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1.
Global sensitivity analysis of wheat grain yield and quality and the related process variables from the DSSAT-CERES model based on the extended Fourier Amplitude Sensitivity Test method
LI Zhen-hai, JIN Xiu-liang, LIU Hai-long, XU Xin-gang, WANG Ji-hua
Journal of Integrative Agriculture 2019, 18 (
7
): 1547-1561. DOI:
10.1016/S2095-3119(18)62046-5
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A crop growth model, integrating genotype, environment, and management factor, was developed to serve as an analytical tool to study the influence of these factors on crop growth, production, and agricultural planning. A major challenge of model application is the optimization and calibration of a considerable number of parameters. Sensitivity analysis (SA) has become an effective method to identify the importance of various parameters. In this study, the extended Fourier Amplitude Sensitivity Test (EFAST) approach was used to evaluate the sensitivity of the DSSAT-CERES model output responses of interest to 39 crop genotype parameters and six soil parameters. The outputs for the SA included grain yield and quality (take grain protein content (GPC) as an indicator) at maturity stage, as well as leaf area index, aboveground biomass, and aboveground nitrogen accumulation at the critical process variables. The key results showed that: (1) the influence of parameter bounds on the sensitivity results was slight and less than the impacts from the significance of the parameters themselves; (2) the sensitivity parameters of grain yield and GPC were different, and the sensitivity of the interactions between parameters to GPC was greater than those between the parameters to grain yield; and (3) the sensitivity analyses of some process variables, including leaf area index, aboveground biomass, and aboveground nitrogen accumulation, should be performed differently. Finally, some parameters, which improve the model’s structure and the accuracy of the process simulation, should not be ignored when maturity output as an objective variable is studied.
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2.
Detection of Internal Leaf Structure Deterioration Using a New Spectral Ratio Index in the Near-Infrared Shoulder Region
LIU Liang-yun, HUANG Wen-jiang, PU Rui-liang , WANG Ji-hua
Journal of Integrative Agriculture 2014, 13 (
4
): 760-769. DOI:
10.1016/S2095-3119(13)60385-8
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Spectral reflectance in the near-infrared (NIR) shoulder (750-900 nm) region is affected by internal leaf structure, but it has rarely been investigated. In this study, a dehydration treatment and three paraquat herbicide applications were conducted to explore how spectral reflectance and shape in the NIR shoulder region responded to various stresses. A new spectral ratio index in the NIR shoulder region (NSRI), defined by a simple ratio of reflectance at 890 nm to reflectance at 780 nm, was proposed for assessing leaf structure deterioration. Firstly, a wavelength-independent increase in spectral reflectance in the NIR shoulder region was observed from the mature leaves with slight dehydration. An increase in spectral slope in the NIR shoulder would be expected only when water stress developed sufficiently to cause severe leaf dehydration resulting in an alteration in cell structure. Secondly, the alteration of leaf cell structure caused by Paraquat herbicide applications resulted in a wavelength-dependent variation of spectral reflectance in the NIR shoulder region. The NSRI in the NIR shoulder region increased significantly under an herbicide application. Although the dehydration process also occurred with the herbicide injury, NSRI is more sensitive to herbicide injury than the water-related indices (water index and normalized difference water index) and normalized difference vegetation index. Finally, the sensitivity of NSRI to stripe rust in winter wheat was examined, yielding a determination coefficient of 0.61, which is more significant than normalized difference vegetation index (NDVI), water index (WI) and normalized difference water index (NDWI), with a determination coefficient of 0.45, 0.36 and 0.13, respectively. In this study, all experimental results demonstrated that NSRI will increase with internal leaf structure deterioration, and it is also a sensitive spectral index for herbicide injury or stripe rust in winter wheat.
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3.
Exploring the Feasibility of Winter Wheat Freeze Injury by Integrating Grey System Model with RS and GIS
WANG Hui-fang, GUO wei, WANG Ji-hua, HUANG Wen-jiang, GU Xiao-he, DONG Ying-ying, XU Xin-gang
Journal of Integrative Agriculture 2013, 12 (
7
): 1162-1172. DOI:
10.1016/S1671-2927(00)8927
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Winter wheat freeze injury is one of the main agro-meteorological disasters affecting wheat production. In order to evaluate the severity of freeze injury on winter wheat systematically, we proposed a grey-system model (GSM) to monitor the degree and the distribution of the winter wheat freeze injury. The model combines remote sensing (RS) and geographic information system (GIS) technology. It gave examples of wheat freeze injury monitoring applications in Gaocheng and Jinzhou of Hebei Province, China. We carried out a quantitative evaluation method study on the severity of winter wheat freeze injury. First, a grey relational analysis (GRA) was conducted. At the same time, the weights of the stressful factors were determined. Then a wheat freezing injury stress multiple factor spatial matrix was constructed using spatial interpolation technology. Finally, a winter wheat freeze damage evaluation model was established through grey clustering algorithm (GCA), and classifying the study area into three sub-areas, affected by severe, medium or light disasters. The evaluation model were verified by the Kappa model, the overall accuracy reached 78.82% and the Kappa coefficient was 0.6754. Therefore, through integration of GSM with RS images as well as GIS analysis, quantitative evaluation and study of winter wheat freeze disasters can be conducted objectively and accurately, making the evaluation model more scientific.
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4.
Spectroscopic Leaf Level Detection of Powdery Mildew for Winter Wheat Using Continuous Wavelet Analysis
ZHANG Jing-cheng, YUAN Lin, WANG Ji-hua, HUANG Wen-jiang, CHEN Li-ping, ZHANG Dong-yan
Journal of Integrative Agriculture 2012, 12 (
9
): 1474-1484. DOI:
10.1016/S1671-2927(00)8679
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Powdery mildew (Blumeria graminis) is one of the most destructive crop diseases infecting winter wheat plants, and has devastated millions of hectares of farmlands in China. The objective of this study is to detect the disease damage of powdery mildew on leaf level by means of the hyperspectral measurements, particularly using the continuous wavelet analysis. In May 2010, the reflectance spectra and the biochemical properties were measured for 114 leaf samples with various disease severity degrees. A hyperspectral imaging system was also employed for obtaining detailed hyperspectral information of the normal and the pustule areas within one diseased leaf. Based on these spectra data, a continuous wavelet analysis (CWA) was carried out in conjunction with a correlation analysis, which generated a so-called correlation scalogram that summarizes the correlations between disease severity and the wavelet power at different wavelengths and decomposition scales. By using a thresholding approach, seven wavelet features were isolated for developing models in determining disease severity. In addition, 22 conventional spectral features (SFs) were also tested and compared with wavelet features for their efficiency in estimating disease severity. The multivariate linear regression (MLR) analysis and the partial least square regression (PLSR) analysis were adopted as training methods in model development. The spectral characteristics of the powdery mildew on leaf level were found to be closely related with the spectral characteristics of the pustule area and the content of chlorophyll. The wavelet features performed better than the conventional SFs in capturing this spectral change. Moreover, the regression model composed by seven wavelet features outperformed (R2=0.77, relative root mean square error RRMSE=0.28) the model composed by 14 optimal conventional SFs (R2=0.69, RRMSE=0.32) in estimating the disease severity. The PLSR method yielded a higher accuracy than the MLR method. A combination of CWA and PLSR was found to be promising in providing relatively accurate estimates of disease severity of powdery mildew on leaf level.
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5.
Estimating Wheat Grain Protein Content Using Multi-Temporal Remote Sensing Data Based on Partial Least Squares Regression
LI Cun-jun, WANG Ji-hua, WANG Qian, WANG Da-cheng, SONG Xiao-yu, WANG Yan, HUANG Wen-jiang
Journal of Integrative Agriculture 2012, 12 (
9
): 1445-1452. DOI:
10.1016/S1671-2927(00)8676
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1304
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Estimating wheat grain protein content by remote sensing is important for assessing wheat quality at maturity and making grains harvest and purchase policies. However, spatial variability of soil condition, temperature, and precipitation will affect grain protein contents and these factors usually cannot be monitored accurately by remote sensing data from single image. In this research, the relationships between wheat protein content at maturity and wheat agronomic parameters at different growing stages were analyzed and multi-temporal images of Landsat TM were used to estimate grain protein content by partial least squares regression. Experiment data were acquired in the suburb of Beijing during a 2-yr experiment in the period from 2003 to 2004. Determination coefficient, average deviation of self-modeling, and deviation of crossvalidation were employed to assess the estimation accuracy of wheat grain protein content. Their values were 0.88, 1.30%, 3.81% and 0.72, 5.22%, 12.36% for 2003 and 2004, respectively. The research laid an agronomic foundation for GPC (grain protein content) estimation by multi-temporal remote sensing. The results showed that it is feasible to estimate GPC of wheat from multi-temporal remote sensing data in large area.
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6.
Assessment of L and Suitability Potentials for Selecting Winter Wheat Cultivation Areas in Beijing, China, Using RS and GIS
WANG Da-cheng, LI Cun-jun, SONG Xiao-yu, WANG Ji-hua, YANG Xiao-dong, HUANG Wen-jiang
Journal of Integrative Agriculture 2011, 10 (
9
): 1419-1430. DOI:
10.1016/S1671-2927(11)60135-1
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It is very important to provide reference basis for winter wheat quality regionalization of cultivation area. The aim of this article was based on factors affecting wheat quality and setting realistic spatial models in each part of the land for assessment of land suitability potentials in Beijing, China. The study employed artificial neural network (ANN) analysis to select factors and evaluate the relative importance of selected environment factors on wheat grain quality. The spatial models were developed and demonstrated their use in selecting the most suitable areas for the winter wheat cultivation. The strategy overcomes the non-accurate traditional statistical methods. Satellite images, toposheet, and ancillary data of the study area were used to find tillable land. These categories were formed by integrating the various layers with corresponding weights in geographical information system (GIS). An integrated land suitability potential (LSP) index was computed considering the contribution of various parameters of land suitability. The study demonstrated that the tillable land could be categorized into spatially distributed agriculture potential zones based on soil nutrient and assembled weather factors using RS and GIS as not suitable, marginally suitable, moderately suitable, suitable, and highly suitable by adopting the logical criteria. The sort of land distribution map made by the factors with their weights showed more truthfulness.
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Assimilation of Remote Sensing and Crop Model for LAI Estimation Based on Ensemble Kalman Filter
LI Rui, LI Cun-jun, DONG Ying-ying, LIU Feng, WANG Ji-hua, YANG Xiao-dong , PAN Yu-chun
Journal of Integrative Agriculture 2011, 10 (
10
): 1595-1602. DOI:
10.1016/S1671-2927(11)60156-9
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1915
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Data assimilation in agricultural remote sensing research is of great significance to integrate with remote sensing observations and model simulations for parameters estimation. The present investigation not only designed and realized the Ensemble Kalman Filtering algorithm (EnKF) assimilation by combing the crop growth model (CERES-Wheat) with remote sensing data, but also optimized and updated the key parameters (LAI) of winter wheat by using remote sensing data. Results showed that the assimilation LAI and the observation ones agreed with each other, and the R2 reached 0.8315. So assimilation remote sensing and crop model could provide reference data for the agricultural production.
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