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| 1 | 基于Sentinel-2遥感影像的玉米冠层叶面积指数反演显示文摘叶面积指数是描述玉米冠层结构的重要参数之一,决定玉米冠层的光合作用、呼吸作用、蒸腾和碳循环等生物物理过程,因此精确反演叶面积指数对玉米长势监测具有重要意义。以河北省保定市的涿州市、高碑店市、定兴县为研究区,利用Sentinel-2遥感影像和LAI-2000地面同步实测数据进行玉米冠层叶面积指数反演,使用归一化差异光谱指数和比值型光谱指数两类指数,构建了单变量和多变量玉米冠层叶面积指数反演模型,通过决定系数(R2)和均方根误差(RMSE)筛选出最佳模型。研究结果表明,由NDSI(783,705)构建的单变量模型为最优反演模型,其决定系数为0.534 2,均方根误差为0.288 5。因此,基于Sentinel-2遥感影像利用植被指数反演玉米冠层叶面积指数的方法可作为判断玉米长势状况的初步判断依据。 | 苏伟 侯宁 李琪 张明政 赵晓凤 蒋坤萍 | 2018 | 农业机械学报2018，49，1： | 39 |
| 2 | Estimating the crop leaf area index using hyperspectral remote sensing显示文摘The leaf area index(LAI) is an important vegetation parameter,which is used widely in many applications.Remote sensing techniques are known to be effective but inexpensive methods for estimating the LAI of crop canopies.During the last two decades,hyperspectral remote sensing has been employed increasingly for crop LAI estimation,which requires unique technical procedures compared with conventional multispectral data,such as denoising and dimension reduction.Thus,we provide a comprehensive and intensive overview of crop LAI estimation based on hyperspectral remote sensing techniques.First,we compare hyperspectral data and multispectral data by highlighting their potential and limitations in LAI estimation.Second,we categorize the approaches used for crop LAI estimation based on hyperspectral data into three types:approaches based on statistical models,physical models(i.e.,canopy reflectance models),and hybrid inversions.We summarize and evaluate the theoretical basis and different methods employed by these approaches(e.g.,the characteristic parameters of LAI,regression methods for constructing statistical predictive models,commonly applied physical models,and inversion strategies for physical models).Thus,numerous models and inversion strategies are organized in a clear conceptual framework.Moreover,we highlight the technical difficulties that may hinder crop LAI estimation,such as the 'curse of dimensionality' and the ill-posed problem.Finally,we discuss the prospects for future research based on the previous studies described in this review. | LIU Ke ZHOU Qing-bo WU Wen-bin XIA Tian TANG Hua-jun | 2016 | Journal of Integrative Agriculture2016，15，2： | 14 |
| 3 | 作物生长模型与定量遥感参数结合研究进展与展望显示文摘作物生长模型与定量遥感参数的结合,不仅满足前者实现区域应用的需求,也有助于提高后者的反演精度,在生态、农业、资源调查与全球气候变化等研究上意义重大。该文从作物生长模型空间应用拓展的角度,对国内外主流作物生长模型、定量遥感参数以及两者结合的参数与方法进行了概述,分析了典型作物生长模型的主要模拟过程及其驱动、初始化、输出等参数,总结了当前定量遥感正反演结果可为作物生长模型区域应用提供的参数数据;建立了作物生长模型模拟过程与定量遥感参数的对应关系,对比分析了作物生长模型与定量遥感参数的不同结合方式。基于以上内容,对作物生长模型面应用的限制因素及其与定量遥感参数的关系、作物生长模型面应用时参数尺度效应的影响、作物生长模型与定量遥感参数耦合方法的发展3个方面展开了讨论,以期为作物生长模型与定量遥感参数开展更好的结合研究提供参考。 | 吴蕾 柏军华 肖青 杜永明 柳钦火 徐丽萍 | 2017 | 农业工程学报2017，33，9： | 13 |
| 4 | Multi-scale MSDT inversion based on LAI spatial knowledge显示文摘Quantitative remote sensing inversion is ill-posed.The Moderate Resolution Imaging Spectroradiometer at 250 m resolution(MODIS_250m) contains two bands.To deal with this ill-posed inversion of MODIS_250m data,we propose a framework,the Multi-scale,Multi-stage,Sample-direction Dependent,Target-decisions(Multi-scale MSDT) inversion method,based on spa-tial knowledge.First,MODIS images(1 km,500 m,250 m) are used to extract multi-scale spatial knowledge.The inversion accuracy of MODIS_1km data is improved by reducing the impact of spatial heterogeneity.Then,coarse-scale inversion is taken as prior knowledge for the fine scale,again by inversion.The prior knowledge is updated after each inversion step.At each scale,MODIS_1km to MODIS_250m,the inversion is directed by the Uncertainty and Sensitivity Matrix(USM),and the most uncertain parameters are inversed by the most sensitive data.All remote sensing data are involved in the inversion,during which multi-scale spatial knowledge is introduced,to reduce the impact of spatial heterogeneity.The USM analysis is used to implement a reasonable allocation of limited remote sensing data in the model space.In the entire multi-scale inversion process,field data,spatial knowledge and multi-scale remote sensing data are all involved.As the multi-scale,multi-stage inversion is gradually refined,initial expectations of parameters become more reasonable and their uncertainty range is effectively reduced,so that the inversion becomes increasingly targeted.Finally,the method is tested by retrieving the Leaf Area Index(LAI) of the crop canopy in the Heihe River Basin.The results show that the proposed method is reliable. | ZHU XiaoHua FENG XiaoMing ZHAO YingShi | 2012 | Science China Earth Sciences2012，55，8： | 5 |
| 5 | 基于数据机理的植被叶面积指数遥感反演研究显示文摘定量获取地表植被高精度时序及空间覆盖的叶面积指数(Leaf Area Index,LAI)是生态监测及农业生产应用的重要研究内容。通过使用Moderate Resolution Imaging Spectroradiometer(MODIS)植被冠层多角度观测MOD09GA数据及叶面积指数MOD15A2数据,发展了一种参数化的叶面积指数遥感反演方法并完成了必要的检验分析。研究使用基于辐射传输理论的RossThick LiSparse Reciprocal(RTLSR)核驱动模型及Scattering by Arbitrarily Inclined Leaves with Hotspot(SAILH)模型进行植被冠层辐射特征的提取,使用Anisotropic Index(ANIX)异质性指数作为指示植被冠层二向反射分布Bidirectional Reflectance Distribution Function(BRDF)的辅助特征信息,发展了基于数据机理(Data-Based Mechanistic,DBM)的植被叶面积指数建模和估算方法。通过必要的林地、农作物、草地植被实验区反演及数值分析可得知:①时间序列多角度遥感观测数据结合数据机理的叶面积指数估算方法,可实现模型参数的时序动态更新,改进叶面积指数估算结果的时序完整性及精度。②异质性指数可以用做指示植被冠层二向反射分布特征信息,可降低因观测数据几何条件差异所导致的反演结果不确定情况,同时能够补充植被时序生长过程表现的植被结构变化等动态特征。经研究实践,可将算法应用于时空尺度的叶面积指数估算,并能够为生态、农业应用提供植被的高精度遥感监测指标。 | 郭利彪 刘桂香 运向军 张勇 孙世贤 | 2020 | 遥感技术与应用2020，35，5： | 3 |
| 6 | 采用DBM方法的时间序列LAI建模与估算显示文摘运用DBM(Data Based Mechanistic)方法,使用MODIS数据,建立了遥感观测反射率数据与叶面积指数(LAI)在时间序列上的统计关系模型(LAI_DBM模型),并结合部分Bigfoot站点实测LAI数据进行了模型检验。结果显示,LAI_DBM模型能够较好表达时间序列反射率与LAI的动态变化关系。LAI_DBM模型使用遥感观测数据实时估算得到的LAI,在数据质量和时间连续性上比MODISLAI有改进。 | 陈平 王锦地 梁顺林 | 2012 | 遥感学报2012，16，3： | 1 |
| 7 | A Methodology for Estimating Leaf Area Index by Assimilating Remote Sensing Data into Crop Model Based on Temporal and Spatial Knowledge显示文摘In this paper,a methodology for Leaf Area Index(LAI) estimating was proposed by assimilating remote sensed data into crop model based on temporal and spatial knowledge.Firstly,sensitive parameters of crop model were calibrated by Shuffled Complex Evolution method developed at the University of Arizona(SCE-UA) optimization method based on phenological information,which is called temporal knowledge.The calibrated crop model will be used as the forecast operator.Then,the Taylor′s mean value theorem was applied to extracting spatial information from the Moderate Resolution Imaging Spectroradiometer(MODIS) multi-scale data,which was used to calibrate the LAI inversion results by A two-layer Canopy Reflectance Model(ACRM) model.The calibrated LAI result was used as the observation operator.Finally,an Ensemble Kalman Filter(EnKF) was used to assimilate MODIS data into crop model.The results showed that the method could significantly improve the estimation accuracy of LAI and the simulated curves of LAI more conform to the crop growth situation closely comparing with MODIS LAI products.The root mean square error(RMSE) of LAI calculated by assimilation is 0.9185 which is reduced by 58.7% compared with that by simulation(0.3795),and before and after assimilation the mean error is reduced by 92.6% which is from 0.3563 to 0.0265.All these experiments indicated that the methodology proposed in this paper is reasonable and accurate for estimating crop LAI. | ZHU Xiaohua ZHAO Yingshi FENG Xiaoming | 2013 | Chinese Geographical Science2013，23，5： | 1 |
| 8 | 结合Sentinel-1B和Landsat8数据的针叶林叶片含水量反演研究显示文摘为研究SAR影像结合光学影像反演叶片含水量的可行性,本文以吉林省长春市净月潭国家森林公园为研究区,以Sentinel-1B、Landsat8 OLI遥感影像和通过外业调查获取的叶片含水量为数据源,通过相关性分析,选取出与叶片含水量相关性较大的波段组合和植被指数,并对其进行主成分提取,建立主成分与叶片含水量之间的线性、二次多项式、三次多项式和指数模型,并利用精度最高的模型反演出净月潭国家森林公园的叶片含水量。结果表明:(1) Sentinel-1B的VV极化、VH/VV极化比和OLI传感器的短波红外1波段、短波红外2波段、归一化水分指数(NDWI)、比值植被指数(RVI)与叶片含水量相关性较大;(2) Sentinel-1B和Landsat8 OLI数据结合相较于仅使用Landsat8 OLI数据、提取出的主成分与叶片含水量相关性较高;(3)利用提取出的主成分与叶片含水量建立的反演模型中三次多项式模型的拟合精度最高(R2=0.629 9,RMSE=0.035 8)。表明Sentinel-1B结合Landsat8 OLI数据能较好得反演出针叶林的叶片含水量。 | 王长青 邢艳秋 汪献义 邢万里 张蓉鑫 | 2018 | 森林工程2018，34，4： | 0 |
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