氮素营养指数（NNI）是作物氮素诊断的可靠指标。然而，目前还没有适用于多生育时期NNI反演的光谱指数。为克服传统NNI直接反演方法（NNI_T1）和通过反演生物量（AGB）和植株氮浓度（PNC）进行NNI间接反演方法（NNI_T2）在多生育期应用的局限性，本文构建了一个新的NNI遥感指数（NNI_RS）。本文基于连续四年（2012–2013（Exp.1），2013–2014（Exp.2），2014–2015（Exp.3）和2015–2016（Exp.4））的冬小麦田间试验，采用交叉验证方法利用氮素相关植被指数和生物量相关植被指数构建了遥感关键氮浓度稀释曲线（N_{c_RS}）和根据NNI构建原理得到的NNI_RS进行综合评价。结果表明：（1）由标准叶面积指数决定指数（sLAIDI）和红边叶绿素指数（CI_{red edge}）构建的NNIR_S模型表达式为NNI_RS=CI_{red edge}/（a×sLAIDI^b），在Exp.1/2/4，Exp.1/2/3，Exp.1/3/4和Exp.2/3/4中参数“a”分别等于2.06，2.10，2.08和2.02，参数“b”分别等于0.66，0.73，0.67和0.62；（2）与NNI_T1和NNI_T2模型相比，NNI_RS模型的精度最高（R²的范围为0.50–0.82，RMSE的范围为0.12–0.14）；（3）NNI_RS在验证数据集中也达到了较好的精度，RMSE分别为0.09，0.18，0.13和0.10。因此，本文认为NNI_RS模型在氮素遥感诊断中具有较大的潜力。

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.

Sensitivity analysis (SA) is an effective tool for studying crop models; it is an important link in model localization and plays an important role in crop model calibration and application.  The objectives were to (i) determine influential and non-influential parameters with respect to above ground biomass (AGB), canopy cover (CC), and grain yield of winter wheat in the Beijing area based on the AquaCrop model under different water treatments (rainfall, normal irrigation, and over-irrigation); and (ii) generate an AquaCrop model that can be used in the Beijing area by setting non-influential parameters to fixed values and adjusting influential parameters according to the SA results.  In this study, field experiments were conducted during the 2012–2013, 2013–2014, and 2014–2015 winter wheat growing seasons at the National Precision Agriculture Demonstration Research Base in Beijing, China.  The extended Fourier amplitude sensitivity test (EFAST) method was used to perform SA of the AquaCrop model using 42 crop parameters, in order to verify the SA results, data from the 2013–2014 growing season were used to calibrate the AquaCrop model, and data from 2012–2013 and 2014–2015 growing seasons were validated.  For AGB and yield of winter wheat, the total order sensitivity analysis had more sensitive parameters than the first order sensitivity analysis.  For the AGB time-series, parameter sensitivity was changed under different water treatments; in comparison with the non-stressful conditions (normal irrigation and over-irrigation), there were more sensitive parameters under water stress (rainfall), while root development parameters were more sensitive.  For CC with time-series and yield, there were more sensitive parameters under water stress than under no water stress.  Two parameters sets were selected to calibrate the AquaCrop model, one group of parameters were under water stress, and the others were under no water stress, there were two more sensitive parameters (growing degree-days (GDD) from sowing to the maximum rooting depth (root) and the maximum effective rooting depth (rtx)) under water stress than under no water stress.  The results showed that there was higher accuracy under water stress than under no water stress.  This study provides guidelines for AquaCrop model calibration and application in Beijing, China, as well providing guidance to simplify the AquaCrop model and improve its precision, especially when many parameters are used.  

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.