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运用动态谐波回归模型(Dynamic Harmonic Regression,DHR)对MODIS的长时间序列的LAI产品进行分析,可以从中分离出LAI随时间变化的多年趋势、季节变化及残差等主要成分,通过建立的模型实现LAI年间变化的短时预测。本文将所述DHR模型分析方法试用于遥感数据产品随时间变化的信息提取,对LAI年间变化的预测结果证明该方法用于遥感像元尺度LAI产品的时间序列分析与预测的效果良好。 相似文献
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真实性检验是评价遥感反演产品质量和验证遥感应用产品是否准确、真实地反映实际情况的重要途径。叶面积指数(LAI)是表征陆地植被结构和长势的关键参数,全面准确评价和验证LAI产品是产品用于陆面过程模型的前提。本文以MODIS LAI与GLASS LAI产品为研究对象,在尺度效应和尺度转换的基础上,建立了针对非均匀像元的低分辨率LAI产品真实性检验方法。在考虑空间异质性和植被长势差异的情况下,借助中分辨率的遥感影像,分别利用1 km像元平均叶面积指数和反演表观叶面积指数实现了对LAI算法和产品的真实性检验。为了比较作物长势差异和地表非均匀度对产品的影响,本文选择有代表性的河南鹤壁和甘肃张掖两个地区进行两种LAI产品真实性检验研究。研究结果表明,GLASS LAI和MODIS LAI产品均存在明显的低估现象。这并不是产品算法的问题,而是由于地表异质性和非均匀度的影响。在异质性更显著的张掖盈科灌区,低估现象更明显。GLASS LAI产品是多种LAI产品的融合,它的平均LAI比MODIS更接近真实情况,但是LAI的动态范围比MODIS窄。 相似文献
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遥感估算叶面积指数(LAI)时空动态变化对全球气候变化研究具有重要的意义,为了提高遥感估算时间序列叶面积指数的精度,需要耦合遥感观测数据与LAI动态过程模型。本文提出一种基于双集合卡尔曼滤波(Dual EnKF)的时间序列LAI反演方法,同时更新LAI估计值和LAI动态过程模型中的敏感性参数,得到LAI和动态过程模型敏感参数的最优估计值来优化动态过程模型。一方面使得动态过程模型可以更好地描述LAI随时间的变化过程,降低模型预测误差,从而提高LAI动态过程模型的预测能力;另一方面通过耦合动态过程模型和辐射传输模型,集成遥感观测数据与动态过程模型的预测值,进而得到优化的LAI估计值。为检验算法,分别选取作物、草地和林地等典型植被验证站点进行Dual EnKF LAI时间序列估算,并分别与MODIS LAI产品及其SG滤波曲线、集合卡尔曼滤波方法反演LAI、未优化的动态过程模型模拟LAI结果进行比较,并配以一些站点地面实测点数据作为参考。结果表明,采用Dual EnKF方法得到的LAI不但保持了时间上的连续性,而且通过改善动态过程模型的预测能力,即使在缺乏高质量遥感观测数据时,也能够获得符合LAI发展趋势的估算值,没有出现跳跃、波动现象,时间序列曲线较稳定,更符合植被LAI变化规律,表明基于Dual EnKF的时间序列LAI遥感估算方法是提取LAI时间廓线的一种有效途径。 相似文献
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由于地表空间异质性的普遍存在,遥感反演模型的非线性必然会导致不同分辨率观测的遥感结果不一致,从而产生遥感产品尺度效应。本文研究了遥感产品尺度效应概念、模拟方法和定量计算模型,并利用锡林浩特草原研究区的实测数据,对尺度效应模型和方法进行了定量计算与验证分析。首先,基于不同升尺度方法与多尺度遥感成像机理之间的机理联系,通过“先反演再平均”与“先平均再反演”之间的差异,可计算“高”分辨率与“低”分辨率之间的遥感产品尺度差异。其次,分别以红光、近红外两波段反射率和归一化植被指数(NDVI)为自变量,对叶面积指数(LAI)非线性遥感模型进行泰勒展开,研究了模型非线性、遥感数据空间异质性对LAI遥感产品尺度差异的影响,发现高阶项可忽略,利用二阶导数项和遥感数据方差项可定量计算遥感产品尺度差异,经过二阶导数项纠正后的尺度差异相对偏差从5.6%分别降低到0.78%和1.45%。最后,分析了LAI遥感产品尺度效应的特征规律,得出以下结论:随着植被覆盖的增大,同等遥感空间异质性的LAI遥感产品尺度差异越大,且红光波段比近红外波段的尺度差异敏感性高近2个数量级;对于绝大部分陆地植被区域,存在“低分辨率低估”尺度效应,且遥感产品尺度差异的主导要素为LAI模型非线性,NDVI变量自身非线性对尺度效应贡献占23.5%;对于湿地类植被与水体混合情形,NDVI变量非线性的贡献为主导贡献,出现“低分辨率高估”尺度效应,必须利用红光、近红外两波段的二阶导数项非线性尺度差异,才能解释这一类型的LAI遥感产品尺度效应。本文建立了具有一定普适意义的遥感产品尺度效应定量模拟与尺度纠正方法,对推动定量遥感的尺度问题研究有一定参考价值。 相似文献
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山地叶面积指数反演理论、方法与研究进展 总被引：2，自引：0，他引：2
叶面积指数LAI（Leaf Area Index）是表征叶片疏密程度和冠层结构特征的重要植被参数,在气候变化、作物生长模型以及碳、水循环研究中发挥着重要作用。遥感是获取区域及全球尺度LAI的一个重要手段,当前LAI产品主要基于遥感数据反演得到,但是多数LAI产品算法并未考虑地形特征的影响,导致山地LAI遥感反演精度不确定性大。提高山地LAI遥感反演精度亟需考虑地形因子对冠层反射率的影响,其中山地冠层反射率模型和遥感数据地形校正是提升山地LAI遥感反演精度的关键。本文围绕山地LAI遥感反演理论与方法,综合分析了国内外山地冠层反射率模型和地形校正模型的研究进展,总结了目前山地LAI遥感反演存在的问题,并讨论了未来研究的发展趋势。 相似文献
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叶面积指数(leaf area index,LAI)是描述植被冠层结构的重要参数,准确获取果树的LAI对果树长势监测和果树估产均有重要作用。以美国加州中部的果园为研究区,基于沿太阳主平面飞行成像的机载MODIS/ASTER模拟传感器(MODIS/ASTER airborne simulator,MASTER)数据,利用实测LAI数据与归一化差值植被指数(normalized difference vegetation index,NDVI)、归一化差值红外指数(normalized difference infrared index,NDII)和归一化差值水体指数(normalized difference water index,NDWI)分别建立回归模型,并选取NDWI进行研究区LAI的反演。结果表明:由于地物的二向性反射,垂直太阳主平面飞行获取的遥感数据具有明显的亮度梯度现象,而沿太阳主平面飞行获取的遥感数据几乎不受亮度梯度的影响;NDVI在高植被覆盖区容易达到饱和,而NDWI比NDVI和NDII具有更高的拟合度和更小的均方根误差,更加适合研究区LAI的遥感反演;该研究结果可以丰富LAI反演理论,也可以为研究LAI尺度问题提供理论和数据支持。 相似文献
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叶面积指数LAI (Leaf Area Index)是表征植被生长状态的一个重要的冠层结构参数。MODIS LAI产品是全球常用的遥感LAI产品之一。然而,由于地表异质性、数据质量、模型精度等多方面的差异,MODIS LAI产品质量各有不同。基于无线传感器网络的LAINet仪器可以自动获取时间频率更密集的LAI实测数据,为验证卫星遥感LAI产品质量提供了有力支持。本文基于2018年和2019年黑河中游时间序列地面实测LAI数据与高空间分辨率卫星遥感植被指数数据,建立经验回归模型。将该模型反演高空间分辨率卫星遥感LAI作为参考LAI真值,对MODIS LAI产品进行了精度验证与稳定性评价,分析了MODIS LAI与LAINet地面测量的差异原因。结果表明：与Landsat 8参考真值相比,MODIS LAI生长季的质量（RMSE2018=1.17,RMSE2019=1.14）优于衰落季（RMSE2018=1.39,RMSE2019=1.84）,MODIS LAI总体低估,尤其是生长季后期。时间序列上,MODIS LAI产品能够刻画植被生长和凋落的季节特征,但生长前期波动性要强于后期。与L... 相似文献
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水稻叶面积指数（leaf area index,LAI）是评价其长势的重要农学参数,高光谱遥感能够实现叶面积指数的快速无损监测。为了寻找反演水稻LAI的最优植被指数,扩展水稻LAI高光谱估测模型的普适性,选取宁夏引黄灌区水稻为研究对象,通过设置不同氮素处理,借助相关分析、回归分析等方法研究高光谱植被指数与水稻LAI之间的定量关系,并通过确立的最优波段组合,构建4种植被指数与水稻LAI的高光谱反演模型。结果表明,水稻LAI在抽穗末期达到最大值,并随氮素水平的增加而增加;水稻冠层原始光谱反射率在400~722 nm和1 990~2 090 nm波段与LAI达到极显著负相关水平,在近红外区域760~1 315 nm与LAI呈极显著正相关。模型检验结果表明,以比值植被指数RVI（850,750）为变量建立的水稻LAI估测模型最佳,研究结果可为水稻LAI的高光谱估测提供地域参考。 相似文献
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《测绘与空间地理信息》2013,(11)
遥感定量分析方法能够通过生物量相关因子直观反映路域植被的生长过程,为探索公路建设运营对自然环境的影响及其影响的复杂性提供强有力的技术手段。利用高分辨率IKONOS影像,研究两种遥感解译方法反演路域植被叶面积指数(LAI),对比分析其结果与精度,探索适用于路域生态环境这一特殊生态区域植被LAI的反演方法。研究表明,物理模型法较经验模型法更具有适普性和准确性,更适用于路域植被LAI的反演,精度更高。 相似文献
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A leaf area index is a key parameter reflecting the growth changes of vegetation and one of the most important canopy structural parameters for performing quantitative analyses of many ecological and climate models. Although using high-resolution satellite data and the radiative transfer model (RTM) can be used to generate high resolution LAI products, the RTM method has some problems because its temporal resolution is low, the input parameters are more appropriate for a physics model, and some parameters are difficult to obtain. Problems that urgently need to be solved include improving the temporal-spatial resolution for LAI products and localizing LAI products. To explore an applicable method for the high-resolution LAI products in a small basin and to improve the inversion accuracy, we propose an approach for GF-1 WFV LAI retrieval using MOD15A2 data and the measured LAI of the Poyang Lake watershed. Empirical models were used to retrieve high resolution LAI values, and the results show that these models are well designed for analyzing time-series satellite data. Good correlations were obtained between the NDVI of the GF-1 WFV data, the retrieved LAI values and the MODIS LAI data from samples acquired in both summer and winter. The exponential NDVI model obtained the best LAI value estimation results from the GF-1 WFV data (R2 = 0.697, RMSE = 1.100); the best synthetic validation of the RMSE is 0.883, close to the optimum model. Therefore, the retrieval results more fully reflect the growth process of the different features. This study proposed an upscale method for developing a high spatial resolution GF-1 satellite standard LAI products retrieval model using MODIS data. The proposed method will be helpful for efficiently improving the temporal-spatial resolution of LAI products to benefit the extraction of vegetation parameter information and dynamic land use monitoring. 相似文献
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基于时间序列叶面积指数稀疏表示的作物种植区域提取 总被引：3，自引：0，他引：3
以华北平原黄河以北地区为研究区域,以时间序列叶面积指数LAI(Leaf Area Index)傅里叶变换的谐波特征作为不同作物识别的数据源,利用稀疏表示的分类方法识别2007年—2016年冬小麦、春玉米、夏玉米等主要农作物种植区域。首先利用上包络线Savitzky-Golay滤波分别对2007年—2016年的时间序列MODIS LAI曲线进行重构,进而对重构的年时间序列LAI进行傅里叶变换,以0—5级谐波振幅、1—5级谐波相位作为作物识别的依据,基于各类地物的训练样本,通过在线字典学习算法构建稀疏表示方法的判别字典,对每个待测样本利用正交匹配追踪算法求解稀疏系数,从而计算对应于各类地物的重构误差,根据最小重构误差判定待测样本的作物类型,并对作物识别结果的位置精度进行验证。结果表明,2007年—2016年作物识别的总体精度为77.97%,Kappa系数为0.74,表明本文提出的方法可以用于研究区域主要作物种植区域的提取。 相似文献
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V. K. Sehgal C. V. S. Sastri N. Kalra V. K. Dadhwal 《Journal of the Indian Society of Remote Sensing》2005,33(1):131-136
A study aimed at generating wheat yield maps of farmer’s fields by using remote sensing (RS) inputs was undertaken during
the rabi season of 1998-99 in six villages of Alipur Block of Delhi State. RS derived leaf area index (LAI) were linked to
wheat simulation model WTGROWS by adopting a strategy christened “Modified Corrective Approach”. This essentially uses an
empirical relation of grain yield and LAI, which was derived from WTGROWS simulation model by running model for a combination
of input resources, management practices and soil types occurring in the area. This biometric relationship was applied to
all the wheat fields of the study area for which the LAI was derived from single acquisition of IRS LISS-III data (Jan 27,
99). The LAI-NDVI relation adopted was logarithmic in nature (R2=0.83) and was based on ground measurements of LAI in farmer’s fields in the same area. A comparison of predicted grain yield
by the modified corrective approach and actual observed yield for the 22 farmer’s fields showed high correlation coefficient
of 0.8 and a root mean square error (RMSE) of 597 kg ha-1 which was 17% of the observed mean yield. Thus linking of RS information and crop simulation model provides an alternative
for mapping and forecasting crop yield under highly variable cropping environment of Indian farms, which is a pre-requisite
for implementing Precision Crop Management (PCM). 相似文献
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基于遥感数据与作物生长模型同化的冬小麦长势监测与估产方法研究 总被引：23，自引：2，他引：23
本文以LAI作为结合点，讨论了利用复合型混合演化（SCE—UA）算法实现CERES—Wheat模型与遥感数据同化的可行性。CERES—Wheat模型同化后主要生育期和产量的模拟值分别与真实条件下模型相应模拟值以及实测值进行比较。结果表明，同化后CERES—Wheat模型的模拟精度对LAI外部同化数据的误差并不十分敏感。并且在LAI同化数据较少时，也可获得较好的同化结果。这一特点体现了SCE—UA算法应用于同化过程的优越性，为同化策略在区域冬小麦长势监测及估产中的应用提供了基础。 相似文献
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Remote sensing images are widely used to map leaf area index (LAI) continuously over landscape. The objective of this study is to explore the ideal image features from Chinese HJ-1 A/B CCD images for estimating winter wheat LAI in Beijing. Image features were extracted from such images over four seasons of winter wheat growth, including five vegetation indices (VIs), principal components (PC), tasseled cap transformations (TCT) and texture parameters. The LAI was significantly correlated with the near-infrared reflectance band, five VIs [normalized difference vegetation index, enhanced vegetation index (EVI), modified nonlinear vegetation index (MNLI), optimization of soil-adjusted vegetation index, and ratio vegetation index], the first principal component (PC1) and the second TCT component (TCT2). However, these image features cannot significantly improve the estimation accuracy of winter wheat LAI in conjunction with eight texture measures. To determine the few ideal features with the best estimation accuracy, partial least squares regression (PLSR) and variable importance in projection (VIP) were applied to predict LAI values. Four remote sensing features (TCT2, PC1, MNLI and EVI) were chosen based on VIP values. The result of leave-one-out cross-validation demonstrated that the PLSR model based on these four features produced better result than the ten features’ model, throughout the whole growing season. The results of this study suggest that selecting a few ideal image features is sufficient for LAI estimation. 相似文献
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北京地区冬小麦冠层光谱数据与叶面积指数统计关系研究 总被引：4，自引：1，他引：3
以北京地区冬小麦为研究对象,利用TM传感器的光谱响应函数处理地面测量获得的冬小麦冠层光谱数据,得到对应于TM传感
器红光波段和近红外波段的反射率,进而计算出冬小麦冠层的归一化植被指数NDVI。建立了LAI与NDVI之间的不同经验关
系模型,对实验结果进行分析后得出,LAI与NDVI之间具有高度的指数相关性。 相似文献
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《International Journal of Digital Earth》2013,6(4):319-337
Abstract The leaf area index (LAI) is an important parameter to quantitatively describe the structure of vegetation and crops. Uncertainty in the relationship between the LAI and polarimetric parameters is the key problem for LAI estimation from polarimetric synthetic aperture radar (POLSAR) data. However, the existing POLSAR data have difficulties meeting the demand of the aforementioned research. This paper analyses the correlations between the LAI and the polarimetric parameters derived from Cloude and Freeman decompositions using simulated POLSAR data based on a coherent scattering model for maize and wheat. The results show: (1) The POLSAR data at C-band with a large incidence angle (40 degrees) are very suitable for finding the LAI for maize and wheat. (2) For maize there is a strong correlation between the scattering type angle and the LAI at C-band with a large incidence angle, and the coherency entropy, anisotropy, and the power of the double-bounce scattering power component also have significant correlations with the LAI. (3) For wheat at C-band with a high incidence angle, although the correlation coefficient is low, there is still a correlation between the entropy, anisotropy and LAI. Besides, the volume scattering is suitable for extracting the LAI for wheat at X-band. 相似文献
18.
《International Journal of Digital Earth》2013,6(11):881-896
Satellite-based remote sensed phenology has been widely used to assess global climate change. However, it is constrained by uncertain linkages with photosynthesis activity. Two dynamic threshold methods were employed to retrieve spring phenology metrics from four Moderate Resolution Imaging Spectroradiometer (MODIS) products, including fraction of Absorbed Photosynthetically Active Radiation (fAPAR), Leaf Area Index (LAI), Normalized Difference Vegetation Index (NDVI), and Enhanced Vegetation Index (EVI) for three temperate deciduous broadleaf forests in North America between 2001 and 2009. These MODIS-based spring phenology metrics were subsequently linked to the photosynthetic curves (daily gross primary productivity, GPP) measured by an eddy covariance flux tower. The 20% dynamic threshold spring onset metrics from MODIS products were closer to the photosynthesis onset metrics at the date of 2% GPP increase for NDVI and fAPAR, and closer to the date of 5% and 10% increase of GPP for EVI and LAI, respectively. The 50% dynamic threshold onset metrics were closer to the photosynthesis onset metrics at the date of 10% GPP increase for NDVI, and closer to the date of 20% GPP increase for fAPAR, LAI and EVI, respectively. These results can improve our knowledge on the photosynthesis activity status of remotely sensed spring phenology metrics. 相似文献
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《地理信息系统科学与遥感》2013,50(2):212-231
A fine-resolution leaf area index (LAI) data set over a 150 km × 150 km region in central Kazakhstan is retrieved using Landsat ETM+ imagery and ground-based LAI inferred from hemispherical photography and direct measurements. Regression analysis and geostatistics are applied for developing empirical models of LAI from Landsat ETM+ data. The best accuracy is achieved using a model employing a canonical index that combines all the contributions of individual Landsat ETM+ bands into a single index (R 2 = 0.67; RMSE = 0.21). This model is then applied for mapping LAI at a regional scale. 相似文献