Regression Models for Soil Water Storage Estimation Using the ESA CCI Satellite Soil Moisture Product: A Case Study in Northeast Portugal

The European Space Agency Climate Change Initiative Soil Moisture (ESA CCI SM) product provides soil moisture estimates from radar satellite data with a daily temporal resolution. Despite validation exercises with ground data that have been performed since the product’s launch, SM has not yet been c...

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Bibliographic Details
Published in:Water (Basel) 2021-01, Vol.13 (1), p.37
Main Authors: de Figueiredo, Tomás, Royer, Ana Caroline, Fonseca, Felícia, de Araújo Schütz, Fabiana Costa, Hernández, Zulimar
Format: Article
Language:English
Subjects:
Atmosphere
Bands
Case studies
Climate change
Datasets
Depletion
Dielectric properties
Ground stations
Hydrologic cycle
Hydrology
Meteorological data
Moisture content
Precipitation
Radar
Radar data
Radar satellites
Radiometers
Regression analysis
Satellites
Soil moisture
Soil water
Soil water storage
Storage capacity
Temporal resolution
Vegetation
Water balance
Water storage
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Summary:The European Space Agency Climate Change Initiative Soil Moisture (ESA CCI SM) product provides soil moisture estimates from radar satellite data with a daily temporal resolution. Despite validation exercises with ground data that have been performed since the product’s launch, SM has not yet been consistently related to soil water storage, which is a key step for its application for prediction purposes. This study aimed to analyse the relationship between soil water storage (S), which was obtained from soil water balance computations with ground meteorological data, and soil moisture, which was obtained from radar data, as affected by soil water storage capacity (Smax). As a case study, a 14-year monthly series of soil water storage, produced via soil water balance computations using ground meteorological data from northeast Portugal and Smax from 25 mm to 150 mm, were matched with the corresponding monthly averaged SM product. Linear (I) and logistic (II) regression models relating S with SM were compared. Model performance (r2 in the 0.8–0.9 range) varied non-monotonically with Smax, with it being the highest at an Smax of 50 mm. The logistic model (II) performed better than the linear model (I) in the lower range of Smax. Improvements in model performance obtained with segregation of the data series in two subsets, representing soil water recharge and depletion phases throughout the year, outlined the hysteresis in the relationship between S and SM.
ISSN:2073-4441
2073-4441
DOI:10.3390/w13010037