Development of Improved Algorithms for Downscaling SMAP-Derived Soil Moisture Using Visible/Inferred satellite observations

Document Type : Original Article

Authors

1 Ph.D. Student Department of Water Engineering, Ferdowsi University, Mashhad, Iran

2 Professor, Water Engineering Department, Faculty of Agriculture, Ferdowsi University of Mashhad., Mashhad., Iran

3 Department of water engineering, agriculture faculty, Ferdowsi university of Mashhad, Mashhad, Iran

Abstract

In this research, a new downscaling method was proposed to improve the spatial resolution of Soil Moisture Active Passive (SMAP) soil moisture estimates with the use of higher resolution from the visible/infrared satellite (MODIS). The model relies on a physical linear relationship between the soil moisture content and shortwave infrared transformed reflectance (STR). Finally, the physical downscaling model was compared against the traditional empirical triangle method to evaluate the performance of using STR in place of land surface temperature (LST). In this study, MODIS and SMAP satellites observations from 2015 to 2018 were used and the evaluation of the downscaled soil moisture was undertaken at the COSMOS sites in Arizona. Results show that while both downscaling algorithms have decreased Root Mean Square Error (RMSE) values, the performance of physical algorithm generally is better than empirical. However, the spatial correlation (R) values decreased using downscaling algorithms, but the RMSE values improved from 0.032 of SMAP soil moisture to 0.3 and 0.031 for the physical and empirical downscaling algorithms, respectively. Also the values of bias improved from 0.011 of SMAP soil moisture to 0.0016 and 0.0076 for the physical and empirical downscaling algorithms, respectively. In conclusion, the result show the proposed physical downscaling algorithm nicely improves the limited spatial variability of SMAP soil moisture and replacing LST with STR can yield a new insight on the downscaling issues.

Keywords

References

A., N.M., H., N.M., J., S.J., G., R.K., & R., S.J. (2007). Sediment yields from unit-source semiarid watersheds at Walnut Gulch. Water Resources Research, 43
Adegoke, J.O., & Carleton, A.M. (2002). Relations between Soil Moisture and Satellite Vegetation Indices in the U.S. Corn Belt. Journal of Hydrometeorology, 3, 395-405
Carlson, T. (2007). An Overview of the "Triangle Method" for Estimating Surface Evapotranspiration and Soil Moisture from Satellite Imagery. Sensors, 7, 1612
Carlson, T.N. (2013). Triangle Models and Misconceptions. International Journal of Remote Sensing Applications, 3, 155-158
Carlson, T.N., Gillies, R.R., & Perry, E.M. (1994). A method to make use of thermal infrared temperature and NDVI measurements to infer surface soil water content and fractional vegetation cover.   Remote Sensing Reviews, 9, 161-173
Chauhan, N.S., Miller, S., & Ardanuy, P. (2003). Spaceborne soil moisture estimation at high resolution: a microwave-optical/IR synergistic approach. International Journal of Remote Sensing, 24, 4599-4622
Chen, N., He, Y., & Zhang, X. (2017). NIR-Red Spectra-Based Disaggregation of SMAP Soil Moisture to 250 m Resolution Based on OzNet in Southeastern Australia. Remote Sensing, 9, 51
Das, N., Entekhabi, D., & G. Njoku, E. (2011). An Algorithm for Merging SMAP Radiometer and Radar Data for High-Resolution Soil-Moisture Retrieval.
Entekhabi, D., G. Njoku, E., E. O'Neill, P., Kellogg, K.H., Crow, W., Edelstein, W.N., Entin, J., D. Goodman, S., J. Jackson, T., Johnson, J., Kimball, J., Piepmeier, J., D. Koster, R., Martin, N., McDonald, K., Moghaddam, M., Moran, S., Reichle, R., Shi, J., & van Zyl, J. (2010). The Soil Moisture Active and Passive (SMAP) mission.
Hain, C.R., Crow, W.T., Mecikalski, J.R., Anderson, M.C., & Holmes, T. (2011). An intercomparison of available soil moisture estimates from thermal infrared and passive microwave remote sensing and land surface modeling. Journal of Geophysical Research: Atmospheres, 116
Jian, P., Alexander, L., Olivier, M., & C., V.N.E. (2017). A review of spatial downscaling of satellite remotely sensed soil moisture. Reviews of Geophysics, 55, 341-366
Knipper, K., Hogue, T., Franz, K., & Scott, R. (2017). Downscaling SMAP and SMOS soil moisture with moderate-resolution imaging spectroradiometer visible and infrared products over southern Arizona. Journal of Applied Remote Sensing, 11, 026021
Liu, Y.Y., Parinussa, R., A. Dorigo, W., de Jeu, R., Wagner, W., van Dijk, A., McCabe, M., & Evans, J. (2011). Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals.
Mallick, K., Bhattacharya, B.K., & Patel, N.K. (2009). Estimating volumetric surface moisture content for cropped soils using a soil wetness index based on surface temperature and NDVI. Agricultural and Forest Meteorology, 149, 1327-1342
McColl, K.A., Alemohammad, S.H., Akbar, R., Konings, A.G., Yueh, S., & Entekhabi, D. (2017). The global distribution and dynamics of surface soil moisture. Nature Geoscience, 10, 100
Merlin, O., Chehbouni, A., Walker, J.P., Panciera, R., & Kerr, Y.H. (2008). A Simple Method to Disaggregate Passive Microwave-Based Soil Moisture. IEEE Transactions on Geoscience and Remote Sensing, 46, 786-796
Moran, M.S., Clarke, T.R., Inoue, Y., & Vidal, A. (1994). Estimating crop water deficit using the relation between surface-air temperature and spectral vegetation index. Remote Sensing of Environment, 49, 246-263
Petropoulos, G., Carlson, T.N., Wooster, M.J., & Islam, S. (2009). A review of Ts/VI remote sensing based methods for the retrieval of land surface energy fluxes and soil surface moisture. Progress in Physical Geography: Earth and Environment, 33, 224-250
Piles, M., Camps, A., Vall-llossera, M., Corbella, I., Panciera, R., Rüdiger, C., H. Kerr, Y., & Walker, J. (2011). Downscaling SMOS-derived soil moisture using MODIS Visible/Infrared data.
Ritchie, J.C., Nearing, M.A., Nichols, M.H., & Ritchie, C.A. (2005). Patterns of Soil Erosion and Redeposition on Lucky Hills Watershed, Walnut Gulch Experimental Watershed, Arizona. CATENA, 61, 122-130
Sadeghi, M., Babaeian, E., Tuller, M., & Jones, S.B. (2017). The optical trapezoid model: A novel approach to remote sensing of soil moisture applied to Sentinel-2 and Landsat-8 observations. Remote Sensing of Environment, 198, 52-68
Stisen, S., Sandholt, I., Nørgaard, A., Fensholt, R., & Jensen, K.H. (2008). Combining the triangle method with thermal inertia to estimate regional evapotranspiration — Applied to MSG-SEVIRI data in the Senegal River basin. Remote Sensing of Environment, 112, 1242-1255
Zreda, M., Shuttleworth, W., Zeng, X., Zweck, C., Desilets, D., Franz, T., & Rosolem, R. (2012). COSMOS: the COsmic-ray soil moisture observing system.
Iranian Journal of Irrigation & Drainage
Volume 14, Issue 2 - Serial Number 80
July and August 2020
Pages 650-660

Files

Share

How to cite

Statistics