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Surface and soil moisture monitoring, estimations, variations, and retrievals


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This presentation explored five leading articles in the remotely sensed and in situ surface and soil moisture monitoring, estimations, variations, and retrievals for global environmental change. The …

This presentation explored five leading articles in the remotely sensed and in situ surface and soil moisture monitoring, estimations, variations, and retrievals for global environmental change. The presentation gives insight to the purpose of each study, subjects of investigations, methods used to collect and analyze data sets, results and implications, and conclusions. This project is in fulfillment of the course on remote sensing for global environmental change and precedes our preview on water resources monitoring. This project was conducted by Christina Geller, 5th year accelerated graduate student in Geographic Information Systems for Development, and Environment and Jenkins Macedo, 2nd year graduate students in Environmental Science and Policy at the Department of International Development, Community, and Environment (IDCE) at Clark University. All academic materials used in this study were appropriately referenced (see bibliography for details).

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  • 1. Monitoring of Surface + Ground Water Resources Christina Geller Jenkins Macedo Remote Sensing for Global Climate Change November 4, 2013
  • 2.
  • 3. Approaches to and Problems with Measuring Soil Moisture 1. in situ field measurements a. b. c. d. short duration intensive field experiments very sparse field or regional mean soil moisture not properly represented 2. land surface models a. limited measurements of model physical parameters b. input data errors 3. remote sensing observations a. shallow depth b. scale is overly coarse
  • 4. Remote Sensing Observations Advanced Microwave Scanning Radiometer Earth Observing System (AMSR-E) on the Aqua satellite o May 2002 o modified passive microwave radiometer on Advanced Earth Observing Satellite-II (ADEOS-II) o measures brightness temperatures at 6 frequencies o 6.9 GHz (C band) and 10.7 Ghz (X band) o soil moisture algorithm uses a microwave transfer model to compare observed and computed brightness temperature o calculated by NSIDC and VUA-NASA (de Jeu et al., 2008)
  • 5. Electromagnetic Spectrum
  • 6. Remote Sensing Observations (cont) ERS scatterometer from the ERS-1 and ERS-2 o monitors wind speed and direction over the oceans o configured a real aperture radar providing 2 radar images o 50 km spatial resolution o 500 km swath width o active microwave sensors: sends out a signal and measures how much of that signal returns after interacting with the target o ERS-1 mission: 1991 to March 10, 2000 o ERS-2: 1995 to September 5, 2011
  • 7. Remote Sensing Observations (cont) SMOS o carried on Proteus o measures microwave radiation emitted from Earth’s surface within the ‘L-band’ (around a frequency of 1.4 GHz) o provide:  global maps of soil moisture every three days at a spatial resolution of 50 km  global maps of sea-surface salinity down to 0.1 practical salinity units for a 30-day average over an area of 200×200 km
  • 8.
  • 9. AMSR-E (Aqua) ERS SMOS 6.9, 10.7, 18.7, 23.8, 36.5, 89.0 C-band (5.3 Ghz) L-band (1.4 GHz) varies from 5.4 km at 89 GHz to 56 km at 6.9 GHz 50 km 50 km Orbital Near-circular, polar, Sunsynchronous Syn-synchronous Return Frequency ascending (1:30pm) and descending (1:30am) mode 35 days cycle Global coverage every 3 days Temporal Duration 2001 (no data from 89 Ghz after 2004; stopped spinning on Oct 4) EM frequencies (GHz) Spatial resolutions Orbital or Geostationary 1991 to March 10, 2000 (ERS-1) 1995 to September 5, 2011 (ERS-2) 2009 - present
  • 10. “Remote sensing observatory validation of surface soil moisture using Advanced Microwave Scanning Radiomater E, Common Land Model, and ground based data: Case study in SMEX03 Little River Region, Georgia, U.S.” Chou et al., 2008
  • 11. Purpose • compare soil moisture estimations from: o AMSR-E o ground-based measurement o Soil-Vegetation-Atmosphere Transfer (SVAT) model  combine land surface and atmosphere processes modeling using both water and energy balances • Common Land Model (CLM)  require model forcing data and certain parameters
  • 12. Methods: AMSR-E iterative multi-channel inversion procedure o microwave transfer model to compare observed brightness temp (TB) and computed brightness temp (TBP)  affected by soil volumetric water content, vegetation water content (VWC), and surface temp (Ts)
  • 13. Methods: SVAT
  • 14. Results • • • • agreed well in drying and wetting patterns average soil moisture: 0.122 to 0.167 m3/m3 AMSR-E o lower variability o weak agreement with in situ & CLM o did not capture temporal variability during SMEX03 period CLM o wetter than observed o followed patterns during SMEX03 period
  • 15. “Global Soil Moisture Patterns Observed by Space Borne Microwave Radiometers and Scatterometers.” de Jeu et al., 2008
  • 16. Purpose global evaluation of: ○ ERS scatterometer ■ ○ obtained from 50 km scatterometer originally designed for measuring winds over the oceans AMSR-E soil moisture data ■ uses low frequency microwave brightness temperatures to obtain soil moisture
  • 17. Methods: AMSR-E Soil Moisture • contribution of the atmosphere to observed brightness temperature o function of physical temperature of the radiating body and its emissivity
  • 18. Methods: AMSR-E Soil Moisture • contribution of the atmosphere to observed brightness temperature o function of physical temperature of the radiating body and its emissivity • radiation from the land surface observed above canopy
  • 19. (a) Comparison of smooth surface emissivity and the soil dielectric constant according to the Fresnel relations with an incidence angle of 55 degrees. (b) Comparison of the soil dielectric constant and soil moisture for typical sand, loam and clay soils.
  • 20. Methods: SRS scatterometer • soil moisture derived using retrieval method proposed by Wagner et al. (1999, 2003) change detection approach tracks relative soil moisture changes rather than absolute o dry and wet reference conditions identified based on multi-year backscatter time series o
  • 21. Average soil moisture for 2006: (a) and (b) derived from ERS data, (c) and (d) from C-band AMSR-E, and (e) and (f) from X-band AMSR-E.
  • 22. Results • AMSR-E (X-band and C-band) o • active radar instruments on the ground cause Radio Frequency Interference (RFI) in C-band  ex) eastern part of the USA ERS scatterometer o volume scattering in dry soil or reduced sensitivity of dielectric constant  ex) wet region in northern Mexico
  • 23. Results (cont) • Comparison between ERS scatterometer and AMSR-E  low and negative values found in deserts and more densely vegetated regions • • due to low sensitivity of dielectric constant in desert effect of mountains • average correlation coefficient of 0.83 • • -0.08 and 0.33, respectively explained by limited soil moisture retrieval capabilities  strong similarity in sparse to moderate vegetated regions  low correlations in densely vegetated areas and deserts • potential to combine both products
  • 24. “Analysis of Terrestrial Water Storage Changes from GRACE and GLDAS.” Syed et al., 2008
  • 25. Purpose ● Spatial-temporal variations in Terrestrial Water Storage Changes (TWSC) ● Compared results with those simulated Global Land Data Assimilation Systems (GLDAS) ● Additionally, GLDAS simulated to infer TWSC partitioned in snow, canopy water, and to understand how variations in the hydrologic fluxes act to enhance or dissipate stores.
  • 26. Methods • To investigate water storage changes o Groundwater storage monitoring  GRACE-driven TWSC  Global Land Data Assimilation System (GLDAS) o Data frame  GRACE-driven data Center for Space Research RL01 (April 2002-July 2004, & June, 2003).  collected corrected GRACE Stokes coefficients expanded to degree and order 60 smoothed with 1000 km half-width Gaussian averaging kernel to different gravity estimates.  lesser degree, the degree two, and zero were not considered due to their quantifiable errors.  smoothed spherical harmonics coefficients were transformed into 1 x 1 degree gridded data, which represented vertically integrated water mass changes over 100 kilometers with accuracy of about 1.5 cm equivalent water thickness.
  • 27. Methods (cont) ● PRIMARY LAND SURFACE FLUX DATA ○ NASA GLDAS ○ 1 degree, 3 hourly outputs from 1979 to present (Noah Land Surface ModelGLDAS). ○ Hydrologic fluxes and storages were gathered from January, 2002-December, 2004. ● GRACE-DRIVEN TWSC ESTIMATES ○ by differencing the monthly anomalies. ■ were derived from the mean gravity field from each monthly GRACE resolutions. ■ estimates TWSC as average changes in TWS from one month to the other. ○ TWS (Total Soil Moisture, Snow Water Equivalent, & Canopy Water Storage).
  • 28. Methods (cont) 1 Equation for the comparable replication of GRACE observations from GLDAS land surface output: Following the results of equation (1), estimates of TWSC from GLDAS that closely approximate GRACE were computed; where, the terms to the right of the equations are 15th day averages of each calendar of the year with the assumption that average 15th day can be representative of approximately 30 days average. 2 S, represents the average TWS for the index day (i), and the subscripts (i) and (N) represent day of month and month respectively, and (t) is time. Source: Syed et al. 2008 Calculating TWSC using the monthly basin-scale terrestrial water balance provides approximations; where, P (Precipitation), R (Runoff), and E (Evapotranspiration) 3
  • 29. Results Terrestrial Water Storage ● 1a). TWS peaked during the NH Winter (DJF) with an amplitude of 0.6 cm/month 1b). Shows seasonal averages with strongest water storage change signals in a SH 0 to 30 degree S latitude band with lesser peak in NH subtropics at 60 degree N. 1c). Shows associated peaks in amplitude of seasonal cycle in the zonally averaged absolute value of TWSC in corresponding regions (two issues: 1st global TWSC data & TWS is predictable to precipitation & evaporation). 1a 1b 1c Source: Syed et al. 2008 ● ●
  • 30. Source: Syed et al. 2008 Results (cont) ● Increase in annual mean in Europe (0.32 cm/month), South America (0.30 cm/month, and Asia (0.08 cm/month), lesser depletion of total water storage in Australia (-0.14 cm/month, Africa (-0.02 cm/mont, & N. America (-0.06 cm/month. ● Tropical basins in NH gain water during JJA from precipitation, while basins in the SH tropics and those in NH mid-to-high latitudes lose water. ● Higher seasonal averaged amplitudes TWSC were noted in the tropics of the SH of latitudinal variability compared to tropics of the NH.
  • 31. Results (cont) ● GRACE-GLDAS Comparison ○ computed using equation 1 & 2 ○ global model output from GLDAS captures the magnitude and variations of terrestrial hydrology. ○ good overall agreement between the two estimates with RMSE ranging about 1 cm/month in JJA and about 0.7 cm/month in DJF.
  • 32. Results (cont) ● TIME SERIES OF TWSC from GRACE & GLDAS ○ GLDAS estimates agreed very well with GRACE, with RMSE values of about 1.5 cm/month in the Mississippi and Mackenzie River Basins ○ And about 2.5 cm/month in the Amazon and Parana River Basins ○ Overall, Figures 4 & 5 show agreements in spatial-temporal variability of TWSC estimates from GRACE and GLDAS.
  • 33. Conclusion • The study characterized TWSC variations using GRACE and GLDAS: o Global, zonal, and basin-scale estimates of GRACE-driven storage changes indicate a wide range in variability and magnitude with emphasis on the space-time heterogeneity in TWSC response. o Continental and hemispheric differentiations in precipitation were noted. o Averaged TWSC was found to have greatest amplitudes zonally in the tropics of the SH (about 7 cm/month). o At the river basin-scale, comparative analyses between GLDAS and GRACE-driven estimates of TWSC agreed well. o Noah Land Model used in the GLDAS simulations did not include surface and groundwater stores because of the inability to quantify their contribution to storage change.
  • 34. Comparison and assimilation of global soil moisture retrievals from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSRE) and the Scanning Multichannel Microwave Radiometer (SMMR).” “ Reichle et al., 2007
  • 35. Purpose • • to compare two satellite data sets of surface soil moisture retrievals. assimilate the preliminary products into NASA Catchment Land Surface Model (CLSM) to determine retrieved soil moisture using multiyear means and temporal variability as units to determine the difference.
  • 36. Methods • • Global soil moisture retrievals o NASA Catchment Land Surface Model (CLSM) o Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) on the Aqua satellite Scanning Multichannel Microwave Radiometer (SMMR). o Data frame o Satellite-driven soil moisture retrievals  infer soil moisture from microwave signals o Land model integrations  relates soil moisture to antecedent meteorological forcing o Ground-based measurements  provides direct and accurate measurements of soil moisture
  • 37. Methods (cont) Satellite-driven soil moisture retrievals o Using NASA Level-2B AMSR-E “AE_Land Product o infer soil moisture from microwave signals  surface temperature inputs are from SMMR 37 GHz vertical polarization channel (which are stored on 0.25 degree grid) their resolution is about 120 km based on footprint of 148 km by 965 km.  both day and night overpasses were used..  Quality control measures > used AMSR-E data points corresponding flags for light vegetation, no rain, no snow, no frozen ground, and no Radio Frequency Interference (RFI).
  • 38. Methods (cont) • Land integration Model o obtained from the integration of NASA CLSM.  computational unit used is the hydrological catchment.  global land surface divided into catchment excluding inland water & ice-covered area.  in each catchment, vertical soil moisture profile were determined based.  it incorporates meteorological forcing inputs that rely on observed data o 2002-2006 AMSR-E forcing data are from GLDAS project (3-hourly time step at 2 degree and 2.5 degree resolution in latitude and longitude corrected using CMAP.  based on global atmospheric data assimilation system at the NASA GMAO. • o 1979-1987 SMMR forcing data based on ECMWF 15 years reanalysis at 6-hourly time steps corrected using the monthly mean observations (precipitation, radiation, temperature, and humidity data). Ground-based measurements (in situ measurements) o o USDA Soil Climate Analysis Network (SCAN) were used to validate AMSR-E (2002-2006). Global Soil Moisture Data Bank (GSMDB) were used to validate SMMR (1979-1989).
  • 39. Methods (cont) ● 10 soil moisture retrievals per month were available due to power constraints of platform and swath width. ● 50 AMSR-E soil moisture retrievals data were available. ● Satellite soil moistures are available for low-latitude regions with little vegetation (Northern & Southern Africa, and Australia). ● Freezing and snow cover limits data availability, which impacts yearly averages. ● Data are not available for densely forested ecoregions (South America, East Asia and temperate and boreal forest of NA and Euroasia). Source: Reichle et al., 2007
  • 40. Results • • • • • Validation against situ data indicates that for both data sets soil moisture fields from the assimilation are superior to either satellite or model data. Global analysis reveals how changes in the model and observations error parameters may enhance filter performance in future experiments. For surface soil moisture anomalies, both satellite data show similar skill in reproducing the corresponding in situ data, with R = 0.38 for AMSR-E and R = 0.32 for SMMR (based on different algorithms). The model estimates agree somewhat better than the satellite data with the in situ data and that recent AMSR-E years are superior to that of SMMR historic period. Time series improvements reveal statistically significant correlation with CI exceeding 99.99% (AMSR-E) for surface and root zone soil moisture, and 99.9% for surface (root zone) soil moisture (SMMR).
  • 41. Conclusion • • • • demonstrated that the assimilation of surface soil moisture retrievals from AMSR-E into NASA Catchment land surface model to provide estimates of surface and root zones soil moisture validated with in situ data. compared AMSR-E and SMMR soil moisture retrievals found significant difference in their climatologies. AMSR-E retrievals are considerably drier and show less temporal variability than the SMMR data (Figure 3 and 4). global analysis of model produced by the data assimilation system can add value to Lband retrievals of soil moisture from the planned SMOS and Aquarius missions.
  • 42. “Estimating profile soil moisture and groundwater variations using GRACE and Oklahoma Mesonet soil moisture data.” Swenson et al., 2008
  • 43. Purpose • • • to estimate time series of regional groundwater anomalies by combining terrestrial water storage estimates from GRACE with in situ soil moisture observations from the Oklahoma Mesonet with supplementary data from DOE’s Atmospheric Radiation Measurement Network (DOE ARM). develop an empirical scaling factor to assess soil moisture variability within the top 75cm sampled sites. to provide efficient and effective mechanism to monitor and assess groundwater resources both above and below the surface.
  • 44. Methods • Water balance approach • Oklahoma Mesonet • GRACE o estimate variations in groundwater averaged over a region centered on of OK. o collected real-time hydrometeorological observations > 100 stations. o in situ soil moisture measurements were conducted every 30 mins at depth of 5cm, 25cm, 60cm, and 75cm. o estimate variations and data sets from > 100 stations were combined with total water estimates from GRACE using a water balance equation (not provided in text). o combine time series of spatially averaged groundwater storage variations.
  • 45. Methods (cont) o GRACE   used the Released 4 (RL04) data produced by Centered for Space Research (CSR). employed the post-processing technique to produce water storage estimates averaged over a region of 280,000 square km. o OK Mesonet    soil moisture detection sensors were added to 60 sites (2,25,60, and 75cm depth) and 43 stites at (2 and 25cm depth). volumetric water content is determined from a soil water retention curve. automated algorithm assessed the quality of soil moisture data. o DOE ARM Network   Soil Water and Temperature System (SWAT) 21 sites to collect hourly profiles of soil temperature and water at eight depths (0.05 to 1.75m) below the surface. Average inter-site distance was about 75 km. 10 sites spanned the period 2002 to present.
  • 46. Results OVERVIEW Overall, results are comparatively observed with well level data from a larger surrounding region and the data reveals consistent phase and relative inter-annual variability in relations to soil moisture estimates. groundwater storage estimated from approximately 40 USGS well levels in the region around OK, scaled weight of the GRACE averaging kernel in Figure 1. Over 40% of the variability in unsaturated zone water storage occurs below the deepest OM sensors. • • •
  • 47. Results (cont) Figure 3 shows the time series of soil moisture expressed as monthly anomalies of volumetric water content at each four depths at which OKM sensors are located. Figure 4 shows the monthly averaged soil moisture anomalies expressed as volumetric water content with increasing phase lag with depth. Source: Swenson et al. 2008
  • 48. Results (cont) ● Figure 10 shows the best results for confirming the upper panel. The results compared two groundwater estimates. ● The well level groundwater (dark gray line) confirms the general characteristics of the regional groundwater signal estimated as a residual from GRACE (light gray line). Both results show similar seasonal cycle, and the phases of the time series agree well. Source: Swenson et al. 2008
  • 49. Conclusion • • • • • in view of the discrepancies that exist in both spatial and temporal sampling between the data used to create two groundwater estimates, the overall agreement is good. both time series illustrate similar interannual variability: o relatively dry 2004 preceded by much wetter 2005 and 2003 signal lying b/w the other years. smaller amplitude of well level-derived time series is not surprising, where signals separated by larger distances are likely to be less well correlated. which indicates that variations in both soil moisture and groundwater are well correlated at scales. The correlation between month-to-month changes in the two times series may also indicate that the method for estimating GRACE is pessimistic.
  • 50. Bibliography • Choi, M., Jacobs, J.M., and Bosch, D.D. (2008). Remote Sensing Observatory Validation of Surface Soil Moisture using Advanced Microwave Scanning Radiometer E, Common Land Model, and Ground-based Data: Case Study in SMEX03 Little River Region, Georgia, U.S. Water Resources Research, Vol. 44, pg. 1-14. • de Jeu, A.M., Wagner, W., Holmes, T.R.H., Dolman, A.J., van de Giesen, N.C., and Friesen, J. (2008). Global Soil Moisture Patterns Observed by Space Borne Microwaves Radiometers and Scatterometers. Survey Geophysics, Vol. 29, pg. 399-420. • Reichle, R.H., Koster, R.D., Lui, P., Mahanama, S.P.P., Njoku, E.G., and Owe, M., (2007). Comparison and Assimilation of Global Soil Moisture Retrievals from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) and the Scanning Multichannel Microwave Radiometer (SMMR). Journal of Geophysical Research, Vol. 112, pg. 1-14. • Swenson, S., Famiglietti, J., Basara, J., and Wahr, J. (2008). Estimating Profile Soil Moisture and Groundwater Variations using Gravity Recovery and Climate Experiment (GRACE) and Oklahoma Mesonet Soil Moisture Data. Water Resource Research, Vol. 44, pg. 1-12. • Syed, T.H., Famiglietti, J.S., Rodell, M., Chen, J., and Wilson, C.R. (2008). Analysis of Terrestrial Water Storage Changes from Gravity Recovery and Climate Experiment (GRACE) and Global Land Data Assimilation System (GLDAS). Water Resources Research, Vol. 44, pg. 1-15.
  • 51. Thanks! 