Optimizing spatial window selection for rice SPAD value retrieval using multispectral UAV images

Unmanned aerial vehicle (UAV) remote sensing has emerged as a crucial approach in precision agriculture, due to the high timeliness, low cost of data acquisition, and superior spatial resolution. Physical and chemical parameters of crops can be estimated to enable the dynamic monitoring of crop growth using UAV multispectral imagery. However, the high spatial resolution of UAV imagery often leads to the misalignment between ground sampling points and corresponding image pixels, even the accuracy of inversion models. This study aims to investigate the optimal spatial window for the UAV-based multispectral inversion of rice chlorophyll content. A DJI Phantom 4-M UAV was employed to obtain the multispectral images from a rice experimental field in the National Agricultural Science and Technology Park of the Jinggangshan in Xingqiao Town, Ji'an City, Jiangxi Province, China. The images were also collected during the boosting, heading, and maturation stages of rice growth, with a uniform resolution of 2.7 cm per pixel. The UAV was equipped with a multispectral sensor consisting of five spectral bands (450 nm (blue), 560 nm (green), 650 nm (red), 730 nm (red edge), and 840 nm (near-infrared)) and a TimeSync time synchronization system with the centimeter-level positioning accuracy. The ground chlorophyll content (SPAD) measurements were obtained concurrently with the UAV multispectral data acquisition using a SPAD-502Plus chlorophyll content meter. The sample area was selected at the center of each rice paddy, where three to five rice plants were sampled. The SPAD value was measured for the upper, middle, and lower sections of each rice leaf. The average value of each rice plant was then determined to represent the SPAD value of the sample. Additionally, the latitude and longitude of the sampling points were recorded using network RTK services. Spatial windows of varying sizes and shapes were employed to process the acquired images. Various vegetation indices were computed using the processed images. The correlation coefficients between the vegetation indices generated with different windows and the ground-measured SPAD values were examined. The spatial window corresponding to the vegetation indices with the highest correlation coefficients was identified as the optimal spatial window. Subsequently, the vegetation indices were selected with the ground-measured SPAD values. The support vector machines (SVM), random forests (RF), extreme learning machines (ELM), generalized linear models (GLM), and multiple linear stepwise regression models (MLSR) were constructed to evaluate the inversion accuracy of SPAD values at distinct rice growth stages. The results showed that: 1) The correlation coefficients between various vegetation indices and SPAD values were significantly improved after processing with a spatial window. In circular spatial windows, the optimal window radius was determined as 35, 25, and 25 pixels for each growth stage, respectively. In square spatial windows, the optimal side length was 71, 41, and 61 pixels for each growth stage, respectively. The results of the square window were very similar to those of the circular window. 2) The support vector machine model demonstrated the highest efficacy in retrieving rice SPAD values. The peak inversion accuracy was achieved during the boosting stage with a coefficient of determination of 0.718, a root mean square error (RMSE) of 1.849, and a mean absolute error of 1.465. The UAV-based multi-spectral data during the boosting stage were input into the SVM model to spatially invert rice SPAD values. The resulting map effectively reflected the fertilization treatment of the experimental field, thereby offering guidance for agricultural production. This finding can be expected to serve as a valuable reference for the selection of spatial windows in the inversion of biochemical parameters of various crops. Furthermore, a technical foundation was established for the UAV-based multispectral monitoring of crop growth, thereby contributing to the advancement of precision agriculture.