Estimation of Soil Water Content Based on Hyperspectral Features and the ANN Model.

Soil water content (θ) is an important factor for the crop growth and crop production. The objectives of this study were to (i) test various regression models for estimating θ based on spectral feature parameters, and (ii) compare the performance of the proposed models by using artificial neural networks (ANN) and spectral feature parameters. The θ data of sand and loam and concurrent spectral parameters were acquired at the laboratory experiment in 2014. The results showed that: (1) the maximum reflectance with 900～970 nm and the sum reflectance within 900～970 nm estimate θ had the significant, when sand bulk density was 1.40 g·cm-3; the maximum reflectance with blue edge and the sum reflectance within 900～970 nm had the best correlation (R2>0.70) when sand bulk density was 1.50 g·cm-3; while soil bulk density was 1.60 g·cm-3, the sum reflectance within 780～970 nm and normalized absorption depth in 560～760 nm reached a significant (R2>0.90); when soil bulk density was 1.70 g·cm-3, the maximum reflectance with 900～970 nm and the sum reflectance within 900～970 nm had the best correlation estimate θ (R2>0.88). 2) When the soil type was loam, the maximum reflectance with 900～970 nm and the sum reflectance within 900～970 nm had a best correlation estimate θ. The spectral feature parameters the sum reflectance within blue edge (R2=0.26 and RMSE=0.09 m3·m-3) and 780～970 nm absorption depth (R2=0.32 and RMSE=0.10 m3·m-3) were best correlated with θ in the sand. The θ model based on maximum reflectance with 900～970 nm (R2=0.92 and RMSE=0.05 m3·m-3) and the sum reflectance within 900～970 nm had a high correlation (R2=0.92 and RMSE=0.04 m3·m-3) in the loam. The BP-ANN model presented a better estimation accuracy of θ (R2=0.87 and RMSE=0.05 m3·m-3) in two soils. Thus, the ANN model has great potential for estimating θ. Thus, the BP-ANN model has great potential for θ estimation.

[Estimated nitrogen nutrition index based on the hyperspectral for wheat of drip irrigation under mulch].

The accurate wheat management needs a reasonable nitrogen application, and it is one of the key measures for real-time and quantitatively monitoring of nitrogen status to gain the higher yield of wheat. In the present study, two field experiments were conducted with different nitrogen stress and wheat cultivars, the relationship was analyzed between spectral parameters and the partial factor productivity from applied N (PFPn), and the estimating model was established for PFP, in the growth stages of wheat. The result indicated that there was a highly significant correlation between the PFP, and GreenNDVI at jointing, the correlation coefficient (r) was 0.6404, the estimating model of PFPn was established, and the root mean square errors (RMSE) was 0.4597. The result indicated that the PFPn can be effectively estimated by using spectral parameters.

[Monitoring canopy nitrogen status in winter wheat of growth anaphase with hyperspectral remote sensing].

Biomass, leaf area index (LAI) and nitrogen status are important parameters for indicating crop growth potential and photosynthetic productivity in wheat. Nondestructive, quick assessment of leaf dry weight, LAI and nitrogen content is necessary for nitrogen nutrition diagnosis and cultural regulation in wheat production. In order to establish the monitoring model of nitrogen richness in winter wheat of growth anaphase, studying the relationship between the nitrogen richness (NR) containing nitrogen density, LAI and leaf dry weight and the difference of hyperspectral reflectance rates (deltaR), we conducted a comparable experiment with five winter wheat varieties under nitrogen application level of 0, 100, 200 and 400 kg x N x ha(-1). The results indicated the NRs of the different varieties of winter wheat leaves increased with increasing growth stage while in the different nitrogen levels it was sequenced as: NO>N3>N1>N2. Twelve vegetation indices were compared with corresponding NR. The NR had significantly negative correlation to TCARI and VD672 in those vegetation indices, and their correlations (r) arrived at 0.870 and 0.855, respectively. The coefficients of determination (R2) of two models were 0.757 and 0.731 by erecting model with the two indexes and NR Root mean square error (RMSE), relative error (RE) and determination coefficient between measured and estimated NR were employed to test the model reliability and predicting accuracy. Accuracy rates of the models based on TCARI and VD672 achieved 84.56% and 80.13%. The overall results suggested that leaf nitrogen status of growth anaphase in winter wheat has stable relationships with some vegetation indexes, especially index of TCARI and VD672.

Estimation of wheat agronomic parameters using new spectral indices.

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 R(2) and the lowest RMSEs were 0.711 and 0.78 (OSAVI×EVI2), 0.785 and 3.98 g/m(2) (OSAVI×CIred edge) and 0.846 and 0.65 g/m(2) (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.