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

Abstract

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 sensedand 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 m 3 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.

Seasonal ecosystem vulnerability to climatic anomalies in the Mediterranean

“…Coupling of land surface models with satellite-based surface soil moisture can further enhance knowledge on the status of rootzone soil moisture in future Tramblay et al, 2020). Furthermore, it should be noted that validations of the ESA CCI soil moisture data set with in situ observations from Mediterranean sites in Spain, France and Turkey showed high agreement (Albergel et al, 2013;Dorigo et al, 2015;Bulut et al, 2019). Also the FAPAR product from the CGLS has been validated with observation data from Tunisia, Italy, Spain and France, primarily for a variety of crop types as well as a deciduous broadleaf forest in Italy and a needleleaf forest in Spain (Fuster et al, 2020).…”

Comparative Evaluation of Microwave L-Band VOD and Optical NDVI for Agriculture Drought Detection over Central Europe

“…The normalized difference of NIR and SWIR radiances of Landsat Thematic Mapper (TM), called ND45 [6], normalized difference vegetation index (NDVI; [7]), and enhanced vegetation index (EVI; [8]) are examples of these indices that have been shown to be effective in the monitoring of vegetation water content, crop phenology, and patterns of crop production in different regions of the world [9][10][11][12]. Among different VIs, the NDVI is the most widely used VI due to its simplicity in transforming spectral bands, the easy procedure of its calculation, and its availability for a long time period [13][14][15][16].…”

Model Prediction of the Soil Moisture Regime and Soil Nutrient Regime Based on DEM-Derived Topo-Hydrologic Variables for Mapping Ecosites

“…In addition to field assessments using easily observable site features, indicator vegetation, and easily identified soil properties [7], many studies focused on the model predictions of SNRs and SMRs. The models are often based on interpolation schemes [8] and statistics-based schemes [9,10] using varying model predictors from field-based plant indicators [7], model-based clay content [9], model-based soil drainage [11], remote sensing data [12], and map-based soil texture [10], with varying class number, map resolution, and model accuracies. However, there is a lack of model studies that estimated SNRs and SMRs with high resolution (i.e., ≤10 m) and high accuracy using easily accessible model predictors.…”