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Soil temperature (ST) plays a critical role in ecosystems. Monitoring high-resolution ST profiles remains challenging due to the inherent heterogeneity of ST in space, time, and depth. To address this challenge, in this study, we integrate remote sensing techniques and deep learning methods to retrieve spatiotemporal continuous ST from 2011 to 2020 at four depths (5, 10, 20, and 40 cm) over the central Tibetan Plateau (TP). Landsat and MODIS observations were fused to obtain land surface properties at different resolutions. The fused variables were integrated with in-situ ST and soil moisture (SM) measurements to estimate spatiotemporally continuous (0.0005°, 0.0025°, and 0.0125°) ST profile through deep learning-based training models. The deep belief network (DBN) was applied to estimate ST: (a) from layer to layer (LW-DBN), and (b) from land surface properties directly (Direct-DBN). The ten-fold cross-validation indicates that both approaches achieve promising results (R2 > 0.836, MAE < 2.152 °C), and the Direct-DBN outperformed the LW-DBN at all spatial scales and depths. ST retrieval at deeper depths and coarser resolutions tend to have better monitoring accuracy. Further analysis indicates an increment of ST at all four depths over the study period, which provides valuable insights into global warming. [ABSTRACT FROM AUTHOR]

Soil moisture, especially surface soil moisture (SSM), plays an important role in the development of various natural hazards that result from extreme weather events such as drought, flooding, and landslides. There have been many remote sensing methods for soil moisture retrieval based on microwave or optical thermal infrared (TIR) measurements. TIR remote sensing has been popular for SSM retrieval due to its fine spatial and temporal resolutions. However, because of limitations in the penetration of optical TIR radiation and cloud cover, TIR methods can only be used under clear sky conditions. Microwave SSM retrieval is based on solid physical principles, and has advantages in cases of cloud cover, but it has low spatial resolution. For applications at the local scale, SSM data at high spatial and temporal resolutions are important, especially for agricultural management and decision support systems. Current remote sensing measurements usually have either a high spatial resolution or a high temporal resolution, but not both. This study aims to retrieve SSM at both high spatial and temporal resolutions through the fusion of Moderate Resolution Imaging Spectroradiometer (MODIS) and Land Remote Sensing Satellite (Landsat) data. Based on the universal triangle trapezoid, this study investigated the relationship between land surface temperature (LST) and the normalized difference vegetation index (NDVI) under different soil moisture conditions to construct an improved nonlinear model for SSM retrieval with LST and NDVI. A case study was conducted in Iowa, in the United States (USA) (Lat: 42.2°~42.7°, Lon: -93.6°~-93.2°), from 1 May 2016 to 31 August 2016. Daily SSM in an agricultural area during the crop-growing season was downscaled to 120-m spatial resolution by fusing Landsat 8 with MODIS, with an R² of 0.5766, and RMSE from 0.0302 to 0.1124 m³/m³. [ABSTRACT FROM AUTHOR]

Surface soil moisture (SSM), the average water content of surface soil (up to 5 cm depth), plays a key role in the energy exchange within the ecosystem. We estimated SSM in areas with vegetation cover (grassland) by combining microwave and optical satellite measurements in the central Tibetan Plateau (TP) in 2015. We exploited TERRA moderate resolution imaging spectroradiometer (MODIS) and Sentinel-1A synthetic aperture radar (SAR) observations to estimate SSM through a simplified water-cloud model (sWCM). This model considers the impact of vegetation water content (VWC) to SSM retrieval by integrating the vegetation index (VI), the normalized difference water index (NDWI), or the normalized difference infrared index (NDII). Sentinel-1 SAR C-band backscattering coefficients, incidence angle, and NDWI/NDII were assimilated in the sWCM to monitor SSM. The soil moisture and temperature monitoring network on the central TP (CTP-SMTMN) measures SSM within the study area, and ground measurements were applied to train and validate the model. Via the proposed methods, we estimated the SSM in vegetated area with an R2 of 0.43 and a ubRMSE of 0.06 m3/m3 when integrating the NDWI and with an R2 of 0.45 and a ubRMSE of 0.06 m3/m3 when integrating the NDII. [ABSTRACT FROM AUTHOR]

Surface charges can produce strong electric field around soil particles, and profoundly influence soil water movement. The influence of soil electric field on water transport in soil column with a permanently charged soil was studied. It was found that soil electric field influences soil water transport by affecting: (1) the electrostatic forces between the electric field and water molecule dipole; and (2) soil water conductivity through controlling stability of soil aggregates. When soil electric field was high, soil aggregates were broken and soil electric field influenced soil water transport through its effect on the interaction forces between water dipole and the electric field. Conversely, when soil electric field was low, the effect of soil electric field on soil water transport was mainly through its influence on soil aggregate stability. The surface potential of 153 mV for the soil was the critical potential, below this potential, the influence of soil electric field on soil aggregate stability was the main mechanism affecting soil water transport; and above this potential, the effect of soil electric field on the interaction forces between water dipole and the electric field was the main influence on soil water transport. This study suggests that it might be possible to regulate soil water transport by adjusting soil electric field using electrolytes. [ABSTRACT FROM AUTHOR]