Monitoring leaf potassium content using hyperspectral vegetation indices in rice leaves

Abstract

Potassium (K) is one of three main crop nutrients, and the high rate of potash fertilizer utilization (second only to nitrogen) leads to high prices. Therefore, efficient application, as well as rapid and time monitoring of K in crops is essential. Several turnover box and field experiments were conducted across multiple years and cultivation factors (i.e., potassium levels and plant varieties) yielding 340 groups of leaf samples with different K contents; these samples were used to examine the relationship between reflectance spectra (350–2500 nm) and leaf K content (LKC). The correlation between LKC and the two-band spectral indices computed with random two bands from 350 to 2500 nm were determined for the published K vegetation indices in rice. Results showed that the spectral reflectance, R, of the shortwave infrared (1300–2000 nm) region was sensitive to the K levels and significantly correlated with rice LKC. New shortwave infrared two-band spectral indices, Normalized difference spectral index [NDSI (R1705, R1385)], Ratio spectral index [RSI (R1385, R1705)], and Difference spectral index [DSI (R1705, R1385)], showed good correlations with LKC (R2 up to 0.68). Moreover, the three-band spectral indices (R1705 − R700)/(R1385 − R700) and (R1705 − R1385)/(R1705 + R1385 − 2 × R704) were developed by adding red edge bands to improve accuracy. Three-band spectral indices had an improved prediction accuracy for rice LKC (R2 up to 0.74). However, several previously published K-sensitive vegetation indices did not yield good results in this study. Validation with independent samples showed that the indices (R1705 − R700)/(R1385 − R700) and (R1705 − R1385)/(R1705 + R1385 − 2 × R704) had higher accuracies and stabilities than two-band indices and are suitable for quantitatively estimating rice LKC. The widescale application of these proposed vegetation indices in this paper still needs to be verified in different environmental conditions. This study provides a technical basis for LKC monitoring using spectral remote sensing in rice.