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 m3/m3 (synthetic data) and 0.06 m3/m3 (actual data) for bare soil. The accuracy decreases in the presence of vegetation (root mean square in the range 0.08e0.12 m3/m3), as expected.

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

Pulvirenti, Luca;Cenci, Luca;Boni, Giorgio;
2018

Abstract

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 m3/m3 (synthetic data) and 0.06 m3/m3 (actual data) for bare soil. The accuracy decreases in the presence of vegetation (root mean square in the range 0.08e0.12 m3/m3), as expected.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11567/894166
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