Sentinel-2数据的冬小麦地上干生物量估算及评价   总被引：3，自引：0，他引：3  

郑阳  吴炳方  张淼 《遥感学报》2017,21(2):318-328

作物生物量快速精确的监测对于农业资源的合理利用与农田的精准管理具有重要意义。近年来,遥感技术因其独特的优势已被广泛用于作物生物量的估算中。本文主要针对不同宽波段植被指数在冬小麦生物量(文中的生物量均是指地上干生物量)估算方面的表现进行探索。首先利用欧洲空间局最新的Sentinel-2A卫星数据提取出17种常见的植被指数,之后分别构建其与相应时期内采集的冬小麦地上生物量间的最优估算模型,通过分析两者间的相关性与敏感性,获取适宜进行生物量估算的指数。最后,绘制了研究区的生物量空间分布图。结果表明,所选的植被指数均与生物量显著相关。其中,红边叶绿素指数(CI_(re))与生物量的估算精度最高(决定性系数R~2为0.83;均方根误差RMSE为180.29 g·m~(–2))。虽然相关性较高,但部分指数,如归一化差值植被指数(NDVI)等在生物量较高时会出现饱和现象,从而导致生物量的低估。而加入红边波段的指数不仅能够延缓指数的饱和趋势,而且能够提高反演精度。此外,通过敏感性分析发现,归一化差值指数和比值指数分别在作物生长的早期和中后期对生物量的变化保持较高的敏感性。由于红边比值指数(SR_(re))和MERIS叶绿素敏感指数(MTCI)在冬小麦全生长季内一直对生物量的变化保持高灵敏性,二者是生物量估算中最为稳定的指数。  相似文献   

Erratum: Self-similarity and scaling behaviour of infrared emission from radiatively heated dust -- I. Theory     

《Monthly notices of the Royal Astronomical Society》1999,303(4):864-864

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The Correlation Bias for Two-Dimensional Simulations by Turning Bands     

Tilmann Gneiting 《Mathematical Geology》1999,31(2):195-211

The turning bands method (TBM) generates realizations of isotropic Gaussian random fields by summing contributions from line processes. We consider two-dimensional simulations and study the correlation bias attributable to the use of only a finite number L of lines. Our analytical and numerical results confirm that the maximal bias is of order 1/L, and that L = 64 lines suffice for excellent covariance reproduction. The notorious banding observed in simulations with an insufficient number of lines is a related but different phenomenon and depends strongly on the choice of the line simulation technique. Clear-cut recommendations for the number of lines necessary to avoid the effect can only be based on practical experience with the specific code at hand.  相似文献   

Exploring hyperspectral bands and estimation indices for leaf nitrogen accumulation in wheat     

Xia Yao  Yan ZhuYongChao Tian  Wei FengWeiXing Cao 《International Journal of Applied Earth Observation and Geoinformation》2010

Hyperspectral sensing can provide an effective means for fast and non-destructive estimation of leaf nitrogen (N) status in crop plants. The objectives of this study were to design a new method to extract hyperspectral spectrum information, to explore sensitive spectral bands, suitable bandwidth and best vegetation indices based on precise analysis of ground-based hyperspectral information, and to develop regression models for estimating leaf N accumulation per unit soil area (LNA, g N m⁻²) in winter wheat (Triticum aestivum L.). Three field experiments were conducted with different N rates and cultivar types in three consecutive growing seasons, and time-course measurements were taken on canopy hyperspectral reflectance and LNA under the various treatments. Then, normalized difference spectral indices (NDSI) and ratio spectral indices (RSI) based on the original spectrum and the first derivative spectrum were constructed within the range of 350–2500 nm, and their relationships with LNA were quantified. The results showed that both LNA and canopy hyperspectral reflectance in wheat changed with varied N rates, with consistent patterns across different cultivars and seasons. The sensitive spectral bands for LNA existed mainly within visible and near infrared regions. The best spectral indices for estimating LNA in wheat were found to be NDSI (R₈₆₀, R₇₂₀), RSI (R₉₉₀, R₇₂₀), NDSI (FD₇₃₆, FD₅₂₆) and RSI (FD₇₂₅, FD₅₁₆), and the regression models based on the above four spectral indices were formulated as Y = 26.34x^1.887, Y = 5.095x − 6.040, Y = 0.609 e^3.008x and Y = 0.388x^1.260, respectively, with R² greater than 0.81. Furthermore, expanding the bandwidth of NDSI (R₈₆₀, R₇₂₀) and RSI (R₉₉₀, R₇₂₀) from 1 nm to 100 nm at 1 nm interval produced the LNA monitoring models with similar performance within about 33 nm and 23 nm bandwidth, respectively, over which the statistical parameters of the models became less stable. From testing of the derived equations, the model for LNA estimation on NDSI (R₈₆₀, R₇₂₀), RSI (R₉₉₀, R₇₂₀), NDSI (FD₇₃₆, FD₅₂₆) and RSI (FD₇₂₅, FD₅₁₆) gave R² over 0.79 with more satisfactory performance than previously reported models and physical models in wheat. It can be concluded that the present hyperspectral parameters of NDSI (R₈₆₀, R₇₂₀), RSI (R₉₉₀, R₇₂₀), NDSI (FD₇₃₆, FD₅₂₆) and RSI (FD₇₂₅, FD₅₁₆) can be reliably used for estimating LNA in winter wheat.  相似文献   

基于实测高光谱数据的鄱阳湖湿地植被光谱差异波段提取   总被引：1，自引：0，他引：1  

况润元  曾帅  赵哲  肖阳 《湖泊科学》2017,29(6):1485-1490

高光谱遥感技术的出现为有效解决湿地植被种类的精细识别和分类提供了可能.通过实地测取鄱阳湖湿地5种植被的高光谱数据,在对数据预处理的基础上,提出一种基于数据误差范围和植被光谱均值差的植被光谱差异波段提取方法.将该方法应用于包络线变换前后的光谱曲线提取植被的光谱差异波段,最后利用马氏距离法检验植被识别效果.结果表明:本文中的方法有效提取了植被光谱差异波段,其中变换前光谱差异波段分别为663~688 nm,变换后为581~636、660~695和1225~1236 nm.在光谱差异波段范围内,同种植被的马氏距离值小于异种植被的马氏距离值,可有效对植被进行识别.研究结果为湿地植被分类识别奠定了理论基础,同时为湖泊湿地植被以及湖泊生态环境的保护决策提供科学依据.  相似文献   

应用MODIS遥感数据监测巢湖水质   总被引：18，自引：1，他引：17  

吴敏  王学军 《湖泊科学》2005,17(2):110-113

以巢湖为研究对象,对MODIS的各个波段辐射率与水质参数叶绿素a浓度、悬浮物浓度和透明度进行拟合,分析了MODIS各个波段辐射率的拟合在监测大型内陆湖泊水质中的可行性.结果表明:MODIS波段辐射率的组合能与巢湖水质参数进行较好的匹配,MODIS波段1—4和10—11对于监测巢湖中叶绿素a浓度、悬浮物浓度和透明度有重要意义.  相似文献   

An experimental investigation of wave‐induced sediment responses in a natural silty seabed: New insights into seabed stratification          下载免费PDF全文

Xiaolei Liu  Yonggang Jia  Jiewen Zheng  Mingzheng Wen  Hongxian Shan 《Sedimentology》2017,64(2):508-529

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不同植被红边指数在城市草地健康判别中的对比研究     

方灿莹  王琳  徐涵秋 《地球信息科学学报》2017,19(10):1382-1392

遥感红边指数与表征绿色植物生长状况的重要生化参数有密切的关系,是植被长势监测的重要因子。为寻找出最适用于城市草地生长状况监测的红边指数,本文基于Sentinel-2A数据,对比分析了不同红边指数在城市草地健康状况估算方面的差异。本文以福州市和厦门市的城市草地为例,在全面分析各种健康水平草地光谱响应特征差异的基础上,选取了6种与草地生化参数相关的红边指数,即红边位置REP、地面叶绿素指数MTCI、归一化差值红边指数NDRE1、新型倒红边叶绿素指数IRECI、红边叶绿素指数CI_red-edge以及叶绿素吸收指数MCARI2,然后采用独立样本T检验及欧式距离对这6种红边指数在草地健康判别中的优劣进行了定量对比。结果表明：IRECI指数对草地健康状况最为敏感,该指数在不同健康等级草地的值域区间和均值都存在显著性差异,其判别总精度均大于85%;NDRE1和MCARI2指数次之,其他3个指数则难以判别草地的健康状况。因此,在基于Sentinel-2A影像的城市草地健康遥感判别中,推荐使用IRECI指数。  相似文献   

基于MODIS影像多特征的CART决策树分类     

张会  闫金凤 《地理空间信息》2013,11(2):111-113

以山东省为研究区域,利用2009年9月MODIS的8 d合成波段反射率产品MOD09,选择特征变量植被指数(NDVI、EVI)、NDWI、NDMI、NDSI及辅助信息DEM,通过选取其中的影像特征组合来确定分类方案,构建各波段组合的CART决策树,对MODIS影像进行分类,得到CART决策树的最优波段组合。结果表明,特征变量DEM、NDVI、EVI对分类结果贡献较大;将CART决策树的分类结果与其相对应的最大似然分类结果进行比较可知,基于影像多特征的CART决策树分类方法能明显提高分类精度。  相似文献   

Introducing sensor spectral response into the classification process     

《International Journal of Applied Earth Observation and Geoinformation》2013

Many sensors have their bands overlapped and therefore do not set a normal space. If a spectral distance is measured, as in first-order statistical classifiers, the direct consequence is that the result will not be the most accurate. Image classification processes are independent of the spectral response function of the sensor, so this overlap is usually ignored during image processing. This paper presents a methodology that introduces the spectral response function of sensors into the classification process to increase its accuracy. This process takes place in two steps: first, incident energy values of the sensors are reconstructed; second, the energy of the bands is set in an orthonormal space using a matrix singular value decomposition. Sensors with and without overlapping spectral bands were simulated to evaluate the reconstruction of energy values. The whole process was implemented on three types of images with medium, high and very high spatial resolution obtained with the sensors ASTER, IKONOS and DMC camera, respectively. These images were classified by ISODATA and minimum distance algorithms. The ISODATA classifier showed well-defined features in the processed images, while the results were less clear in the original images. At the same time, the minimum distance classifier showed that overall accuracy of the processed images increased as the maximum tolerance distance decreased compared to the original images.  相似文献