The value of satellite remote sensing soil moisture data and the DISPATCH algorithm in irrigation fields

• Published in: Hydrology and earth system sciences Vol. 22; no. 11; pp. 5889 - 5900
• Main Authors: Fontanet, Mireia, Fernàndez-Garcia, Daniel, Ferrer, Francesc
• Format: Journal Article Publication
• Language: English
• Published: Katlenburg-Lindau Copernicus GmbH 14.11.2018; Copernicus Publications
• Subjects: Agricultural land; Agricultural management; Agriculture; Algorithms; Data; Disaggregation; Enginyeria civil; Fields; Geologia; Gravimetry; Heterogeneity; Hidrologia; Humitat; Hydrology; Imaging techniques; Irrigation; Irrigation scheduling; Irrigation water; Land surface temperature; Measurement; Mesurament; Normalized difference vegetative index; Rain; Rainfall; Reflectometry; Remote sensing; Resolution; Satellite data; Satellite soil moisture estimates; Satellites; Soil; Soil moisture; Soil moisture measurements; Spatial discrimination; Spatial distribution; Spatial resolution; Spatial variability; Spatial variations; Spectroradiometers; Sprinkler irrigation; Surface temperature; Sòls; Time domain analysis; Vegetation index; Water balance; Water management; Watershed management; Àrees temàtiques de la UPC
• ISSN: 1607-7938; 1027-5606; 1607-7938
• DOI: 10.5194/hess-22-5889-2018

Soil moisture measurements are needed in a large number of applications such as hydro-climate approaches, watershed water balance management and irrigation scheduling. Nowadays, different kinds of methodologies exist for measuring soil moisture. Direct methods based on gravimetric sampling or time domain reflectometry (TDR) techniques measure soil moisture in a small volume of soil at few particular locations. This typically gives a poor description of the spatial distribution of soil moisture in relatively large agriculture fields. Remote sensing of soil moisture provides widespread coverage and can overcome this problem but suffers from other problems stemming from its low spatial resolution. In this context, the DISaggregation based on Physical And Theoretical scale CHange (DISPATCH) algorithm has been proposed in the literature to downscale soil moisture satellite data from 40 to 1 km resolution by combining the low-resolution Soil Moisture Ocean Salinity (SMOS) satellite soil moisture data with the high-resolution Normalized Difference Vegetation Index (NDVI) and land surface temperature (LST) datasets obtained from a Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. In this work, DISPATCH estimations are compared with soil moisture sensors and gravimetric measurements to validate the DISPATCH algorithm in an agricultural field during two different hydrologic scenarios: wet conditions driven by rainfall events and wet conditions driven by local sprinkler irrigation. Results show that the DISPATCH algorithm provides appropriate soil moisture estimates during general rainfall events but not when sprinkler irrigation generates occasional heterogeneity. In order to explain these differences, we have examined the spatial variability scales of NDVI and LST data, which are the input variables involved in the downscaling process. Sample variograms show that the spatial scales associated with the NDVI and LST properties are too large to represent the variations of the average soil moisture at the site, and this could be a reason why the DISPATCH algorithm does not work properly in this field site.