A hyperspectral index sensitive to subtle changes in the canopy chlorophyll content under arsenic stress

Arsenic stress induces in subtle changes in the canopy chlorophyll content (CCC). Therefore, the establishment of a spectral index that is sensitive to subtle changes in the CCC is important for monitoring crop arsenic contamination in large areas by remote sensing. Experimental sites with three contamination levels were selected and were located in Chang Chun City, Jilin City, Jilin Province, China. Arsenic stress can induce small changes in the CCC, reflecting in the crop spectrum. This study created a new index to monitor the CCC. Then, the results from the index were compared with these from other indices and the random forest model, respectively. The final purpose of this study is to find an optimal index, which is sensitive to small changes in the CCC under arsenic stress for monitoring regional CCC in rice. The results indicate that the distribution of the CCC is aligned with the distribution of the arsenic stress level and that NVI (R640, R732, and R752) is the best index for monitoring CCC. The correlation coefficient R2 between the predicated values using NVI and the measured values of canopy chlorophyll content is 0.898, which performs better than the random forest model and other indices.