Search

Accurately monitoring the crop water conditions (CWC) is vital for agricultural water management. Traditional in situ measurements are limited by inefficiency and lack of spatial information. However, the development of unmanned aerial vehicle (UAV) applications in agriculture now provides a high throughput and cost-effective method to obtain field crop growth information. Unfortunately, current UAV-based drought indices do not capture the time series information, or the accuracy is limited. This study uses UAV-based multispectral and thermal information and site-observed air temperature to obtain the following three UAV-based drought indices: the normalized relative canopy temperature (NRCT), the temperature vegetation drought index (TVDI), and the three-dimension drought index (TDDI). We evaluate the accuracy with which these indices can be used to characterize the CWC of field maize by comparing them with in situ vegetation moisture contents (VMC). This study aims to (i) evaluate the pertinence of the TDDI for characterizing VMC, (ii) compare the performance of TDDI with that of NRCT and TVDI, and (ii) analyze the spatiotemporal variation of the three drought indices. The results show that (i) TDDI provides the best estimates of VMC (r = 0.71), (ii) NRCT and TVDI are comparable for characterizing VMC (r = 0.59 and 0.63, respectively) and are strongly correlated (r = 0.92), (iii) the three indices characterize the spatial distribution of VMC well, but the multi-phase image information used by TDDI makes it significantly better for studying VMC temporal variations than NRCT and TVDI. The results of this study prove that UAV-based observations can be used to accurately monitor field crop water conditions. In addition, the TDDI provides new insights into the study of remote sensing-based drought indices.

The high proportion of soil background pixels in UAV remote sensing images is an important reason for the uncertainty of high-precision leaf area index (LAI) estimation at early growth stages of crops. Although the traditional method of removing soil pixels from images based on canopy coverage (CC) eliminates pure soil pixels, it can cause spectral saturation at the early stages and therefore affect the accuracy of LAI estimation. In this study, a new method called reduced soil contribution (CS) was constructed to improve the accuracy of LAI estimation. This method can be improved by introducing a quantitative method to account for the contribution of soil information, which can be used to correct the calculation of vegetation indices and eliminate soil interference in maize LAI estimation. A six-rotor UAV equipped with a multispectral camera was used to collect field image data. Experimental plots with different maize breeding varieties were laid out to carefully evaluate the accuracy of the estimation model using UAV multispectral images collected at different growth stages. The performance of four estimation models, a light gradient boosting machine, gradient-boosting decision tree, random forest regression model and extreme gradient boosting, for LAI estimation was evaluated. The CS-based approach significantly improved the accuracy of the LAI model estimation, reducing the rRMSE by 1.89% for a single growing season compared to the traditional method. On average, the rRMSE for all growth stages decreased by 3.5%, demonstrating its effectiveness in improving maize LAI estimation accuracy. Randomness in error measured by Moran’s I metrics showed that the GBDT (gradient-boosting decision trees) model based on the CS method showed less spatial aggregation. These results showed that the CS model can effectively reduce the influence of soil on the estimation of the maize LAI and improve the LAI estimation accuracy compared with the direct removal of soil background pixels from an image.

Study region: Two typical flux towers and surrounding 100 km × 100 km areas in Northern China. Study focus: Up-scaling the instantaneous latent heat flux (LE) to daily-scale is an important step for estimate daily evapotranspiration based on satellite observations. This study evaluated the performance three up-scaling methods: constant evaporation fraction (CEF), diurnal LE variation (DLEV) and geosynchronous satellite (GS) by using flux tower and satellite observations at site-scale and regional scales, respectively. The purpose was to improve the accuracy of up-scaling LE. New hydrological insights for the region: The results suggested that: (1) DLEV showed slightly better accuracy (rRMSE = 16.27–26.33%) in estimating daily LE than CEF (rRMSE = 17.21–25.14%) at site-scale; (2) At regional scale, GS showed best accuracy (rRMSE = 34.05%), followed by DLEV (rRMSE = 36.46%), CEF had the worst accuracy (rRMSE = 38.96%); (3) DELV can accurately describe the LE variation during the 8:00–17:00 and show a good agreement with flux tower observations and GS observations, but DLEV cannot guarantee the estimation accuracy outside of this period. In general, this study provided a reference for understanding diurnal LE variation during a single day. Additionally, The DLEV proposed in this study can provide a better up-scaling accuracy of regional LE estimation than the CEF which is currently commonly used.