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 no water stress. Two parameters sets were selected to calibrate the AquaCrop model, one group of parameters were under water stress, and the other were under no water stress, there were two more sensitive parameters (growing degree-days (GDD) from sowing to the maximum rooting depth (root) and 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

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Since the early 1960s, multispectral imagery has been served as the data source for earth observational remote sensing(RS) in the last thirty years; the advancement of sensor technology had made it accessible to colleting hundreds continues spectral bands-hyperspectral RS. Hyperspectral RS (HRS) is a new technique for observing the earth, which is different from the multispectral RS because of several hundreds of contiguous spectral bands. With a long history of development, HRS is widely used currently. This review details the development of HRS, data processing, characteristics, imaging mode of hyperspectral sensors and its applications, such as detecting and identifying the surface, monitoring agriculture and forest status, environmental studies, and military surveillance, etc.