Results of Improved SMAP Soil Moisture Retrieval Using a Deep Neural Network-based Replacement of Radiative Transfer and Roughness Model

This repository contains: A deep neural network (DNN) based soil moisture (SM) estimates (NN) based on the SMAP TB (Descending, 6 AM) and SMAP SCA-V ancillary data as the input variables. (SMAP_NN_36km_20150331_20220326.nc) Temporally averaged roughness parameter (hNN) and scattering albedo (omegaNN) which are retrieved by inversely tracking the DNN model. (SMAP_hNN_omegaNN_36km_temporal_average_201503_202103.nc) Summary: The DNN model has been developed by relating SMAP TB and SMAP SCA-V ancillary data with in-situ SM data from the international soil moisture network (ISMN) using DNN. To minimize scale mismatch between gridded SMAP data and point in-situ data, the triple collocation analysis was conducted. The SM estimated from the DNN algorithm (NN) showed a good agreement with the ISMN data that was not used in the model training. Moreover, for a densely vegetated region located in the Amazon (Tambopata site) the NN showed less bias compared to available SM retrievals. Two parameters hNN and omegaNN are retrieved by ingesting NN to the modified dual channel algorithm. When the SM retrieval was conducted using the hNN and omegaNN, the result showed good agreement with the NN (DNN-based SM) with R of 0.986, ubRMSD of 0.015 m3/m3, and bias of -0.001 m3/m3. The paper "Improved SMAP Soil Moisture Retrieval Using a Deep Neural Network-based Replacement of Radiative Transfer and Roughness Model" published in the Transactions on Geoscience and Remote Sensing. For more details, please contact me ([e-mail])