Estimating Soil Evaporation Using Drying Rates Determined from Satellite-Based Soil Moisture Records

The implications of climate change on freshwater resources in the arid and semiarid Mediterranean environments using hydrological modeling, GIS tools, and remote sensing

Precipitation partitioning in arid and semiarid environments is not well understood due to scanty precipitation, its temporal distribution, and the lack/absence of adequate measurements of the hydrometeorological components. Simulation methods have the potential to bridge the data gap, thereby providing a window to estimate the water balance components. The present investigation evaluates the water balance components of a typical watershed situated in the southeastern Mediterranean for the period 1979 through 2019 using daily meteorological data and a grid spacing of 250 m. Generated runoff results were commensurate with corresponding values obtained using the SWAT model. Computed groundwater recharge is also compatible with recharge values calculated using the chloride mass balance method. Results show that average runoff and groundwater recharge for the entire period was ⁓24 mm a-1 and 19 mm a-1, giving a precipitation ratio of 9.5% and 7.5%, respectively. Substantial interannual variability in the water balance components was observed during the study period which reflected the significant precipitation fluctuations typifying the Eastern Mediterranean. Results show that the period extending from 1998/1999 through 2018/2019 witnessed an 18% drop in annual precipitation, while surface runoff and groundwater recharge experienced a reduction of ⁓34% and ⁓67%, respectively. Although groundwater recharge is a complex function of numerous meteorological and geological factors, the NDVI can provide an excellent indicator of groundwater recharge in marginal Mediterranean environments. This is highly beneficial in areas where climate records are scanty or absent. The presented results emphasize the significant impacts of global warming and aridification on the future availability of water resources in the semiarid marginal climates in the Eastern Mediterranean and point out clearly that water resources in this area will become scarcer, leading to multiple security threats at national and regional levels.

Spatiotemporal Analysis of Soil Moisture Variation in the Jiangsu Water Supply Area of the South-to-North Water Diversion Using ESA CCI Data

The South-to-North Water Transfer Jiangsu Water Supply Area (JWSA) is a mega inter-basin water transfer area (water source) that provides water resources from JiangHuai, combines drainage and flooding management, and regulates nearby rivers and lakes. Analyzing the spatiotemporal soil moisture dynamics in the area will be informative regarding agricultural drought along with flood disaster assessment and will provide early warning studies. Therefore, we evaluated the quality of European Space Agency Climate Change Initiative Soil Moisture (ESA CCI_SM) data in the South-North Water Transfer JWSA. Furthermore, we utilized ensemble empirical modal decomposition, Mann-Kendall tests, and regression analysis to study the spatiotemporal variation in soil moisture for the past 29 years. The CCI _SM data displayed a high correlation with local soil measurements at nine sites. We next analyzed the CCI_SM data from three pumping stations (the Gaogang, Hongze, and Liushan stations) in the South-North Water Transfer JWSA. These stations had similar periodic characteristics of soil moisture, with significant periodic fluctuations around 3.1 d. The overall soil moisture at the three typical pumping stations demonstrated an increasing trend. We further investigated whether abrupt soil moisture changes existed at each station or not. The spatial distribution of soil moisture in the South-North Water Transfer JWSA was characterized as “dry north and wet south”, with higher soil moisture in winter, followed by autumn, and low soil moisture in spring and summer. Although the linear trend of soil moisture in the South-North Water Transfer JWSA varied in significance, the overall soil moisture in the JWSA has increased over the past 29 years. The areas with significantly enhanced soil moisture are mostly distributed in the Yangzhou and Huai’an areas in the southeastern part of the study area. The areas with significantly decreased soil moisture are small in size and mostly located in northern Xuzhou.

A continental-scale soil evaporation dataset derived from Soil Moisture Active Passive satellite drying rates

This manuscript describes an observationally-based dataset of soil evaporation for the conterminous U.S. (CONUS), gridded to a 9 km resolution for the time-period of April 2015-March 2019. This product is termed E-SMAP (Evaporation-Soil Moisture Active Passive) in which soil evaporation is estimated from the surface layer, defined by the SMAP sensing depth of 50 mm, between SMAP overpass intervals that are screened on the basis of precipitation and SMAP quality control flags. Soil evaporation is estimated using a water balance of the surface soil that we show is largely dominated by SMAP-observed soil drying. E-SMAP soil evaporation is on average 0.72 mm day-1, which falls within the range of soil evaporation estimates (0.17-0.89 mm day-1) derived from operational land surface models and an alternative remote sensing product. E-SMAP is independent from existing soil evaporation estimates and therefore has the potential to improve understanding of evapotranspiration partitioning and model development.

Soil Moisture Data Assimilation to Estimate Irrigation Water Use

Knowledge of irrigation is essential to support food security, manage depleting water resources, and comprehensively understand the global water and energy cycles. Despite the importance of understanding irrigation, little consistent information exists on the amount of water that is applied for irrigation. In this study, we develop and evaluate a new method to predict daily to seasonal irrigation magnitude using a particle batch smoother data assimilation approach, where land surface model soil moisture is applied in different configurations to understand how characteristics of remotely sensed soil moisture may impact the performance of the method. The study employs a suite of synthetic data assimilation experiments, allowing for systematic diagnosis of known error sources. Assimilation of daily synthetic soil moisture observations with zero noise produces irrigation estimates with a seasonal bias of 0.66% and a correlation of 0.95 relative to a known truth irrigation. When synthetic observations were subjected to an irregular overpass interval and random noise similar to the Soil Moisture Active Passive satellite (0.04 cm3 cm-3), irrigation estimates produced a median seasonal bias of <1% and a correlation of 0.69. When systematic biases commensurate with those between NLDAS-2 land surface models and Soil Moisture Active Passive are imposed, irrigation estimates show larger biases. In this application, the particle batch smoother outperformed the particle filter. The presented framework has the potential to provide new information into irrigation magnitude over spatially continuous domains, yet its broad applicability is contingent upon identifying new method(s) of determining irrigation schedule and correcting biases between observed and simulated soil moisture, as these errors markedly degraded performance.

An Improved Spatio-Temporal Adaptive Data Fusion Algorithm for Evapotranspiration Mapping

Continuous high spatio-temporal resolution monitoring of evapotranspiration (ET) is critical for water resource management and the quantification of irrigation water efficiency at both global and local scales. However, available remote sensing satellites cannot generally provide ET data at both high spatial and temporal resolutions. Data fusion methods have been widely applied to estimate ET at a high spatio-temporal resolution. Nevertheless, most fusion methods applied to ET are initially used to integrate land surface reflectance, the spectral index and land surface temperature, and few studies completely consider the influencing factor of ET. To overcome this limitation, this paper presents an improved ET fusion method, namely, the spatio-temporal adaptive data fusion algorithm for evapotranspiration mapping (SADFAET), by introducing critical surface temperature (the corresponding temperature to decide soil moisture), importing the weights of surface ET-indicative similarity (the influencing factor of ET, which is estimated from remote sensing data) and modifying the spectral similarity (the differences in spectral characteristics of different spatial resolution images) for the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM). We fused daily Moderate Resolution Imaging Spectroradiometer (MODIS) and periodic Landsat 8 ET data in the SADFAET for the experimental area downstream of the Heihe River basin from April to October 2015. The validation results, based on ground-based ET measurements, indicated that the SADFAET could successfully fuse MODIS and Landsat 8 ET data (mean percent error: −5%), with a root mean square error of 45.7 W/m2, whereas the ESTARFM performed slightly worse, with a root mean square error of 50.6 W/m2. The more physically explainable SADFAET could be a better alternative to the ESTARFM for producing ET at a high spatio-temporal resolution.

Soil Moisture Remote Sensing across Scales

Soil moisture plays an important role in the water, carbon, and energy cycles. We summarize the 13 articles collected in this Special Issue on soil moisture remote sensing across scales in terms of the spatial, temporal, and frequency scales studied. We also review these papers regarding the data, the methods, and the different applications discussed.