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    De6.2 report De6.2 report Document Transcript

    • Preliminary Algorithm Theoretical Basis Documents for Snow/Ice/Frozen soil Properties Fraction Cover, Water Equivalent, and Frozen/Thaw Status Deliverable De6.2 The WorkPackage 6 group1,2,31 Cold and Arid Regions Envrironmental and Engineering Research Institute, CAS, P.R. China2 Institute Tibetan Plateau Research, Chinese Academy of Science, P.R.China 3 Beijing Normal University, Chinese Academy of Science, P.R.China Dissemintation level: Programme Participants Lead beneficiary ID: CAREERI
    • ISSN/ISBN: c 2010 Edited by the CEOP-AEGIS Project Office LSIIT/TRIO, University of Strasbourg BP10413, F-67412 ILLKIRCH Cedex, France Phone: +33 368 854 528; Fax: +33 368 854 531 e-mail: management@ceop-aegis.orgNo part of this publication may be reproduced or published in any formor by any means, or stored in a database or retrieval system, without thewritten permission of the CEOP-AEGIS Project Office.
    • CEOP-AEGIS Report De 6.2 MODIS SNOW PRODUCTS ALGORITHM ABSTRACT......................................................................................................................................................... 1 1. INTRODUCTION ............................................................................................................................................. 2 1.1 Identification ......................................................................................................................................... 2 1.2 Overview ................................................................................................................................................ 3 2. ALGORITHM DESCRIPTION OF SNOW COVER .............................................................................................. 5 2.1 Introduction........................................................................................................................................... 5 2.2 Background and Data........................................................................................................................... 6 2.3 Calculation of ground reflectance ....................................................................................................... 8 2.4 Adjust of NDSI .................................................................................................................................... 10 2.5 Additional Algorithms ........................................................................................................................ 11 2.6 Image fusion ........................................................................................................................................ 12 2.7 Backup Algorithm............................................................................................................................... 13 3. ALGORITHM DESCRIPTION OF FRACTIONAL SNOW COVER ..................................................................... 13 4. VALIDATION PLAN ..................................................................................................................................... 13 4.1 Introduction......................................................................................................................................... 13 4.2 Approach ............................................................................................................................................. 14 4.3 Validation Sites.................................................................................................................................... 14 4.4 Auxiliary Measurements .................................................................................................................... 14 4.5 Scaling .................................................................................................................................................. 14 5. ANCILLARY DATA ...................................................................................................................................... 15 6. PROGRAMMING AND PROCEDURAL CONSIDERATIONS ............................................................................ 15 6.1 Programming Issues ........................................................................................................................... 15 6.2 Processing Issues ................................................................................................................................. 15 6.3 Quality Assurance............................................................................................................................... 15 REFERENCES .................................................................................................................................................. 15 PART II SNOW WATER EQUIVALENT RETRIEVAL ALGORITHM ABSTRACT....................................................................................................................................................... 19 1. INTRODUCTION ........................................................................................................................................... 19 1.1 Identification ....................................................................................................................................... 19 1.2 Overview .............................................................................................................................................. 20 2. ALGORITHM DESCRIPTION ........................................................................................................................ 21 2.1 Introduction......................................................................................................................................... 21 2.2 Theoretical Basis of the Algorithm.................................................................................................... 24 2.3 Description of Retrieval Concept ...................................................................................................... 25 2.4 Description of Retrieval Algorithm................................................................................................... 25 2.5 Backup Algorithm............................................................................................................................... 26 3. ALGORITHM PROTOTYPING ...................................................................................................................... 26 3.1 Data Analysis....................................................................................................................................... 26 3.2 Prototyping of the Algorithm............................................................................................................. 28 II
    • CEOP-AEGIS Report De 6.2 4. VALIDATION PLAN ..................................................................................................................................... 29 4.1 Introduction......................................................................................................................................... 29 4.2 Approach ............................................................................................................................................. 29 4.3 Validation Sites.................................................................................................................................... 32 4.4 Auxiliary Measurements .................................................................................................................... 32 4.5 Scaling .................................................................................................................................................. 32 4.6 Data Protocols and Dissemination..................................................................................................... 32 4.7 Proposed Validation Tests.................................................................................................................. 32 5. ANCILLARY DATA ...................................................................................................................................... 32 6. PROGRAMMING AND PROCEDURAL CONSIDERATIONS ............................................................................ 33 6.1 Programming Issues ........................................................................................................................... 33 6.2 Processing Issues ................................................................................................................................. 33 6.3 Quality Assurance............................................................................................................................... 33 References.................................................................................................................................................. 33 PART III SURFACE SOIL FREEZE/THAW STATE DATASET USING THE DECISION TREE CLASSIFICATION ALGORITHMABSTRACT....................................................................................................................................................... 371. INTRODUCTION......................................................................................................................................... 37 1.1 IDENTIFICATION ....................................................................................................................................... 38 1.2 OVERVIEW ................................................................................................................................................ 382. ALGORITHM DESCRIPTION .................................................................................................................. 39 2.1 INTRODUCTION ......................................................................................................................................... 39 2.2 TARGETS TO BE OBSERVED ...................................................................................................................... 39 2.3 RADIATIVE TRANSFER PROBLEM ............................................................................................................ 39 2.4 MATHEMATICAL BASIS OF THE ALGORITHM ......................................................................................... 40 2.5 DESCRIPTION OF RETRIEVAL CONCEPT ................................................................................................. 41 2.6 DESCRIPTION OF RETRIEVAL ALGORITHM ............................................................................................ 41 2.7 BACKUP ALGORITHM ............................................................................................................................... 413. ALGORITHM PROTOTYPING ................................................................................................................ 41 3.1 DATA ANALYSIS........................................................................................................................................ 41 3.1.1 Analysis of the brightness temperature characteristics of each land surface type .................... 41 3.1.2 Cluster analysis and decision tree for freeze/thaw status classification...................................... 444. VALIDATION PLAN................................................................................................................................... 45 4.1 INTRODUCTION ......................................................................................................................................... 45 4.2 APPROACH ................................................................................................................................................ 46 4.3 VALIDATION SITES ................................................................................................................................... 49 4.4 AUXILIARY MEASUREMENTS ................................................................................................................... 49 III
    • CEOP-AEGIS Report De 6.2 4.5 SCALING .................................................................................................................................................... 49 4.6 DATA PROTOCOLS AND DISSEMINATION ................................................................................................ 49 4.7 PROPOSED VALIDATION TESTS ............................................................................................................... 495. ANCILLARY DATA .................................................................................................................................... 496. PROGRAMMING AND PROCEDURAL CONSIDERATIONS............................................................ 50 6.1 PROGRAMMING ISSUES ............................................................................................................................ 50 6.2 PROCESSING ISSUES ................................................................................................................................. 50 6.3 QUALITY ASSURANCE .............................................................................................................................. 50REFERENCES.................................................................................................................................................. 50 IV
    • PART I MODIS Snow Products AlgorithmAuthors: Xiaohua Hao, Jian Wang, Hongyi Li, Zhe LiAffiliations: Cold and Arid Regions Environment and Engineering Research Institute, Chinese Academy of Sciences.
    • CEOP-AEGIS Report De 6.2 MODIS Snow Products Algorithm Abstract The algorithms of MODIS Terra and MODIS Aqua versions of the snow products havebeen developed by the NASA National Snow and Ice Data Center (NSIDC). The MODISglobal snow-cover products have been available through the NSIDC Distributed ActiveArchive Center (DAAC) since February 24, 2000 to Terra and July 4, 2002 to Aqua. TheMODIS snow-cover maps represent a potential improvement relative to hemispheric-scalesnow maps that are available today mainly because of the improved spatial resolution andsnow/cloud discrimination capabilities of MODIS, and the frequent global coverage. InChina, the snow distribution is different to other regions. Their accuracy on Qinghai-TibetPlateau (QTP), however, has not yet been established. There are some drawbacks aboutNSIDC global snow cover products on QTP:1. The characteristics of snow depth distribution on QTP: Thin, discontinuous. Our researchindicated the MODIS snow-cover products underestimated the snow cover area in QTP(Hao xiaohua, 2008).2. The snow on QTP belongs to alpine snow. Errors due to the effects of topography can belarge. Without the terrain correction of a digital elevation model, the NSIDC global snowproducts can underestimate the snow cover in QTP.3. The snow products can separate snow from most obscuring clouds. However, there arestill many cloud pixels in daily snow cover product. The study developed a new daily snow cover algorithm through improving the NSIDCsnow algorithms and combining MODIS-Terra and MODIS-Aqua data in QTP. The studyalso developed a method of mapping fractional snow cover from MODIS in QTP. The newsnow cover products will provide daily snow cover at 500-m resolution in QTP. The newsnow cover algorithm employs the CIVCO topographic correction, a grouped-criteriatechnique using the Normalized Difference Snow Index (NDSI) and other spectral thresholdtests and image fusion technology to identify and classify snow on a pixel-by-pixel basis.The usefulness of the NDSI is based on the fact that snow and ice are considerably morereflective in the visible than in the shortwave IR part of the spectrum, and the reflectance of 1
    • CEOP-AEGIS Report De 6.2most clouds remains high in the short-wave IR, while the reflectance of snow is low. Inorder to reduce the effect on cloud, snow cover over MODIS-Terra and MODIS-Aqua iscomposed as maximum snow extent. At last, a MODIS-Terra fractional snow cover datawere added to the product base on linear relationship between NDSI and fractional snowcover. Validation of the MODIS snow cover and fractional snow cover products is an on-goingprocess. Two types of validation are addressed in the study-absolute and relative. To deriveabsolute validation, the MODIS maps are compared with field measurements. Relativevalidation refers to comparisons with other high resolution image snow cover maps, whichare considered to be the ‘truth’ snow maps. We have validated the daily snow cover productMOD10A1 and 8-day snow cover product MOD10A2 using snow depth from 47 climatestations in North Xinjiang, China. The accuracy of MODIS snow cover mapping algorithmunder varied topography, snow depth and land cover types was analyzed. Analysis showedthat the MODIS snow cover underestimated the snow cover area in alpine regions.Vegetation cover has an important influence in the accuracy of MODIS snow cover maps.We also validated the MOD10A1 by Landsat-ETM+ images with 30-m resolution in QTP.Results suggest that the snow mapping algorithm of MODIS also underestimates the snowcover. We intend to design a field experiment focused on validating our snow coverproducts in QTP this winter. Recent advances in the area of snow remote sensing have leadto further algorithm development to more accurately measure snow cover from differentsensors. In future, a blended snow product to map snow cover area utilizing MODIS,AMSR-E passive microwave data, QuikSCAT scatterometer data and ICESTA laser radardata will be developed.1. Introduction1.1 Identification Snow is an important, though highly variable, earth surface cover (Klein et al., 1998).Because of its high albedo, snow is an important factor in determining the radiation balance,with implications for global climate studies (Foster and Chang, 1993). Midlatitude alpinesnow cover and its subsequent melt can dominate local to regional climate and hydrology,and more and more notice in the world’s mountains regions snow cover. Because of itsimportance, accurate monitoring of snow cover extent is an important research goal in thescience of Earth systems. Satellites are well suited to measurement of snow cover becausethe high albedo of snow presents a good contrast with most other natural surfaces except 2
    • CEOP-AEGIS Report De 6.2cloud. Fortunately, the physical properties of snow make it highly amenable to monitoringvia remote sensing. The objective of the MODIS snow mapping is to generate snow coverarea and fractional snow cover products on Qinghai-Tibet Plateau.1.2 Overview Remote sensing of snow cover is more than 40 years old. Snow was observed in thefirst image obtained from the TIROS-1 weather satellite following its April 1960 launch(Singer and Popham, 1963). However, it was in the mid-1960s that snow was successfullymapped from space on a weekly basis following the launch of the ESSA-3 satellite. ESSA-3carried the Advanced Vidicon Camera System (AVCS) that operated in the spectral range of0.5 - 0.75 mm with a spatial resolution at nadir of 3.7 km. Using a variety of sensors,including the Scanning Radiometer (SR), Very High Resolution Radiometer (VHRR) andAVHRR sensors, snow cover has been mapped in the Northern Hemisphere on a weeklybasis since 1966 by NOAA (Matson et al., 1986; Matson, 1991). Initially, the weeklyNOAA National Environmental Satellite Data and Information System (NESDIS)operational product was determined from visible satellite imagery from polar-orbiting andgeostationary satellites and surface observations. Where cloud cover precluded the analyst’sview of the surface for an entire week, the analysis from the previous week was carriedforward (Ramsay, 1998). The maps were hand drawn, and then digitized using an 89 89line grid overlaid on a stereographic map of the Northern Hemisphere. In 1997, the older,weekly maps were replaced in 1997, by the IMS product. The IMS product provides a dailysnow map that is constructed through the use of a combination of techniques includingvisible, near-infrared and passive-microwave imagery and meteorological-station data at aspatial resolution of about 25 km (Ramsay, 1998 and 2000). Regional snow products, with1-km resolution, are produced operationally in 3000 - 4000 drainage basins in NorthAmerica by the National Weather Service using NOAA National Operational HydrologicRemote Sensing Center (NOHRSC) data (Carroll, 1990 and Rango, 1993). Passive-microwave sensors on-board the Nimbus 5, 6, and 7 satellites and the DefenseMeteorological Satellite Program (DMSP) have been used successfully for measuring snowextent at a 25 to 30 km resolution through cloud-cover and darkness since 1978 (Chang etal., 1987). Passive-microwave sensors also provide information on global snow depth(Foster et al., 1984). The NOAA/AVHRR and the DMSP Special Sensor Microwave Imager(SSM/I) are currently in operation. The Landsat Multispectral Scanner (MSS) and TMsensors, with 80-m and 30-m resolution, respectively, are useful for measurement of snow 3
    • CEOP-AEGIS Report De 6.2covered area over drainage basins (Rango and Martinec, 1982). Additionally, Landsat TMdata are useful for the quantitative measurement of snow reflectance (Dozier et al., 1981;Dozier, 1984 and 1989; Hall et al., 1995; Winther, 1992). The Moderate Resolution Imaging Spectroradiometer (MODIS), a major NASA EOSinstrument, was launched aboard the Terra satellite on December 18, 1999 (10:30 AMequator crossing time, descending) for global monitoring of the atmosphere, terrestrialecosystems, and oceans. On May 4, 2002, a similar instrument was launched on the EOS-Aqua satellite (1:30 PM equator crossing time, descending) (Salomonson et al., 2001).MODIS data are now being used to produce snow-cover products from automatedalgorithms at Goddard Space Flight Center in Greenbelt, MD. The products are transferredto the National Snow and Ice Data Center (NSIDC) in Boulder, CO, where they are archivedand distributed via the Warehouse Inventory Search Tool (WIST). The MODIS snowproducts are produced as a series of six products, including MOD10_L2, MOD10L2G,MOD10A1, MOD10A2, MOD10C1 and MOD10C2. MOD10_L2 is swath product that isgenerated using the MODIS calibrated radiance data products (MOD02HKM andMOD021KM), the geolocation product (MOD03), and the cloud mask product (MOD35_L2)as inputs. The MODL2G product is the result of mapping all the MOD10_L2 swathsacquired during a day to grid cells of the Sinusoidal map projection. The Earth is dividedinto an array of 36 x 18, longitude by latitude, tiles, about 10°x10° in size in the Sinusoidalprojection. The daily snow product MOD10A1 is a tile of data gridded in the sinusoidalprojection. Tiles are approximately 1200 x 1200 km (10°x10°) in area. Snow data arrays areproduced by selecting the most favorable observation (pixel) from the multiple observationsmapped to a cell of the MOD10_L2G gridded product from the MOD10_L2 swath product.In addition to the snow data arrays mapped in from the MOD10_L2G, snow albedo iscalculated. There are four SDSs (or data fields) of snow data; snow cover map, fractionalsnow cover, snow albedo and QA in the data product file. The MOD10A2 is eight-daycomposited of MOD10A1. The MOD10A2 is generated by merging all the MOD10A1products (tiles) for an eight-day period. MOD10C1 and MOD10C2 snow product gives aglobal view of snow cover at 0.05° resolution global climate modeling grid (CMG) by ageographic projection. The detail of MODIS products can be found from MODIS SnowProducts User Guide (Riggs et al. 2003). MODIS snow-cover products represent potentialimprovement to or enhancement of the currently available operational products mainlybecause the MODIS products are global and 500-m resolution, and have the capability toseparate most snow and clouds. The MODIS snow-mapping algorithms are automated, 4
    • CEOP-AEGIS Report De 6.2which means that a consistent data set may be generated for longterm climate studies thatrequire snow-cover information. MODIS Terra and MODIS Aqua versions of the snowproducts are generated. Bias to Terra is because the snow detection algorithm is based onuse of near infrared data at 1.6 µm. A primary key to snow detection is the characteristic ofsnow to have high visible reflectance and low reflectance in the near infrared, MODIS band6. MODIS band 6 (1.6 µm) on Terra is fully functional however, MODIS band 6 on Aqua isonly about 30% functional; 70% of the band 6 detectors non-functional. That situation onAqua caused a switch to band 7 (2.1 µm) for snow mapping in the swath level algorithm. Inaddition, a fractional snow cover data array has been added to the product from collection 5. In our study, mapping snow cover in mountainous regions remains an omissionlimitation to the MODIS snow products from NSIDC (Hao Xiaohua et al. 2008). TheMODIS snow cover products rely on analysts to fine-tune the maps. So we describe andvalidate a method that retrieves snow-covered area in Xinjiang and Qinghai-Tibet Plateauregions, China by Terra MOD09 surface reflectance data. Develop an improved algorithmsuited for mapping MODIS snow cover and fraction snow cover on Qinghai-Tibet Plateau.2. Algorithm Description of snow cover2.1 IntroductionThe new snow cover algorithm employs the CIVCO topographic correction, a grouped-criteria technique using the Normalized Difference Snow Index (NDSI) and other spectralthreshold tests and image fusion technology to identify and classify snow on a pixel-by-pixel basis. The new algorithm was selected for the following reasons:(1) The new snow cover algorithm is more accurate than algorithm of NSIDC on Qinghai- Tibet Plateau.(2) It corrects the effect of atmospheric and topographic conditions.(3) It can minimize the limitation of the cloud.(4) It runs automatically and fast. It is straightforward, computationally frugal, and thus easy for the user to understand exactly how the product is generated. Snow has strong visible reflectance and strong short-wave IR absorbing characteristics.The Normalized Difference Snow Index (NDSI) is an effective way to distinguish snowfrom many other surface features. Both sunlit and some shadowed snow is mappedeffectively. A similar index for vegetation, the Normalized Difference Vegetation Index(NDVI) has been proven to be effective for monitoring global vegetation conditionsthroughout the year (Tucker, 1979 and 1986). Additionally, some snow/cloud discrimination 5
    • CEOP-AEGIS Report De 6.2is accomplished using the NDSI. Other promising techniques, such as traditional supervisedmultispectral classifications, spectral-mixture modeling, or neural-network analyses havenot yet been shown to be usable for automatic application at the global scale. However,these techniques may progress to regional applications.2.2 Background and Data2.2.1 Area of interest The Qinghai-Tibet Plateau is the highest plateau over the world. It not only had animportant influence on the atmospheric circulation of the northern hemisphere, but alsodirectly affected the climatic and eco-environmental evolution of China in the Quaternaryperiod (Huairen and Xin, 1985).The Qinghai-Tibet Plateau is the largest, nonpolar colddesert in the world, with an average elevation above 4000 m. The presence of snow coverplays a key role in the cold desert ecosystem by affecting the hydrology, ecology andclimate. Snow cover in Qinghai-Tibet Plateau is highly variable both spatially andtemporally. Thin, discontinuous sheets of snow can occur year round (Zheng et al. 2000). Inthe absence of snow, soils are more vulnerable to freezing and potentially decreased rates ofmicrobial transpiration, which can alter the soil’s ability to sequester carbon. Due to theremoteness and topographic complexity of the Qinghai-Tibet Plateau, remote sensing offersthe most practical tool for monitoring its snow cover area.2.2.2 Elevation data The Digital Elevation Model (DEM) of the area at 500 m spatial resolution was createdfrom SRTM (Shuttle Radar Topography Mission) data at 3 arc-seconds, which is 1/1200thof a degree of latitude and longitude, or about 90 meters as a source of topographycorrection. From the DEM dataset, information about the slope, aspect and illuminationaccording to the sun angle and elevation were generated for input to the topographiccorrections algorithms for MODIS image.2.2.3 MODIS data In the new algorithm, we rely on MOD09 surface reflectance products (MOD09GA,MYD09GHK) to get the MODIS snow cover. MOD09 (MODIS Surface Reflectance) is aseven-band product computed from the MODIS Level 1B land bands 1 (620-670 nm), 2(841-876 nm), 3 (459-479), 4 (545-565 nm), 5 (1230-1250 nm), 6 (1628-1652 nm), and 7(2105-2155 nm). MOD is the MODIS/Terra data and MYD is the MODIS/Aqua data. Theproduct is an estimate of the surface spectral reflectance for each band as it would have been 6
    • CEOP-AEGIS Report De 6.2measured at ground level as if there were no atmospheric scattering or absorption. It correctsfor the effects of atmospheric gases, aerosols, and thin cirrus clouds. The data can beobtained from the National Snow and Ice Data Center Distributed Data Archive. SixMOD09 tiles (h23v05, h24v05, h25v05, h26v05, h2506, h26v06) were used in the studyregion. Other MODIS product suite that include cloud mask data (MOD35 and MYD35) andtemperature data (MOD11A1 and MYD11A1) were regard as auxiliary inputs. The MODISdaily snow cover product (MOD10A1 and MYD10A1) is regard as the reference data of thesnow cover from the new algorithms.2.2.4 Landsat-ETM+ data and analysis The ETM+ was launched on April 15, 1999, on the Landsat-7 satellite(http://www.landsat.gsfc.nasa.gov/project/satellite.html). The ETM+ has eight discretebands ranging from 0.45 to 12.5 Am, and the spatial resolution ranges from 15 m in thepanchromatic band, to 60 m in the thermal-infrared band. All of the other bands have 30-mresolution. Landsat-ETM+ data provide a high-resolution view of snow cover that can becompared with the MODIS and operational snow-cover products. In the study, Landsat-ETM+ path 143 row 30, path 136 row 38, path134 row 38, path 136 row 39, path134 row 40path were used to produce a validation dataset for the MODIS snow cover products. Thefigure1 shows the detail of study region. 7
    • CEOP-AEGIS Report De 6.2 Figure 1. The study region and the Landsat-ETM+ location. A, B ,C, D and E are respectively path 143 row 30, path 136 row 38, path134 row 38, path 136 row 39, path134 row 40.2.3 Calculation of ground reflectance The objective of any radiometric correction of airborne and spaceborne imagery ofoptical sensors is the extraction of physical earth surface parameters such as reflectance,emissivity, and temperature. The imagery available in the MOD09 (MODIS surfacereflectance product) provides measurements of surface reflectance with the atmospherecorrection by ‘6S’ model. However, in rugged terrain and in the case of multi-temporaldataset these measurements are affected strongly by changes of topographic conditions. Ourresearch indicates that such variability reduces the identification of snow in shadow. Togetting the true ground reflectance the topography correction of the MOD09 is necessary inQTP. The problem of differential terrain illumination on satellite imagery has beeninvestigated for at least 20 years. At present, there are many methods in terrain correction,such as physical models, Semi-empirical and empirical models. Although physical models canbe quite successful to eliminate atmospheric and topographic effects they inherently rely on an 8
    • CEOP-AEGIS Report De 6.2accurate spectral and radiometric sensor calibration and on the accuracy and appropriate spatialresolution of a digital elevation model (DEM) in rugged terrain and the computer is complex. TheMODIS data is large quantity. The empirical based approach offers the fast and accuratecorrection. Law (2004) tested and compared three topographic correction methods, whichare the Cosine Correction, Minnaert Correction and a CIVCO model. By comparing, heoffered an improved CIVCO model. In our study, we used the improved CIVCO model. The CIVCO method used here is modified from the two stage normalization proposedby Civco, 1989, and consists of two stages. In the first stage, shaded relief models,corresponding to the solar illumination conditions at the time of the satellite image arecomputed using the DEM data. This requires the input of the solar azimuth and altitudeprovided by the metadata of the satellite image. The resulting shaded relief model wouldhave values between 0 and 1. After the model is created, a transformation of each of theoriginal bands of the satellite image is performed to derive topographically normalizedimages using equation (1) and (2). (1) ( 2)where !Ref"ij= the normalized radiance data for pixel(i, j) in band(!)Ref"ij= the raw radiance data for pixel(i, j) in band(!)µk= the mean value for the entire scaled shaded relief model (0,1)µij= the scaled (0,1) illumination value for pixel(i, j)C" = the correction coefficient for band(!)N! = the mean on the slope facing away the sun in the uncalibrated data for the forestcategoryS! = the mean on the slope facing to the sun in the uncalibrated data for the forest categoryµk = the mean value for the entire scaled shaded relief modelµN = the mean of the illumination of forest on the slope facing away from the sun. 9
    • CEOP-AEGIS Report De 6.2µS = the mean of the illumination of forest on the slope facing to the sun.By the topography correction, we can get the MODIS surface reflectance. It will improvethe accuracy of snow cover mapping in mountainous regions.2.4 Adjust of NDSI The MODIS snow cover products algorithm is essentially designed for the evaluation ofthe threshold value of the NDSI (Normalize Difference Snow Index) threshold value. ForMODIS data the NDSI is calculated as: ê éé à (3)The NDSI threshold of the MODIS snow cover products distributed by the NSIDC is 0.40.The NDSI values of the MODIS scenes greater than or equal to 0.40 represent snow coverpixels. In addition, since water may also have an NDSI 0.4, an additional test is necessary toseparate snow and water. Snow and water may be discriminated because the reflectance ofwater is <11% in MODIS band 2. Hence, if the reflectance of MODIS band 4 >11%, and theNDSI 0.40, the pixel is initially considered snow covered. However, validation of thecurrent NDSI threshold has being accomplished only by the measurements in the UnitedStates and Europe. In China, therefore, there is not reliable NDSI threshold value for theMODIS snow mapping and a credible threshold can be established. In the study, the snow cover area of A, B and C were selected for this study. First, theLandsat-ETM+ snow cover maps were produced by the method of the SNOMAP. Then, thesnow cover maps, produced obtained from the way mentioned above, were compared withthe ones derived by the manual photo interpretation classification technique. Overallagreement which is simply a comparison of the number of snow pixels, is high at 96%. Thus,the Landsat-ETM+ snow cover maps can be reliable served as the “groudtruth” withwhich then the snow cover maps of the study area extracted from the MOD09measurements by NDSI method were compared. For the MODSI snow cover maps of thestudy areas, the NDSI threshold value for snow was increased gradually for 0.30 to 0.40 insteps of 0.01. At Last, the comparisons focused on comparing the MODIS snow cover mapsfollowing with NDSI threshold value and the Landsat-ETM+ snow cover maps serving asabsolute standard. The result suggests that the MODIS snow cover products distributed bythe NSIDC using NDSI threshold of 0.40 underestimated the SCA (snow-covered area) of 10
    • CEOP-AEGIS Report De 6.2the study areas. In the study areas, the credible NDSI threshold value is respectively 0.34,0.36and0.38 in A, B and C regions. As computer the average value, it is approximately0.36,which is less than the one from the 0.40 of NSIDC. Table 1. MODIS snow cover accuracy of different NDSI threshold in A, B and C region. NDSI The overall accuracy, Kappa The overall accuracy, Kappa The overall accuracy, Kappathreshold coefficient and fractional snow coefficient and fractional snow coefficient and fractional snow value cover area of A region. cover area of B region. cover area of C region. 0.39 93.00% 0.669 11.37% 86.82% 0.676 27.73% 94.73% 0.708 10.17% 0.38 93.02% 0.672 11.53% 86.81% 0.678 28.36% 94.74% 0.711 10.48% 0.37 93.07% 0.675 11..66% 86.76% 0.679 29.02% 94.62% 0.709 10.79% 0.36 93.11% 0.679 11.83% 86.73% 0.680 29.63% 94.51% 0.707 11.08% 0.35 93.16% 0.683 11.97% 86.63% 0.679 30.25% 94.39% 0.706 11.48% 0.34 93.17% 0.685 12.13% 86.54% 0.679 30.87% 94.26% 0.703 11.82% 0.33 92.89% 0.678 12.66% 86.45% 0.679 31.51% 94.16% 0.702 12.16% 0.32 92.91% 0.681 12.80% 86.28% 0.677 32.13% 94.04% 0.700 12.53% 0.31 92.91% 0.683 12.98% 86.13% 0.676 32.66% 93.88% 0.697 12.89% 0.30 92.90% 0.684 13.18% 86.05% 0.676 33.23% 93.69% 0.692 13.28%2.5 Additional Algorithms In forested locations, many snow covered pixels have an NDSI lower than 0.4. Tocorrectly classify these forests as snow covered, a lower NDSI threshold is employed. Thenormalized difference vegetation index (NDVI) and the NDSI are used together in order todiscriminate between snow-free and snow covered forests. The NDSI-NDVI field isdesigned to capture as much of the variation in NDSI-NDVI values observed in the snowcovered forests as possible while minimizing inclusion of non-forested pixels. It wasdesigned to include forestcovered pixels that have NDSI values lower than 0.4, yet haveNDVI values lower than would be expected for snow-free conditions (Klein et al., 1998).For MODIS data the NDVI is calculated as: ê éé à ( 4)Last, a threshold of 10% in MODIS band 4 was used to prevent pixels with very low visiblereflectances, for example black spruce stands, from being classified as snow as haspreviously been suggested (Dozier, 1989). The NDSI can separate snow from most obscuring clouds, it does not always identify or 11
    • CEOP-AEGIS Report De 6.2discriminate optically-thin cirrus clouds from snow. Clouds are masked by using theMODIS cloud masking data product (MOD35). One of the problems facing the MODIS snow-mapping algorithm is the mapping ofsnow in regions where it is known not to exist. One of the more common locations for thisproblem is in dark, dense forests, particularly in the tropics. The nature of the snow-mapping algorithm is such that it is particularly sensitive to small changes in the NDSI orNDVI over dark, dense vegetation. To correct false-snow mappings in tropical forests, theMODIS temperature mask product (MOD11) was used to improve the accuracy of snowcover map. A tentative threshold of 277 K has been set. When this threshold is applied intropical regions, e.g., the Congo, it eliminates from 93% to 98% of the false snow (Barton,et al. 2001).2.6 Image fusion MODIS cloud masking data product was used to map MODIS snow cover product.Nevertheless, inaccurate detection of clouds in the MOD35 cloud mask product revealed tobe problematic in high-elevation regions such as the QTP, China (Hall et al. 2002). TheCollection 5 of the MODIS snow products has been infused and expanded with informationregarding characteristics and quality of snow products at each level. It improves the cloudmask product, thus permitting more snow covet to be mapped. However, the accuratemonitoring of SCA using optical imagery of high spatial resolution is seriously reduced bycloud cover due to the similar reflective nature of snow and clouds. The ground object undercloud remains unknown. Whether in MODIS terra or MODIS aqua daily snow coverproduct, either way, its always was effected by large cloud. In the context of remote sensing, image fusion consists of merging images fromdifferent sources, which hold information of a different nature, to create a synthesized imagethat retains the most desirable characteristics of each source (Pohl & Genderen, 1998). Inmy study, the method was applied to composite the MODIS/Terra and MODIS/Aqua snowcover product to minimize the effect of cloud. In selecting the image fusion technique forthe daily composites, we decided that it would be most useful to use maximum snow cover.In other words, if snow were present on any image in any location on the Terra or Aqua. tileproduct, it will show up as snow-covered on the daily composite product. Maximum snowcover is a more useful parameter than minimum or average snow cover. Using eitherminimum or average snow cover would result in failure to map some snow cover. The 12
    • CEOP-AEGIS Report De 6.2compositing technique also minimizes cloud cover. The figure 2 shows the flow process of ournew MODIS snow cover map algorithm. MOD09GA MYD09GA CIVCO Terrain correction NDSI 0.36, B2 0.11 other Snow Snow in forest Klein MODEL b4>0.1 Cloud, Other Cloud Other LST mask:MOD11A1 Cloud mask: MOD35 Threshold value 283 Land/water mask: MOD03 MODSNOW Maximum Composition MYDSNOW Snow Cover Map Figure 2. The flow process chart of the new snow cover algorithms.2.7 Backup Algorithm Future enhancements to MODIS snow cover maps include improving snow coverresolution, fusing the polygenetic remote sensing data and producing more abundant appliedsnow products.3. Algorithm Description of fractional snow coverThe work are doing.4. Validation Plan4.1 Introduction The accuracy of snow cover products from optical remote sensing is of particularimportance in hydrological applications and climate models. In the study, using in situobservation data during the five snow seasons at 47 climatic stations from January 1 to 13
    • CEOP-AEGIS Report De 6.2March 31of year 2001 and from November 1 to March 31 of year 2001 to 2005 in northernXinjiang area, China, the accuracy of MODIS snow cover products (MOD10A1 andMOD102) and VEGETATION snow cover products (VGT-S10 snow cover products)algorithm under varied terrain and land cover types were analyzed. The study shows theoverall accuracy of MOD10A1 MOD10A2 and VGT-S10 snow cover products is high at91.3%, 90.6%, 87.9% respectively in all climatic stations. However, the overall accuracy ofthe snow cover products in mountain regions is low. In mountain climatic stations the snowomission of the three products is 32.4 21.7% 36.3% respectively. The cloud limitationratio of MOD10A1 reaches to 61.8%.;but the MOD10A2 and VGT-S10 are only 7.6%,1.8%. The comparison result of user-defined 10-day MODIS snow products and VGT-S10snow products shows that the snow identification ability of MODIS are more accuracy thanVGT-S10 snow cover products. However, the VGT-S10 snow cover products are littleaffected by cloud than MODIS snow cover products.4.2 Approach Two types of validation are addressed in our study-absolute and relative. To deriveabsolute validation, the MODIS maps are compared with ground measurements ormeasurements of snow cover from Landsat data, which are considered to be the ‘truth’ forthis work. Relative validation refers to comparisons with other snow maps, most of whichhave unknown accuracy. Thus for the studies of relative validation, it is not generallyknown which snow map has a higher accuracy.4.3 Validation SitesQTP-Naqu. Lake Namtso.4.4 Auxiliary MeasurementsSnow density, snow water liquid, snow grain size, snow temperature and snow pit works.4.5 Scaling 14
    • CEOP-AEGIS Report De 6.25. Ancillary Data DEM data , snow depth from climate stations.6. Programming and Procedural Considerations6.1 Programming Issues The difficulty in establishing the accuracy of any of these maps is that it is not knownwhich map is the ‘‘truth’’ (if any) and the techniques used to map snow cover in the variousmaps are different, resulting in different products.6.2 Processing Issues6.3 Quality AssuranceReferencesBarton, J.S., D.K. Hall and G.A. Riggs, unpublished document, 2001: Thermal and geometric thresholds in the mapping of snow with MODIS, July 11, 2001.Carroll T R. Operational airborne and satellite snow cover products of the National Operational Hydrologic Remote Sensing Center[C]. Proceedings of the forty-seventh annual Eastern Snow Conference, Bangor, Maine, CRREL Special Report. June 7-8, 1990: 90-44.Chang, A.T.C., J.L. Foster and D.K. Hall. Microwave snow signatures (1.5 mm to 3 cm) over Alaska, Cold Regions Science and Technology. 1987, 13:153-160.Civco D L. Topographic Normalization of Landsat Thematic Mapper Digital Imagery[J]. Photogrammetric Engineering and Remote Sensing. 1989, 55(9): 1303-1309.Dozier J, Schneider S R, McGinnis J D F. Effect of grain size and snowpack water equivalence on visible and near-infrared satellite observations of snow[J]. Water Resources Research.1981,17(4): 1213-1221.Dozier, J. Snow reflectance from Landsat-4 thematic mapper. I.E.E.E. Transactions on Geoscience and Remote Sensing, 1984,22: 323-328.Dozier, J. Spectral signature of alpine snow cover from the Landsat Thematic Mapper, Remote Sensing of Environment. 1989, 28: 9-22. 15
    • CEOP-AEGIS Report De 6.2Foster, J.L., D.K. Hall, A.T.C. Chang and A. Rango. An overview of passive microwave snow research and results. Reviews of Geophysics. 1984, 22: 195-208.Foster, J.L., A.T.C. Chang. Snow cover. In Atlas of Satellite Observations Related to Global Change R.J. Gurney, C.L. Parkinson, and J.L. Foster (eds.), Cambridge University Press, Cambridge. 1993: 361-370.Hao Xiaohua, Wang Jian, Li Hongyi. Evaluation of the NDSI threshold value in mapping snow cover of MODIS—A case study of snow in the middle Qilian Mountains. Journal of Glaciology and Geogryology. 2008,30 (1): 132-138.Hall D K, Riggs G A, Salomonson V V. Development of methods for mapping global snow cover using moderate resolution imaging spectroradiometer data. Remote Sensing of Environment. 1995, 54: 127–140.Hall D K, Riggs G A, Salomonson V V, et al. MODIS snow-cover products[J]. Remote Sensing of Environment. 2002, 83: 181-194.Law K H, Nichol J. Topographic correction for differential illumination effects on IKONOS satellite imagery[C]. ISPRS Congress, Istanbul, Turkey Commission 3. 12-23 July 2004.Huairen Y. Climatic change in Quaternary. In: Tingdong L. Contribution to the Quaternary glaciology and Quaternary geology, Geological Publishing House, P.R. China,1985,2:135–144.Klein A, Hall D K, Riggs G A. Global snow cover monitoring using MODIS. In 27th International Symposium on Remote Sensing of Environment. June 8-12, 1998: 363-366.Matson, M., C.F. Ropelewski and M.S. Varnadore. An atlas of satellitederived northern hemisphere snow cover frequency, National Weather Service, Washington, D.C. 1986, 75 pp.Matson, M.. NOAA satellite snow cover data, Palaeogeography and Palaeoecology. 1991, 90: 213-218.Pohl, C., & Genderen, J. L. V. (1998). Multisensor image fusion in remote sensing: Concepts, methods and applications. International Journal of Remote Sensing, 19(5), 823#854.Ramsay, B. The interactive multisensor snow and ice mapping system. Hydrological Processes. 1998, 12:1537-1546.Ramsay B. Prospects for the interactive multisensor snow and Ice Mapping System (IMS) [C]. Proceedings of the 57th Eastern Snow Conference, Syracuse, NY, East Snow Conference. 2000: 161-170.Rango, A. Snow hydrology processes and remote sensing. Hydrological Processes. 1993, 7:121-138.Rango, A. and J. Martinec. Snow accumulation derived from modified depletion curves of snow coverage, Symposium on Hydrological Aspects of Alpine and High Mountain Areas, IAHS Publication. 1982,138:83-90.Salomonson V V, Guenther B, Masuoka, E A. A summary of the status of the EOS Terra Misson MODIS and attendant data product development after one year of on-orbit performance. In: Proceedings of the 16
    • CEOP-AEGIS Report De 6.2International Geoscience and Remote Sensing Symposium/IGARSS’2001, Sydney, Australia, 9-13 July, 2001.Singer, F.S. and R.W. Popham. Non-meteorological observations from weather satellites, Astronautics and Aerospace Engineering. 1963, 1(3): 89-92.Tucker, C.J. Red and photographic infrared linear combinations for monitoring vegetation, Remote Sensing of Environment. 1979, 8: 127-150.Tucker, C.J. Maximum normalized difference vegetation index images for sub-Saharan Africa for 1983-1985, International Journal of Remote Sensing, 1986,7: 1383-1384. Winther, J.G. Landsat thematic mapper (TM) derived reflectance from a mountainouswatershed during the snow melt season, Nordic Hydrology. 1992, 23: 273-290. 17
    • PART II Snow Water Equivalent Retrieval Algorithm Authors: Tao CheAffiliations: Cold and Arid Regions Environment and Engineering Research Institute, Chinese Academy of Sciences.
    • CEOP-AEGIS Report De 6.2 Snow Water Equivalent Retrieval Algorithm Abstract We report spatial and temporal distribution of seasonal snow depth derived frompassive microwave satellite remote sensing data (e.g. SMMR from 1978 to 1987 and SMM/Ifrom 1987-2006) in China. We first modified the Chang algorithm and then validated itusing meteorological observations data, considering the influences from vegetation, wetsnow, precipitation, cold desert and frozen ground. Furthermore, the modified algorithm isdynamically adjusted based on the seasonal variation of grain size and snow density. Thesnow depth distribution is indirectly validated by MODIS snow cover products bycomparing the snow extent area from this work. The final snow depth datasets from 1978 to2006 show that the inter-annual snow depth variation is very significant. The spatial andtemporal distribution of snow depth is illustrated and discussed, including the steady snowcover regions in China and snow mass trend in these regions. Though the area extent ofseasonal snow cover in the Northern Hemisphere indicates a weak decrease in a long timeperiod, there is no clear trend in change of snow cover area extent in China. However, snowmass over the Qinghai-Tibet Plateau and Northwestern China has increased, while it hasweakly decreased in Northeastern China. Overall, snow depth in China during the past threedecades shows significant inter-annual variations with a weak increasing trend.1. Introduction1.1 Identification Snow plays an important role at the climatic system due to its high surface albedo andheat insulation effect which influences energy exchange between the land surface and theatmosphere. It also influences the hydrological processes though snow water storage andrelease. To obtain the large scale and long time period snow depth datasets, the passivemicrowave remote sensing data (e.g. SMMR and SSM/I) have shown their capability in thepast three decades (Armstrong and Brodzik, 2002). The deeper the snowpack, the moresnow crystals are available to scatter microwave energy away from the sensor. Hence,microwave brightness temperatures are generally lower for deep snowpack while they are 19
    • CEOP-AEGIS Report De 6.2higher for shallow snowpack (Chang and others, 1987). Based on this fact, both snow depthand snow water equivalent retrieval algorithms were developed by using brightnesstemperature difference between 18 and 37 GHz (spectral gradient, e.g. Chang and others,1987). With the utility of the Chang algorithm in the global scale, it was shown that a singlealgorithm cannot describe all kinds of snow conditions (Foster and others, 1997). Regionalalgorithms to retrieve snow depth have been developed in the past decade for NorthAmerica and Eurasia snowpack (Foster and others, 1997; Tait, 1998; Kelly and others,2003).1.2 Overview In fact, the global snow depth retrieval algorithms overestimate snow depth in Chinaaccording to the records of meteorological station observations (Chang and others, 1992).Snow depth retrieved from passive microwave remote sensing data can be influenced by thecondition of snowpacks, such as snow crystal (England, 1975; Chang and others, 1976;Foster and others, 1997), snow density (Wiesmann and Matzler, 1999; Foster and others,2005), and vegetation (Foster and others, 1997). Tait (1998) reported the differentalgorithms for different snow features. For this reason, it is necessary to develop analgorithm favorable to snow depth study in China. It is reported that snow grain size and density determine the coefficient of spectralgradient for snow depth retrieval. For example, using the Chang algorithm with a grain sizeof 0.3 mm, the coefficient is 1.59, and with a grain size of 0.40 mm, the coefficient becomes0.78 (Foster and others, 1997). Josberger and Mognard (2002) reported that while thesnowpack was constant, the spectral gradient continued to increase with time due to themetamorphism of snow. Larger snow grains cause increased microwave scattering with theresult that an algorithm based on a fixed value for grain size will tend to overestimate snowdepth. (Armstrong and others, 1993). So, the spectral gradient will increase with the timelapses due to the grouping snow grain size and snow density. Liquid water content in snow layer (Ulaby and others, 1986; Matzler, 1994) and largewater bodies (Dong, 2005) can also lead to large errors in retrieving snow water equivalent.These two factors should be considered before the linear regression for the coefficientmodification as in the Chang algorithm. Microwave radiation will not determine snow depthaccurately when snow is wet (Matzler, 1994). The dry snow and wet snow criteria were 20
    • CEOP-AEGIS Report De 6.2used to discriminate the wet snow brightness temperature data, while the lake and land-seaboundary were collected for removing the meteorological stations that near to the largewater body. After the work of Neale and others (1990), the NOAA-NASA SSM/I Pathfinder(NNSP) program also uses SSM/I data to derive land surface classifications and to establishcriteria of dry snow and wet snow (Singh and Gan, 2000). Grody (1991) reported it was necessary to remove the rain signal to identify snow cover.When it is raining, snow parameters may not be retrieved. For obtaining the long-time seriesdataset of snow depth, the Grody’s decision tree method based on the passive microwaveremote sensing data can be adopted so that the snow depth retrieval algorithm only isfocused on the snow pixels. In this study, we will modify the Chang snow algorithm to make it suitable for snowdepth retrieval in China using SMMR and SSM/I remote sensing data and snow depth datarecorded at the China national meteorological stations. We will further analyze the accuracyand uncertainty of the new snow product produced from the modified Chang algorithm. Thedaily snow depth datasets in China from 1978/1979 to 2005/2006 will be produced, andtheir spatial and temporal characteristics will be analyzed.2. Algorithm Description2.1 IntroductionThe coefficient of spectral gradient algorithm Based on theoretical calculations and empirical studies, Chang and others (1987)developed an algorithm for passive remote sensing of snow depth over relative uniformsnowfields utilizing the difference between the passive microwave brightness temperatureof 18 and 37 GHz in horizontal polarization. SD = 1.5*(TB(18H) – TB(37H)) 1 SD is snow depth in cm, and TB(18H) and TB(37H) are brightness temperature at 18and 37 GHz in horizontal polarization, respectively. Here, brightness temperature at 37GHzis sensitive to snow volume scattering, while that at 18GHz includes the information from 21
    • CEOP-AEGIS Report De 6.2the ground under the snow. Therefore, the basic theory of the spectral gradient algorithm isthe snow volume scattering, which can be used to estimate the snow depth after thecoefficient (slope) was modified by the snow depth observations in the field. Based on Foster and others’s results (1997) of forest influence, the forest area fractionwas considered here: SD = a*(TB (18H) – TB (37H))/ (1-f) 2where a is the coefficient, while f is the forest area fraction. In this study, snow depth observations at the meteorological stations in 1980 and 1981were regressed with the spectral gradient of SMMR at 18 and 37GHz in horizontalpolarization. Before regression, the adverse factors should be taken into account, such asliquid water content within the snowpack, which lead to a large uncertainty due to the bigdifference between dry snow and water dielectric characteristics. The brightnesstemperature data influenced by liquid water content were eliminated based on the followingdry snow criteria: TB(22V)-TB(19V) 4, TB(19V)-TB(19H)+TB(37V)-TB(37H)>8,225<TB(37V)<257, and TB(19V) 266 (Neale and others 1990). Mixed pixels with largewater bodies were removed according to the Chinese lake distribution map and the Chinesecoastline maps. According to the regression between the spectral gradient of TB(18H) and TB(37H) andthe snow depth measured at the meteorological stations, the coefficient (slope) is 0.78 andthe standard deviations from the regression line is 6.22cm for SMMR data. For the SSM/Ibrightness temperature data, the 19GHz channel replaced the 18GHz of SMMR. Resultsshow that the coefficient is 0.66 and the standard deviations from the regression line are5.99cm. So, the modified algorithm is: SD = 0.78*(TB(18H) – TB(37H))/(1-f) (for SMMR data from 1978 to 1987) SD = 0.66*(TB(19H) – TB(37H))/(1-f) (for SSM/I data from 1987 to 2006) (3) There are 2217 snow depth observations available in 1980 and 1981, while 6799observations in 2003 due to the SSM/I has an improved swath width and acquiring periodthan the SMMR has (See Figure 1 and 2). 22
    • CEOP-AEGIS Report De 6.2 Figure 1. Snow depth estimated from passive microwave brightness temperature data and observed in meteorological stations: (a) SMMR in 1980 and 1981 and (b) SSM/I in 2003. Figure 2 Percentage of error frequency distribution of snow depth estimated from passive microwave brightness temperature data and observed in meteorological stations. (a) SMMR in 1980 and 1981 and (b) SSM/I in 2003.A simple dynamically adjusted algorithm Snow density and grain size are two sensitive factors affecting microwave emissionfrom snowpacks (Foster and others, 1997, 2005), because it can partly affect the volumescattering coefficient of snow. Although Josberger and Mognard (2002) developed adynamic snow depth algorithm, it is difficult to use the algorithm to mapping snow depthestimation in China because the lack of reliable ground and air temperature data for eachpassive microwave remote sensing pixel. In this study, we adopted a statistical regressionmethod to adjust the coefficient dynamically based on the error increasing ratio within thesnow season from October to April. The original Chang algorithm underestimated the snowdepth in the beginning of snow season and overestimated snow depth in the end of snowseason (Figure 4). As the results of statistic, the average offsets can be obtained in everymonth for SMMR and SSM/I, respectively (Table 1). Table 1 Average offsets to remove the influence from snow density and grain size variations for each month within the snow season based on the linear regression method 23
    • CEOP-AEGIS Report De 6.2 Average offset (cm) Month SMMR SSM/I Oct -3.64 -4.18 Nov -3.08 -3.58 Dec -1.91 -1.93 Jan -0.19 0.29 Feb 1.51 2.15 Mar 2.65 3.31 Apr 3.32 3.80Figure 3 Error increases from snow density and grain size variations within the snow season from October to next April based on the estimations of SMMR and SSM/I data and observations in meteorological stations. Here (a): SMMR and (b): SSM/I2.2 Theoretical Basis of the Algorithm To obtain the large scale and longtime period snow depth datasets, the passivemicrowave remote sensing data (e.g. SMMR and SSM/I) have shown their capability in thepast three decades (Armstrong and Brodzik, 2002). The deeper the snowpacks, the moresnow crystals are available to scatter microwave energy away from the sensor. Hence,microwave brightness temperatures are generally lower for deep snowpacks while they arehigher for shallow snowpacks (Chang and others, 1987). Based on this fact, both snowdepth and snow water equivalent retrieval algorithms were developed by using brightnesstemperature difference between 18 and 37 GHz (spectral gradient, e.g. Chang and others,1987). With the utility of the Chang algorithm in the global scale, it was shown that a singlealgorithm cannot describe all kinds of snow conditions (Foster and others, 1997). Regionalalgorithms to retrieve snow depth have been developed in the past decade for NorthAmerica and Eurasia snowpacks (Foster and others, 1997; Tait, 1998; Kelly and others,2003). 24
    • CEOP-AEGIS Report De 6.22.3 Description of Retrieval Concept2.4 Description of Retrieval Algorithm The spectral gradient algorithm for the snow depth retrieval is based on the volumescattering of snowpacks, which means other scattering surfaces can influence the results aswell. However, it will overestimate the snow cover area if the spectral gradient algorithm isdirectly used to retrieve snow depth (Grody and Basist,1996). This is because that the snowcover produces a positive difference between low and high-frequency channels, but theprecipitation, cold desert, and frozen ground show a similar scattering signature. Grody andBasist (1996) developed a decision tree method for the identification of snow. Theclassification method can distinguish the snow from other scattering signatures (i.e.precipitation, cold desert, frozen ground). Within the decision tree flowchart, there are four criteria related to the 85GHz channel.For its utility of SMMR brightness temperature data which do not have the 85GHz channel,we only adopted other relationships, such as the TB(19V)-TB(37V) as the scatteringsignature rather than the TB(22V)-TB(85V). For the SMMR measures, the simplifieddecision tree can be described as following relationships: 1. TB(19V)-TB(37V)>0, for scattering signature; 2. TB(22V)>258 or 258$TB(22V)%254 and TB(19V)-TB(37V)$2, for precipitation; 3. TB(19V)-TB(19H)%18 and TB(19V)-TB(37V)$10, for cold desert; 4. TB(19V)-TB(19H) 8K and TB(19V)-TB(37V) 2K and TB(37V)-TB(85V) 6K, for frozen ground. For the more detail description of the decision tree method, please see Grody and Basist(1996). In this study, we adopted the Grody’s decision tree method to obtain snow cover fromSMMR (1978-1987) and SSM/I (1987-2004). Then, the snow depth data were calculatedonly on those pixels by the snow depth retrieval algorithm. The return periods of SMMRand SSM/I measurements are about every 3-5 days depending on the latitude. To obtain the 25
    • CEOP-AEGIS Report De 6.2daily snow depth dataset, the intervals between swaths were filled up by the most recentdata available.2.5 Backup Algorithm3. Algorithm Prototyping3.1 Data AnalysisPassive microwave remote sensing data The Scanning Multichannel Microwave Radiometer (SMMR) is an imaging 5-frequencyradiometer (6, 10, 18, 21, and 37 GHz) flown on the Nimbus-7 earth satellites launched in1978. The SSM/I sensors on the DMSP satellite collect data for 4 frequencies: 19, 22, 37,and 85 GHz. Both vertical and horizontal polarizations are measured for all except 22 GHz,for which only the vertical polarization is measured. At NSIDC (National Snow and IceData Center), the SMMR and SSM/I brightness temperatures are gridded to the NSIDCEqual-Area Scalable Earth grids (EASE-Grids). Because China is located in a mid-latituderegion, we used the brightness temperature data with the global cylindrical equal-areaprojection (Armstrong and others, 1994; Knowles and others, 2002).Meteorological station snow depth observations Snow depth observations at national meteorological stations from the ChinaMeteorological Administration (CMA) were used to modify and validate the coefficient ofthe Chang algorithm. We used 178 stations within the main snow cover regions in China,covering the Northeastern China, Northwestern China, and the QTP (Qinghai-Tibet Plateau)(Figure 4). For modification of the Chang algorithm, we collected snow depth data from thedaily observations in 1980 and 1981 for SMMR, and 2003 for SSM/I, respectively. Then,snow depth data in 1983 and 1984 (for SMMR) and 1993 (for SSM/I) were used to validatethe modified algorithm.MODIS snow cover area products Hall and others (2002) described the Moderate Resolution Imaging Spectroradiometer(MODIS) snow cover area algorithm for the EOS Terra satellite. At present, the MODIS 26
    • CEOP-AEGIS Report De 6.2snow products are created as a sequence of products beginning with a swath (scene) andprogressing, through spatial and temporal transformations, to an eight-day global griddedproduct. In the NASA Goddard Space Flight Center (GSFC), the daily Climate ModelingGrid (CMG) snow product gives a global view of snow cover at 0.05 degree resolution.Snow cover extent is expressed as a percentage of snow observed in the raw MODIS cells at500 m when mapped into a grid cell of the CMG at 0.05 degree resolution. These MODISsnow cover products can be downloaded from NASA Earth Observing System DataGateway. In this study, we projected the 0.05 degree daily CMG product to register with theEASE-Grids projection for the accuracy assessment of snow area extent derived frompassive microwave satellite data.Vegetation distribution map in China Snow depth retrieval from passive microwave remote sensing data will be influenced byvegetation, in particular, the dense forest. Hu (2001) published the vegetation atlas of China(1:1,000,000), which is the most detailed and accurate vegetation map of the whole countryup to now. It was based on the result of the nationwide vegetation surveys and theirassociated researches in 50 years since 1949 and the relevant data from the aerial remotesensing and satellite images, as well as geology, pedology and climatology. In this study, wedigitized and vectorized the vegetation atlas of China, and projected it into cylindricalequal-area projection to register the EASE-GRID data. The forest area fraction will be usedto reduce the forest influence for the snow depth retrieval from passive microwavebrightness temperature data. 27
    • CEOP-AEGIS Report De 6.2 Figure 4. Position of meteorological stations within main snow cover regions in China (NWC: Northwestern China, QTP: Qinghai-Tibet Plateau, NEC: Northeastern China, and other region).Lake distribution map/Land-sea boundary Based on the results of Dong and others (2005), large water bodies will seriouslyinfluence the brightness temperature. Before the modification of snow depth retrievalalgorithm, those brightness temperature data and meteorological station data nearby thelakes or ocean were removed to eliminate the mixed pixel effect. We used the 1:1,000,000lake distribution maps from the Lake Database in China, which was produced by theNanjing Institute of Geography and Limnology, Chinese Academy of Sciences (CAS) andwas shared for scientific and educational group at Data-Sharing Network of Earth SystemScience, CAS (http://www.geodata.cn). The Data-Sharing Network also archived the1:4,000,000 coastline maps. These spatial data also was projected to register the EASE-GRID data.3.2 Prototyping of the Algorithm We adopted the Grody’s decision tree method to obtain snow cover from SMMR (1978-1987) and SSM/I (1987-2004). Then, the snow depth data were calculated only on thosepixels by the snow depth retrieval algorithm. The return periods of SMMR and SSM/Imeasurements are about every 3-5 days depending on the latitude. To obtain the daily snowdepth dataset, the intervals between swaths were filled up by the most recent data available.The flow chart to obtain the snow depth data in China can be described by Figure 5. 28
    • CEOP-AEGIS Report De 6.2Figure 5 Flow chart of snow depth data in China derived from passive microwave brightness temperature data.4. Validation Plan4.1 Introduction The validation used meteorological observations data, considering the influences fromvegetation, wet snow, precipitation, cold desert and frozen ground. The snow depthdistribution is indirectly validated by MODIS snow cover products by comparing the snowextent area from this work.4.2 ApproachAccuracy assessment (Snow depth) To assess the accuracy of snow depth retrieved from the modified algorithm, we usedmeasured snow depth data at the meteorological stations in 1983 and 1984 to compare withthe SMMR results, and that in 1993 for the SSM/I results. Both of the absolute errors lessthan 5cm hold about 65% of all data (Figure 6). The standard deviations are 6.03cm and5.61cm for SMMR and SSM/I, respectively.Accuracy assessment (Snow cover) 29
    • CEOP-AEGIS Report De 6.2 We collected MODIS snow cover products from December 3, 2000 to February 28,2001 to compare with the results of this study. Though MODIS snow cover products can notprovide snow depth information, we can compare the agreement or disagreement of MODISand SSM/I snow extent in each of SSM/I pixels by resampling the MODIS snow coverproducts into the EASE-Grids projection. For a SSM/I pixel, when the snow depth is largerthan 2cm, we consider the pixel to be snow covered. For the resampled MODIS pixel, thesnow cover area is a fraction of snow covered, and when the snow cover area is larger than50% we consider it as a snow cover pixel. Congalton (1991) described several accuracyassessment methods of remotely sensed data. First of all, we considered the MODIS snowcover products as the truth because the optical remote sensing has higher spatial resolutionand better comprehensive algorithm than the passive microwave remote sensing. Then, weestablished the error matrixes of the SSM/I results for each day according to MODIS snowcover products. Finally, two methods (overall accuracy and kappa analysis) were used toassess the accuracy. The two data sets have a good agreement by the overall accuracy analysis. The overallaccuracy is about from 0.8 to 0.9 after using Grody’s decision tree method (Grody andBasist, 1996), while the accuracy from 0.7 to 0.8 without using the method (Figure 7(a)).The results show that the overall accuracy can be improved by Grody’s decision treemethod by 10%. The Kappa analysis is a more strict method to assess the coincidence in two data sets.The Khat statistic was defined as (Congalton, 1991): (4) Where r is the number of rows in the error matrix, xii is the number of MODISobservations in row i and column i, xi+ and x+i are the marginal totals of row i and columni, respectively. N is the total number of data. The results of Khat statistics show that theaccuracy can be improved by Grody’s decision tree method by 20% (Figure 7(b)). 30
    • CEOP-AEGIS Report De 6.2 Figure 7 Accuracy assessment of overall accuracy and Kappa analysis methods based on the MODIS dailysnow cover area products from December 1, 2000 to February 28, 2001. Solid line is the results with Grody’sdecision tree method to identify the snow cover, and Dash line is the results without the decision tree method. (a) Overall accuracy, and (b) Kappa coefficient.UncertaintyEffect of Vegetation Vegetation cover has a significant influence on snow depth estimation from remotesensing data (Foster and others, 1997, 2005). In this study, we used the forest coverparameter to remove this influence (Foster and others, 1997). In fact, this method is notappropriate out for dense forest regions. We overlap the stable snow cover map with theChinese Vegetation Map and find dense forests with a large forest cover fraction (greater0.5) mainly distribute in the Xing’aling regions (Heilongjiang Province and the Eastern InterMongolia) with about 160 EASE-Grid pixels (100,000km2). Although snow depth derivedfrom the modified algorithm may be questionable, the total area of the dense forest regionsis very limited.Effect of Snow Crystal 31
    • CEOP-AEGIS Report De 6.2 The snow grain size can influence the algorithm coefficient of snow depth retrieval (e.g.formula (1) and (2)). With a snow grain size of 0.3mm the coefficient is 1.59, but with asnow grain size of 0.4mm the coefficient becomes 0.78 (Foster and others, 1997). Snowcrystal size can depend on the snowfall condition, such as the wind and temperature. It alsovaries with snow metamorphism after the snow is on the ground. In this study, wecharacterized this influence using a statistical regression method and adjusted the seasonaloffsets. These offsets can not interpret the regional differences of snow conditions.Effect of Liquid Water Content The snow depth can not be retrieved when snow is wet because the liquid water withinsnow layer will remove the volume scatter of microwave signals. Therefore, only morningbrightness temperature data were used to minimize the errors associated with melting snowin the afternoon.4.3 Validation Sites The specific validation sites still under-investigation which will be presented in latervrsion4.4 Auxiliary Measurements Still under-investigation which will be presented in later version4.5 Scaling Still under-investigation which will be presented in later version4.6 Data Protocols and Dissemination4.7 Proposed Validation Tests Still under-investigation which will be presented in later version5. Ancillary Data 32
    • CEOP-AEGIS Report De 6.2 The ancillary data need in this algorithm is: meteorological station snow depthobservations, MODIS snow cover area products, vegetation distribution map in China andlake distribution map/Land-sea boundary. Detailed information for each dataset can be fundin Section 3.1 Data Analysis6. Programming and Procedural Considerations The whole part still under-investigation which will be presented in later version6.1 Programming Issues6.2 Processing Issues6.3 Quality AssuranceReferences1. Armstrong, R. L., A. T. C. Chang, A. Rango, and E. Josberger. 1993. Snow depths and grain-size relationships with relevance for passive microwave studies, Ann. Glaciol., 17, 171–176.2. Armstrong, R. L., K. W. Knowles, M. J. Brodzik and M. A. Hardman. 1994, updated current year. DMSP SSM/I Pathfinder daily EASE-Grid brightness temperatures, [list dates of data used]. Boulder, Colorado USA: National Snow and Ice Data Center. Digital media3. Armstrong, R.L., and M.J. Brodzik. 2002. Hemispheric-scale comparison and evaluation of passive-microwave snow algorithms. Ann. Glaciol,. 34, 38-44.4. Chang, A. T. C., P.Gloersen, T. Schmugge, T. T. Wilheit, and H. J.Zwally. 1976. Microwave emission from snow and glacier ice. J. Glaciol., 16, 23-39.5. Chang, A. T. C., J. L. Foster, and D. K. Hall. 1987. Nibus-7 SMMR derived global snow cover parameters. Ann. Glaciol,. 9, 39-44.6. Chang, A. T. C., D. A. Robinson, L. Peiji, and C. Meisheng. 1992. The use of microwave radiometer data for characterizing snow storage in western China. Ann. 33
    • CEOP-AEGIS Report De 6.2 Glaciol., 16, 215-219.7. Congalton, R. 1991. A review of assessing the accuracy of classification of remotely sensed data. Remote Sens. Environ,.37, 35-46,.8. Dong, J. R., J. P.Walker, and P. R. Houser. 2005. Factors affecting remotely sensed snow water equivalent uncertainty. Remote Sens. Environ, 97, 68-82.9. England, A.W. 1975. Thermal microwave emission from a scattering layer. J. Geophys. Res., 80 (32), 4484-4496.10. Foster, J. L., A. T. C. Chang, and D. K. Hall, 1997. Comparison snow mass estimates from a prototype passive microwave snow algorithm, a revised algorithm and snow depth climatology. Remote Sens. Environ. 62, 132-142, 1997.11. Foster, J.L., C.J. Sun, J.P. Walker, R. Kelly, A.C.T. Chang, J.R. Dong, H. Powell. 2005. Quantifying the uncertainty in passive microwave snow water equivalent observations. Remote Sens. Environ. 94, 187-203.12. Grody, N C. 1991. Classification of snow cover and precipitation using the Special Sensor Microwave Imager. J. Geophys. Res., 96, 7423-7435.13. Grody, N. C., and A. N. Basist. 1996. Global identification of snowcover using SSM/I measurements. IEEE Trans. Geosci. Remote Sensing.34, 237-249.14. Hall, D. K., G. A. Riggs, V. V. Salomonson, N. E. DiGirolamo, and K. J. Bayr. 2002. MODIS snow-cover products. Remote Sens. Environ.83, 181-194.15. Hu, X. Y. 2001. The Vegetation Atlas of China (1:1,000,000). Beijing: Science press.16. Josberger, E. G., and Mognard, N. M. 2002. A passive microwave snow depth algorithm with a proxy for snow metamorphism. Hydrological Processes, 16(8), 1557- 1568.17. Kelly, R.E., A.C.T. Chang, and T. Leung T. 2003. A prototype AMSR-E global snow area and snow depth algorithm. IEEE Trans. Geosci. Remote Sens., 41(2), 230-242.18. Knowles, K., E. Njoku, R. Armstrong, and M.J. Brodzik. 2002. Nimbus-7 SMMR Pathfinder daily EASE-Grid brightness temperatures. Boulder, CO: National Snow and 34
    • CEOP-AEGIS Report De 6.2 Ice Data Center. Digital media and CD-ROM.19. Li, P. J. and D. S. Mi. 1983. Distribution of snow cover in China. Journal of glaciology and cryopedology, 5(4), 9-18. (In Chinese)20. Matzler, C. 1994. Passive microwave signatures of landscapes in winter. Meteorol. Atmos. Phys. 54, 241–260.21. Neale, C. M. U., M. L. McFarland, and K. Chang. 1990. Land-surface-type classification using microwave brightness temperatures from the special sensor microwave/imager. IEEE Trans. Geosci. Remote Sens. 28(5), 829-837.22. Qin, D., S. Liu, and P. Li. 2006. Snow cover distribution, variability, and response to climate change in Western China. J. Climate, 19(9), 1820-1833.23. Rikiishi, K. and N. Nakasato. 2006. Height dependence of the tendency for reduction in seasonal snow cover in the Himalaya and the Tibetan Plateau region, 1966-2001. Ann. Glaciol., 43, 369-377.24. Singh, P. R., and T. Y. Gan. 2000. Retrieval of snow water equivalent using passive microwave brightness temperature data. Remote Sens. Environ, 74, 275-286.25. Tait, A.B. 1998. Estimation of snow water equivalent using passive microwave radiation data. Remote Sens. Environ.64, 286-291.26. Ulaby, F., R.Moore, , and A. Fung. 1986. Microwave Remote Sensing, Artech House, Dedham, MA, Vol. III, 1602-1634.27. Wiesmann, A, and C. Matzler. 1999. Microwave emission model of layered snowpacks. Remote Sens. Environ, 70, 307-316. 35
    • CEOP-AEGIS Report De 6.2 PART IIISurface Soil Freeze/Thaw State Dataset Using The Decision Tree Classification Algorithm Authors: Rui JinAffiliations: Cold and Arid Regions Environment and Engineering Research Institute, Chinese Academy of Sciences.
    • CEOP-AEGIS Report De 6.2Surface Soil Freeze/Thaw State Dataset Using The Decision Tree Classification AlgorithmAbstract A new decision tree algorithm to classify the surface soil freeze/thaw states has beendeveloped. The algorithm uses SSM/I brightness temperatures recorded in the early morning.Three critical indices are introduced as classification criteria—the scattering index (SI), the37 GHz vertical polarization brightness temperature (T37V), and the 19 GHz polarizationdifference (PD19). And the discrimination of the desert and precipitation from frozen soil isconsidered, which improve the classification accuracy. Long time series of surface soilfreeze/thaw statuses can be obtained using this decision tree, which potentially can providea basic dataset for research on climate and cryosphere interactions, carbon cycles,hydrological processes, and general circulation models.1. Introduction Globally, about 50&106 km2 of surface soil undergoes freeze/thaw cycles annually(Kimball et al., 2001; Zhang et al., 2003a). The soil freeze/thaw status has a profoundinfluence on the energy and water exchange between the land surface and the atmosphere,the hydrological cycle, crop growth, and the carbon cycle (Cao & Chang, 1997; Goodison etal., 1998; Judge et al., 1997; Zhang & Armstrong, 2001; Zuerndorfer et al., 1990;Zuerndorfer & England, 1992). The timing, duration, and area of surface soil freeze/thawstatus can be taken as an indicator of climate change because of its sensitivity (Goodison etal., 1998; Li et al., 2008; Zhang & Armstrong, 2001; Zhang et al., 2003b). A new decision tree algorithm was developed to classify the soil freeze/thaw state withSSM/I data. New indices are introduced, and the discrimination of the desert andprecipitation from frozen soil is considered. Long time series of surface soil freeze/thawstatuses can be obtained using this decision tree, which potentially can provide a basicdataset for research on climate and cryosphere interactions, carbon cycles, hydrological 37
    • CEOP-AEGIS Report De 6.2processes, and general circulation models (Allison et al., 2001; Jin & Li, 2002; Judge et al.,1997; Zhang & Armstrong, 2001; Zuerndorfer et al., 1990).1.1 Identification1.2 Overview Many studies were published during the 1980s and 1990s on detecting the surface soilfreeze/thaw state using passive microwave radiometers such as SMMR and SSM/I. Thereare two major types of near-surface soil freeze/thaw states classification algorithmcomprising the dual-indexes algorithm (Zuerndorfer et al., 1990; Zuerndorfer et al., 1992;Judge et al., 1997; Zhang and Armstrong, 2001; Zhang et al., 2003), and change detectionalgorithm (Smith et al., 2004). All above algorithms were based on the unique microwaveradiative characteristics associated with frozen soils, such as lower thermo-dynamicaltemperature, higher emissivity and volume scattering darkening effect. (1) Dual-indexes Algorithm The dual-indexes algorithm using T37 brightness temperature and the spectral gradient(SG) between T37 and T18/T19 was most widely used in 1990s. The dual-index algorithmwas easily for the operational application with the unified thresholds throughout theresearch region, however the thresholds of both indices were determined through astatistical analysis of training samples, which need to be recalibrated when applied in otherregions (Jin and Li, 2002). (2) Change Detection Algorithm The change detection algorithm for surface soil freeze/thaw states classification wasoriginated from the active microwave remote sensing based on the time series of thebackscattering coefficient. Smith developed an algorithm applicable to passive microwaveremote sensing (Smith et al., 2004) by using the difference between the brightnesstemperature at 37 and 19 (or 18) GHz to identify the transition from frozen to thawed soil.However, the gradual process of soil temperature with freezing, the coarse spatial resolutionof the passive microwave radiometers, and the opposite effect of increased emissivity anddecreased thermal temperature of frozen soil on the brightness temperature may resulted inno abrupt changes in brightness temperature or spectral signals at the daily scale. 38
    • CEOP-AEGIS Report De 6.2 Furthermore, both of above algorithms only separate frozen and thawed soil. Thedesert in the winter season and snow were both easily misclassified as frozen soil because oftheir similar volumetric scattering characteristics (Fiore Jr & Grody, 1992; Cao & Chang,1997). In addition, precipitation may mask the radiation emitted from the land surface(Grody & Basist, 1996). Therefore, it is necessary to distinguish these types to improve theclassification accuracy of frozen/thawed soil.2. Algorithm Description2.1 Introduction A new decision tree algorithm was developed to classify the soil freeze/thaw state withSSM/I data. New indices, i.e. scattering index, polarization difference, are introduced, andthe discrimination of the desert and precipitation from frozen soil is considered, which willimprove the classification accuracy of the surface soil freeze/thaw states.2.2 Targets to be observed Due to the coarse spatial resolution of passive microwave remote sensing, “pure”training samples from SSM/I data need to be collected to analyze the brightness temperaturecharacteristics of different land surface types and to determine the threshold of each node inthe decision tree. We selected four types of samples, including frozen soil, thawed soil,desert and snow. The latter two sample types have volume scattering characteristics similarto those of frozen soil. Grody’s method was adequately validated by previous research(Grody & Basist, 1996), so it was adopted directly to identify precipitation.2.3 Radiative Transfer Problem The soil brightness temperature Tb can be simply expressed as the product of the soileffective temperature Teff and the emissivity e if we consider the soil as a semi-infinitemedium (Ulaby et al., 1986). When the soil freezes, its thermodynamic temperaturedecreases, but the emissivity increases due to the decreased permittivity. Therefore, thechange in radiobrightness may be either positive or negative, mainly depending on the soilmoisture (Zuerndorfer et al., 1990; Zuerndorfer & England, 1992). For dry soil, the soilemissivity changes little between the thawed and frozen states, so the brightness temperaturegenerally decreases with soil temperature. For moist soil, the emissivity increasessignificantly when it changes from the thawed to the frozen state, but the Teff may only 39
    • CEOP-AEGIS Report De 6.2drop a few Kelvin, so the Tb may increase (Dobson et al., 1985; Jin & Li, 2002; Zuerndorferet al., 1990). According to the above analysis, although the brightness temperature of frozensoil is low, the brightness temperature cannot be taken as an unambiguous index to identifythe soil freeze/thaw status (Zuerndorfer et al., 1990). Moreover, the brightness temperatureof moist regions near rivers and lakes is also low because of abundant moisture and thecorresponding lower emissivity, which may cause confusion in distinguishing betweenfrozen soil and very moist soil when using the brightness temperature alone (England, 1990). Both the permittivity and the dielectric loss factor decrease with soil freezing (Hoekstraet al., 1974). The dielectric loss factor is reduced more than the permittivity, resulting in adecrease of the loss tangent ( ), which means that the emission depth will begreater and there will be volume scattering. The effective emission depth Ze (1-e-1 of thetotal emission in the zenith direction originates above Ze) is about 10% of the free spacewavelength in moist soil, and increases to more than 30% of the free space wavelengthwhen the soil is frozen (Zuerndorfer et al., 1990). The higher the microwave frequency themore heterogeneous the soil column is, and the stronger the scattering volume will be (Cao& Chang, 1997; England et al., 1991; Zuerndorfer et al., 1990). The brightness temperatureof frozen soil at high frequencies is therefore generally lower than that at low frequencies. In summary, the microwave emissions and scattering characteristics have severaldifferences between frozen and thawed soil, such as a lower thermodynamic temperatureand brightness temperature, a higher emissivity, and a stronger volume scatter darkeningeffect that can be used to select proper indices to identify the soil freeze/thaw state.2.4 Mathematical Basis of the Algorithm There are three critical indices used in the decision tree: (1) Scattering Index (SI): The SI was proposed based on a regression analysis of thetraining data covering various land surface types and atmospheric conditions (Grody, 1991),expressed as follows: , (1) 40
    • CEOP-AEGIS Report De 6.2 where, T19V, T22V and T85V are vertical polarization brightness temperatures at 19,22 and 85 GHz, respectively. F represents the simulated 85 GHz vertical polarizationbrightness temperature under the ideal condition of no scattering effect. SI is the deviationof the actual SSM/I T85V observation from F. Because the volume scattering darkening offrozen soil at 85 GHz is stronger than that at lower frequencies, SI is a more reliable indexthan SG for distinguishing between scatterering and non-scatterering samples. (2) 37 GHz vertical polarization brightness temperature (T37V): A correlation analysiswas carried out between the SSM/I brightness temperature at each channel and the SMTMS4 cm deep soil temperature, revealing that T37V has the highest correlation coefficient of0.87 with the 4 cm deep soil temperature. T37V was therefore used as a criterion to indicatethe thermal regime of the surface soil. (3) 19 GHz Polarization Difference (PD19 = T19V - T19H). The polarizationdifference at 19 GHz reveals the surface roughness. A rougher surface decreases thecoherent reflection and increases incoherent scattering, resulting in the tendency of thesurface reflectivity to be independent of polarization, diminishing the polarization difference.The PD19 was used to identify the desert, which has a relatively small roughness.2.5 Description of Retrieval Concept2.6 Description of Retrieval Algorithm2.7 Backup Algorithm3. Algorithm Prototyping3.1 Data Analysis3.1.1 Analysis of the brightness temperature characteristics of each landsurface type The variation of the time series of the above three indices was analyzed for eachsample type, providing a priori knowledge necessary to create a decision tree. Frozen/thawed soil Figure 1 shows the time series of T37V, SI and PD19 at the Tuotuohe and MS3608stations from June 29, 1997 to August 31, 1998. The SMTMS 4 cm deep soil temperatures 41
    • CEOP-AEGIS Report De 6.2and soil moistures are also shown as ancillary information to indicate the surface soilfreeze/thaw status. Both stations are located in the seasonally frozen ground region. The soilmoisture of MS3608 was higher than that of Tuotuohe. Although the hydrothermal conditions are different between the two stations, the threeindices have many characteristics in common when the soil is frozen or thawed. In themiddle of October, the 4 cm deep soil temperature fell below the soil freezing point; theliquid water in the soil changed its phase to ice and suddenly dropped. The 37 GHzbrightness temperature therefore decreased, and the SI increased due to volume scatteringdarkening. When the reverse phase change process occurred during middle to late April ofthe next year, the 4 cm deep soil temperature increased; the 37 GHz brightness temperatureaccordingly increased and the SI decreased due to dominant surface scattering. The frozensoil scatters with an SI between 10 and 3 because the volume fraction of soil matrix and iceparticles in the frozen soil is very large, about 0.5 to 0.8, which results in the attenuation ofthe volume scattering effect. The high value of SI at the MS3608 station in December 1997resulted from the snow cover. The PD19 of frozen soil fluctuated modestly with soiltemperature and soil moisture, and was commonly smaller than 25. (1) Desert Two years (1999-2000) of SSM/I brightness temperatures and daily mean airtemperatures were acquired for the Tazhong station, located in the hinterland of theTaklimakan desert and operated by the CMA (China Meteorological Administration). Therewere no soil temperature observations at the Tazhong station. The polarization difference ofthe desert at each SSM/I channel was larger than that of other land types because it issmoother (Neale et al., 1990). Fig. 2 shows that the PD19 of the desert was above 30 formost of the year, the SI was mainly between 5 and 10, and the brightness temperaturevariation of the desert agreed well with the air temperature variation due to the very lowmoisture content in the desert. Compared to dry snow and frozen ground, the desert is aweaker scatterer due to the large volume fraction, and the homogeneous particle size anddielectric properties. The effective emissivity of the desert at 37 GHz vertical polarizationwas about 0.95 on average, calculated by dividing the 37 GHz vertical polarizationbrightness temperature by the daily mean air temperature. 42
    • CEOP-AEGIS Report De 6.2 (a) Tuotuohe (b) MS3608Fig. 1 Time series of T37V, SI and PD19 of frozen/thawed soil at Tuotuohe (a) and MS3608 (b) station 43
    • CEOP-AEGIS Report De 6.2 Fig. 2 Time series of T37V, SI and PD19 of the desert at Tazhong station, Taklamakan Desert. (2) Snow cover The microwave radiative characteristics of snow cover are very similar to those offrozen soil, including a low temperature, a low complex dielectric constant, and strongvolume scattering (Edgeton et al., 1971). The shallow and dry snow is transparent tomicrowaves, so most of the brightness contribution comes from the underlying soil, whichmay cause confusion in separating shallow snow and frozen soil. The snow depth for eachsnow sample was calculated using Equation 2 (Che et al., 2008). The SI of shallow snowsamples (<10 cm) are generally between 0 and 20, close to the SI of frozen soil. An increasein the snow depth enhances the volume scattering effect in snow. Therefore, the SI of snowdeeper than 10 cm is above 30, and even reaches 80 for deep snow. (2) Furthermore, the patchily-distributed shallow snow cover over China cannoteffectively play a role in the heat preservation and insulation of the underlying soil. The soilunder the snow cover remained frozen most of the time (Cao et al., 1997). The snow coverwas therefore not targeted as a classification type in this decision tree.3.1.2 Cluster analysis and decision tree for freeze/thaw status classification The spatial distribution of the randomly selected training samples shows that each typeconverges as a cluster in the 3-dimensional space composed by the three indices (Fig. 3a).The decision rules in the decision tree (Fig. 4) were determined from the mean and standarddeviation of each index calculated for each type. These rules are: (1) The PD19 of desert is 36.28±2!2.22 (mean±2!standard deviation), obviously largerthan that of other land surface types. A threshold of PD19>30 was used to identify mostdesert (Fig. 3b), and the remaining desert can be further separated in the sub-branches of thedecision tree by using PD19>25. (2) Both frozen soil and snow are strong scatterers withhigh SI values. The threshold of SI"5.0 was used to separate more than 95% (18.69±2&6.04)(Fig. 3c) of frozen soil samples into the left branch of the decision tree (Fig. 4). (3) In termsof brightness temperature, the T37V of frozen soil is 232.57±2&9.40, while that of thawedsoil is 259.1±2&5.33. The threshold of T37V=252 K can separate frozen and thawed soilsamples with the least misclassification (Fig. 3a and d). (4) Because of the strong scatteringfrom ice particles, some of the precipitation pixels would be divided into the left branch of 44
    • CEOP-AEGIS Report De 6.2the decision tree after using SI"5.0. However, the precipitation is still warmer than frozenground. Grody’s index T22V"165+0.49&T85V was therefore directly adopted to identifydeep convective precipitation with ice particles. Furthermore, the discriminantT85V/T19V<0.9 was used to identify hail clouds and rainstorms (He & Chen, 2006). Forprecipitation with weak scattering, the discrimination of 254K$T22V$258K and SI$2 wereused in the right branch of the decision tree (Grody & Basist, 1996). The decision tree toclassify soil surface freeze/thaw status was finally set up in Fig. 4.3.2 Prototyping of the Algorithm4. Validation Plan4.1 Introduction In order to evaluate the accuracy of the decision tree algorithm, the daily classificationresults were first validated by SMTMS 4 cm deep soil temperature observations at the localtime of 6:00 am for eight stations on the Qinghai-Tibetan Plateau measured during CEOP-EOP3. We also conducted a grid-to-grid validation by the Kappa statistics using the map ofgeocryological regionalization and classification in China (Zhou et al., 2000) as a reference(Fig. 5b). 45
    • CEOP-AEGIS Report De 6.2 Fig. 3 Cluster analysis on the samples of frozen soil, thawed soil, desert and snow (a) and the statistical characteristics of PD19 (b), SI (c) and T37V (d) for different land surface types.4.2 Approach In order to evaluate the accuracy of the decision tree algorithm, the daily classificationresults were first validated by SMTMS 4 cm deep soil temperature observations at the localtime of 6:00 am for eight stations on the Qinghai-Tibetan Plateau measured during CEOP-EOP3. Only the classification of frozen or thawed soil was validated. The number ofvalidated pixels was 1695, and the number of misclassifications was 219. The averageclassification accuracy reached 87% (Table 1). 46
    • CEOP-AEGIS Report De 6.2 Fig. 4 Flow chart of the decision tree for the surface soil freeze/thaw status classification. Table 1. Validation of the classification results by 4 cm deep soil temperature observations at selected CEOP stations. Station Validation data Misclassified data Accuracy (%) AMDO 219 25 88.58 MS3608 207 24 88.41 MS3637 209 27 87.08 D66 217 15 93.09 D105 209 39 91.34 D110 211 41 80.57 BJ 207 19 90.82 Tuotuohe 216 29 86.57 Total 1695 219 87.08 As for the misclassification, among 219 pixels, 18 cases of thawed soil weremisclassified into the desert type due to the high PD19 value of the flat and dry surfaces.This kind of misclassification can be avoided using a reliable desert map. The freeze orthaw statuses of the remaining 201 pixels were misclassified. We first analyzed this kind ofmisclassification from the viewpoint of soil temperature; it was found that 40% and 73% ofthe misclassification occurred when the 4 cm deep soil temperature was in the range of -0.5°C-0.5 °C and -2.0 °C-2.0 °C, respectively, according to the frequency histogram ofmisclassification pixels numbered against 4 cm deep soil temperatures (Fig. 6a). Then wedetermined that from the viewpoint of timing, most misclassifications occurred during thetransition period between the cold and warm seasons. For instance, the proportions of errorin April-May and September-October to the total number of misclassifications were about33% and 38%, respectively (Fig. 6b). It is understandable that most of the misclassificationswere in the transition periods because the heterogeneity within pixels is more significant atthese times. Furthermore, the frozen soil is defined according to the temperature regime. 47
    • CEOP-AEGIS Report De 6.2However, most of the water in the soil still remains in the liquid state when the soiltemperature is just below the soil freezing point, which shows similar dielectric propertiesas the thawed soil and would result in misclassification between frozen and thawed soil. Fig. 5 actual number of frozen days in China (a) and Map of geocryological regionalization andclassification in China (b) for the period from Oct. 1, 2002 to Sep. 31, 2003. We also conducted a grid-to-grid validation by the Kappa statistics using the map ofgeocryological regionalization and classification in China (Zhou et al., 2000) as a reference(Fig. 5b), a widely used method to measure the agreement between the reference data andthe classified result in grid format (Congalton, 1991). For comparability, we first obtainedthe actual number of frozen days for one year—during the period from October 1, 2002 toSeptember 31, 2003—over China based on the pentad compositions by counting the frozendays for each pixel (Fig. 5a). Then, the map of the frozen soil area was delineated byassuming that the pixels that were frozen for more than 15 days should be seasonally frozensoil or permafrost. The pixels that were frozen for less than 15 days represent short timefrozen soil (Zhou et al., 2000). The new frozen soil area map derived from the decision treeclassification result using the SSM/I data was compared with the reference map. The resultsshow that the overall classification accuracy was 91.66%, which was calculated from theerror matrix, and the Kappa index was 80.5%. The boundary between the frozen and thawedsoil in the new map (Fig. 5a) was consistent with the southern limit of seasonally frozenground in the reference map (Fig. 5b). 48
    • CEOP-AEGIS Report De 6.2 Fig. 6 Frequency histograms of the soil temperature and occurrence time for the misclassified pixels.4.3 Validation Sites Table 2. Stations used in algorithm development and validation (Wang et al., 2000, Zhou et al., 2000)Station Situation Altitude(m) Geocryological regionalization Landscape 91.63ºE;AMDO 4700 predominantly continuous permafrost subhumid alpine 32.24ºN 91.78ºE;MS3608 4610 predominantly continuous and island permafrost subhumid alpine 31.23ºN 91.66ºE;MS3637 4820 predominantly continuous and island permafrost subhumid alpine 31.02ºN 93.78ºE; D66 4600 predominantly continuous permafrost semi-arid desert steppe 35.52ºN 91.94ºE; D105 5020 predominantly continuous permafrost N/A 33.07ºN 91.88ºE; subhumid swamp D110 5070 predominantly continuous permafrost 32.69ºN meadow 91.90ºE; BJ 4509 predominantly continuous and island permafrost N/A 31.37ºN 92.43ºE;Tuotuohe 4535 predominantly continuous permafrost semi-arid alpine 34.22ºN 83.4ºE;Tazhong 1099 desert desert 39.0ºN4.4 Auxiliary Measurements4.5 Scaling4.6 Data Protocols and Dissemination4.7 Proposed Validation Tests5. Ancillary Data The daily F13 SSM/I brightness temperatures during the period from Oct. 1, 2002 to Sep.30, 2003 were provided by the National Snow and Ice Data Center (NSIDC) at theUniversity of Colorado in the Equal Area Scalable Earth Grid (EASE-Grid) format. Theglobal level 3 products were used in this study, and the spatial resolution is 25 km. TheSSM/I radiometer passes over the same region twice daily at 6:00 (descending orbit) and18:00 (ascending orbit) local time. Because the surface soil temperature at 6:00 local timeapproximates the daily minimal surface temperature, the descending orbit data was selected 49
    • CEOP-AEGIS Report De 6.2to capture the daily freeze/thaw cycle. The atmospheric influence was not corrected for theSSM/I brightness temperature since it has an insignificant effect. Due to the coarse spatial resolution of passive microwave remote sensing, “pure”training samples from SSM/I data need to be collected to analyze the brightness temperaturecharacteristics of different land surface types and to determine the threshold of each node inthe decision tree. We selected four types of samples, including frozen soil, thawed soil,desert and snow. The ancillary data used to ensure the purity of samples include the dailyMODIS snow cover product with 0.05º resolution (MOD10C1), the map of geocryologicalregionalization and classification in China, and the Chinese land use map at 1:1,000,000scale. All the training samples were randomly selected according to the following criteria,and a training sample corresponds to a SSM/I pixel. The frozen soil samples were selectedin the seasonally frozen ground region and the permafrost region from the map ofgeocryological regionalization and classification in China from winter data. The thawed soilsamples were picked from the unfrozen region, and the short-term frozen ground regionfrom summer data. The desert samples came from the hinterland of Taklimakan accordingto the Chinese land use map. The snow samples were determined if the snow fractionderived from MODIS snow cover products was larger than 0.75 in a 25 km EASE-grid pixel.The number of samples of frozen soil, thawed soil, desert and snow are 207, 317, 467 and362, respectively. The 4 cm deep soil temperatures observed by the Soil Moisture and TemperatureMeasuring System (SMTMS) of the GEWEX-Coordinated Enhanced Observing Period(CEOP) (http://monsoon.t.u-tokyo.ac.jp/ceop2/index.html) were used as validation data.Table 1 shows the locations of the CEOP stations used in the paper.6. Programming and Procedural Considerations6.1 Programming Issues6.2 Processing Issues6.3 Quality AssuranceReferences 50
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    • AcknowledgmentsThe work described in this publication has been supported by the EuropeanCommission (Call FP7-ENV-2007-1 Grant nr. 212921) as part of the CEOP-AEGIS project (http://www.ceop-aegis.org) coordinated by the Universityof Strasbourg, France.