Chunhua Xiao

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 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.

Spectrum Characteristics of Cotton Canopy Infected with Verticillium Wilt and Inversion of Severity Level

Verticillium wilt of cotton is one of the diseases of cotton with extensive occurrence and maximal harming in our country even in the world .Hyper spectrum remote sensing with the fine spectrum information has becoming the efficient method to monitor the Verticillium wilt of cotton. The research was conducted in Xinjiang, the largest cotton plant region of China. The paper used data which was collected both canopy spectrum infected with verticillium wilt and SL (severity level) in the year 2005 2006, the quantitative correlation were analyzed between SL and canopy reflectance spectrum derivative spectrum. The tested results indicated that spectrum characteristics of cotton canopy infected with verticillium wilt had better regularity with the increase of SL in different periods and varieties. Spectrum reflectance increased in visible light region (620 700nm) with the increase of the SL, which inverted in nearinfrared region, and extreme signification in 78

Monitoring Chlorophyll and Nitrogen Contents in Cotton Leaf Infected by Verticillium wilt with Spectra Red Edge Parameters

The relationship between chlorophyll (Chl) content, leaf nitrogen content (LNC) and red edge parameters were analyzed, and diagnose models of spectra red edge parameters were established for cotton leaf infected by Verticillium wilt. Results showed that: (1) Chl a, Chl b, Chl a+b content, and LNC decreased with increasing in severity level (SL) of Verticillium wilt in cotton leaves, in which Chl a showed the highest and Chl b showed the lowest decrement rate, respectively. (2) Spectrum reflectance increased with increasing severity of Verticillium wilt in the visible region (400–700 nm), near-infrared region (700–1300 nm) and short infrared region (1300–2500 nm), and significantly higher increment was detected in 525–680 nm region (P0.01). Spectrum absorption decreased significantly with increasing SL of Verticillium wilt in the visible region and short infrared region (P0.01), and which increased first and then decreased in near-infrared region. (3) Decrease of REP, Dr, Lo, Depth672, Area672 and increase of Lwidth was detected among red edge parameters, in which Area672 showed the highest and Dr showed the Lowest decrement rate, respectively. (4) There was significantly positive correlation between Chl a, Chl b, Chl a+b, LNC of cotton leaves and REP, Lo, Depth672, Area672 of red edge parameters, significantly negative correlation was found for Lwidth of red edge parameter, while no significant correlation was found for Dr of red edge parameter. (5) Diagnose models of Chl a, Chl a+b,edge parameter, while no significant correlation was found for Dr of red edge parameter. (5) Diagnose models of Chl a, Chl a+b, and LNC for Verticillium wilt in cotton leaves with the independent variables Area672, and Chl b with the independent variables Lo reached the best estimated precision (P0.01). This could diagnose severity level of Verticillium wilt in cotton leaves effectively.

A Leaf Layer Spectral Model for Estimating Protein Content of Wheat Grains

The spectral signatures of crop canopies in the field provide much information relating morphological or quality characteristics of crops to their optical properties. This experiment was conducted using two winter-wheat (Triticum aestivum) cultivars, Jingdong8 (with erect leaves) and Zhongyou9507 (with horizontal leaves). We analysiced the relation between the direction spectral characteristics and the laeves nitrogen content(LNC). The result showed that the spectral information observed at the 0° angle mainly provided information on the upper canopy and the lower layer had little impact on their spectra. However, the spectral information observed at 30° and 60° angles reflected the whole canopy information and the status of the lower layer of the canopy had great effects on their spectra. Variance analysis indicated that the ear layer of canopy and the topmost leaf blade made greater contributions to CDS. The predicted grain protein content (GPC) model by leaf layers spectra using 0° view angle was the best with root mean squares (RMSE) of 0.7500 for Jingdong8 and 0.6461 for Zhongyou9507. The coefficients of determination, R2 between measured and estimated grain protein contents were 0.7467 and 0.7599. Thus, grain protein may be reliably predicted from the leaf layer spectral model.

Estimating wheat grain protein content from ground-based hyperspectral data using a improved detecting method

Chinese Academy of Agricultural Sciences Room 104, Institute of Crop Sciences, No.12 South Street, Zhongguancun, Beijing, 100081,China. luylwheat@hotmail.com *shaokun0004@sina.com.cn. AbstractThat high correlation between nitrogen concentration and grain protein content (GPC) made it possible to predict grain quality indirectly by measuring nitrogen status in wheat. However, former studies all based on the canopy spectral reflectance (Rc) measurement method, by which mixed spectra such as ears, leaves and soil background spectrum were achieved. So it is very difficult to abstract useful information from the canopy spectra, and the precision can not be heightened authentically. Our objective was to test the feasibility of measuring ear-layer spectral reflectance (Rel) using a improved method and to realize prediction of wheat grain quality before harvest. The results showed that Rel measured by the improved method was proved more effective to estimate GPC of wheat than Rc measured by traditional method. In relation to Rc, Rel was much closer to pure ear spectral reflectance(Re), for the Rel measured by the improved method was not disturbed by factors such as soil background and plant-type. In this article, the correlations between ear nitrogen total content (ETNC) and 20 spectral characteristic parameters were also analyzed respectively, and the results indicated that ear layer spectral characteristic parameters have stronger correlativity with ETNC than canopy ones. Rel and GPC all had highly correlative relation with ETNC, so a new model was established to predict indirectly GPC using ratio vegetable index (RVI[890,670]) through two models linking, and by testing, R=0.6617, RSME=0.8509 by the improved method and R=0.8653 RSME=0.7339 by traditional method. it was proved that the improved method produced higher coefficient of determination and lower root mean square error (RMSE) than traditional method under the same condition, and the precision by RVI[890,670] calculated from Rel was 13.75 percent higher than from Rc, which indicated that the predictive model established with the improved measurement method is more reliable and practicability than with the traditional method. This study made it possible to predict wheat grain quality using RVI and laid the foundation for portable nitrogen and protein monitor exploiting.