植被含水量的遥感反演方式共24页
植被参数遥感反演
2019‐06‐15植被参数遥感反演种间竞争条件下互花米草光谱特征分析及叶绿素含量反演研究目录研究背景1数据来源2光谱分析与叶绿素反演3总结401研究背景面临外来物种入侵等威胁长江口盐沼湿地互花米草vs 芦苇等湿地生态系统重要的生态服务价值面积占5.8%丰富的生态系统产品和服务宏观研究→精细化研究单一物种→多物种混合-入侵物种与本地物种的竞争-生态学–光谱学–遥感科学湿地生态遥感以国产高分系列为例-空间分辨率GF2: 1m-光谱分辨率GF5: 0.45~12.5μm ，12个谱段-时间分辨率GF4: 分钟级机遇挑战种间竞争条件下互花米草光谱特征分析及叶绿素含量反演研究种间竞争生态学研究多(入侵机制、扩散方式、影响因子等)光谱学研究少互花米草生态学研究多(环境影响、生物多样性、驱动因子等)光谱学研究少，遥感主要针对纯物种分类和制图长江口盐沼湿地：华东师大、复旦大学、同济大学、南大、中科院、上师大叶绿素反演农田研究多，湿地研究少光谱指数多，集成应用少123入侵机制-Yokomizo,2009;Z. Ge, 2013; Hu,2015等扩散方式-Paradis,2014;H.Liu,2017影响因子-B.Li,2009;Medeiros,2013环境影响-B.Li,2012;C.Zhang,2017等生物多样性-C. Wang,2006;L. Tang;2013光谱-Z.Gao,2006;B. Zhao, 2015制图-Davranche,2013;Ai,2017叶绿素-Jacquemoud,2009;Main,2013等生物量-Quan,2011;Verrelst,2013;Pastor,2015;LAI-Ustin, S.2009;Tian,2013;B.Liu,2016等01数据来源数据来源研究意义研究区崇明东滩野外实验基地长江口北部典型盐沼湿地典型湿地植被互花米草VS 芦苇。
实验课1-定量遥感--植被覆盖度反演PPT优秀课件
实验一 植被覆盖度的遥感反演
三、实习仪器与数据
（1）Landsat 8数据：LC81290392013110LGN01 仁寿县的县界*.shp文件
（2）根据自己的兴趣选择研究区，遥感影像以及矢量数据可以从网上获取。 数据来源：从网站下载免费数据，如：
◦ /data/ ◦ ◦ / 数据源请从（1）和（2）中任意选择一个。
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实验一 植被覆盖度的遥感反演
◦ 3、计算植被覆盖度 方法一：(两种方法都要做)
根据公式（1），我们可以将整个地区分为三个部 分：
• 当NDVI小于NDVI0 ， fv取值为0； • NDVI大于NDVIv ， fv取值为1； • 介于两者之间的像元使用公式（1）计算。 利用ENVI主菜单->Basic Tools->Band Math，在公式 输入栏中输入进行计算. 请回顾ENVI中公式的写法
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实验一 植被覆盖度的遥感反演
在线性像元分解模型中，最简单的模型假设像元只有植被 和非植被两部分构成。所得的光谱信息也只有这两个组分因子 线性合成。他们各自的面积在像元中所占的比率即为各因子的 权重，其中植被覆盖部分所占像元的百分比即为该像元的植 被覆盖度。
NDVI = fv*NDVIv + (1- fv)* NDVI0
(公式1）
NDVI为像元NDVI值，fv为像元的植被覆盖度，NDVIv和NDVI0分别 为植被覆盖部分和非植被覆盖部分的NDVI值。NDVIv和NDVI0这两个 参数值的确定是关键，将直接影响到植被覆盖度估算结果。
在实际工作中因缺少大面积地表实测数据作参考，以及不可避 免存在噪声，所以通常对NDVI 统计直方图给定置信区间，求该区间 内的最小和最大值来作为NDVI0和NDVIv值，或者取5%和95%频率的 NDVI 值作为NDVI0和NDVIv值。本实验中采取后一种取值方法。
植被含水量的遥感反演方式
总结
统计模型相对比较简单,适用性强,在地面实况不清或遥感信号产生机理过于复 杂的情况下,是一种很好的工具来暂时回避困难,留待以后继续研究。 但是随着地面知识的积累和遥感观测波段的增加,统计模型的这一优势逐渐减弱。 并且当这些方法从实验室状态推广到室外冠层遥感数据的时候,就出现了大量的干扰 因素,包括不同的照明强度和角度、观测状态、冠层结构、下覆地表和大气状态等。 到目前为止,发展新的光谱指数仍然是一个活跃的研究领域,但是不论是经验或 半经验统计方法都缺乏鲁棒性和可移植性。可能在某些地点和时间,某种方法或指数 能够取得很好的效果,但事异时移,它们很可能就不适用了,因此人们逐渐考虑利用物 理模型反演得到植物的组分含量。
在使用PROSPECT 模型时考虑了三种生化组分:叶绿素,水分,干物质。其中干物 质代表纤维素、半纤维素、木质素、蛋白质、淀粉等,这些物质或者因其在叶片 内的含量极其微量,或者由于它们的吸收作用非常微弱,很难将他们的作用单独表 示出来,因此采用了总的干物质来表达这些物质的综合作用。PROSPECT 模型 是目前公认的叶片尺度最好的辐射传输模型之一,其输入参数只有4 个,为反演带 来了很大的方便。在这4个输入参数中,只有叶肉结构参数n 的确定无法通过测量
• 例如Penuelas等发现用水分指数WI(WI=R970/R900)能清楚地指示水分状况的变化.
• Penuelas和Inoue在随后的研究中还表明WI(WI=R900/R970)与NDVI(NDVI=(R900R680)/(R900+R680))的比值WI/NDVI不仅可以用来预测叶片的水分含量,还可以用来预 测植株或冠层的含水量,且显著提高了预测的精度.
物理模型方法：
叶片光学模型基于生物物理机制,通过描述光子在叶片内的散 射和吸收,模拟叶片的光谱特性,其前向过程通常都包含生化组分含 量,这些参数通常无法获得解析表达式,但是可以通过反向反演得到 。进一步可以将叶片模型耦合到冠层模型中,就可以利用冠层光谱 数据反演得到组分含量。由于物理模型解释了光与叶片物质的作 用机制,原理清楚,加之在模型的初始假设范围内,不受限于时间地点 等因素,因此成为植被生化组分参数提取研究的又一个方向。 目前应用于反演植被含水量的物理模型主要考虑基于辐射传 输方程的叶片光学模型PROSPECT 和冠层模型SAIL 及其耦合模 型。
植被覆盖地表土壤水分遥感反演
植被覆盖地表土壤水分遥感反演一、概述植被覆盖地表土壤水分遥感反演是当前遥感科学与农业科学交叉领域的重要研究方向。
随着遥感技术的不断进步，利用遥感手段对植被覆盖地表下的土壤水分进行反演，已经成为监测土壤水分动态变化的有效手段。
本文旨在深入探讨植被覆盖地表土壤水分遥感反演的基本原理、方法进展及实际应用，以期为相关领域的研究和实践提供有益的参考。
植被覆盖地表土壤水分遥感反演的基本原理在于，通过遥感传感器获取地表植被和土壤的综合信息，进而利用特定的反演算法提取出土壤水分含量。
这一过程中，植被覆盖对遥感信号的影响不可忽视，如何有效去除植被覆盖的影响，成为植被覆盖地表土壤水分遥感反演的关键问题。
在方法进展方面，近年来国内外学者提出了多种植被覆盖地表土壤水分遥感反演方法，包括基于植被指数的反演方法、基于热惯量的反演方法、基于微波遥感的反演方法等。
这些方法各有特点，适用于不同的研究区域和植被类型。
随着深度学习等人工智能技术的快速发展，其在植被覆盖地表土壤水分遥感反演中的应用也逐渐受到关注。
在实际应用方面，植被覆盖地表土壤水分遥感反演在农业、生态、环境等领域具有广泛的应用前景。
通过实时监测土壤水分状况，可以为农业生产提供科学的灌溉指导，提高水资源的利用效率也可以为生态环境监测和评估提供重要的数据支持，有助于维护生态平衡和可持续发展。
植被覆盖地表土壤水分遥感反演是一项具有重要意义的研究工作。
随着遥感技术的不断进步和反演算法的不断优化，相信这一领域的研究将会取得更加丰硕的成果。
1. 背景介绍：植被覆盖地表土壤水分的重要性及其在农业、生态和环境监测中的应用。
植被覆盖地表的土壤水分是地球水循环的重要组成部分，它直接影响着植被的生长和生态系统的平衡。
在农业领域，土壤水分是作物生长的关键因素之一，其含量和分布直接影响着作物的产量和品质。
准确获取植被覆盖地表的土壤水分信息，对于指导农业生产、优化水资源管理具有重要意义。
在生态方面，土壤水分与植被覆盖度之间存在着密切的相互作用关系。
《典型草原不同植被条件下土壤水分遥感反演研究》
《典型草原不同植被条件下土壤水分遥感反演研究》篇一一、引言在农业、生态学以及地理学等众多领域中，土壤水分的测量和评估扮演着重要的角色。
特别是对于草原地区，其生态环境的脆弱性及土地资源的有限性使得土壤水分的动态监测尤为关键。
传统方法通常需要地面实测或取样分析，这不仅效率低下，还可能无法实现大面积的连续监测。
而遥感技术的引入为解决这一问题提供了新的途径。
本文旨在探讨典型草原不同植被条件下，如何利用遥感技术进行土壤水分的反演研究。
二、研究区域与数据源本研究选取了具有代表性的草原地区作为研究对象，该地区植被类型多样，包括草地、灌木丛、稀树草原等。
数据源主要来自卫星遥感数据和地面实测数据。
卫星遥感数据包括多光谱、高分辨率以及热红外等不同类型的数据，用于获取地表信息及土壤水分的间接估计。
地面实测数据则用于验证遥感反演结果的准确性。
三、遥感反演方法本研究采用了多种遥感反演方法，包括植被指数法、归一化水体指数法、温度植被干旱指数法等。
这些方法根据不同的植被类型和土壤水分特性，通过分析地表光谱特征、植被覆盖度、地表温度等因素，间接估算土壤水分。
同时，还结合了地理信息系统（GIS）技术，对反演结果进行空间分析和可视化表达。
四、不同植被条件下的土壤水分反演1. 草地条件下的土壤水分反演在草地条件下，采用植被指数法进行土壤水分的反演。
首先，根据多光谱数据计算归一化植被指数（NDVI），然后结合地面实测数据建立NDVI与土壤水分之间的回归模型。
通过该模型，可以估算出草地条件下的土壤水分含量。
2. 灌木丛条件下的土壤水分反演在灌木丛条件下，采用归一化水体指数法进行土壤水分的反演。
由于灌木丛地区存在一定量的地表水体，通过分析水体的光谱特征和空间分布，可以估算出该地区的土壤水分含量。
同时，结合高分辨率遥感数据，可以更准确地识别地表水体的分布和变化。
3. 稀树草原条件下的土壤水分反演在稀树草原条件下，采用温度植被干旱指数法进行土壤水分的反演。
《2024年内蒙古典型草原植被地上生物量遥感反演》范文
《内蒙古典型草原植被地上生物量遥感反演》篇一一、引言内蒙古作为我国典型的草原地区，其植被地上生物量的研究对于了解草原生态系统的结构和功能具有重要意义。
随着遥感技术的不断发展，利用遥感数据进行植被地上生物量的反演已成为研究热点。
本文旨在探讨内蒙古典型草原植被地上生物量的遥感反演方法，以期为草原生态保护和可持续发展提供科学依据。
二、研究区域与数据本研究区域选取内蒙古典型草原，涵盖了多种草地类型。
数据来源包括遥感数据和地面实测数据。
遥感数据包括多时相、多光谱的卫星和无人机遥感影像，地面实测数据包括植被高度、叶面积指数、生物量等相关参数。
三、方法与技术（一）遥感数据处理遥感数据处理包括影像预处理、植被指数计算等步骤。
首先，对遥感影像进行辐射定标、大气校正等预处理，以提高数据的准确性。
然后，计算归一化植被指数（NDVI）等植被指数，以反映植被的生长状况。
（二）地上生物量反演模型根据前人研究成果和实地调查数据，建立地上生物量与遥感数据之间的数学模型。
通过对比不同模型的反演精度，选择最优模型进行地上生物量的反演。
四、结果与分析（一）遥感数据与地上生物量的关系通过分析遥感数据与地上生物量的关系，发现NDVI等植被指数与地上生物量之间存在显著的正相关关系。
这表明遥感数据可以有效地反映草原植被的生长状况和地上生物量。
（二）反演模型的精度评价采用地面实测数据对反演模型进行验证，结果表明所选模型的反演精度较高，可以有效地反映草原植被的地上生物量。
同时，对比不同模型的反演结果，发现某些模型在特定区域的反演效果更佳。
（三）空间分布特征通过反演得到的草原植被地上生物量空间分布图，可以看出内蒙古典型草原植被地上生物量的空间分布特征。
在不同草地类型、不同海拔、不同坡度等条件下，植被地上生物量存在显著的差异。
五、讨论与展望本研究通过遥感反演方法，得到了内蒙古典型草原植被地上生物量的空间分布特征。
然而，仍存在一些不足之处，如模型普适性有待提高、反演精度有待进一步提升等。
光学与ＳＡＲ 遥感协同反演植被覆盖区土壤含水量
光学与ＳＡＲ遥感协同反演植被覆盖区土壤含水量作者：杨晶晶邓清海李莎张丽萍陈桥孙桂宗孙振洲来源：《人民黄河》2023年第11期摘要：在进行土壤含水量反演时，单纯使用传统遥感反演模型很难有效消除干扰因素。
以山东省东营市为研究区，基于光学遥感与合成孔径雷达（ＳＡＲ）数据，采用植被光谱指数修正水云模型中的植被含水量，并将修正后的水云模型与高级积分方程模型（ＡＩＥＭ）耦合，以消除植被含水量和土壤粗糙度对土壤含水量反演结果的影响，从而达到提高遥感模型反演土壤含水量精度的目的。
结果表明：基于比值植被指数（ＳＲ）的二次函数修正水云模型后，与ＡＩＥＭ模型耦合反演土壤含水量的精度最高，决定系数大于０．５，均方根误差（ＲＭＳＥ）小于等于２．２９０；土壤含水量在空间上呈现西北部大，向南逐渐减小的连续空间分布特征，该耦合模型具有普适性。
关键词：植被光谱指数；植被含水量；ＡＩＥＭ模型；多源遥感协同反演中图分类号：Ｓ５１２．１１；Ｓ１５２．７；Ｓ１２７文献标志码：Ａｄｏｉ：１０．３９６９／ｊ．ｉｓｓｎ．１０００－１３７９．２０２３．１１．０２０引用格式：楊晶晶，邓清海，李莎，等．光学与ＳＡＲ遥感协同反演植被覆盖区土壤含水量［Ｊ］．人民黄河，２０２３，４５（１１）：１０６－１１０．旱情监测是农作物产品估值研究等的基础［１］。
近几十年来，多源遥感对地监测技术迅速发展，突破了传统土壤含水量监测的局限性，使得大面积连续土壤含水量监测成为可能［２］。
微波遥感技术可以穿透地表植被覆盖对地进行监测，并利用微波信号与土壤物理性质之间的高度相关性，灵敏地探测土壤含水量的动态变化［３］。
基于此，有学者利用可见光、短红外、近红外图像结合微波探测技术，研究得出了一系列反演土壤含水量模型，如常用的水云模型（ＷＣＭ）、Ｄｕｂｏｉｓ模型以及高级积分方程模型（ＡＩＥＭ）［４］等，其中ＷＣＭ模型被广泛应用于植被覆盖区土壤含水量反演、ＡＩＥＭ模型可以去除地表粗糙度对土壤含水量反演效果的影响。
《典型草原不同植被条件下土壤水分遥感反演研究》范文
《典型草原不同植被条件下土壤水分遥感反演研究》篇一一、引言随着遥感技术的不断发展，其在生态学、环境科学和农业科学等领域的应用越来越广泛。
其中，土壤水分的遥感反演研究是近年来关注的热点之一。
典型草原作为我国重要的生态系统之一，其植被覆盖类型多样，土壤水分状况对草原生态系统的稳定性和可持续性具有重要影响。
因此，本文旨在研究典型草原不同植被条件下土壤水分的遥感反演方法，为草原生态保护和可持续发展提供科学依据。
二、研究区域与数据本研究选取了我国北方某典型草原为研究对象，该地区植被类型多样，包括草地、灌木、森林等。
研究所用的数据包括遥感数据、气象数据和实地采样数据。
遥感数据主要包括Landsat、Sentinel-2等多光谱卫星数据；气象数据来自当地气象局提供的气象观测数据；实地采样数据则用于验证遥感反演结果的准确性。
三、土壤水分遥感反演方法1. 植被指数法：通过计算多光谱卫星数据中的植被指数（如NDVI、EVI等），分析植被覆盖情况对土壤水分的影响。
在此基础上，建立植被指数与土壤水分之间的回归模型，实现土壤水分的遥感反演。
2. 物理模型法：基于土壤-植被-大气之间的能量平衡和水热传输过程，建立物理模型，通过模型参数的估计和优化，实现土壤水分的遥感反演。
3. 机器学习方法：利用机器学习算法（如随机森林、支持向量机等），对多光谱卫星数据、气象数据等进行分析和训练，建立土壤水分与遥感数据之间的非线性关系模型，实现土壤水分的遥感反演。
四、不同植被条件下土壤水分反演结果与分析1. 草地条件下的土壤水分反演结果：在草地条件下，采用植被指数法、物理模型法和机器学习等方法进行土壤水分反演。
通过对比分析，发现机器学习方法在草地条件下的反演效果较好，能够较好地反映土壤水分的空间分布和变化趋势。
2. 灌木条件下的土壤水分反演结果：在灌木条件下，由于植被覆盖度较高，采用植被指数法进行土壤水分反演的效果较好。
同时，物理模型法也能够较好地反映土壤水分的状况。
植被覆盖地表土壤水分遥感反演
1007-4619 (2010) 05-959-15 Journal of Remote Sensing 遥感学报Received : 2009-11-20; Accepted : 2010-04-20Foundation : Project of National Scientific Foundation of China (No. 40861020); the Open Fund of State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (No. 09R03); Key Projects of Sciences Research of Education Ministry and Natural Science Foundation of XinJiang, China (No. 200821128).First author biography : ZHOU Peng, (1980— ), male, Post graduate of College of Resources and Environment Science, Xinjiang University, maily engaged in the study of resources, environment and remote sensing application in arid area. E-mail: zp5226@Corresponding author : DING Jianli, E-mail: watarid@Retrieval methods of soil water content in vegetation covering areasbased on multi-source remote sensing dataZHOU Peng, DING Jianli, WANG Fei, Guljamal.Ubul, ZHANG ZhiguangLab for Oasis Ecosystem of Xinjiang , College of Resource and Environmental Science , Xinjiang University , Urumqi 830046, ChinaAbstract: This paper takes the delta oasis of Weigan and Kuqa rivers in Xinjiang as the study area. Fusion image of SAR(Radar-sat image) combined with visible spectrum remote sensing image (TM image) is used to extract soil and vegetation water content in arid oasis. Based on the Normalized Difference Moisture Index extracted from homochronous visible spectrum re-mote sensing data, this thesis utilizes “water-cloud model” to wipe off vegetation influence from total backscattering coefficient of radar data and sets up the relationship between soil backscattering coefficient and soil moisture. Correlation coefficient for HH Polarization is R 2 =0.5227, for HV Polarization is R 2 =0.3277. Result shows that in arid and semi-arid area where the main crops are cotton and corn, the combination of C- band HH polarization radar data with visible image performs well in the study of removing vegetation influence while retrieving soil water content in medium vegetated areas.Key words: soil moisture, remote sensing, vegetable, backscatter coefficient, water-cloud model CLC number: TP751.1 Document code: A1 INTRODUCTIONSoil moisture is a very important component of the earthecosystems, which is the tie of the surface water and ground-water. Soil moisture plays a very important role in the global water cycle, it is also an important parameter in the hydrologi-cal, meteorological and agricultural research (Yuan et al., 2004). Large-scale soil moisture monitoring is an important content of agricultural water management and crop drought forecast-ing .At the same time, in regional and even the global-scale, soil moisture is also an essential parameter in the research of the land surface processes model, Which plays an important role in improving the regional and global climate models (Gao et al., 2001). Recently, the monitoring of regional scale soil moisture helps to resolve the problems of hydrological model in arid drainage basin and monitor the growth of crops. Tradition-ally, the estimation of soil moisture requires intensive labour operations in the field and needs to do some complex post-treatment processes, which are not only time-consuming, but also difficult to obtain a wide range of synchronous soil moisture information. The development of remote sensing technology provides an effective means of accessing the re-gional scale soil moisture information (Cashion et al., 2005; Urso & Minacapilli, 2006). At present, the methods of moni-toring soil moisture based on remote sensing are mainly ther-mal inertia, thermal infrared, crop water stress index, anomalies of vegetation index and microwave remote sensing (Chen et al., 1999; Guo & Zhao 2004). However, microwave remote sensing has the characters of all day and night, high repeated coverage, the penetrability through some surface objects and not restricted by weather conditions, which make microwave remote sensing monitoring of soil moisture widely used in arid and semi-arid area.Soil moisture monitoring of microwave remote sensing has experienced 30-year history, and has been established many backscattering coefficient models such as small perturbation model, Kirchhoff model, two-scale model and the integral equation model and so on. In addition, there are experience and semi-empirical models (Baghdadi et al., 2002; Liao et al., 2002; Wickel et al., 2001; Xiong & Shao, 2006). However, in the vegetated areas of arid and semi-arid oasis, the application of these retrieval models are confined by surface roughness and vegetation coverage. Higher vegetation coverage will cause lower estimation on soil moisture and the higher estimation on surface roughness (Liu et al., 2005) which make the acquisition of soil moisture became complex. The key question to the study of soil moisture is how to eliminate the influence of surface roughness and separate the vegetation scattering and absorption960Journal of Remote Sensing 遥感学报 2010, 14(5)from the soil moisture. In these models, vegetation water con-tent (VWC) is an important parameter. As it is very difficult tomeasure the parameter in the field, people usually use the opti-cal image to predict and then establish the relationship between normalized difference moisture index (NDMI) and VWC. Based on the use of optical image data, this paper uses NDMI to estimate the study area’s vegetation water content, then, em-ploys multi-polarization satellite-based radar data together with microwave scattering Water-Cloud Model eliminating the im-pact of the vegetation layer and isolating the contributions of vegetation scattering and absorption from the total backscat-tering coefficient, Which is the research on the estimation of surface soil moisture in vegetated areas in arid oasis.2 STUDY AREA AND DATA SOURCE 2.1 Status of study areaThe study area in this paper locates in the north of Tarim Basin, the lower reaches of Weigan and Kuqa Rivers, the mid-dle of Tianshan and the north of Taklamagan desert. The aver-age elevation is 920—1100m, which belongs to landwarm-temperate zone extreme arid climate. The average annual evaporation is 2420.23 mm, the average annual precipitation is 43.1mm, and the ratio of evaporation to precipitation is about 54 to 1. The image range of study area is that the coordinate of east longitude is from 82º15′ to 82º53′ and north latitude is from 41º15′ to 42º36′, which is confirmed according to the samples in field. Farm belt is dominant in study area, and the types of vegetation is not complex and is mainly composed of cotton (55%) and corn (15%) combining with other halophytes shrubs and salt secretion plants such as Tamarix, bulrush, Populus eupfratica and Alhagi sparsifolia Shap.2.2 Status of satellite dataRadarsat-2 satellite was successfully launched in December 14, 2007 at space launch base in Kazakhstan's Baikonur, it can provide 11 kinds of beam patterns, the highest resolution is 3m, the maximum imaging width is 500 km, and the maximum data rate is 445.4Mb/s, the range of incidence angle is 10°—60°. The C-Band can effectively extract soil moisture of 0—5cm soil layer. The primary parameters of RADARSAT-2 System are showed in Table 1.Table 1 Primary parameters of RADARSAT-2 systemCarrier frequency Polarization mode Bandwidth/MHz Antenna dimension/m 2Antenna quality/kgActive antenna Proposed isolation/dB C-Band (5.405GHz) HH, HV VH, VV11.6, 17.3, 30, 50, 10015×1.5750C-Band T/R>25The multi-polarization data of Radarsat-2 in September 10,2008 is chosen in this study, and the polarization is HH and HV. The homochronous visible data is Landsat-5 TM data which includes mid-infrared and near-infrared band for calculating NDMI. The image of study area is as follows:Fig. 1Polarization composite image of RADARSAT 2 HH/HVFig. 2 RGB composite image of Landsat5 TM 5432.3 Collection and analysis of samplesIn this study, Landsat-5 TM data obtained in Sep 27, 2007 is used as a reference map to choose the typical samples in the middle and the southwest of the oasis of Weigan and Kuqa rivers combining with GPS positioning technology. Sampling points are selected regularly distribution as much as possible and com-prehensive considering of the local soil, vegetation type and other factors. Sampling time is from sep 16, 2008 to sep 26, 2008 and there are 30 located sampling points in this study. Set 5 sampling points as Plum-shape in the range of 30m of each located points and profile mining each sample points and the profiles of each sample are 0—5cm. Borrow these soil samples and vegetation samples around of the soil sample dots back to laboratories, and dry them with the weighing method to obtain the soil avoirdupois moisture and vegetation water content.3 PREPROCESSINGImage data pre-processing mainly includes image registra-tion, geometric correction, noise reduction and color enhance-ment and so on. As it is need to find out the relationship be-tween normalized difference moisture index (NDMI) and vege-tation water content (VWC), the atmospheric correction of TM data is absolutely necessary. The level of SAR image data ob-tained is SLC, so it should do noise reduction, radiometric cali-bration and geometric correction. 3.1 Optical image processingIn order to keep the aboriginality, the image only did at-ZHOU Peng et al .: Retrieval methods of soil water content in vegetation covering areas based on multi-source remote sensing data 961mospheric correction and geometric correction neglecting otherpre-processing, which assure the objectivity of estimation. Be-cause of the impacts of atmosphere on visible-near-infrared and thermal infrared are different, it is necessary to separately carry out atmospheric correction using 6S model (Liu & Zhao, 2002). While geometric correction was performed to do multinomial correction of TM image using ground control points.3.2 Radar image processing 3.2.1 Radiometric calibrationAs the radiometric error caused by the difference of the dis-tance between ground scattering cell and radar, it is need to becorrected.Radiometric calibration equations is :20sin()nD Kσα= (1) where, D n 2 is the pixel intensity value, α is the radar incidence angle of the target location, K is the absolute calibration factor of the image. The values of K and α are obtained from the header files. SIGMA parameter correction is selected. Figure out the distance orientational incident angle of every pixels, then RADARSAT-2 satellite image data is changed to backscattering coefficient using the below calibration expres-sion:212()10log()/10log(sin())n D A A σθθ°=++ (2) where, D n is the gray-level value of radar image, A 1, A 2 are automatic gain control coefficient of radar system, θ is the dis-tance orientational incident angle of every pixels.3.2.2 Noise treatmentThe interrelated treatment in the processing of Radar-sat imaging causes a large number of spots (Speckle), which pro-duce an obstacle on feature extraction, so the noise treatment is used to eliminate the impact. After several rounds of test analy-sis, 5 × 5 enhanced Lee filter to the original SAR image has the best filtering effect, which can eliminate the most spots. 3.2.3 Geometric calibrationIn this thesis, the study area is flat, so cubic polynomial is selected as the correction method.4 RETRIEVAL OF SOIL MOISTURE CONTENTIN VEGETATED AREASThe vegetation water content (VWC) is extracted based on a choice of appropriate microwave scattering model. As it is definitely difficult to do large-scale area survey and extracting, which also definitely destroys vegetation, this paper uses Landsat-5 TM data to figure out normalized difference moisture index (NDMI) and find out the relationship between NDMI and VWC, then validates the vegetation water content. With that, multi-polarization satellite-based radar data together with mi-crowave scattering water-cloud model are employed eliminat-ing the impact of the vegetation layer on radar backscattering and finally retrieves the soil water content of study area.4.1 Choice of microwave scattering model of soil moisturecontent in vegetated areasIn the study on soil moisture of Microwave remote sensing,vegetated areas will interfere with soilbacks cattering signal. Therefore, it is necessary to establish a rational vegetation scat-tering model and remove the impact of the vegetation in soil moisture retrieval algorithm. A lot of foreign scholars havebeen studied on vegetation scattering model: a group of Michi-gan State University in USA, proposed “MIMICS” Model in 1990, MIMICS Model (Fung et al., 1992)was established fortall plants such as arbor, it took into account three levels ofvegetation canopy, tree trunks and the bottom surface ,whichmore realistically simulate the surface microwave backscat-tering. In 1978, Attma, Ulaby etc. using crops as the research object, proposed “Water-Cloud” model. “Water-Cloud” model assumes vegetation as homogeneous scatter and ignores themultiple scattering between vegetation layer and the surface. The overall backscattering of vegetation coverage area is sim-ply divided into two parts, namely, the direct reflection by the vegetation back scattering and the ground backscattering after the double attenuation of crop. Because there are mostly crops and other low vegetation in study area, “Water-Cloud” model is simple and practical. Be-sides, it uses very few parameters.The model is expressed as follows:0020can veg soil ()()()()σθσθγθσθ=+ (3)where, 0can()σθ is the total radar backscattering coefficient in vegetated areas, 0veg ()σθ is the direct backscattering coeffi-cient of vegetation layer, 0soil ()σθ is the direct backscatteringcoefficient of surface, γ2(θ) is the double attenuation factor of radar wave penetrating crops, where: 02veg veg ()cos()(1())A m σθθγθ=⋅⋅⋅− (4)22pp veg ()exp(2sec())Bm γθθ=− (5)In the expressions above, A and B are parameters depending on the type of vegetation which can be respectively obtained through regression arithmetic after the utilization of MIMICS model to simulate the surface parameters. m veg is vegetation water content (kg/m 3), θ is the angle of incidence of radar wave.4.2 Extracting of characteristic parameters and vegetationwater content estimationThe vegetation water content is defined as the water content of plants per unit and per area. As a significant input parameter of Water-Cloud model, VWC performs very crucial effect on the retrieval of soil moisture in vegetated areas (Liu et al., 2008). Now, the study of the regional retrieval of VWC using visible image has been already mature, and the relativity estab-lished between spectrum index and VWC is high(Jackson et al.,962Journal of Remote Sensing 遥感学报 2010, 14(5)2004; Rosnay et al., 2006; Wang et al., 2008). So, this study uses visible remote sensing to retrieve and field survey data ofsamples to validate the VWC in study area. In view of the spec-trum characteristic of soil, vegetation and water between 0.8 to1.7μm, Landsat5 TM(sep 16,2008) homochronous with fieldsurvey date is chose which includes near- infrared band 4(0.76μm)and mid-infrared band 5(1.55μm). The spectrumbandwidths are respectively 25 nm and 20 nm. According to the characteristic of vegetation that it has higher reflectivity in near-infrared band and lower reflectivity in mid-infrared band because of the absorbing effect of leave’s water content, NDMI (Gao,1996; ZARCO-TEJADA et al., 2003)is applied to extractvegetation water content information. As the vegetation instudy area are most cotton and other low salt secretion plants,based on relevant research (Chen et al., 2005), the equation isshowed as expressions 6, while expressions 7 is established to gain VWC according to the relationship between vegetationwater content of field survey and NDMI. NDMI (NIR MIR)/(NIR MIR)=−+ (6) VWC 2.15NDMI 0.32=+ (7) There are rivers, lakes and reservoirs in study area whichdisturb the extracting of vegetation water content. So, the ef-fects of these water bodies must be removed in the process of the retrieval work. This paper uses MNDWI index (Xu, 2008)which is improved a little according as the regional circs toextract water body information, the equation is as follows:MNDWI (1+0.5)(GREEN-MIR)/(GREEN+MIR)= (8) After that, masking the original image and the water body effects are removed. Analysis and validation indicate that NDMI performs well in the retrieval of vegetation water con-tent.4.3 Calculating of backscattering in vegetated areasThe soil moisture estimation of initiative microwave remotesensing is mainly affected by vegetation coverage and surface roughness. The soil backscattering in vegetated area is com-posed of body scattering from vegetation, surface scattering from earth and the multiple scattering between vegetation layer and the surface. These factors are taken into account in the estimation of soil moisture content (Li et al., 2002). As the hypsography in study area is flat, and mostly is covered by low plants, vegetation is considered seriously while the effect of surface roughness is not taken into account when calculating the soil backscattering coefficient.In the study of soil moisture estimation in vegetated area, it is necessary to use “Water-Cloud” model to remove the contri-bution of vegetation in the backscattering. Parameters A and B are experience constants and they are changed by regional limit. A relatively mature method is applied to calibrate the parame-ters (Chen et al., 2007; Gao et al., 2008). Firstly, Water-Cloud and MIMICS model are used to simulate the backscattering coefficient of vegetated area then obtain the surface direct scat-tering and the variational relationship between surface scatter-ing and incident angle. Follow that, parameters of the dominate plants in study area is showed in Table 2. Based on the simu-lating results, calibrate the vegetation parameters A and B men-tioned in Water-Cloud model through non-linear least squares method.Table 2 Parameters input to MIMICS modelClass Parameters ValueFrequency /GHz 5.4Sensor Polarization mode HH/HVWater content 0.17 Root mean square height /cm 0.817 Land surface Correlative length /cm 7.528 Water content 0.39Radius /cm 2.6 Thickness /cm 0.04 LeavesHeight /m 0.9 Radius /cm 0.3 Length /cm 21Branch Density /m 38.6Climate Temperature /(°)22 After the analysis of regression, A is 0.0019 and B is 0.137. In term of Eq.(4) and Eq.(5), bare soil backscattering is ac-quired with Water-Cloud model removing the vegetation influ-ence, the expressions are as follows:02veg veg ()0.0019cos()(1())m σθθγθ=×××− (9)2veg ()exp(20.137sec())m γθθ=−× (10)00veg 0soil 2()()()()σθσθσθγθ−=(11)The total backscattering coefficient of surface can be calcu-lated from Eq. (2) and soil backscattering coefficient after eliminating vegetation effect is got from Eq.(11). In order to clear up the connection between HH and HV , there separately does a correlation analysis of backscattering coefficients of HH and HV before and after eliminating vegetation effect. Consid-ering that the change of soil moisture can not be reflected effec-tively from radar backscattering coefficient in some high vege-tated area, ambiguous dots are canceled in the analysis, results show:Fig. 3 and Fig. 4 indicate that: whatever before or after eliminating vegetation effect, correlation coefficients of HHFig. 3 Simulated curve between the backscattering coefficient of HHand HV polarization before eliminating vegetation effectZHOU Peng et al.: Retrieval methods of soil water content in vegetation covering areas based on multi-source remote sensing data 963Fig. 4 Simulated curve between the backscattering coefficient of HHand HV polarization after eliminating vegetation effectand HV polarization backscattering coefficients are all very lowwhich are respectively 0.2534 and 0.2884. Therefore, the nextwork is to discuss the soil water content retrieval of HH, HVpolarization.4.4 Analysis of sample dots dataIn order to analyze the effect factors of backscatteringcoefficient in vegetation coverage area, we got 30 sample dotsfrom field survey which include water content in the soil layerof 0—5cm and the coordinates of these samples. Backscatteringcoefficient and water content of soil of the samples are showedin Table 3. According to the data in Table 3, respectively ana-lyze the relationship between HH, HV polarization backscat-tering coefficient and water content of soil before and afterremoving vegetation influence and discuss the impact tobackscattering coefficient of vegetation.Table 3 Backscattering coefficient and water content of soilSamples HH polarization/dBHV polarization/dBWater content in 5cm%C 1 −13.2507 −17.7713 5.1470 C 2 −10.7600−19.8877 4.9754 C 3 −11.5946−18.4416 5.0036 C 4 −11.3997−17.61538.4150 C 5 −12.2587−18.45157.6217 C 6 −12.5959−15.775814.7993 C 7 −11.5938−17.819411.7455 C 8 −10.7801−17.229313.1799 C 9 −14.3954−19.3163 3.6656 C 10 −9.6117−21.013910.7864 C 11 −12.8598−19.3812 4.3598 C 12 −15.9316−17.5230 3.2380 C 13 −14.9207−18.9558 2.2208 C 14 −9.7728−16.767318.8682 C 15 −7.8132−16.776320.1818 C 16 −13.1535−17.999312.1819 C 17 −12.8540−19.6966 4.9591 C 18 −14.1582−16.0084 4.1625 C 19 −13.8883−19.6414 4.0697 C 20 −13.5933−20.4478 4.8147 C 21 −12.8223−22.6523 3.1433 C 22 −10.5719−29.8080 1.1135 C 23 −9.2110−22.3394 6.7921 C 24 −8.8493−22.076813.1973 C 25 −12.9640−22.8122 1.3152 C 26 −11.4630−18.05148.5359 C 27 −15.0914−14.8482 2.0410 C 28 −11.0910−20.98437.2859 C 29 −9.4889−19.748315.7987 C 30 −8.8780−19.678310.3772Obtain the corresponding backscattering coefficient of the 30 samples in the image according as their coordinates, the relationship between before and after removing vegetation in-fluence of HH, HV polarization backscattering coefficient dis-played in Fig. 5.Fig. 5 Graph of relationship between HH, HV polarizationbackscattering coefficient(a) HH polarization; (b) HV polarizationIt is showed in Fig. 5 that, HH, HV polarization backscat-tering coefficient are becoming attenuation when separating the scattering and absorbing of vegetation using water-cloud model. The change of HH polarization data is thin, the reason is that the cropland of these samples is over the irrigation time and the evaporation is high, which results in lower water content of leaves and rhizome. Thus, the impact to scattering and absorb-ing of vegetation is tiny. HV polarization data is influenced by the scattering of plant canopy and branch. The study area is mostly covered by low crops and shrub. It is supposed that the vegetation is a orbicular scattering body covered with the earth’s surface and ignoring its size, figure and the distributing character of the orientation, which results in a tiny change after removing the influence of vegetation.The influencing analysis of vegetation on backscattering co-efficient 0soil()σθof bare soil:The backscattering coefficient of vegetation 0veg()σθ is acquired from Water-Cloud model, then the contribution of vegetation’s scattering and absorbing can be separated from the total backscattering of radar, and then the backscattering coeffi-cient 0soil()σθ of bare soil is obtained. The relationship be-tween HH, HV polarization backscattering coefficient and soil water content before and after eliminating vegetation effect are respectively showed in Fig. 6 and Fig. 7.964Journal of Remote Sensing 遥感学报 2010, 14(5)Fig. 6 Scatter plot of HH, HV polarization backscattering coefficient and soil water content before eliminating vegetation effect(a) HH polarization; (b) HV polarizationFig. 7 Scatter plot of HH, HV polarization backscattering coefficient and soil water content after eliminating vegetation effect(a) HH polarization; (b) HV polarizationFig. 6 and Fig. 7 illustrate the correlation coefficient of HH, HV polarization backscattering coefficient and soil water con-tent before eliminating vegetation effect are respectively 0.3769 and 0.1919,while them rise to 0.5227 and 0.3277 with the use of Water-Cloud Model after eliminating vegetation effect. Therefore, the relativity of soil backscattering coefficient and soil water content is increasing while eliminating the influence of vegetation. Research indicated HH polarization is muchmore sensitive to soil water content (Bao et al., 2006), data in this paper validate that HH polarization combining with Wa-ter-Cloud Model performs well in the separation of vegetation’s scattering and absorbing from the total backscattering of soil. Based on the above conclusion, establishing the connection model of soil water content and HH polarization backscattering coefficient, the regress equation is as follows:Water5=112.91e 0.1906δ (R 2=0.5227,n =30) (12)Use the Eq. (12) to retrieve the samples’ soil water content,results is as Fig. 8.Fig. 8 Soil water content mapField survey shows some of the river ways inside of the oa-sis are dry. The top left corner of the image is oasis-desertecotone, where the soil water content is less than 8%, as the vegetation cover area is out of the irrigating time, the soil water content are mostly between 7% and 15%, and only the soil water content of a hand of marsh are a little higher. The results of field survey are consistent with the detection of the soil moisture.5 CONCLUSION AND DISCUSSIONIn this paper, a case study is the Delta oasis of Weigan and Kuqa rivers in the Xinjiang where provided with predominance of terrain characteristic. Expert the advantages of initiative microwave remote sensing Radarsat-2 data and visible remotesensing TM data to extract water content. Firstly, using TM datato extract water content of plant, then combining with Wa-ter-Cloud Model to eliminate the impact of vegetation to back-scattering of soil, finally, simulating the relationship between backscattering coefficient and soil water content and validating and analysis the results, conclusions show:(1) Comparing with soil water content data and backscat-tering coefficient of SAR image, visible data combing with radar data performs well to extract the information of vegeta-tion and soil water content, and HH polarization backscattering coefficient is much closer to the change of soil water content. (2) Years’ difference has influence on character of vegetation. Although the year and the month of the soil water content data from field survey are contemporaneous with the image, the days are not, Which are likely to lead to differences of waterZHOU Peng et al.: Retrieval methods of soil water content in vegetation covering areas based on multi-source remote sensing data 965ingredient, leaves and rhizome water content and soil water content and them will disturb the extracting of plant water con-tent. The continuing study can try to relatively transform the spectrum data and construct some index to eliminate the effect of exoteric factors.(3) The hypsography in study area is flatness, and mostly covered by low plants, accordingly, the effect of surface roughness is not taken into account when calculating the soil water content. While in practical application the impact of sur-face roughness to radar echo is not neglected, which is a prob-lem in this research. 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Vegetation water content map-ping using Landsat data derived normalized difference water index for corn and soybeans. Remote Sensing of Environment, 92: 475—482Liao J J, Guo H D, Shao Y, LI X W and Veneziani N. 2002. Method and model of surface feature detection in arid to Semi-arid Area Using SAR Interferometry. Journal of Remote Sensing, 6(6): 431—434Li Z, Guo H D and Shi J C. 2002. Measuring the change of soil mois-ture with vege tation cover integration passive and active micro-wave data. Journal of Remote Sensing, 6(6): 481—484Liu W X,Liu X L and Wang J.2008.Romote sensing retrieval of soil moisture using ENVISAT-ASAR and MODIS images in vegetated areas of Huanan. Agricultural Research in the Arid Areas, 26(3): 39—43Liu W, Shi J C and Wang J M. 2005. Applying the decomposition tech-nique in vegetated surface to estimate soil moisture by multi- temporal measurements. Remote Sensing Information, (4): 3—6 Liu Z H and Zhao Y S.2002. The application study of MODTRAN and 6S model on atmospheric correction of MODIS image. Journal of Remote Sensing, 6: 217—222Rosnay P D, Calvet J C and Kerr Y. 2006.SMOSREX:A long term field campaign experiment for soil moisture and land surface processes remote sensing. Remote Sensing of Environment, (102): 377—389 Urso G D and Minacapilli M. 2006. A semi-empirical approach for surface soil water content estimation from radar data without a-priori information on surface roughness. Journal of Hydrology, 321: 297—310Wang J and Xu R S. 2008.Methods and research developments for retrival of vegetable water content by remote sensing. Remote Sensing Information, (1): 100—105Wickel A J, Jackson T J and Wood E F. 2001. Multitemporal monitoring of soil moisture with radarsat SAR during the 1997 Southern Great Plains hydrology experiment. International Journal of Re-mote Sensing, 22(8): 1571—1583Xiong W C and Shao Y. 2006. 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植被BRDF模型,FAPAR遥感反演
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三、植被BRDF模型—概述
（逆向拟合） （正向模拟） 将植被冠层分为三种类型：
遥感数据
模型选择
反演方法
应用
连续植被
行播作物
离散植被
辐射传输模型（RT）
RT或GO模型
几何光学模型(GO）
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三、植被BRDF模型—概述
结构参数：
• 总的长、宽、高
• LAI(leaf area index)
• FAVD(Foliage area volume density)：某一高度上单位体积内 叶面积的总和，单位1/m。
LAI2000 LAI2200
7/17/2015
15 15
二、LAI地面测量方法
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三、植被BRDF模型
• 概述 • BRDF模型研究进展
• 植被二向性统一模型
17 17
Directional Radiative Transfer Model
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Bidirectional Reflectance Distribution Function (BRDF)
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（图片来源：http://rami-benchmark.jrc.ec.europa.eu/HTML/RAMI-IV/RAMI-IV.php）
植被二向性反射统一模型 • 出发方程
• 群聚效应 • 离散植被BRDF模型
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三、植被BRDF模型—统一模型
出发方程
不论连续植被还是离散植被，目标对太阳辐射的反射率可近似表达 为一次散射和多次散射的贡献之和 1 m
北京大学暑期研究生定量遥感精品课程班
植被叶面积指数与FAPAR遥感反演 ——植被BRDF模型与LAI反演
《典型草原不同植被条件下土壤水分遥感反演研究》范文
《典型草原不同植被条件下土壤水分遥感反演研究》篇一一、引言随着遥感技术的快速发展，其在农业、环境监测等领域的应用越来越广泛。
其中，土壤水分作为草原生态系统的重要参数，其监测与反演技术对于草原生态保护、草地资源管理和农牧业生产具有重要意义。
本文针对典型草原不同植被条件下的土壤水分遥感反演进行研究，旨在为草原生态环境监测提供技术支撑。
二、研究区域与方法2.1 研究区域本研究选取我国北方典型草原区作为研究区域，该区域植被类型丰富，生态环境脆弱，对土壤水分的变化较为敏感。
2.2 研究方法本研究采用遥感技术手段，结合地面实测数据，对典型草原不同植被条件下的土壤水分进行反演研究。
具体方法包括：（1）遥感数据获取与处理：收集研究区域的遥感数据，包括多时相、多光谱的卫星遥感数据，进行辐射定标、大气校正等预处理。
（2）植被指数计算：根据预处理后的遥感数据，计算归一化植被指数（NDVI）等植被指数，用于表征植被覆盖度和生长状况。
（3）土壤水分反演模型构建：结合地面实测土壤水分数据，构建土壤水分反演模型，包括统计模型、物理模型和机器学习模型等。
（4）不同植被条件下的土壤水分反演：根据构建的反演模型，对不同植被条件下的土壤水分进行反演，分析土壤水分的时空分布规律和变化趋势。
三、不同植被条件下的土壤水分反演结果3.1 草地类型对土壤水分的影响研究发现在草地类型对土壤水分的影响显著。
草地类型主要包括草原、草甸、荒漠草原等。
不同草地类型的植被覆盖度、根系分布、土壤类型等均存在差异，导致土壤水分的分布和变化规律也不同。
通过遥感反演技术，可以有效地监测不同草地类型的土壤水分状况，为草原生态保护和草地资源管理提供科学依据。
3.2 季节变化对土壤水分的影响季节变化对土壤水分的影响也不可忽视。
春季和夏季是草原生长的主要时期，植被覆盖度较高，土壤水分相对较充足；而秋季和冬季，草原生长减缓或进入休眠期，植被覆盖度降低，土壤水分相对减少。
通过遥感反演技术，可以实时监测季节变化对土壤水分的影响，为农牧业生产提供决策支持。
植被含水量的遥感反演方式
统计分析方法
• 确定了植被水分的敏感光谱后,在统计分析的基础上,前人提出了很多不 同的指数和方法来诊断植物的含水量。这些统计模型可以大致分为以下 三类:
• 1:建立光谱指数 • 2:基于光谱导数变量建立模型 • 3包络线消除法
植被含水量光谱反演原理及 水分的敏感光谱波段
• 1 植被含水量光谱反演原理
• 2 水分的敏感光谱波段
1:植被含水量光谱反演原理
• 每一种物质对不同波长的电磁波的吸收和反射都不同,物质的这 种对不同波段光谱的响应特性叫光谱特性。植被光谱诊断便是基 于植被的光谱特性来进行的。
• 植被反射波谱中某些波长的光谱反射和吸收差异是由植被中化学 组分分子结构的化学键在一定辐射水平的照射下发生振动引起的, 从而产生了不同的光谱反射率,且该波长处光谱反射率的变化对 该化学组分的含量多少非常敏感(故称敏感光谱) 。植被含水量 光谱诊断的实现便是以植被水分敏感光谱的反射率与水含量的相 关关系为基础的。
• 研究表明,FMC、RWC和EWT是表征含水量的三个不相关量,是定量提取含水量 的不同方法。
• 研究发现在用遥感数据反演含水量时,短波红外波段与EWT相关性较好,而与 FMC相关性较差,并且表明用EWT来表征含水量要优于用FMC表示,因为FMC要受 叶子中的干物质影响。
• 在研究EWT和FMC的关系时发现,对于桉树叶,其近红外波段反射光谱与FMC有 很好的相关性,而短波红外波段的反射光谱与EWT高度相关。
建立光谱指数
• 建立的光谱指数一般是两个波段或多个波段的组合,如简单的加减组合、比值或者 是归一化比值,这是根据植被波谱的物理特性和半经验方法提出的。
《2024年内蒙古典型草原植被地上生物量遥感反演》范文
《内蒙古典型草原植被地上生物量遥感反演》篇一一、引言随着科技的不断进步，遥感技术作为一种高效的地球观测手段，已被广泛应用于多个领域，其中，草原生态环境的监测和评估显得尤为重要。
内蒙古自治区拥有典型的草原生态系统，其植被地上生物量的变化直接关系到草原生态系统的健康状况。
因此，本研究旨在利用遥感技术对内蒙古典型草原植被地上生物量进行反演，以期为草原生态保护和恢复提供科学依据。
二、研究区域与数据源本研究以内蒙古典型草原为研究区域，该区域地理位置优越，生态类型多样。
所采用的数据源为遥感数据，包括卫星遥感数据和无人机遥感数据。
卫星遥感数据具有覆盖范围广、获取周期短等优点，可获取大范围、连续的地面信息；无人机遥感数据则具有高分辨率、高精度等优势，可获取地面详细信息。
三、方法与技术本研究采用遥感反演技术对内蒙古典型草原植被地上生物量进行估算。
首先，对遥感数据进行预处理，包括辐射定标、大气校正等步骤，以提高数据的精度和可靠性。
其次，根据植被指数与地上生物量的关系，选取合适的植被指数（如NDVI、EVI 等），建立植被指数与地上生物量的回归模型。
最后，利用回归模型对遥感数据进行反演，得到植被地上生物量的空间分布情况。
四、结果与分析通过遥感反演技术，我们得到了内蒙古典型草原植被地上生物量的空间分布情况。
结果表明，不同地区的植被地上生物量存在显著差异，这与当地的气候、土壤、植被类型等因素密切相关。
此外，我们还发现，植被指数与地上生物量之间存在显著的线性关系，这为建立回归模型提供了依据。
通过对回归模型的分析，我们发现模型的精度较高，可有效估算植被地上生物量。
五、讨论与结论本研究利用遥感技术对内蒙古典型草原植被地上生物量进行了反演，取得了较好的结果。
然而，仍存在一些问题和挑战。
首先，遥感数据的精度和分辨率对反演结果的准确性有较大影响，因此，需要进一步提高遥感数据的精度和分辨率。
其次，植被指数与地上生物量之间的关系受多种因素影响，如气候、土壤、植被类型等，因此，需要进一步深入研究这些因素对反演结果的影响。
基于光谱指数的植被含水率遥感反演模型研究——以岷江上游毛尔盖地区为例
基于光谱指数的植被含水率遥感反演模型研究——以岷江上游毛尔盖地区为例潘佩芬;杨武年;简季;戴晓爱【摘要】Based on the measured vegetation moisture content and vegetation spectrum of samples in the study area,this paper firstly established the mathematical model between vegetation moisture content and vegetation spectral index and then inversed the vegetation moisture content by using the vegetation spectral index method.Results show that:the simple ratio spectral index has good correlation to vegetation moisture content,and the linear model is more suitable for retrieving vegetation moisture content.The vegetation moisture content retrieval results in 1999 and 2007 show that:the vegetation moisture content has raised during the 9 years,and the area of increased vegetation moisture content has also increased.%利用研究区植被样本实测含水率和实测光谱数据,基于植被光谱指数法,建立植被含水率与植被光谱指数之间的数学模型,同时利用该模型对研究区的遥感数据进行分析,反演植被含水率.结果证明:简单比值光谱指数与植被含水率有较好的相关性,线性模型更适合该研究区的植被含水率反演.1999年和2007年两年的植被含水率反演结果显示:9年间植被含水率提高,含水率高的面积增大.【期刊名称】《遥感信息》【年(卷),期】2013(028)003【总页数】5页(P69-73)【关键词】光谱指数;植被含水率;遥感反演模型;毛尔盖【作者】潘佩芬;杨武年;简季;戴晓爱【作者单位】成都理工大学地学空间信息技术国土资源部重点实验室,成都610059;成都理工大学地学空间信息技术国土资源部重点实验室,成都610059;成都理工大学地学空间信息技术国土资源部重点实验室,成都610059;成都理工大学地学空间信息技术国土资源部重点实验室,成都610059【正文语种】中文【中图分类】TP791 引言植被含水率能够反映植被的生长状况，对于森林等的生态环境和生态安全具有重大的意义。
植被含水量的遥感反演方式学习教案
Chen等在用Landsat数据反演(fǎn yǎn)含水量时发现短波红外位于1550～ 1750nm波段较佳,并且发现1640nm、2130nm波段处对水分的吸收很敏感。
第9页/共24页
第十页，共24页。
植被(zhíbèi)含水量反演方法
• 在明确了水分于近红外-短波(duǎnbō)红外波段比较敏感后,许多学 者对用光谱反射率诊断植株水分状况 进行了可行性分析。国内 外研究植被水分含量与光谱特征之间的关系,主要集中在两个方 面:
• Penuelas等也发现用近红外波段的一阶导数的最小值或 其所在的波长能清楚地指示RWC状况的变化。沈艳等讨 论了利用基于导第1数4页/共光24页 谱变量分析方法建立叶片含水量 模型的可行性,并同时发现,针对不同的表征方法,用FMC 反演单子叶叶片含水量精第十五度页，共2大4页。 于EWT,而用EWT反演双
• 叶片生化组分对应特定光谱的吸收特征,利用多元回归 可以确定化学组分和光谱数据相关程度高的波段和波段 组合,从而反演出化学组分含量。在进行回归分析的过 程中,采用逐步回归的方法,通过F检验,使对因变量贡献 大的因子随时(suíshí)可以进入方程,贡献小的因子又可以 随时(suíshí)剔除,从而建立最优回归方程。
《典型草原不同植被条件下土壤水分遥感反演研究》
《典型草原不同植被条件下土壤水分遥感反演研究》篇一一、引言草原生态系统对环境变化非常敏感，其健康状态对气候变化和生态安全具有重要意义。
而土壤水分作为草地生态系统中关键的水文参数，直接影响植被生长、能量交换等重要生态过程。
然而，传统的土壤水分测量方法通常费时费力，且难以在较大空间范围内进行连续监测。
因此，利用遥感技术进行土壤水分的反演研究显得尤为重要。
本文旨在研究典型草原不同植被条件下土壤水分的遥感反演方法，以期为草原生态环境的监测与保护提供科学依据。
二、研究区域与方法本研究选取了具有代表性的典型草原区域，通过收集该区域的遥感数据、气象数据以及地面实测数据，开展土壤水分的遥感反演研究。
（一）研究区域研究区域位于我国某典型草原区，具有丰富的植被类型和地形地貌特征。
（二）方法与技术路线1. 数据收集：收集遥感数据（包括Landsat、Sentinel-2等卫星数据）、气象数据以及地面实测数据。
2. 遥感图像处理：对遥感数据进行预处理，包括辐射定标、大气校正等，以提高数据的信噪比。
3. 植被指数计算：根据预处理后的遥感数据，计算归一化植被指数（NDVI）等植被指数。
4. 土壤水分反演：利用统计模型、机器学习等方法，建立植被指数与土壤水分之间的关系模型，进行土壤水分的反演。
5. 结果分析：对反演结果进行统计分析、空间分析等，探讨不同植被条件下土壤水分的分布特征及变化规律。
三、植被条件下的土壤水分反演模型构建（一）统计模型本研究采用多元线性回归模型进行土壤水分的反演。
以NDVI等植被指数为自变量，土壤水分含量为因变量，建立统计模型。
通过分析自变量与因变量之间的关系，得出土壤水分含量的估算模型。
（二）机器学习模型除了统计模型外，本研究还采用了机器学习算法进行土壤水分的反演。
利用支持向量机、随机森林等算法，建立植被指数与土壤水分之间的非线性关系模型。
通过训练和验证，得出适用于不同植被条件的土壤水分反演模型。
四、结果分析（一）统计模型结果分析通过对比反演结果与地面实测数据，发现多元线性回归模型的估算精度较高，能够在一定程度上反映实际土壤水分状况。
《典型草原不同植被条件下土壤水分遥感反演研究》
《典型草原不同植被条件下土壤水分遥感反演研究》篇一一、引言随着遥感技术的不断发展，其在生态环境监测、土地资源管理等领域的应用越来越广泛。
其中，土壤水分作为生态系统的重要参数之一，其准确获取对于草原生态保护、农业发展等具有重要意义。
典型草原作为我国重要的生态屏障和农业生产基地，其不同植被条件下的土壤水分遥感反演研究显得尤为重要。
本文旨在通过遥感技术手段，探究典型草原不同植被条件下土壤水分的分布特征和变化规律，为草原生态保护和农业生产提供科学依据。
二、研究方法本研究采用遥感技术手段，结合地理信息系统和统计分析方法，对典型草原不同植被条件下的土壤水分进行反演研究。
具体包括以下步骤：1. 数据收集：收集典型草原区域的遥感数据、气象数据、植被数据等。
2. 数据处理：对遥感数据进行预处理，包括辐射定标、大气校正、图像融合等。
3. 植被分类：利用遥感数据对典型草原进行植被分类，划分出不同植被类型区域。
4. 土壤水分反演：采用遥感反演模型，对不同植被类型区域的土壤水分进行反演。
5. 结果分析：对反演结果进行统计分析，探究土壤水分的分布特征和变化规律。
三、不同植被条件下土壤水分的分布特征1. 草地土壤水分分布草地是典型草原的主要植被类型之一，其土壤水分分布受到气候、地形、植被等多种因素的影响。
通过遥感反演，我们发现草地土壤水分在空间上呈现出一定的分布规律。
在降雨较多的季节，草地土壤水分较高，而在干旱季节则相对较低。
同时，草地土壤水分的分布还受到地形的影响，如在山谷、河谷等低洼地区，土壤水分相对较高。
2. 灌木林土壤水分分布灌木林是典型草原的另一种重要植被类型，其土壤水分的分布与草地有所不同。
由于灌木林的根系较为发达，能够更好地吸收和保持土壤水分，因此其土壤水分相对较高。
同时，灌木林的分布也受到地形的影响，如在山腰、山坡等地区，灌木林的土壤水分相对较高。
3. 其他植被类型土壤水分分布除了草地和灌木林之外，典型草原还存在着其他植被类型，如荒漠、湖泊等。