Article Title :
Space-borne Active Microwave Remote Sensing of Soil Moisture: A Review
1 (2017)
53-86
Uncertainty , Validation , Modeling , Calibration , Dielectric properties , Polarimetry , Scatterometer , SAR


Space-borne active microwave remote sensing is an efficient technique to acquire knowledge of land surface soil moisture (SM). Several studies have reported comparable results of surface SM using space-borne scatterometer responses to backscattering from soil layer. However, detection and measuring of SM using these techniques require an appropriate filtering of data, site-specific calibration of surface roughness parameters, prior knowledge of the study area, specific research purpose, careful selection of model, different suitable datasets with appropriate time series, etc. Reported success studies are very site-, data- and situation-specific and show uncertainty in SM estimations therefore, insufficient to reach global conclusions and applications. Scientific challenge before the community is to develop or modify models and appropriate datasets for SM estimations with simplification and high precision with global applicability for complex bio-physical units. The field is new, active, attractive, challenging and interesting area of research for sustainable land and climate change management.

Space-borne active microwave remote sensing is promising technique for soil moisture retrievals.
Several techniques and models are available for soil moisture retrievals.
Results of these models are very site-, data and situation specific.
More studies require for wide applications in different bio-physical environment.
Bourgeau-Chavez, L. L., Kasischke, E. S., Riordan, K., Brunzell, S., Nolan, M., Hyer, E., Slawski, J., Medvecz, M., Walters, T. and Ames, S., 2007. Remote monitoring of spatial and temporal surface soil moisture in fire disturbed boreal forest ecosystems with ERS SAR imagery. Int. J. Rem. Sens., 28(10), 2133-2162.
Entekhabi, D., Entekhabi, D. Njoku, E. G., O’Neill, P. E., Kellogg, K. H., Crow, W. T., Edelstein, W. N., Entin, J. K., Goodman, S. D., Jackson, T. J., Johnson, J., Kimball, J., Piepmeier, J. R., Koster, R. D., Martin, N., McDonald, K. C., Moghaddam, M., Moran, S., Reichle, R., Shi, J. C., Spencer, M. W., Thurman, S.W., Tsang, L. and Van Zyl, J., 2010. The soil moisture active passive (SMAP) mission. Proceedings of the IEEE, 98.5, 704-716.
Fernandez-Moran, R., Wigneron, J.-P., Lopez-Baeza, E., Al-Yaari, A., Coll-Pajaron, A., Mialon, A., Miernecki, M., Parrens, M., Salgado-Hernanz, P. M., Schwank, M., Wang, S. and Kerr, Y. H., 2015. Roughness and vegetation parameterizations at L-band for soil moisture retrievals over a vineyard field. Remote Sensing of Environment, 170, 269-279.
Filion, R., Bernier, M., Paniconi, C., Chokmani, K., Melis, M., Soddu, A., Talazac, M., and Lafortune, F-X, 2016. Remote sensing for mapping soil moisture and drainage potential in semi-arid regions: applications to the Campidano plain of Sardinia, Italy. Science of the Total Environment. 573, 862-876.
Fung, A.K., 1994. Microwave Scattering and Emission Models and Their Applications, Norwood, MA: Artech House.
Hornáček, M., Wagner, W., Sabel, D., Truong, H.-L., Snoeij, P., Hahmann, Diedrich, E. and Doubková, M., 2012. Potential for high resolution systematic global surface soil moisture retrieval via change detection using Sentinel-1. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5(4), 1303-1311.
Huang, S., Tsang, L., Njoku, E.G. and Chan, K.S., 2010. Backscattering Coefficients, Coherent Reflectivities, and Emissivities of randomly rough soil surfaces at L-band for SMAP applications based on numerical solutions of Maxwell equations in three-dimensional simulations. IEEE Transactions on Geoscience and Remote Sensing, 48(6), 2557-2568.
Jensen, J.R., 1996, Principal component analysis. In Introductory Digital Image Processing; A Remote Sensing Perspective, K. C. Clarke (Ed.), 172–179.
Liu, L., Liao, J., Chen, X., Zhou, G., Su, Y., Xiang, Z., Wang, Z., Liu, X., Li, Y., Wu, J., Xiong, X. and Shao, H., 2017. The microwave temperature vegetation drought index (MTVDI) basedon AMSR-E brightness temperatures for long-term drought assessmentacross China (2003-2010). Remote Sensing of Environment, 199, 302-320.
Pasolli, L., Notarnicola, C., Bertoldi, G., Dellachiesa, S., Niedrist, G., Bruzzone, L., Tappeiner, U. and Zebisch, M., 2014. Soil moisture monitoring in mountain areas by using high-resolution SAR images: results from a feasibility study. European Journal of Soil Science,
Rowell, D. L., 1994. Soil Science: Methods and Applications (Harlow: Longman Group).
Srivastava, H. S., Sharma, P.K, Kumar, D., Sivasankar, T., Mishra, R.S., Mishra, M. and Patel, P., 2015. Soil moisture variation over parts of Saharanpur and Haridwar districts (India) during November-2006 to June-2007 as observed by multi-polarized (VV/HH and VV/VH) ENVISAT-1 temporal ASAR data. International Journal of Advanced Engineering Research and Science (IJAERS), 2(1), 31-39.
Thoma, D., Moran, M., Bryant, R., Holifield Collins, C., Rahman, M. and Skirvin, S., 2004. Comparison of two methods for extracting surface soil moisture from C-band radar imagery. In Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS 04), Anchorage, AK, USA, 827-830.
Ulaby, F. T., Moore, R. K. and Fung, A. K., 1982. Microwave Remote Sensing: Active and Passive, Vol. II, Addison-Wesley: Reading, MA, U.S.A.
Ulaby, F. T., Moore, R. K. and Fung, A. K., 1986. Microwave Remote Sensing: Active and Passive, Vol. III, Artech House: Dedham, MA, U.S.A.