Abstract

The main goal of this study was to evaluate four major remote sensing soil moisture (SM) products over the state of Texas. These remote sensing products are: (i) the Advanced Microwave Scanning Radiometer—Earth Observing System (AMSR-E) (2002–September 2011); (ii) the Soil Moisture Ocean Salinity system (SMOS, 2010–present); (iii) AMSR2 (2012–present); and (iv) the Soil Moisture Active Passive system (SMAP, 2015–present). The quality of the generated SM data is influenced by the accuracy and precision of the sensors and the retrieval algorithms used in processing raw data. Therefore, it is important to evaluate the quality of these satellite SM products using in situ measurements and/or by inter-comparing their data during overlapping periods. In this study, these two approaches were used where we compared each satellite SM product to in situ soil moisture measurements and we also conducted an inter-comparison of the four satellite SM products at 15 different locations in Texas over six major land cover types (cropland, shrub, grassland, forest, pasture and developed) and eight climate zones along with in situ SM data from 15 Mesonet, USCRN and USDA-NRCS Scan stations. Results show that SM data from SMAP had the best correlation coefficients range from 0.37 to 0.92 with in situ measurements among the four tested satellite surface SM products. On the other hand, SM data from SMOS, AMSR2 and AMSR-E had moderate to low correlation coefficients ranges with in situ data, respectively, from 0.24–0.78, 0.07–0.62 and 0.05–0.52. During the overlapping periods, average root mean square errors (RMSEs) of the correlations between in situ and each satellite data were 0.13 (AMSR-E) and 0.13 (SMOS) cm3/cm3 (2010–2011), 0.16 (AMSR2) and 0.14 (SMOS) cm3/cm3 (2012–2016) and 0.13, 0.16, 0.14 (SMAP, AMSR2, SMOS) cm3/cm3 (2015–2016), respectively. Despite the coarser spatial resolution of all four satellite products (25–36 km), their SM measurements are considered reasonable and can be effectively used for different applications, e.g., flood forecasting, and drought prediction; however, further evaluation of each satellite product is recommended prior to its use in practical applications.

Highlights

  • Soil moisture (SM) controls hydrological, ecological and meteorological processes and plays a critical role in the partitioning of available water and energy exchange among the soil, plant and atmosphere continuum [1,2,3,4]

  • Results show that SM data from Soil Moisture Active Passive (SMAP) had the best correlation coefficients range from 0.37 to 0.92 with in situ measurements among the four tested satellite surface SM products

  • This study evaluated satellite surface SM products by comparing the satellite diurnal measurements to each other and comparing them with in situ surface SM measurements from soil moisture monitoring networks which are considered as ground truths

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Summary

Introduction

Soil moisture (SM) controls hydrological, ecological and meteorological processes and plays a critical role in the partitioning of available water and energy exchange among the soil, plant and atmosphere continuum [1,2,3,4]. Water 2017, 9, 372 has led to its classification as an essential climate variable (ECV) since 2010 [3,8]. The SM is ranked the second top priority parameter (after precipitation) which is essentially needed and its data have a wide range of applications such as in enhancing weather and climate forecasting, improving agricultural productivity and crop yield predictions, drought and flood monitoring and prediction, disaster and health monitoring and others [9]. Soil moisture is highly variable in space and time because of the distribution and characteristics of precipitation, soil texture, vegetation and topography [11,12,13,14]. While satellite measurements can cover relatively larger areas, steep terrain and dense vegetation add complexity to the data. In situ measurements are scarce and cover relatively smaller areas [15]

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