A Prototype Software Package to Retrieve Soil Moisture From Sentinel-1 Data by Using a Bayesian Multitemporal Algorithm

Abstract

The Sentinel-1 mission will offer the opportunity to obtain C-band radar data characterized by short revisit time, thus allowing for the generation of frequent soil moisture maps. This work presents a prototype software implementing a multitemporal approach to the problem of soil moisture retrieval using Synthetic Aperture Radar (SAR) data. The approach exploits the short revisit time of Sentinel-1 data by assuming the availability of a time series of SAR images that is integrated within a retrieval algorithm based on the Bayesian maximum a posteriori probability statistical criterion. The paper focuses on the combination of on-line and off-line processing that has been designed in order to decrease the time necessary to produce a soil moisture map, which may be a critical aspect of multitemporal approaches. It describes also the optimization of the algorithm carried out to find the set of algorithm parameters that allow obtaining the best tradeoff between accuracy of the estimates and computational efficiency. A set of simulations of C-band SAR data, produced by applying a well-established radar-backscattering model, is used to perform the optimization. The designed system is tested on a series of ERS-1 SAR data acquired on February-April 1994 in Central Italy with a revisit time of three days. The results indicate that the temporal trend of estimated soil moisture is consistent with the succession of rain events occurred throughout the period of ERS-1 acquisitions over the observed geographic area

A Bayesian Approach to Combine Landsat and ALOS PALSAR Time Series for Near Real-Time Deforestation Detection

“…Single-sensor applications include monitoring of annual land cover changes [50][51][52][53], soil moisture [54][55][56] and lake dynamics [57]. Fusing multi-temporal Landsat and SAR data for land cover classification was demonstrated by [58,59].…”

Empirical fitting of forward backscattering models for multitemporal retrieval of soil moisture from radar data at L-band

“…The MLTA was designed in the framework of the "GMES Sentinel-1 Soil Moisture Algorithm Development" project 7 funded by ESA (ESA Contract No. SMAD-TN-ACS-GS-0308).…”

“…However, new SAR missions have been developed to provide data with short revisit time, such as the European Space Agency (ESA) Sentinel-1 (S-1) 5 and the Soil Moisture Active/Passive (SMAP) mission of the National Aeronautics and Space Administration (NASA). 6 A multitemporal algorithm 7 [hereafter denoted as multitemporal algorithm (MLTA)] was initially conceived for deriving a soil moisture product from the data provided by the C-band S-1 mission, characterized by short revisit time (the two-satellite constellation offers 6 days repeat or less over Europe and Canada 8 ). Considering the launch of the SMAP mission, operating in a frequency range (L-band) particularly suited for SMC applications, the MLTA has been adapted to work at L-band and to take advantage of the multipolarization (HH, VV, and HV) capability of SMAP.…”

Multitemporal Soil Moisture Retrieval over Bare Agricultural Areas by Means of Alpha Model with Multisensor SAR Data

“…e multitemporal [11,12], multi-incidence angle [13][14][15], multipolarization [16][17][18][19], and multifrequency [20] SAR data are increasingly applied for soil moisture retrieval to avoid using less observations than the number of unknown parameters [21,22]. e availability of SAR data characterized by short repeating cycles such as Radarsat-2, Sentinel-1, ALOS-2, Cosmo-SkyMed, and TerraSAR-X/TanDEM-X provides possible alternatives for monitoring soil moisture change at fine spatial scales through change detection methods [23].…”