Evaluation of Remotely-Sensed and Model-Based Soil Moisture Products According to Different Soil Type, Vegetation Cover and Climate Regime Using Station-Based Observations over Turkey

• Published in: Remote sensing (Basel, Switzerland) Vol. 11; no. 16; p. 1875
• Main Authors: Bulut, Burak, Yilmaz, M. Tugrul, Afshar, Mehdi H., Şorman, A. Ünal, Yücel, İsmail, Cosh, Michael H., Şimşek, Osman
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2019
• Subjects: Accuracy; Agricultural research; Algorithms; Antecedent precipitation; Antecedent precipitation index; Calibration; Climate change; Data collection; Datasets; Drought; Evaluation; Hydrologic data; Hydrology; In situ measurement; inter-comparison; Meteorology; Moisture content; Precipitation; Quality control; Remote sensing; Rescaling; Root-mean-square errors; Satellites; Scaling; Seasonal variations; sensor calibration; Sensors; Soil moisture; Soil temperature; Soils; station-based measurements; Studies; Time series; Topography; validation; Vegetation; Vegetation cover; Australia; United States > US; Turkey
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs11161875

This study evaluates the performance of widely-used remotely sensed- and model-based soil moisture products, including: The Advanced Scatterometer (ASCAT), the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), the European Space Agency Climate Change Initiative (ESA-CCI), the Antecedent Precipitation Index (API), and the Global Land Data Assimilation System (GLDAS-NOAH). Evaluations are performed between 2008 and 2011 against the calibrated station-based soil moisture observations collected by the General Directorate of Meteorology of Turkey. The calibration of soil moisture observing sensors with respect to the soil type, correction of the soil moisture for the soil temperature, and the quality control of the collected measurements are performed prior to the evaluation of the products. Evaluation of remotely sensed- and model-based soil moisture products is performed considering different characteristics of the time series (i.e., seasonality and anomaly components) and the study region (i.e., soil type, vegetation cover, soil wetness and climate regime). The systematic bias between soil moisture products and in situ measurements is eliminated by using a linear rescaling method. Correlations between the soil moisture products and the in situ observations vary between 0.57 and 0.87, while the root mean square errors of the products versus the in situ observations vary between 0.028 and 0.043 m3 m−3. Overall, according to the correlation and root mean square error values obtained in all evaluation categories, NOAH and ESA-CCI soil moisture products perform better than all the other model- and remotely sensed-based soil moisture products. These results are valid for the entire study time period and all of the sub-categories under soil type, vegetation cover, soil wetness and climate regime.