Estimation of Surface Soil Moisture With Downscaled Land Surface Temperatures Using a Data Fusion Approach for Heterogeneous Agricultural Land

Abstract

Field-scale surface soil moisture (SSM, 0-10 cm), which is closely linked with land surface temperature (LST), is particularly important to agricultural water resource management. Active and passive microwave remote sensing-based SSM retrievals on the order of kilometer squared resolutions are difficult to apply to heterogeneous agricultural land surfaces that may need SSM data at a resolution of 30 m. In this study, the High-resolution Urban Thermal Sharpener and Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model were applied to downscale optical and thermal remote sensing data simultaneously by blending Landsat and MODIS red-near infrared-LST data, with the ultimate goal to generate field-scale SSM values from the trapezoidal approach. To evaluate the performance of the downscaled LST E (based on the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model method) and SSM, an irrigation district (Area 1) in Inner Mongolia and an irrigation district in the North China Plain (Area 2) with varying spatial heterogeneity were selected as the testbeds. Results indicated that the downscaled LST E was highly consistent with synchronous Landsat LST H and in situ LST measurements in Area 1, with the root-mean-square error ranging from 0.73 to 2.75 K. Compared with the MODIS SSM, the average root-mean-square error of the downscaled SSM improved from 0.048 to 0.038 cm 3 /cm 3 for both areas. The downscaled LST E and SSM developed in this study enhance the spatiotemporal resolutions of the SSM estimates, maximizing the potential of remotely sensed information for agricultural water resource management.Plain Language Summary Field-scale (30 m) surface soil moisture (SSM), closely linked with land surface temperature (LST), is particularly important for agricultural water resource management, such as for assessment of agricultural droughts, optimization of irrigation schedules and improvement of water use efficiency, particularly in the heterogeneous agricultural land. Here, the High resolution Urban Thermal Sharpener (HUTS) and Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) were jointly adopted to downscale optical and thermal remote sensing data simultaneously by blending Landsat and MODIS Red-Near infrared-LST data, with the ultimate goal to generate field-scale SSM values from the downscaled Red-Near infrared-LST remote sensing data using the theoretical trapezoidal approach. The field-scale LST and SSM developed in this study maximize the potential of remotely sensed information, improve both the spatial and temporal resolutions of SSM, and provide more valuable information on heterogeneous land surfaces for agricultural water resource management. Key Points: • Downscaled LSTE (30 m), closely linked with SSM, can be acquired jointly using HUTS and ESTARFM methods • Downscaled field-scale SSM (30 m) well represents the actual soil moisture than MODIS SSM in heterogeneous land • Downscaled Red-Near infrared-LSTE improves both the spatial and temporal resolution of SSM

An Analysis of Land Surface Temperature Trends in the Central Himalayan Region Based on MODIS Products

“…The MODIS LST product is widely utilized in regional and global studies because of its daily global coverage ability [28][29][30][31][32]. Meanwhile, previous validation studies indicated that it has a Due to the high fragility of the montane ecosystem and its substantial dependence on agricultural activity, climate change has, in this region, the strongest impact on many vulnerable issues, such as water resources, biodiversity, agricultural development, and food security.…”

“…The MODIS LST product is widely utilized in regional and global studies because of its daily global coverage ability [28][29][30][31][32]. Meanwhile, previous validation studies indicated that it has a relatively high estimation accuracy (within ±1 K in most cases) [33,34], and thus, this product is the best choice for specifying the LST spatial and temporal variability.…”

Application of SEBAL and Ts/VI Trapezoid Models for Estimating Actual Evapotranspiration in the Algerian Semi-Arid Environment to Improve Agricultural Water Management

“…1) Single-source models, such as SEBAL (Surface Energy Balance Algorithm for Land) (Bastiaanssen et al, 1998;Bastiaanssen et al, 2005), METRIC (Mapping Evapotranspiration with Internalized Calibration) (Allen et al, 2007;Ramírez-Cuesta et al, 2020), T s /VI trapezoid model (Jiang and Islam, 2001;Stisen et al, 2008;Zhu et al, 2017), SEBS (Surface Energy Balance System) (Su, 2002;Chen et al, 2019), S-SEBI (Simplified Surface Energy Balance Index) (Roerink et al, 2000;Allies et al, 2020), that do not distinguish between soil evaporation and transpiration. Their simplicity has made the single-source models widely used; 2) dual-source models, such as TSEB (Two Source Energy Balance) (Kustas et al, 2018), ALEXI or Dis-ALEXI (Anderson et al, 2007) and SPARSE (Soil Plant Atmosphere and Remote Sensing Evapotranspiration) that discriminates the soil and vegetation component.…”

“…SEBAL may not be as applicable as other models for ET spatialization over agricultural areas where ground information is scarce or difficult to collect (Khaldi et al, 2011). Moreover, SEBAL has the particularity of using a calibration procedure to compensate for temperature and albedo errors without the need for a complex atmospheric correction (Bastiaanssen et al, 2005).…”

Challenges in UAS-Based TIR Imagery Processing: Image Alignment and Uncertainty Quantification

“…Land surface temperature is a key factor of ecological, cryospheric, and climatic systems [1]. Remote sensing systems that monitor the spatial distribution of ground surface temperature are used in a variety of fields such as agriculture [2], permafrost monitoring [3], soil moisture quantification [3,4], and urban studies [5,6].…”