Abstract
Soil moisture (SM) is a crucial component for understanding, modeling, and forecasting terrestrial water cycles and energy budgets. However, estimating field-scale SM based on thermal infrared remote-sensing data is still a challenging task. In this study, an improved Flexible Spatiotemporal DAta Fusion (FSDAF) method based on land-surface Diurnal Temperature Cycle (DTC) model (DFSDAF) was proposed to fuse Moderate Resolution Imaging Spectroradiometer (MODIS) and Advance Spaceborne Thermal Emission and Reflection Radiometer (ASTER) land-surface temperature (LST) data to generate ASTER-like LST during the night. The reconstructed diurnal LST data at a high spatial resolution (90 m) was then utilized to drive a two-source normalized soil thermal inertia model (TNSTI) for the vegetated surfaces to estimate field-scale SM. The results of the proposed methods were validated at different observation depths (2, 4, 10, 20, 40, 60, and 100 cm) over the Zhangye oasis in the middle region of the Heihe River basin in the northwest of China and were compared with the SM estimates from the TNSTI model and other SM products, including AMSR2/AMSR-E, GLDAS-Noah, and ERA5-land. The results showed the following: (1) The DFSDAF method increased the accuracy of LST prediction, with the determination coefficient (R2) increasing from 0.71 to 0.77, and root mean square error (RMSE) decreasing from 2.17 to 1.89 K. (2) the estimated SMs had the best correlation with the observations at the 10 cm depth (with R2 of 0.657; RMSE of 0.069 m3/m3), but the worst correlation with observations at the 40 cm depth (with R2 of 0.262; RMSE of 0.092 m3/m3); meanwhile, the modeled SMs were significantly underestimated above 40 cm (2, 4, 10, and 20 cm) and slightly overestimated below 40 cm (60 and 100 cm); in addition, the field-scale SM series at high spatial resolution (90 m) showed significant spatiotemporal variation. (3) The SM estimates based on the TNSTI for the vegetated surfaces are more capable of characterizing the SM status in the root zone (~80 cm) or even deeper, while the SMs from AMSR2/AMSR-E, GLDAS-Noah, or ERA5-land products are closer to the SM in the surface layer (the depth is less than 5 cm). The TNSTI provided favorable data supports for hydrological model simulations and showed potential advantages for agricultural refinement managements and smart agriculture.
Highlights
Introduction distributed under the terms andSoil moisture (SM) plays a critical role in monitoring and forecasting land-surface evapotranspiration, agricultural irrigation management, crop yield prediction, weather and climate change, droughts, and hydrologic processes [1–5]
The DFSDAF model is more capable of predicting ASTER-like land-surface temperature (LST) than Flexible Spatiotemporal DAta Fusion (FSDAF)
ASTER-like LSTs during the night were successfully reconstructed by fusing ASTER and Moderate Resolution Imaging Spectroradiometer (MODIS) LST, using DFSDAF
Summary
Introduction distributed under the terms andSoil moisture (SM) plays a critical role in monitoring and forecasting land-surface evapotranspiration, agricultural irrigation management, crop yield prediction, weather and climate change, droughts, and hydrologic processes [1–5]. The spatial representation of field observations suffers weak due to the strong spatial heterogeneity of SM and spare distribution of sites [15]. The emergence of new SM measurement methods, such as the Cosmic-ray Soil Moisture Observing System (COSMOS) [16,17], the fiber optic Distributed Temperature Sensing (DTS) system [18], and the Global Positioning System (GPS) [19], shows promising potentiality, the instruments are expensive, time-consuming, and hard to operate. Remote sensing offers a promising approach for large-area applications, benefiting from its high revisiting time and relatively lower cost. Over the past several decades, a series of microwave SM products at the global scale have been produced, such as the active microwave-based products, including European Remote Sensing Satellites (ERS) [22]; Advanced Land Observation Satellite-Phased Array type L-band Synthetic Aperture Radar (ALOS PALSAR) [23]; Sentinel-1 [24], the passive microwave-based products including
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