An Iterative Generalized Hybrid Decomposition for Soil Moisture Retrieval Under Vegetation Cover Using Fully Polarimetric SAR


An iterative, generalized hybrid polarimetric decomposition, combining model-based mostly and eigen-based techniques along with a generalized vegetation model, is developed for soil moisture retrieval underneath agricultural vegetation cowl. The algorithm is physically based mostly while not the need of empirical calibration or fitting with auxiliary knowledge and runs in 2 iterations. The algorithm is applied on L-band absolutely polarimetric information sets acquired by DLR's E-SAR sensor. The flights were conducted among the AgriSAR, OPAQUE, and SARTEO campaigns dispensed between 2006 and 2008 on three different test sites. The algorithm achieves inversion rates perpetually higher than ninety five% for a variety of crop varieties at totally different phenological stages. The validation is performed against in situ time-domain reflectometry (TDR), frequency-domain reflectometry (FDR), and gravimetric measurements. The moisture levels range from 5 vol.percent to 40 vol.p.c. The achieved root-mean-square error (RMSE) levels stay between vol.percent and 4.four vol.percent for all 3 sites across totally different vegetation and soil types, comprising the entire phenological cycle (e.g., April-July 2006).

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