Comparative Sensitivity of Vegetation Indices Measured via Proximal and Aerial Sensors for Assessing N Status and Predicting Grain Yield in Rice Cropping Systems.

Reflectance-based vegetation indices can be valuable for assessing crop nitrogen (N) status and predicting grain yield. While proximal sensors have been widely studied in agriculture, there is increasing interest in utilizing aerial sensors. Given that few studies have compared aerial and proximal sensors, the objective of this study was to quantitatively compare the sensitivity of aerially sensed Normalized Difference Vegetation Index (NDVI) and Normalized Difference Red-Edge Index (NDRE) and proximally sensed NDVI for assessing total N uptake at panicle initiation (PI-N_UP) and predicting grain yield in rice. Nitrogen response trials were established over a 3-year period (10 site-years) at various locations throughout the Sacramento Valley rice growing region of California. At PI, a multispectral unmanned aircraft system (UAS) was used to measure NDVI_UAS and NDRE_UAS (average ground sampling distance: 3.7 cm pixel⁻¹), and a proximal GreenSeeker (GS) sensor was used to record NDVI_GS. To enable direct comparisons across the different indices on an equivalent numeric scale, each index was normalized by calculating the Sufficiency-Index (SI) relative to a non-N-limiting plot. Kernel density distributions indicated that NDVI_UAS had a narrower range of values that were poorly differentiated compared to NDVI_GS and NDRE_UAS. The critical PI-N_UP where yields did not increase with higher PI-N_UP averaged 109 kg N ha⁻¹ (±4 kg N ha⁻¹). The relationship between SI and PI-N_UP for the NDVI_UAS saturated lower than this critical PI-N_UP (96 kg N ha⁻¹), whereas NDVI_GS and NDRE_UAS saturated at 111 and 130 kg N ha⁻¹, respectively. This indicates that NDVI_UAS was less suitable for making N management decisions at this crop stage than NDVI_GS and NDRE_UAS. Linear mixed effects models were developed to evaluate how well each SI measured at PI was able to predict grain yield. The NDVI_UAS was least sensitive to variation in yields as reflected by having the highest slope (2.4 Mg ha⁻¹ per 0.1 SI). In contrast, the slopes for NDVI_GS and NDRE_UAS were 0.9 and 1.1 Mg ha⁻¹ per 0.1 SI, respectively, indicating greater sensitivity to yields. Altogether, these results indicate that the ability of vegetation indices to inform crop management decisions depends on the index and the measurement platform used. Both NDVI_GS and NDRE_UAS produced measurements sensitive enough to inform N fertilizer management in this system, whereas NDVI_UAS was more limited. [ABSTRACT FROM AUTHOR]