An Improved Soil Moisture Retrieval Algorithm for ERS and METOP Scatterometer Observations

By Vahid Naeimi, Klaus Scipal, Z. Bartalis, S. Hasenauer, Wolfgang Wagner. IEEE Transactions on Geoscience and Remote Sensing, 2009

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Evaluation of the ESA CCI soil moisture product using ground-based observations (2015, methodology): “…for the ECV product using two different spatial resampling techniques: 1) a nearest neighbour resampling, like adopted for ECV_SMgeneration, and 2) a hammingwindow resampling (radius= 36 km), as used in the TU-Wienmethod tomap observations from swath geometry to a fixed grid (Naeimi et al., 2009).”
Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals (2017, neutral): “5 R1 [21], [22].”
Toward Global Soil Moisture Monitoring With Sentinel-1: Harnessing Assets and Overcoming Obstacles (2019, methodology): “This relation of backscatter to LIA is commonly referred as backscatter slope and was essential to previous SSM retrieval algorithms [42], [58].”
Radar Remote Sensing of Agricultural Canopies: A Review (2017, methodology): “[252] made several improvements to address shortcomings in the original algo815 rithm to yield the so-called WARP5 model.”
Estimating error cross‐correlations in soil moisture data sets using extended collocation analysis (2016, neutral): “2 [Wagner et al., 1999; Naeimi et al., 2009].”