Abstract
Crop agronomic parameters (leaf area index (LAI), nitrogen (N) uptake, total chlorophyll (Chl) content ) are very important for the prediction of crop growth. The objective of this experiment was to investigate whether the wheat LAI, N uptake, and total Chl content could be accurately predicted using spectral indices collected at different stages of wheat growth. Firstly, the product of the optimized soil-adjusted vegetation index and wheat biomass dry weight (OSAVI×BDW) were used to estimate LAI, N uptake, and total Chl content; secondly, BDW was replaced by spectral indices to establish new spectral indices (OSAVI×OSAVI, OSAVI×SIPI, OSAVI×CIred edge, OSAVI×CIgreen mode and OSAVI×EVI2); finally, we used the new spectral indices for estimating LAI, N uptake, and total Chl content. The results showed that the new spectral indices could be used to accurately estimate LAI, N uptake, and total Chl content. The highest R2 and the lowest RMSEs were 0.711 and 0.78 (OSAVI×EVI2), 0.785 and 3.98 g/m2 (OSAVI×CIred edge) and 0.846 and 0.65 g/m2 (OSAVI×CIred edge) for LAI, nitrogen uptake and total Chl content, respectively. The new spectral indices performed better than the OSAVI alone, and the problems of a lack of sensitivity at earlier growth stages and saturation at later growth stages, which are typically associated with the OSAVI, were improved. The overall results indicated that this new spectral indices provided the best approximation for the estimation of agronomic indices for all growth stages of wheat.
Highlights
The development of remote sensing has provided opportunities to quantitatively describe agronomic parameter changes across all growth stages of crops
VI6BDW and leaf area index (LAI) at all growth stages, and the resulting R2 was 0.672. These results suggested that the OSAVI6biomass dry weight index was an improvement over the optimized soil-adjusted vegetation index (OSAVI) index, because the OSAVI alone was not sensitive to LAI changes at later growth stages
The results showed that the correlation between biomass dry weight and N uptake was better than that between OSAVI and N uptake, and the R2 value was 0.653 (Figure 2b)
Summary
The development of remote sensing has provided opportunities to quantitatively describe agronomic parameter changes across all growth stages of crops. The LAI is a key variable for the diagnosis and prediction of crop growth and yield. The NDVI does possess certain limitations related to soil background brightness, in that the NDVI tends to be affected by different soil color and moisture conditions [8,9,10]. To overcome this problem, Rondeaux et al proposed using an optimized soil-adjustment factor, and obtained an optimized soil-adjusted vegetation index (OSAVI), which mitigated the effects of soil and moisture conditions [11]
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