A surface soil moisture mapping service at national (Italian) scale based on Sentinel-1 data. (April 2018)

• Record Type: Journal Article
• Authors: Pulvirenti, Luca; Squicciarino, Giuseppe; Cenci, Luca; Boni, Giorgio; Pierdicca, Nazzareno; Chini, Marco; Versace, Cosimo; Campanella, Paolo
• Is Part Of: Environmental modelling & software. Volume 102(2018)
• Page Start: 13
• Page End: 28
• Publication Date: 2018-04
• Subjects: Soil moisture -- Sentinel-1 -- Multi-temporal algorithm -- Plant water content -- Landsat-8 -- Sentinel-2 -- MODIS; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2017.12.022
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: This paper presents MULESME, a software designed for the systematic mapping of surface soil moisture using Sentinel-1 SAR data. MULESME implements a multi-temporal algorithm that uses time series of Sentinel-1 data and ancillary data, such as a plant water content map, as inputs. A secondary software module generates the plant water content map from optical data provided by Landsat-8, or Sentinel-2, or MODIS. Each output of MULESME includes another map showing the level of uncertainty of the soil moisture estimates. MULESME was tested by using both synthetic and actual data. The results of the tests showed that root mean square error is in the range between 0.03 m 3 /m 3 (synthetic data) and 0.06 m 3 /m 3 (actual data) for bare soil. The accuracy decreases in the presence of vegetation (root mean square in the range 0.08–0.12 m 3 /m 3 ), as expected. Highlights: An automated Sentinel-1 based soil moisture mapping service is developed. The service is implemented by the MULESME software. MULESME outputs are generally very accurate under bare soil conditions. The accuracy decreases when the soil is densely vegetated, as expected.