Abstract
Surface soil water content (SWC) is a major determinant of crop production, and accurately retrieving SWC plays a crucial role in effective water management. Unmanned aerial systems (UAS) can acquire images with high temporal and spatial resolutions for SWC monitoring at the field scale. The objective of this study was to develop an algorithm to retrieve SWC by integrating soil texture into a vegetation index derived from UAS multispectral and thermal images. The normalized difference vegetation index (NDVI) and surface temperature (Ts) derived from the UAS multispectral and thermal images were employed to construct the temperature vegetation dryness index (TVDI) using the trapezoid model. Soil texture was incorporated into the trapezoid model based on the relationship between soil texture and the lower and upper limits of SWC to form the texture temperature vegetation dryness index (TTVDI). For validation, 128 surface soil samples, 84 in 2019 and 44 in 2020, were collected to determine soil texture and gravimetric SWC. Based on the linear regression models, the TTVDI had better performance in estimating SWC compared to the TVDI, with an increase in R2 (coefficient of determination) by 14.5% and 14.9%, and a decrease in RMSE (root mean square error) by 46.1% and 10.8%, for the 2019 and 2020 samples, respectively. The application of the TTVDI model based on high-resolution multispectral and thermal UAS images has the potential to accurately and timely retrieve SWC at the field scale.
Highlights
The surface soil water content (SWC) constitutes a small portion of the agroecosystem, but it plays a critical role in agricultural production [1,2]
The results of this study showed that the temperature vegetation dryness index (TTVDI) generally performed better in estimating SWC as compared to the temperature vegetation dryness index (TVDI) for the 2019 and 2020 Unmanned aerial systems (UAS) surveys
We proposed a new model, the TTVDI, to retrieve surface SWC by incorporating soil texture and Ts and the normalized difference vegetation index (NDVI) derived from high-resolution UAS imagery
Summary
The surface soil water content (SWC) constitutes a small portion of the agroecosystem, but it plays a critical role in agricultural production [1,2]. SWC, including field measurements, remote sensing, and soil water balance simulation models [8,9,10,11]. Compared with the cost-prohibitive, labor-intensive, and time-consuming in-situ measurements and complex model predictions, remote sensing technology has demonstrated great potential for estimating and monitoring surface SWC for its timeliness and convenience at larger spatial scales [12]. Studies have reported that satellite data, such as those from MODIS, Landsat, and Sentinel l/2, in the wavelengths of near-infrared, thermal infrared, and microwave, can be applied to monitor SWC at different scales [1,6,13]. Based on various satellite data, a wide variety of models for estimating surface SWC have been developed over the past decades [14,15,16,17].
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