Real-Time Forecast of SMAP L3 Soil Moisture Using Spatial–Temporal Deep Learning Model with Data Integration

Soil Moisture Active Passive (SMAP) Algorithm Theoretical Basis Document SMAP L2 & L3 Radar Soil Moisture (Active) Data Products

Integration of remote sensing and meteorological data to predict flooding time using deep learning algorithm

Accurate flood forecasting is a vital need to reduce its risks. Due to the complicated structure of flood and river flow, it is somehow difficult to solve this problem. Artificial neural networks, such as frequent neural networks, offer good performance in time series data. In recent years, the use of Long Short Term Memory networks hase attracted much attention due to the faults of frequent ne...

Data Assimilation to Extract Soil Moisture Information from SMAP Observations

This study compares different methods to extract soil moisture information through the assimilation of Soil Moisture Active Passive (SMAP) observations. Neural network (NN) and physically-based SMAP soil moisture retrievals were assimilated into the National Aeronautics and Space Administration (NASA) Catchment model over the contiguous United States for April 2015 to March 2017. By constructio...

The Compact Polarimetry alternative for Soil Moisture Estimation using SMAP

In this paper, we investigate the potential of the compact polarimetry mode at longer wavelengths from space for soil moisture estimation using SMAP data. Compact polarimetry consists of transmitting a single polarization while receiving two polarizations. At longer wavelengths, one of the main challenges associated with compact polarimetry from space is Faraday rotation estimation and correcti...

Soil Moisture Active Passive (SMAP) Algorithm Theoretical Basis Document (ATBD) SMAP Calibrated, Time-Ordered Brightness Temperatures L1B_TB Data Product

(Page intentionally left blank) Algorithm Theoretical Basis Documents (ATBDs) provide the physical and mathematical descriptions of the algorithms used in the generation of science data products. The ATBDs include a description of variance and uncertainty estimates and considerations of calibration and validation, exception control and diagnostics. Internal and external data flows are also desc...