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De6.1 report Document Transcript

  • 1. Sample data set and Report on retrievalperformance based on MODIS and AMSR-E data Deliverable De6.1 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
  • 2. 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.
  • 3. CEOP-AEGIS Report De 6.1 CONTENTS PART I A Report for Snow Cover Area Retrieval by MODIS Data1. Task .................................................................................................................................................................. 12. Data .................................................................................................................................................................. 13. Algorithm......................................................................................................................................................... 24. Validation......................................................................................................................................................... 65. References....................................................................................................................................................... 7 PART II Surface Soil Freeze/Thaw State Dataset Using The Decision Tree Classification Algorithm1. Task ............................................................................................................................................................ 102. Data and method ........................................................................................................................................... 10 2.1 Data .......................................................................................................................................................... 10 2.2 Classification indices............................................................................................................................... 11 2.3 Cluster analysis and decision tree for freeze/thaw status classification............................................. 153. Validation....................................................................................................................................................... 164. Summary........................................................................................................................................................ 195. References...................................................................................................................................................... 20 PART III Snow Depth Derived From Passive Microwave Remote Sensing Data in China and Snow Data Assimilation Method1. Task ................................................................................................................................................................ 242. Data ................................................................................................................................................................ 243. Method ........................................................................................................................................................... 27 3.1 Snow depth derived from passive microwave remote sensing data ................................................... 27 3.2 Assimilating of passive microwave remote sensing data ..................................................................... 314. Accuracy assessment of passive microwave snow products...................................................................... 335. Results ........................................................................................................................................................... 356. References..................................................................................................................................................... 40 PART IV Providing Soil Parameter Data Sets for The Entire Plateau from A Microwave Land Data Assimilation System1. Task ............................................................................................................................................................ 452. Algorithm................................................................................................................................................... 453. Data ............................................................................................................................................................ 464. Test estimated soil moisture and parameters......................................................................................... 465. Evaluation of optimized parameter values ............................................................................................. 486. References.................................................................................................................................................. 49 II
  • 4. CEOP-AEGIS Report De 6.1 III
  • 5. CEOP-AEGIS Report De 6.1 PART I A Report for Snow Cover Area Retrieval by MODIS Data Authors: Xiaohua Hao, Jian Wang, Hongyi Li, Zhe LiAffiliations: Cold and Arid Regions Environment and Engineering Research Institute, Chinese Academy of Sciences (CAREERI, CAS).
  • 6. CEOP-AEGIS Report De 6.1 A Report for Snow Cover Area Retrieval by MODIS Data1. Task 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 exceptcloud. 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.2. Data Mapping of the MODIS snow cover use the elevation data, MODIS series data andLandsat-ETM+ data.The Digital Elevation Model (DEM) of the area at 500 m spatialresolution was created from SRTM (Shuttle Radar Topography Mission) data at 3 arc-seconds, which is 1/1200th of a degree of latitude and longitude, or about 90 meters as asource of topography correction. From the DEM dataset, information about the slope, aspectand illumination according to the sun angle and elevation were generated for input to thetopographic corrections algorithms for MODIS image.In the new algorithm, we rely onMOD09 surface reflectance products (MOD09GA, MYD09GHK) to map the MODIS snowcover. The data can be obtained from the National Snow and Ice Data Center DistributedData Archive. Six MOD09 tiles (h23v05, h24v05, h25v05, h26v05, h2506, h26v06) wereused in the study region. Other MODIS product suite that include cloud mask data (MOD35and MYD35) and temperature data (MOD11A1 and MYD11A1) were regard as auxiliaryinputs. The MODIS daily snow cover product (MOD10A1 and MYD10A1) is regard as thereference data of the snow cover from the new algorithms. Landsat-ETM+ data provide ahigh-resolution view of snow cover that can be compared with the MODIS and operationalsnow-cover products. In the study, Landsat-ETM+ path 143 row 30, path 136 row 38, 1
  • 7. CEOP-AEGIS Report De 6.1path134 row 38, path 136 row 39, path134 row 40 path were used to produce a validationdataset for the MODIS snow cover products. The figure1 shows the detail of study region. 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 40.3. Algorithm 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. To getting the true ground reflectance the topographycorrection of the MOD09 is necessary in QTP. Law (2004) tested and compared threetopographic correction methods, which are the Cosine Correction, Minnaert Correction anda CIVCO model. By comparing, he offered an improved CIVCO model. In our study, weused the improved CIVCO model. The CIVCO method used here is modified from the twostage normalization proposed by Civco, 1989, and consists of two stages. In the first stage,shaded relief models, corresponding to the solar illumination conditions at the time of thesatellite image are computed using the DEM data. This requires the input of the solarazimuth and altitude provided by the metadata of the satellite image. The resulting shadedrelief model would have values between 0 and 1. After the model is created, a 2
  • 8. CEOP-AEGIS Report De 6.1transformation of each of the original bands of the satellite image is performed to derivetopographically normalized images using equation (1) and (2). (1) ( 2) where !Ref"ij= the normalized radiance data for pixel(i, j) in band("), Ref"ij= the rawradiance data for pixel(i, j) in band("), µk= the mean value for the entire scaled shaded reliefmodel (0,1), µij= the scaled (0,1) illumination value for pixel(i, j), C" = the correctioncoefficient for band("), N" = the mean on the slope facing away the sun in the uncalibrateddata for the forest category, S" = the mean on the slope facing to the sun in the uncalibrateddata for the forest category, µk = the mean value for the entire scaled shaded relief model ,µN = the mean of the illumination of forest on the slope facing away from the sun., µS = themean of the illumination of forest on the slope facing to the sun. By the topography correction, we can get the MODIS surface reflectance. It willimprove the accuracy of snow cover mapping in mountainous regions. 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 is0.40. The NDSI values of the MODIS scenes greater than or equal to 0.40 represent snowcover pixels. In addition, since water may also have an NDSI 0.4, an additional test isnecessary to separate snow and water. Snow and water may be discriminated because thereflectance of water is <11% in MODIS band 2. Hence, if the reflectance of MODIS band 4>11%, and the NDSI 0.40, the pixel is initially considered snow covered. However,validation of the current NDSI threshold has being accomplished only by the measurementsin the United States and Europe. In China, therefore, there is not reliable NDSI threshold 3
  • 9. CEOP-AEGIS Report De 6.1value for the MODIS 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”with which 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) ofthe 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% In forested locations, to correctly classify these forests as snow covered, a lower NDSIthreshold is employed. The normalized difference vegetation index (NDVI) and the NDSIare used together in order to discriminate between snow-free and snow covered forests. 4
  • 10. CEOP-AEGIS Report De 6.1(Klein et al., 1998). Last, a threshold of 10% in MODIS band 4 was used to prevent pixelswith very low visible reflectances, for example black spruce stands, from being classified assnow as has previously been suggested (Dozier, 1989). In addition, the MODIS cloudmasking data product (MOD35) and MODIS temperature mask product (MOD11) wereserved as inputs for algorithm. MODIS cloud masking data product was used to map MODIS snow cover product.Nevertheless, the ground object under cloud remains unknown. Whether in MODIS terra orMODIS aqua daily snow cover product, either way, its always was effected by large cloud.In the context of remote sensing, image fusion consists of merging images from differentsources, which hold information of a different nature, to create a synthesized image thatretains the most desirable characteristics of each source (Pohl & Genderen, 1998). In mystudy, 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. Thecompositing technique also minimizes cloud cover. The figure 2 shows the flow process ofour new MODIS snow cover map algorithm. 5
  • 11. CEOP-AEGIS Report De 6.1 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.4. Validation 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. 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 toMarch 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 at 6
  • 12. CEOP-AEGIS Report De 6.1 91.3%, 90.6%, 87.9% respectively in all climatic stations. However, the overall accuracy of the snow cover products in mountain regions is low. In mountain climatic stations the snow omission of the three products is 32.4 21.7% 36.3% respectively. The cloud limitation ratio 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-S10 snow products shows that the snow identification ability of MODIS are more accuracy than VGT-S10 snow cover products. However, the VGT-S10 snow cover products are little affected by cloud than MODIS snow cover products. We’ll measure the snow properties in the QTP-Naqu. Lake Namtso in future. The snow density, snow water liquid, snow grain size, snow temperature and snow pit works were done and the data were used to validate and develop the snow retrieval algorithms. Figure 3 shows the sampling plan in field. Figure 3. The sampling plan of snow measurement in field. In addition, the high-resolution remote sensing data, such as TM, ETM+, ASTER, also were applied to validate the new MODIS snow cover map.5. References 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. 7
  • 13. CEOP-AEGIS Report De 6.1Civco D L. Topographic Normalization of Landsat Thematic Mapper Digital Imagery[J]. Photogrammetric Engineering and Remote Sensing. 1989, 55(9): 1303-1309.Dozier, J. Spectral signature of alpine snow cover from the Landsat Thematic Mapper, Remote Sensing of Environment. 1989, 28: 9-22.Foster, 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.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.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.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.Rango, A. Snow hydrology processes and remote sensing. Hydrological Processes. 1993, 7:121-138.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. Maximum normalized difference vegetation index images for sub-Saharan Africa for 1983-1985, International Journal of Remote Sensing, 1986,7: 1383-1384. 8
  • 14. CEOP-AEGIS Report De 6.1 PART II Surface 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 (CAREERI, CAS).
  • 15. CEOP-AEGIS Report De 6.1 Surface soil freeze/thaw state dataset using the decision tree classification algorithm1. Task We have developed a new decision tree algorithm to classify the surface soilfreeze/thaw states. The algorithm uses SSM/I brightness temperatures recorded in the earlymorning. Three critical indices are introduced as classification criteria—the scattering index(SI), the 37 GHz vertical polarization brightness temperature (T37V), and the 19 GHzpolarization difference (PD19). And the discrimination of the desert and precipitation fromfrozen soil is considered, which improve the classification accuracy. Long time series ofsurface soil freeze/thaw statuses can be obtained using this decision tree, which potentiallycan provide a basic dataset for research on climate and cryosphere interactions, carboncycles, hydrological processes, and general circulation models.2. Data and method 2.1 Data The daily F13 SSM/I brightness temperatures during the period from Oct. 1, 2002 toSep. 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(Armstrong et al., 1994). The global level 3 products were used in this study, and the spatialresolution is 25 km. The SSM/I radiometer passes over the same region twice daily at 6:00(descending orbit) and 18:00 (ascending orbit) local time. Because the surface soiltemperature at 6:00 local time approximates the daily minimal surface temperature, thedescending orbit data was selected to capture the daily freeze/thaw cycle (Zhang &Armstrong, 2001). The atmospheric influence was not corrected for the SSM/I brightnesstemperature since it has an insignificant effect (Judge et al., 1997). 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 10
  • 16. CEOP-AEGIS Report De 6.1(Grody & Basist, 1996), so it was adopted directly to identify precipitation. The ancillarydata used to ensure the purity of samples include the daily MODIS snow cover product with0.05º resolution (MOD10C1) (Hall et al., 2006), the map of geocryological regionalizationand classification in China (Zhou et al., 2000), 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) (Koike, 2004) were used asvalidation data. Table 1 shows the locations of the CEOP stations used in the paper. 2.2 Classification indices 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) 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. 11
  • 17. CEOP-AEGIS Report De 6.1 (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. 2.3 Analysis of the brightness temperature characteristics of each land surface 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. (1) 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 temperaturesand 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. 12
  • 18. CEOP-AEGIS Report De 6.1 (a) Tuotuohe (b) MS3608 Fig. 1 Time series of T37V, SI and PD19 of frozen/thawed soil at Tuotuohe (a) and MS3608 (b) stations. (2) Desert Two years (1999-2000) of SSM/I brightness temperatures and daily mean airtemperatures were acquired for the Tazhong station (Table 1), located in the hinterland ofthe Taklimakan desert and operated by the CMA (China Meteorological Administration).There were no soil temperature observations at the Tazhong station. The polarizationdifference of the desert at each SSM/I channel was larger than that of other land typesbecause it is smoother (Neale et al., 1990). Fig. 2 shows that the PD19 of the desert wasabove 30 for most of the year, the SI was mainly between 5 and 10, and the brightnesstemperature variation of the desert agreed well with the air temperature variation due to thevery low moisture content in the desert. Compared to dry snow and frozen ground, thedesert is a weaker scatterer due to the large volume fraction, and the homogeneous particlesize and dielectric properties. The effective emissivity of the desert at 37 GHz vertical 13
  • 19. CEOP-AEGIS Report De 6.1polarization was about 0.95 on average, calculated by dividing the 37 GHz verticalpolarization brightness temperature by the daily mean air temperature. Table 1. Stations used in algorithm development and validation (Wang et al., 2000, Zhou et al., 2000)Station Situation Altitude(m) Geocryological regionalization LandscapeAMDO 91.63ºE; 4700 predominantly continuous permafrost subhumid alpine 32.24ºNMS3608 91.78ºE; 4610 predominantly continuous and island permafrost subhumid alpine 31.23ºNMS3637 91.66ºE; 4820 predominantly continuous and island permafrost subhumid alpine 31.02ºN D66 93.78ºE; 4600 predominantly continuous permafrost semi-arid desert steppe 35.52ºN D105 91.94ºE; 5020 predominantly continuous permafrost N/A 33.07ºN D110 91.88ºE; 5070 predominantly continuous permafrost subhumid swamp 32.69ºN meadow BJ 91.90ºE; 4509 predominantly continuous and island permafrost N/A 31.37ºNTuotuohe 92.43ºE; 4535 predominantly continuous permafrost semi-arid alpine 34.22ºNTazhong 83.4ºE; 1099 desert desert 39.0ºN Fig. 2 Time series of T37V, SI and PD19 of the desert at Tazhong station, Taklamakan Desert. (3) 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 increase 14
  • 20. CEOP-AEGIS Report De 6.1in 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.2.3 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$5.33. The threshold of T37V=252 K can separate frozen and thawed soilsamples with the least misclassification (Fig. 3a and d). (4) Because of the strong scatteringfrom ice particles, some of the precipitation pixels would be divided into the left branch ofthe decision tree after using SI"5.0. However, the precipitation is still warmer than frozenground. Grody’s index T22V"165+0.49$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. 15
  • 21. CEOP-AEGIS Report De 6.1 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.3. Validation 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 2). 16
  • 22. CEOP-AEGIS Report De 6.1 Fig. 4 Flow chart of the decision tree for the surface soil freeze/thaw status classification. 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. 5a). 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. 5b). 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.However, 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. 17
  • 23. CEOP-AEGIS Report De 6.1Table 2. 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 Fig. 5 Frequency histograms of the soil temperature and occurrence time for the misclassified pixels. 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. 6b), 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. 6a). 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 thawed 18
  • 24. CEOP-AEGIS Report De 6.1soil in the new map (Fig. 6a) was consistent with the southern limit of seasonally frozenground in the reference map (Fig. 6b).Fig. 6 actual number of frozen days in China (a) and Map of geocryological regionalization and classification in China (b) for the period from Oct. 1, 2002 to Sep. 31, 2003.4. Summary A decision tree algorithm was developed to identify the surface soil freeze/thaw statestaking the influence of the desert and precipitation into account. The more reliable SI wasintroduced into this decision tree instead of SG to identify the scatterers. The averageaccuracy of the classification result was 87%, which was validated against the 4 cm deepsoil temperature observations. Most misclassifications occurred when the soil temperatureswere near the soil freezing point and during the transition period between the warm and coldseasons. A grid-to-grid Kappa analysis was also conducted to evaluate the consistencybetween the map of the actual number of frozen days obtained using the decision tree 19
  • 25. CEOP-AEGIS Report De 6.1classification algorithm and the map of geocryological regionalization and classification inChina. The results showed that the overall classification accuracy was 91.7%, while theKappa index was 80.5%. Both validation results show that this new decision tree algorithm based on SSM/Ibrightness temperature can produce a long time series of surface soil freeze/thaw status fromthe launch of SSM/I in 1987 until now with an accuracy capable of providing a dataset toanalyze the timing, duration and areal extent of surface soil freeze/thaw status for theresearch on climate and cryosphere interactions, carbon cycles, and hydrological processesin cold regions.5. ReferencesAllison, I., Barry, R. G., & Goodison, B. E. (2001). Climate and Cryosphere (CliC) project science and co-ordination plan. WCRP-114/WMO/TD No.1053Armstrong, R. L., Knowles K. W., & Brodzik M. J. et al. (1994). DMSP SSM/I Pathfinder daily EASE-Grid brightness temperatures. Boulder, Colorado USA: National Snow and Ice Data Center. Digital media.Bartsch, A., Kidd, R. A. & Wagner, W. et al. (2007). Temporal and spatial variability of the beginning and end of daily spring freeze/thaw cycles derived from scatterometer data. Remote Sensing of Environment, 106 (3): 360-374Cao, M. S., Li, X., & Wang, J. et al. (2006). Remote sensing of cryosphere. Beijing: Science Press. in ChineseCao, M. S., Chang, A. C. T. (1997). Monitoring terrain soil freeze/thaw condition on Qinghai Plateau in spring and autumn using microwave remote sensing. Journal of Remote Sensing, 1(2): 139-144. in ChineseChe, T., Li, X., Jin. R. et al. (2008). Snow depth derived from passive microwave remote sensing data in China. Annals of Glaciology, 49: 145-154Congalton, R. G. (1991). A review of assessing the accuracy of classifications of remotely sensed data. Remote sensing of Environment, 37: 35-46Dobson, M. C., Ulaby, F. T., Hallikainen, M. et al. (1985). Microwave dielectric behavior of wet soil- Part : four-component dielectric mixing models. IEEE Transactions on Geoscience and Remote Sensing, GE-23: 35-46Edgeton, A. T., Stogryn, A., & Poe, G. (1971). Microwave radiometric investigations of snowpack. Final Rep. 1285R-4 of Contract 14-08-001England, A. W., Galantowicz, J. F. & Zuerndorfer, B. W. (1991). A volume scattering explanation for the negative spectral gradient of frozen soil. International Geoscience and Remote Sensing Symposium, 3: 1175-1177England, A. W. (1990). Radiobrightness of diurnally heated, freezing soil. IEEE Transactions on Geoscience and Remote Sensing, 28(4): 464-476Fiore Jr, J. V. & Grody, N. C. (1992). Classification of snow cover and precipitation using SSM/I measurement: case studies. International Journal of Remote Sensing, 13(17): 3349-3361Frolking, S., McDonald, K. C. & Kimbal, J. S. et al. (1999). Using the space-borne NASA scatterometer (NSCAT) to determine the frozen and thawed seasons. Journal of Geophysical Research, 104(D22): 27895-27907Givri, J. R. (1997). The extension of the split window technique to passive microwave surface temperature assessment. International Journal of Remote Sensing, 18(2): 335- 353 20
  • 26. CEOP-AEGIS Report De 6.1Goodison, B. E., Brown, R. D. & Grane, R. G. (1998). EOS Science Plan: Chapter 6 Cryospheric System. NASAGrody, N. C. & Basist, A. N. (1996). Global identification of snowcover using SSM/I measurement. IEEE Transactions on Geoscience and Remote Sensing, 34(1): 237-248Grody, N. C. (1991). Classifiaction of snow cover and precipitation using the special sensor microwave imager. Journal of Geophysical Research, 96(D4): 7423-7435Hall D. K., George, A. R. & Vincent, V. S. (2006). MODIS/Terra Snow Cover Daily L3 Global 0.05deg CMG V005. Boulder, Colorado USA: National Snow and Ice Data Center. Digital mediaHe, W. Y. & Chen, H. B. (2006). Analyses of evolutional characteristics of a hailstorm precipitation from TRMM observation. Acta Meteorological Sinica, 64(3): 364-376. in ChineseHoekstra, P. & Delaney, A. (1974). Dielectric Properties of Soils at UHF and Microwave frequency. J. Geophys Res., 79: 1699-1708Jin, R. & Li, X. (2002). A review on the algorithm of frozen/thaw boundary detection by using passive microwave remote sensing. Remote Sensing Technology and Application, 17(6): 370-375. in ChineseJin, R. (2007). Soil Frozen/Thawed Status Detection by Using SSM/I and Active Layer Data Assimilation System. Ph.D thesis. Graduate University of Chinese Academy of SciencesJin, Y. Q. (1997). Analysis of SSM/I data over the desert areas of China. Journal of Remote Sensing, 1(3): 192-197. in ChineseJudge, J., Galantowicz, J. F. & England, A. W. et al. (1997). Freeze/thaw classification for prairie soils using SSM/I radiobrightnesses. IEEE Transaction On Geoscience and Remote Sensing, 35(4): 827-832Kimball, J. S., McDonald, K. C., Keyser, A. R. et al. (2001). Application of NASA scatterometer (nscat) for determining the daily frozen and nonfrozen landscape of Alaska. Remote Sensing of Environment, 75: 113-126Koike, T. (2004). Coordinated Enhanced Observing Period (CEOP) - an initial step for integrated global water cycle observation. World Meteorological Organization Bulletin, 53(2): 115-121Li, X., Cheng, G. D. & Jin, H. J. et al. (2008). Cryospheric change in China. Global and Planetary Change, 62 (34): 210-218, doi:10.1016/j.gloplacha.2008.02.001.Neale, C. M. U., McFarland, M. J., Chang, K. (1990). Land-surface-type classification using microwave brightness temperature from the Special Sensor Microwave/Imager. IEEE Transactions on Geoscience and Remote Sensing, 28(5): 829-838Ulaby, F. T., Moore, R. K. & Fung, A. K. (1986). Microwave remote sensing: active and passive. Dedham MA: Artech House.Wang S. L., Yang M. X., Toshio K. et al. (2000). Application of time-domain-reflectometer to researching moisture variation in active layer on the Tibetan Plateau. Journal of Glaciology and Geocryology, 22(1): 78-84. in ChineseWegmuller, U. (1990). The effect of freezing and thawing on the microwave signatures of bare soil. Remote Sensing of Environment, 33: 123-135Williams, P. J. & Smith, M. W. (1989). The frozen earth. New York: Cambridge University PressYang Meixue, Yao Tandong & He Yuanqing. (2000). The role of soil moisture-energy distribution and melting-freezing processes on seasonal shift in Tibetan plateau. Journal of Mountain Science, 20(5): 553-558Zhang, L. X., Zhao, S. J. & Jiang, L. M. (2009). The time series of microwave radiation from representative land surface in the upper reaches of Heihe River during alternation 21
  • 27. CEOP-AEGIS Report De 6.1 of freezing and thawing. Journal of Glaciology and Geocryology, 31(2): 198-205. in Chinese Zhang, T., Barry R. G., Knowles, K. Ling, F. & Armstrong R. L. (2003a) Distribution of seasonally and perennially frozen ground in the Northern Hemisphere, in Proceedings of the 8th International Conference on Permafrost, Zurich, Switzerland, edited by Phillips M., Springman S. M. & Arenson L. U., pp. 1289-1294, A. A. Balkema, Brookfield, Vt. Zhang, T., Armstrong, R. L. & Smith, J. (2003b). Investigation of the near-surface soil freeze-thaw cycle in the contiguous United States: algorithm development and validation. Journal of Geophysical Research, 108(D22), doi: 10.1029/2003JD003530 Zhang, T. & Armstrong, R. L. (2001). Soil freeze/thaw cycles over snow-free land detected by passive microwave remote sensing. Geophysical Research Letters, 28(5): 763-766 Zhao, Y. S. (2003). Analysis principium and methods of remote sensing application. Beijing: Science Press, 202-208. in Chinese Zhou, Y. W., Guo, D. X., Qiu, G. Q. et al. (2000). Geocryology in China. Beijing: Science Press. in Chinese Zuerndorfer, B., England, A. W., Dobson, M. C. et al. (1990). Mapping freezing/thaw boundary with SMMR data. Agricultural and Meteorology, 52: 199-225Zuerndorfer, B. & England, A. W. (1992). Radiobrightnesses decision criteria for f reeze/thaw boundaries. IEEE Transaction On Geoscience and Remote Sensing, 30(1): 89-102 22
  • 28. CEOP-AEGIS Report De 6.1 PART III Snow depth derived from passive microwave remote sensing data in China and snow data assimilation method Authors: Tao CheAffiliations: Cold and Arid Regions Environment and Engineering Research Institute, Chinese Academy of Sciences (CAREERI, CAS).
  • 29. CEOP-AEGIS Report De 6.1 Snow depth derived from passive microwave remote sensing data in China and snow data assimilation method1. Task Snow, one of the most important components in the cryosphere system, plays a crucialrole in influencing variability in the global climate system over a variety of temporal andspatial scales (Peixoto and Oort, 1992; Ghan and Shippert, 2006). In this study, we reportspatial and temporal distribution of seasonal snow depth derived from passive microwavesatellite remote sensing data (e.g. SMMR from 1978 to 1987 and SMM/I from 1987-2006)in China. We first modified the Chang algorithm and then validated it using meteorologicalobservations data, considering the influences from vegetation, wet snow, precipitation, colddesert and frozen ground. Furthermore, the modified algorithm is dynamically adjustedbased on the seasonal variation of grain size and snow density. We also report a snow data assimilation system, which can directly assimilate thepassive microwave remote sensing data into the snow process model by the EnsembleKalman Filter (EnKF). The Microwave Emission Model of Layered Snowpacks (MEMLS)is used to transfer the snow state variables to the brightness temperature data, so that theEnKF algorithm can create the Kalman gain matrix according to the brightness temperaturedata simulated and observed. The errors from simulation and observation is estimated by thecomparisons and experiences. The experiment is implemented at a single site, where theforcing data from the JMA-GSM operational global data assimilation system (3D-Var), thebrightness temperature data from the AMSR-E, the snow process model from the commonland model (CLM). The paper also discusses several important issues to enhance the currentsystem, such as the utility of VIS/NIR albedo products, the balance between ensemble sizeand computation, dynamic error estimation, microwave radiative transfer models ofatmosphere and snowlayer, and so forth. This work is the preliminary research, and in thefuture we will focus on development of snow data assimilation system in regional scale.2. Data! Passive microwave remote sensing data 24
  • 30. CEOP-AEGIS Report De 6.1 The Scanning Multichannel Microwave Radiometer (SMMR) is an imaging 5-frequency radiometer (6, 10, 18, 21, and 37 GHz) flown on the Nimbus-7 earth satelliteslaunched in 1978. 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 allexcept 22 GHz, for which only the vertical polarization is measured. At NSIDC (NationalSnow and Ice Data Center), the SMMR and SSM/I brightness temperatures are gridded tothe NSIDC Equal-Area Scalable Earth grids (EASE-Grids). Because China is located in amid-latitude region, we used the brightness temperature data with the global cylindricalequal-area projection (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 1). 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. Figure 1. Position of meteorological stations within main snow cover regions in China (NWC: Northwestern China, QTP: Qinghai-Tibet Plateau, NEC: Northeastern China, and other region).! MODIS snow cover area products 25
  • 31. CEOP-AEGIS Report De 6.1 Hall and others (2002) described the Moderate Resolution Imaging Spectroradiometer(MODIS) snow cover area algorithm for the EOS Terra satellite. At present, the MODISsnow 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 influencedby vegetation, in particular, the dense forest. Hu (2001) published the vegetation atlas ofChina (1:1,000,000), which is the most detailed and accurate vegetation map of the wholecountry up 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.! 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 the 26
  • 32. CEOP-AEGIS Report De 6.11:4,000,000 coastline maps. These spatial data also was projected to register the EASE-GRID data.! Experiment sites and data of snow data assimilation The snow data assimilation experiment was implemented in Eastern Siberia Taiga area,which is one of nine cold regions from the CEOP/CAMP. There are seven reference sites inEastern Siberia Taiga area. Snow depth and air temperature were observed in winter (fromOctober to next April). The CLM forcing data usually include precipitation, shortwave radiation, infraredradiation, as well as wind speed, air temperature, specific humidity and atmosphericpressure at the observational height. In general, it is difficult to collect all of theseatmosphere data, particularly in cold regions. In this experiment, the JMA-GMS modeloutputs were pre-processed as the forcing data (Hirai, 2006). We collected the forcing datafrom October 2002 to May 2004. These before October 2003 were used for the spin-up ofCLM, while others for the snow data assimilation periods. The air temperature data in thesesites only were used for the comparison with JMA-GMS model outputs, while snow depthdata for validation of simulation and assimilation results. Satellite brightness data were fromthe AMSR-E. The MEMLS was linked with the CLM to transfer the snow state variables to thebrightness temperature, so that the satellite brightness temperature can be directlyassimilated into the snow assimilation scheme. The model step of the assimilation systemwas one hour, and the AMSR-E pass times were rounded to be compatible with the modeltimes. At the observation time of brightness temperature, the assimilation scheme wasapplied when the snow depth > 2cm. 2cm is threshold at which passive microwavebrightness temperatures can effectively detect snowpacks.3. Method3.1 Snow depth derived from passive microwave remote sensing data! The 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. 27
  • 33. CEOP-AEGIS Report De 6.1 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 fromthe 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)and the snow depth measured at the meteorological stations, the coefficient (slope) is 0.78and the standard deviations from the regression line is 6.22cm for SMMR data. For theSSM/I brightness temperature data, the 19GHz channel replaced the 18GHz of SMMR.Results show that the coefficient is 0.66 and the standard deviations from the regression lineis 5.99cm. 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) 28
  • 34. CEOP-AEGIS Report De 6.1 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 2 and 3). Figure 2. 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 3 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). 29
  • 35. CEOP-AEGIS Report De 6.1Figure 4 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/I 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 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 3.80! Retrieval of Snow Depth 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 scattering 30
  • 36. CEOP-AEGIS Report De 6.1signature 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 andBasist (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 thedaily snow depth dataset, the intervals between swaths were filled up by the most recentdata available. The flow chart to obtain the snow depth data in China can be described byFigure 5.Figure 5 Flow chart of snow depth data in China derived from passive microwave brightness temperature data.3.2 Assimilating of passive microwave remote sensing data 31
  • 37. CEOP-AEGIS Report De 6.1 The data assimilation algorithm is the linkage between the model operator and theobservation operator within the snow data assimilation system. By uncertainty analysis ofsimulation and observation, it can give us the optimal estimation of snow state variables. Atpresent, the usual optimal algorithms in land data assimilation is Kalman Filter (KF) and itsimproved methods (Kalman, 1960; Evensen, 1994, 2003, and 2004), and the particle filter(Arulampalam et al, 2002). A KF combines all available measurement data, plus prior knowledge about a systemand measuring devices, to produce an estimate of the desired variables in such a manner thaterror is minimized statistically. When a system can be described through a linear model andwhen system and measurement noise are white and Gaussian, the best estimates can beobtained from the KF method. The forecasting equation can be described as: (4)Here, is the snow state vector from the snow process model, and is theanalyzed state vector during the previous time step. The error covariance matrix can beestimated by (5) is a prior covariance matrix. So the updating scheme is (6)where is the observation operator such as the MEMLS in this study, while the is theKalman gain matrix: (7)The updated error covariance (8) The updating scheme of KF needs the error covariance matrix for the model predictionand observations. However, the snow process model is a nonlinear and discontinuousmodel, so that it is difficult to develop a linear model and therefore not able to create theerror estimation from the KF scheme directly. To solve this problem, the KF was improvedand expanded by Evensen (1994) as the Ensemble Kalman Filter (EnKF). By adopting theMonte Carlo sampling method, the statistics of forecasting and measurement can be 32
  • 38. CEOP-AEGIS Report De 6.1obtained. Consequently, the error statistics within equations (2) and (5) can be approximatedas (Evensen, 2003): (9) (10)The e within means the error covariance by estimation from ensemble. Therefore, the nonlinear snow process model also can be analyzed within an EnKFbased LDAS. By using the EnKF scheme, the LDAS can assimilate the passive microwavebrightness temperature data into the snow process model.4. Accuracy assessment of passive microwave snow products! Accuracy 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. Figure 6 Percentage of error frequency distribution of validation by the snow depth observations in meteorological stations and the spectral gradient of SMMR in 1983 and 1984 (a, the number of data is 2070) and SSM/I in 1993 (b, the number of data is 6862).! Accuracy assessment (Snow cover) 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 cover 33
  • 39. CEOP-AEGIS Report De 6.1products 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): (11) 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)). 34
  • 40. CEOP-AEGIS Report De 6.1 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.5. Results Based on the daily snow depth data from 1978 to 2006, snow cover in China is mainlylocated in three regions, the QTP, the Northwestern China, and the Northeastern China,while other regions only hold a little of snow mass (Figure 8). Figure 9 clearly illustrates the snow state variables output from the snow dataassimilation system. Figure 9(a) compares the snow depth assimilated and the in-situobservations. The root mean squared errors (RMSE) of snow depth are 0.175 (forsimulation) and 0.087 (for assimilation), while the bias errors of snow depth are 70.2% (forsimulation) and 23.7% (for assimilation), respectively. Figure 9(b), (c), (d) compare thesnow temperature, liquid water content and density of CLM simulation and assimilationresults. 35
  • 41. CEOP-AEGIS Report De 6.1 The scatter plots of snow depth from in situ observations against the CLM simulationsand also the assimilated results are illustrated in Figure 10. The Figure 10a is the scatterplots of snow depth simulated against observations, while the figures 10b and 10c are snowdepth assimilated against observations for all of snow season and accumulation period,respectively. (a) 36
  • 42. CEOP-AEGIS Report De 6.1 (b)Figure 8 (a) Annual average snow depth distributions in China from 1978 to 2006 based on the SMMR andSSM/I data. (b) Average snow depth distributions in China from 1978 to 2006 during winter (December,January, and February) based on the SMMR and SSM/I data.Figure 9 Assimilation results of snow state variables in the research period. (a) the snow depth from in-situobservations, CLM single simulations, and the snow data assimilation system outputs, (b), (c), and (d) thesnow temperature, liquid water content, and density from assimilation system outputs, respectively. 37
  • 43. CEOP-AEGIS Report De 6.1 (a) (b) Figure 10 Scattering plots of snow depth of in- situ observations against the CLM simulations and the assimilated results. (a) in-situ observations against the CLM simulations, (b) in-situ observations against the assimilated results in the whole period, (c) in-situ observations against the assimilated results only in the accumulation period. (c) First of all, the outputs of snow depth were significantly improved by assimilating theAMSR-E brightness temperatures. The initial states of the snow process model werecontinuously updated by the satellite observations, which reduced the uncertainties ofsimulation. On the other hand, the assimilation results included more information than the retrievalof satellite observations only. More snow state variables can be obtained from the snow dataassimilation system, such as the snow temperature, liquid water content, and snow density. 38
  • 44. CEOP-AEGIS Report De 6.1These data come from the simulations of the snow process model, which can be implicitlyimproved by assimilating the observations. For evaluation of assimilation results, MEMLS was used to recalculate the brightnesstemperature at 18.7 and 36.5GHz in horizontal and vertical polarization based on thesnowpacks before and after the assimilation. The TBDs at 18 and 36 GHz predicted byMEMLS before and after the assimilation along with the ASMSR-E observed ones areillustrated in Figure 11. The RMSEs of TBDs before assimilation are 21.105 (H) and 14.625(V), while they after assimilation are 2.515 (H) and 1.905 (V), respectively. The bias errorsbefore assimilation are 69.0% (H) and 59.2% (V), while they after assimilation are 7.2% (H)and 7.5% (V). here (H) and (V) present the horizontal and vertical polarization, respectively. (a) 39
  • 45. CEOP-AEGIS Report De 6.1 (b)Figure 11 Comparisons of TBDs (Brightness temperature differences) between AMSR-E observations andMEMLS simulations before and after assimilation, here (a) for horizontal polarization, and (b) for verticalpolarization.6. ReferencesArmstrong, R. L., Brodzik, M. J. (2002), Hemispheric-scale comparison and evaluation of passive- microwave snow algorithms. Annals of Glaciology, 34: 38~44.Anderson, E. A. (1976). A point energy and mass balance model of a snow cover, NOAA Tech. Rep. NWS, 19, Office of Hydrol., Natl. Weather Serv., Silver Spring, Md.Andreadis K. M. & Lettenmaier D. P. (2006). Assimilating remotely sensed snow observations into a macroscale hydrology model. Advances in Water Resources. 29, 872-886.Aoki, T., Hachikubo A., & Hori M. (2003). Effects of snow physical parameters on shortwave broadband albedos, J. Geophys. Res., 108(D19), 4616, doi:10.1029/2003JD003506.Arulampalam M. S., Maskell S., Gordon N., & Clapp T. (2002). A Tutorial on Particle Filters for Online Nonlinear/Non-Gaussian Bayesian Tracking. IEEE Transactions on Geoscience and Remote Sensing, 50, 174-188.Bonan, G. B., (1996). A land surface model (LSM version1.0) for ecological, hydrological, and atmospheric studies: Technical description and user’s guide. NCAR Tech. Note NCAR/TN- 417+STR, 1-150.Chang A. T. C., Foster J. L., & Hall D. K. (1987). Nibus-7 SMMR derived global snow cover parameters, Annual of Glaciology, 9, 39-44.Chang A. T. C., Gloersen P., Schmugge T, Wilheit T. T. & Zwally H. J.(1976). Microwave emission from snow and glacier ice. Journal of Glaciology, 16, 23-39. 40
  • 46. CEOP-AEGIS Report De 6.1Clark M. P., Slater A. G., Barrett A. P., Hay L. E., McCabe G. J., Rajagopalan B., & Leavesley G. H. (2006). Assimilation of snow covered area information into hydrologic and land-surface models. Advances in Water Resources, 29, 1209-1221.Cline, D., Armstrong R., Davis R., Elder K., & Liston G.. 2002, Updated July 2004. CLPX GBMR Snow Pit Measurements. Edited by M. Parsons and M.J. Brodzik. In CLPX-Ground: Ground Based Passive Microwave Radiometer (GBMR-7) Data, T. Graf, T. Koike, H. Fujii, M. Brodzik, and R. Armstrong. 2003. Boulder, CO: National Snow and Ice Data Center. Digital Media.Dai, Y., & Zeng Q. C. (1997). A land surface model (IAP94) for climate studies, Part I: Formulation and validation in off-line experiments. Adv. Atmos. Sci., 14, 433-460.Dai, Y., et al., (2001). Common Land Model: Technical documentation and user’s guide [Available online at http://climate.eas.gatech.edu/dai/clmdoc.pdf].Dai, Y., Zeng X., Dickinson R. E., Baker I., Bonan G., Bosilovich M., Denning S., Dirmeyer P., Houser P., Niu G., Oleson K., Schlosser A., & Yang Z. L., (2003). The Common Land Model (CLM). Bull. of Amer. Meter. Soc., 84, 1013-1023.Dickinson, R. E., Henderson-Sellers A., Kennedy P. J., & Wilson M. F. (1993). Biosphere-Atmosphere Transfer Scheme (BATS) version 1e as coupled to Community Climate Model. NCAR Tech. Note NCAR/TN-387+STR, 1-72 .Dong J. R., Walker J. P., & Houser P. R. (2005). Factors affecting remotely sensed snow water equivalent uncertainty. Remote Sensing of Environment, 97, 68 – 82.Evensen G. (1994). Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte- Carlo methods to forecast error statistics. Journal of Geophysical Research, 99, 10143-10162.Evensen G. (2003). The Ensemble Kalman Filter: theoretical formulation and practical implementation. Ocean Dynamics, 53, 343-367.Evensen G. (2004). Sampling strategies and square root analysis schemes for the EnKF. Ocean Dynamics, 54, 539-560.Foster J L, Sun C J, Walker J. P., Kelly R., Chang A. C. T., Dong J. R., Powell H. (2005). Quantifying the uncertainty in passive microwave snow water equivalent observations. Remote Sensing of Environment, 94, 187-203.Foster J. L. & Rango A. (1982). Snow cover conditions in the northern hemisphere during the winter of 1981. Journal of Climatology, 20, 171–183.Foster J. L., Chang A. T. C., & Hall D. K. (1997). Comparison snow mass estimates from a prototype passive microwave snow algorithm, a revised algorithm and snow depth climatology, Remote Sensing of Environment. 62,132–142.Fung A. K. (1994). Microwave Scattering and Emission Models and Their Applications. Norwood, MA: Artech House, 1-573.Ghan, S J & Shippert, T. (2006). Physically based global downscaling: Climate change projections for a full century. Journal of Climate, 19, 1589-1604.Hall, D. K. Riggs G. A., Salomonson V. V., DiGirolamo N. E. & Bayr K. J. (2002). MODIS snow-cover products. Remote Sensing of Environment, 83, 181-194. 41
  • 47. CEOP-AEGIS Report De 6.1Hall, D. K., Sturm, M., Benson, C. S., Chang, A. T. C., Foster, J. L., Garbeil, H. & Chacho E. (1991). Passive microwave remote and in-situ measurements of Arctic and Subarctic snow covers in Alaska. Remote Sensing of Environment, 38, 161–172.Hirai M. (2006). Global JMA model output (JMA-GSM). http://www.atd.ucar.edu/projects/ceop/dm/model/Jin Y. Q., & Liang Z. C. (2003). Iterative solution of multiple scattering and emission from an inhomogeneours scatter media. Journal of applied physics, 93, 2251-2256.Jordan, R. (1991). A One-dimensional temperature model for a snow cover, Special. Report. 91-1b, Cold Regions Res. and Eng. Lab., Hanover, N. H.Kalman R E. (1960). A new approach to linear filtering and prediction problems. Trans. ASME, Series D, J. Basic Eng., 82, 35-45.Kelly, R .E. J., & Chang, A. T. C. (2003). Development of a passive microwave global snow depth retrieval algorithm for SSM/I and AMSRE data. Radio Science, 38(4), 8076, doi:10.1029/2002RS002648.Kelly, R. E., Chang, A., Tsang, L., & Foster, J. (2003). A prototype AMSR-E global snow area and snow depth algorithm. IEEE Transactions on Geoscience and Remote Sensing, 41(2), 230– 242.Loth, B., & Graf H. F. (1993). Snow cover model for global climate simulation, J. Geophys. Res., 98, 10,451– 10,464,.Lynch-Stieglitz, M. (1994). The development and validation of a simple snow model for the GISS GCM. Journal of Climate, 7, 1842-1855.Matzler C, & Wiesmann A. (1999). Extension of the microwave emission model of layered snowpacks to coarse-grained snow. Remote Sensing of Environment, 70, 317-325.Matzler C, Wiesmann A., & strozzi T. (2000). Simulation of microwave emission and backscattering of layeredsnowpacks by a radiative transfer model, and validation by surface-based experiments. IGARSS00, v4, 1548-1550.Oleson K. W., Dai Y. J., Bonan G., et al (2004). Technical description of the community land model (CLM). NCAR technical note.Pan M., et al. (2003). Snow process modeling in the North American Land Data Assimilation System (NLDAS): 2. Evaluation of model simulated snow water equivalent. J. Geophys. Res., 108(D22), 8850, doi:10.1029/2003JD003994.Peixoto, J. P., & Oort A. H. (1992). Physics of Climate. American Institute of Physics, New York.Schlosser, C. A., & Mocko D. M. (2003). Impact of snow conditions in spring dynamical seasonal predictions, J. Geophys. Res., 108(D16), 8616, doi:10.1029/2002JD003113.Sheffield J, et al. (2003). Snow process modeling in the North American Land Data Assimilation System (NLDAS): 1. Evaluation of model-simulated snow cover extent. J. Geophys. Res., 108(D22), 8849, doi:10.1029/2002JD003274.Slater A. G., Schlosser C. A., Desborough C. E., Pitman A. J., Henderson-Sellers A., Robock A. E et al. (2001). The Representation of Snow in Land Surface Schemes: Results from PILPS2 (d). Journal of Hydrometeorology, 2, 7-25. 42
  • 48. CEOP-AEGIS Report De 6.1Steppuhn H. (1981). Snow and agriculture. In D.M. Gray and D.N. Male, editors, Handbook of Snow: Principles, Processes, Management and Use, 60–125. Pergamon Press.Stone, R. S., Longenecker D., Duttoo E.G., & Harris J.M.. (2001). The advancing date of spring snowrnelt in the Alaskan Arctic. Eleventh ARM Science Team Meeting Proceedings, Atlanta, Georgia, 19-23.Sun, C., Walker, J. P., & Houser, P. R. (2004). A methodology for snow data assimilation in a land surface model, J. Geophys. Res., 109, D08108, doi:10.1029/2003JD003765.Sun, S., Jin J. M., & Xue Y. (1999), A simplified layer snow model for global and regional studies, J. Geophys. Res., 104, 19,587– 19,597.Tsang L., Chen C., Chang A. T. C., Guo J., & Ding K. H. (2000). Dense media radiative transfer theory based on quasicrystalline approximation with applications to passive microwave remote sensing of snow. Radio Science, 35, 731-749.Ulaby, F., Moore, R., & Fung, A. (1981), Microwave Remote Sensing, Artech House, Dedham, MA, Vol. I, pp1-5Ulaby, F., Moore, R., & Fung, A. (1986), Microwave Remote Sensing, Artech House, Dedham, MA, Vol. III, pp1602-1634.Wiesman A, & Matzler C. (1999). Microwave emission model of layered snowpacks. Remote Sensing of Environment, 70, 307-316.Wiesmann, A., Matzler, C., & Weise, T. (1998), Radiometric and structural measurements of snow samples. Radio Sci., 33, 273-289.Wu T. D., & Chen K. S. (2004). A Reappraisal of the Validity of the IEM Model for Backscattering From Rough Surfaces [J]. IEEE Transaction on Geoscience and Remote Sensing, 42 (4), 743-753.Xue Y., Sun S., Kahan D., & Jiao Y, (2003). Impact of parameterizations in snow physics and interface processes on the simulation of snow cover and runoff at several cold region sites, J. Geophys. Res. 108, D22, doi:10.1029/2002JD003174. 43
  • 49. CEOP-AEGIS Report De 6.1 PART IV Providing soil parameter data sets for the entire plateau from a microwave land data assimilation system Authors: Kun YangAffiliations: Institute of Tibetan Plateau Research, Chinese Academy of Sciences (ITP, CAS)
  • 50. CEOP-AEGIS Report De 6.1 Providing soil parameter data sets for the entire plateau from a microwave land data assimilation system1. Task Soil thermal and hydraulic parameters are the basic parameters for land surfacemodelling, hydrological modelling, and land data assimilation system. Most of currentmodels use available dataset of soil parameters that are derived from soil survey. However,their accuracy is often questionable due to very limited soil samples available. This isparticularly true for the Tibetan Plateau. This task will estimate soil parameters from a landdata assimilation system developed by University of Tokyo (LDAS-UT) presented in Yanget al. (2007).2. Algorithm Figure 1a shows the flowchart of the LDAS-UT system. It assimilates the AMSR-E 6.9GHz and 18.7 GHz brightness temperatures into a LSM, with a RTM as an observationoperator. At first, the LSM produces the near-surface soil moisture ( ), the groundtemperature (Tg), and the canopy temperature (Tc), which are then fed into the RTM tosimulate the brightness temperatures. The difference between simulated Tb (Tbp,est) andobserved Tb (Tbp,obs) is sensitive to the near-surface soil moisture, which is then adjustedto minimize the difference by a global optimization scheme (Duan et al., 1993) Figure 1b shows a dual-pass assimilation algorithm adopted in LDAS-UT. Pass 1, so-called calibration pass, aims at tuning system parameters; Pass 2, so-called assimilationpass, is to estimate soil moisture. The principle behind this algorithm is that the respondingtime scale of a system state to the system parameters is different from the responding timescale to the initial condition. The system parameters have a long-term impact on statevariables (such as soil moisture), and therefore, a long time window (several months orlonger) is required to calibrate the parameters. By contrast, initial near-surface soil moisturehas a short-term effect on the system state variables, and therefore, a short time window (~1day) is selected to estimate its value by minimizing a cost function. It should be noted thatthe parameter calibration presented in LDAS-UT relies on satellite microwave data insteadof surface observations, and thus, it may have a wide applicability. 45
  • 51. CEOP-AEGIS Report De 6.13. Data A dense soil moisture network was deployed through the AMPEX (Advanced EarthObserving Satellite II (ADEOS-II) Mongolian Plateau Experiment for ground truth) projectin order to collect data for development and validation of AMSR/AMSR-E soil moistureretrieval algorithms (Kaihotsu, 2005). The CEOP Mongolia reference site covers a flat areaof in a semi-arid grassland of Mandal Govi, where soil moisture at 3 cmdepth was measured at 16 stations and meteorological data at 4 stations.4. Test estimated soil moisture and parameters Figure 2 shows that the observed soil moisture values are quite diverse in space. Figure3 shows the comparison of soil moisture among the LDAS-UT estimate, LSM estimate, andthe station-averaged observations. Clearly, the LDAS-UT estimate is agreeable with theobservations fairly well, whereas the LSM simulation with default parameter valuesoverestimates soil moisture. A further analysis indicates that the improvement of soil moisture estimations in LDAS-UT is realized through both the parameter calibration and the data assimilation. We alsoevaluated the effect of the accuracy of forcing data on soil moisture estimate and found ageneral decrease of the accuracy of the estimate when the forcing data become worse.Nevertheless, LDAS-UT produces better estimates than the LSM does in all cases. It is alsosurprising that LDAS-UT produces fairly good estimate of soil moisture when precipitationis set to be zero in the forcing data (not shown).The detail of this application can be found inYang et al. (2009). 46
  • 52. CEOP-AEGIS Report De 6.1 Figure 1 (a) LDAS-UT system structure; (b) schematic of the dual-pass assimilationtechnique. , , and are the ground temperature, canopy temperature, and near-surface soil water content, respectively. is the brightness temperature, the costfunction, and the data assimilation window. is soil reflectivity, the opticalthickness of the vegetation. The subscript p denotes the polarization, obs the observed value,and est the estimated value (see details in Yang et al., 2007). 47
  • 53. CEOP-AEGIS Report De 6.1Figure 2 Observed daily-mean near-surface (at 3 cm depth) soil moisture variations at 16 Mongolian AMPEX stations during 2003/4/30-2003/9/30.Figure 3 Comparisons of AMPEX daily-mean station-averaged near-surface soil volumetric water content with (a) LDAS-UT output, and (b) LSM (SiB2) simulation at CEOP Mongolian site during 2003/4/30-2003/9/30.5. Evaluation of optimized parameter values 48
  • 54. CEOP-AEGIS Report De 6.1 Table 1 shows the observed values and the optimized values of soil parameters. The soilporosity value ( ) and soil water potential at saturation ( ) are quite comparable to theobservation averaged over the individual sites, but the estimates of the soil hydraulicconductivity ( ) are one order of magnitude lower than the observed one, and theestimates of the pore size distribution index ( ) are also much higher than the observed one.Therefore, the optimized values of and are tuned values rather than physical ones.The results suggest that the near-surface soil moisture retrieved from the satellite data is notsufficient to physically estimate all the soil parameters, but it is possible to estimate themost sensitive parameters such as the soil porosity. Table 1 The observed and estimated soil parameter values. Sand% Clay% (m) (10-6 m/s) Observed a MGS 0.334 - - 3.37 - 18.7 MGSb 0.388 - - 3.62 -0.12 51.9 DRSa 0.393 - - 3.06 - 44.0 DRSb 0.428 - - 3.00 -0.11 88.6 BTSb 0.456 - - - - 49.3 E4a 0.358 - - 2.93 - 21.3 G6a 0.341 - - 2.70 - 8.9 C2a 0.299 - - 3.10 - 19.3 C4a 0.395 - - 2.82 - 28.5 Ave 0.377 - - 3.08 -0.115 36.7 LDAS-UT estimated 0.368 47 28 7.34 -0.18 4.9a: from Table 1 of Yamanaka et al. (2005);b: from Table I-2 and Table I-3 of Appendix I in Kaihotsu (2005).6. ReferencesDuan, Q., V.K. Gupta, and S. Sorooshian, 1993: A shuffled complex evolution approach for effective and efficient global minimization, J. Optimiz. Theory App., 76, 501-521.Kaihotsu, I., 2005: Grand truth for evaluation of soil moisture and geophysical/vegetation parameters related to ground surface conditions with AMSR and GLI in the Mongolian Plateau (pp.1-113). JAXA, Japan.Yamanaka, T., I. Kaihotsu, D. Oyunbaatar, T. Ganbold, 2005: Regional-scale variability of the surface soil moisture revealed by the AMPEX monitoring network. Grand truth for 49
  • 55. CEOP-AEGIS Report De 6.1 evaluation of soil moisture and geophysical/vegetation parameters related to ground surface conditions with AMSR and GLI in the Mongolian Plateau, JAXA, Japan, 33-42.Yang, K., T. Watanabe, T. Koike, X. Li, H. Fujii, K. Tamagawa, Y. Ma, and H. Ishikawa, 2007: Auto-calibration system developed to assimilate AMSR-E data into a land surface model for estimating soil moisture and the surface energy budget. J. Meteor. Soc. Japan, 85A, 229-242.Yang, K., T. Koike, I. Kaihotsu, and J. Qin, 2009: Validation of a dual-pass microwave landdata assimilation system for estimating surface soil moisture in semi-arid regions, Journal of Hydrometeorology 10, 780-793, DOI: 10.1175/2008JHM1065.1. 50
  • 56. 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.