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1.
西藏高原典型草地地上生物量遥感估算 总被引：3，自引：0，他引：3
准确估算草地地上生物量对合理规划区域畜牧业、评估草地植被的生态效益有重要意义.利用每月两次的野外调查资料和对应的MODIS植被指数,以GIS空间数据处理技术和多元统计分析方法等为手段,建立了西藏高寒草甸、高寒草原和温性草原3个典型草地类型的地上生物量遥感估算模型和方法.结果表明:MODIS植被指数更适合于高寒草甸和高寒草原的地上生物量估算,对于高寒草甸,最佳估算模型是基于归一化植被指数(normalized difference vegetation index,NDVI)的三次多项式,其相关系数为0.82;对高寒草原,则是基于增强型植被指数(enhanced vegetation index,EVI)的三次多项式,相关系数达0.83;由于温性草原存在很强的空间异质性,估算效果较其他2个草地类型差.MODIS植被指数对草地生长期鲜草生物量的估算和模拟效果要优于总地上生物量.在生长期,高寒草甸和高寒草原的鲜草生物量与植被指数之间的相关系数都大于0.8,最高达0.92;对温性草原,两者的相关系数也均大于0.67,其中,NDVI是高寒草甸和温性草原鲜草生物量估算的最佳植被指数,对高寒草原则是EVI.对同一草地类型,由于地上生物量差异较小,使得相比其他模型,线性或多项式回归模型更适合于西藏高原草地地上生物量的估算. 相似文献
2.
《国土资源遥感》2016,(3)
基于多时相的Landsat8 OLI卫星遥感数据,采用面向对象的分类方法,提取双台河口国际重要湿地芦苇分布信息。通过对归一化植被指数(normalized difference vegetation index,NDVI)等6个植被指数与野外实测芦苇地上生物量数据间的统计分析,比较不同植被指数对芦苇地上生物量的敏感性,构建双台河口国际重要湿地芦苇地上生物量遥感反演模型;应用该模型对芦苇地上生物量进行遥感反演以及空间格局分析。结果表明:双台河口国际重要湿地芦苇分布面积为4.39×104hm2,约占该研究区总面积32.96%;选取的6个植被指数均与芦苇地上生物量显著相关(p0.05),其中,以NDVI为变量的幂指数形式的估算模型为芦苇地上生物量遥感估算最优模型,模拟精度为79%,决策系数为0.76;双台河口国际重要湿地芦苇地上生物量呈东高西低和北高南低的分布格局,其平均地上生物量为4 785.5 g/m2,总地上生物量为2.06×106t;本研究结果可为双台河口国际重要湿地生态系统管理和生物多样性保护提供数据支持与科学指导。 相似文献
3.
基于Google Earth Engine与机器学习的省级尺度零散分布草地生物量估算 总被引：4，自引：0，他引：4
大区域草地地上生物量估算对草地资源利用管理及全球碳循环研究具有重要意义。为高效快速地估算大区域零散分布草地地上生物量,本文选取安徽省为研究区,在谷歌地球云引擎（Google Earth Engine）平台的支撑下,通过机器学习方法建立Landsat 8 OLI及其他辅助数据与地面实测草地地上生物量之间的联系,开展了草地零散分布地区省级尺度草地地上生物量高分辨率估算,并与传统的基于归一化植被指数（NDVI）回归模型进行了比较。研究结果表明,综合利用光谱与地形因子的机器学习方法,估算零散化分布草地地上生物量的精度可以达到65%以上,其中分类回归树（CART）模型R2=0.57,预测精度为68.60%,支持向量机（SVM）模型R2=0.59,预测精度为75.74%,而使用NDVI的回归分析产生的误差较大,R2=0.37,预测精度为57.51%,因此机器学习方法相对于传统基于NDVI的回归分析具有明显优势。另外,谷歌地球云引擎平台数据来源广泛、获取方便,可以高效地实现海量影像数据的预处理及计算分析,大大提升了工作效率,与地面调查数据的结合可实现更大区域乃至全国尺度上的零散分布草地地上生物量高分辨率遥感估算。 相似文献
4.
无人机多光谱影像的天然草地生物量估算 总被引：1，自引：0，他引：1
地上草地生物量是衡量天然草地生态系统的重要指标,是草地资源合理利用和载畜平衡监测的重要依据。为了快速、准确、有效地估算天然草地地上生物量,掌握其变化规律,以天山北坡天然牧场为研究区,分析其地上生物量的时空分布特征。根据研究区阴坡与阳坡不同的草地类型和植被种类,利用多旋翼无人机获取的高分辨率多光谱影像(含近红外波段),结合地面实测数据,在进行天然草地地上生物量与植被指数相关性分析的基础上,运用回归分析方法,建立生物量和多种植被指数的估算模型。结果表明:考虑地形因子(阴阳坡)之后,植被地上生物量与各植被指数的相关性系数显著提高;不同坡向,同一植被指数拟合精度差异较大;同一坡向,各个植被指数的敏感性也有所不同。总体上,比值植被指数(RVI)与阴阳坡草地生物量拟合效果最好,模型精度均达到75%以上。利用植被指数建立的生物量估算方法结果与实际相符,可为天然草地生态系统检测和草地资源合理利用提供方法和依据。 相似文献
5.
WorldView-2纹理的森林地上生物量反演 总被引：1，自引：0，他引：1
使用高空间分辨率卫星WorldView-2的多光谱遥感影像,构建植被指数和纹理因子等遥感因子与森林地上生物量的关系方程,并计算模型估测精度和均方根误差,探索高分辨率数据的光谱与纹理信息在温带森林地上生物量估测应用中的潜力。以黑龙江省凉水自然保护区温带天然林及天然次生林为研究对象,通过灰度共生矩阵(GLCM)、灰度差分向量(GLDV)及和差直方图(SADH)对高分辨率遥感影像进行纹理信息提取,并利用外业调查的74个样地地上生物量与遥感因子建立参数估计模型。提取的遥感因子包括6种植被指数(比值植被指数RVI、差值植被指数DVI、规一化植被指数NDVI、增强植被指数EVI、土壤调节植被指数SAVI和修正的土壤调节植被指数MSAVI)以及3类纹理因子(GLCM、GLDV和SADH)。为避免特征变量个数较多对估测模型造成过拟合,利用随机森林算法对提取的遥感因子进行特征选择,将最优的特征变量输入模型参与建模估测。采用支持向量回归(SVR)进行生物量建模及验证,结果显示选入模型的和差直方图均值(sadh_mean)、灰度共生矩阵方差(glcm_var)和差值植被指数(DVI)等遥感因子对森林地上生物量有较好的解释效果;植被指数+纹理因子组合的模型获得较精确的AGB估算结果(R2=0.85,RMSE=42.30 t/ha),单独使用植被指数的模型精度则较低(R~2=0.69,RMSE=61.13 t/ha)。 相似文献
6.
地表生物量对农作物估产、植被长势评估具有很重要的意义。随着遥感技术的发展与应用,遥感为生物量估算提供了一种新的手段。本文以唐山市为例,利用小麦种植区的MODIS遥感影像数据和同期野外调查获得的16组32个生物量数据,对比分析了归一化植被指数(NDVI)、增强型植被指数(EVI)与小麦生物量多个回归方程的相关系数,进而建立了NDVI、EVI与小麦生物量的线性回归模型。结果显示,使用MODIS数据的植被指数能够很好地对研究区地上生物量进行估算,其中使用EVI的三次函数模型拟合精度最高,并且对每组数据进行平均处理会使模型精度进一步提高。 相似文献
7.
采用GF-1号、ZY-3号以及Landsat-8卫星数据,利用回归模型和像元二分模型,通过对建立的四类植被指数NDVI、MSAVI、MVI和RVI,结合野外调查数据,提出NSD的概念来评价模型及方法的精度。实测数据与各类遥感影像的4种植被指数间均存在着显著的相关关系;通过NSD精度验证,说明空间分辨率较低的遥感数据,在一定程度上提高了反演精度;在4类植被指数中,RVI与MSAVI对于三类数据反演精度较高,且MSAVI对于较低分辨率遥感数据可能具有更好的消除土壤背景影响的作用。 相似文献
8.
为了提高北疆地区雪深时空分布监测的准确性,以该区域48个气象站点2006年12月—2007年1月的月平均雪深观测数据为基础,通过分析月均雪深空间自相关性及其与经纬度、高程的相关性,结合MODIS雪盖数据构建了多元非线性回归克里金插值方法,插值获得了北疆地区较高精度的雪深空间分布数据。将插值雪深数据与普通克里金插值法、考虑高程为辅助变量的协同克里金插值法的预测结果进行比较,结果表明:1相对普通克里金和协同克里金方法,多元非线性回归克里金法的12月份雪深预测精度分别提高了15.14%和9.54%,1月份的提高了4.8%和6.7%;2由于充分利用了经纬度和地形信息,多元非线性回归克里金法的雪深预测结果可提供更多细节信息;3预测结果客观地表达了雪深随经纬度和地形变化的趋势,反映了积雪深度的空间变异性;4基于不显著相关的协变量高程的协同克里金插值法预测的雪深数据精度劣于普通克里金插值法的预测结果。 相似文献
9.
结合树龄信息的遥感森林生态系统生物量制图 总被引：10，自引：0，他引：10
森林生态系统是陆地生态系统中的重要组成部分，其中的地上生物量（AGB，Aboveground Biomass）在全球气候变化和碳循环研究中起着重要的作用。本文利用ETM^＋遥感影像，首先建立了实测叶面积指数（LAI，Leaf Area Index）与实测生物量数据的回归关系，基于遥感叶面积指数图像得到初步地上生物量空间分布图；同时在短波植被指数（SWVI，Short Wave Vegetation Index）与实测树龄之间建立了回归关系，在此基础上得到了树龄空间分布图。然后通过将植被指数（VI，Vegetation Index），LAI，树龄等变量针对不同的树种类型进行逐步回归，得到了较好的回归模型，并结合土地利用／土地覆盖估算了贵州省黎平县的地上生物量，绘制了其空间分布图。统计结果显示：总体森林生态系统的AGB与LAI和RSR（Reduced Simple Ratio）之间有一定的相关关系（R^2=0．895）；杉木林的AGB与LAI和归一化植被指数（NDVI，Normalized Difference Vegetation Index）之间有较强的相关性（R^2=0．93）；针叶树种的LAI与年龄是AGB较好的估算因子（R^2=0．937）；阔叶林的AGB与年龄有一定的相关性（R^2=0．792）；混交林的AGB与LAI和SR（Simple Ratio）有较强的相关性（R^2=0．931）。结果表明，将树龄和土地覆盖／土地利用类型的信息加入到地上生物量估算模型的建立中，是一种改善利用多光谱遥感估算精度的较好的方法。结合土地覆盖／土地利用类型的高分辨率的树龄空间分布图，可为森林生态系统的可持续发展和管理提供科学的论据。 相似文献
10.
地上生物量能够有效反映作物的生长状态,其信息的实时估算对产量预测和农田生产管理都有重要意义。作物生长模型因其详尽的生理生化基础和对生长过程数字化描述能力,成为生物量估算的理想模型。近年来,研究人员利用数据同化算法将时间序列遥感数据同化到作物生长模型中,实现了作物模型由基于气象站的点模拟到区域尺度面模拟的外推,使生物量模拟结果同时具备大范围和机理性两个方面的特点。这一模式下,时间序列的遥感数据质量将对生物量模拟精度产生直接影响,作物生长后期受到光谱饱和的影响,遥感数据的作物冠层信息获取能力会出现明显下降,因此有必要对该阶段遥感数据和作物模型的结合方式进行优化,提升生物量模拟精度。本文针对东北地区春玉米生物量遥感估算存在的问题,提出了利用WOFOST作物模型结合无人机（UAV）遥感数据实现作物生长后期生物量准确估算的新思路。新思路首先利用多光谱遥感数据获取WOFOST模型具备较高空间异质性的土壤速效养分参数以提升模型的空间信息模拟能力,使其能在一定程度上摆脱点尺度模拟的限制。同时,结合集合卡尔曼滤波算法将生长前期无人机（UAV）遥感数据同化到模型中,以缩短模型单独运行时间,减少模型运行过程中的参数误差累积,实现无遥感数据参与下的短期作物生长模拟,并输出生长后期相应的生物量模拟结果。最后,本文利用地面实测数据对新方法的生物量模拟精度进行了评价。结果表明,与全生育期数据同化相比,新方法的生物量估算精度有了明显的提升（全生育期同化：R2 = 0.45,RMSE = 4254.30 kg/ha;新方法：R2= 0.86,RMSE = 2216.79 kg/ha）。 相似文献
11.
In many regions, a decrease in grasslands and change in their management, which are associated with agricultural intensification, have been observed in the last half-century. Such changes in agricultural practices have caused negative environmental effects that include water pollution, soil degradation and biodiversity loss. Moreover, climate-driven changes in grassland productivity could have serious consequences for the profitability of agriculture. The aim of this study was to assess the ability of remotely sensed data with high spatial resolution to estimate grassland biomass in agricultural areas. A vegetation index, namely the Normalized Difference Vegetation Index (NDVI), and two biophysical variables, the Leaf Area Index (LAI) and the fraction of Vegetation Cover (fCOVER) were computed using five SPOT images acquired during the growing season. In parallel, ground-based information on grassland growth was collected to calculate biomass values. The analysis of the relationship between the variables derived from the remotely sensed data and the biomass observed in the field shows that LAI outperforms NDVI and fCOVER to estimate biomass (R2 values of 0.68 against 0.30 and 0.50, respectively). The squared Pearson correlation coefficient between observed and estimated biomass using LAI derived from SPOT images reached 0.73. Biomass maps generated from remotely sensed data were then used to estimate grass reserves at the farm scale in the perspective of operational monitoring and forecasting. 相似文献
12.
土壤属性空间分布受复杂地学环境要素影响,空间分异特征十分明显,用单一全局插值模型进行土壤属性模拟,难以实现高精度模拟。对于空间不连续、全局插值模型精度有限及适应性差的特点,本文提出了一种集成学习支持的、融合地学环境变量的土壤属性自适应曲面建模方法（ASM-SP）。利用2013年采集的110个样点数据,使用回归克里金（RK）、贝叶斯克里金（BK）、普通克里金插值法（OK）、反距离加权法（IDW）、ASM-SP,分别对青海湖复杂地貌类型区进行土壤全钾含量的插补。本文采用逐点交叉验证（LOOCV）插值方法模拟精度。结果表明,ASM-SP不仅考虑了地学环境变量与土壤属性的非线性关系,而且融合了多个模型的适应性优势,是实现复杂地貌类型区土壤全钾含量的高精度模拟的一种新方法。 相似文献
13.
《International Journal of Digital Earth》2013,6(3):203-218
Abstract While data like HJ-1 CCD images have advantageous spatial characteristics for describing crop properties, the temporal resolution of the data is rather low, which can be easily made worse by cloud contamination. In contrast, although Moderate Resolution Imaging Spectroradiometer (MODIS) can only achieve a spatial resolution of 250 m in its normalised difference vegetation index (NDVI) product, it has a high temporal resolution, covering the Earth up to multiple times per day. To combine the high spatial resolution and high temporal resolution of different data sources, a new method (Spatial and Temporal Adaptive Vegetation index Fusion Model [STAVFM]) for blending NDVI of different spatial and temporal resolutions to produce high spatial–temporal resolution NDVI datasets was developed based on Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM). STAVFM defines a time window according to the temporal variation of crops, takes crop phenophase into consideration and improves the temporal weighting algorithm. The result showed that the new method can combine the temporal information of MODIS NDVI and spatial difference information of HJ-1 CCD NDVI to generate an NDVI dataset with both high spatial and high temporal resolution. An application of the generated NDVI dataset in crop biomass estimation was provided. An average absolute error of 17.2% was achieved. The estimated winter wheat biomass correlated well with observed biomass (R 2 of 0.876). We conclude that the new dataset will improve the application of crop biomass estimation by describing the crop biomass accumulation in detail. There is potential to apply the approach in many other studies, including crop production estimation, crop growth monitoring and agricultural ecosystem carbon cycle research, which will contribute to the implementation of Digital Earth by describing land surface processes in detail. 相似文献
14.
《地理信息系统科学与遥感》2013,50(3):397-411
Mapping the spatial distribution of soil nutrient contents from sample data has received much attention in the recent decade. Accurately mapping soil nutrients purely based on sample data, however, is difficult due to the sparsity and high cost of samples. Land use types usually influence the contents of soil nutrients at the local level and it is desirable to integrate such information into predictive mapping. The area-and-point kriging (AAPK) method, which was proposed recently, may provide an interpolation technique for such purposes. This study mapped the soil total nitrogen (TN) distribution of Hanchuan County, China, using AAPK with sample data (consisting of 402 points) and land use information. Ordinary kriging (OK) and residual kriging (RK) were compared to evaluate the performance of AAPK. Results showed that: (1) land use types had important impacts on the spatial distribution of soil TN; (2) measured data at 135 validation locations had stronger correlation with the data predicted by AAPK than by RK and OK, and the mean error and root mean square error with AAPK were lower than with RK and OK; and (3) AAPK generated smaller error variances than RK and OK did. This suggests that AAPK represents an effective method for increasing the interpolation accuracy of soil TN. It should be pointed out that some of the land use polygons used in this study are very large and complex, which might impact the effectiveness of AAPK in improving the prediction accuracy. Segmenting them into simple smaller areas might be helpful. 相似文献
15.
Forests play a critical role in ecological functioning, global warming and climate change through its unique potential to capture and hold carbon (C). Biomass is one of the indicator of the status of forests hence accurate assessment and biomass mapping is important for sustainable forest management. The objectives of this study is to estimate above ground biomass (AGB) from field inventory data and to map AGB combining field inventory data, remote sensing and geo-statistical model. In the present study stratified random sampling were used for estimation of biomass in which 59 plots were laid down in different homogenous strata depending on the NDVI values for the region of Maharashtra Western Ghats. The above ground biomass from field ranged from 0.05 to 271 t-dry wt ha?1 in which trees added maximum towards total biomass followed by shrubs and herbs. This paper evaluates the best vegetation indices to estimate biomass. This study was carried out by using Landsat TM satellite data and field inventory data in the Ratnagiri district of Maharashtra, India. A significant correlation was observed between biomass and vegetation indices. The best fit regression equation developed from field above ground biomass and NDVI with R2 value of 0.61 was used for spectral modeling to estimate the geospatial distribution of AGB in the entire region. The results of spatial predictions Geostatistical technique and remotely sensed data as auxiliary variables were compared using statistical error methods. This study employed Mean error, Root-Mean-Square error, Average Standard error and Root-Mean Square Standardized error. The ME, RMSE, Average Standard error and Root-Mean Square Standardized error was 0.078, 8.032, 7.982 and 0.967 respectively. The results showed that cokriging technique is one of the geostatistical method for spatial predictions of biomass in the studied region. The present study revealed that remote sensing technique combined with field sampling provides quick and reliable estimates of above ground biomass and carbon pool and can be used as baseline information for further temporal studies of biomass status of the region and in planning of forest and natural resources management. 相似文献
16.
In North Korea, reliable and timely information on crop acreage and spatial distribution is hard to obtain. In this study, we developed a fast and robust method to estimate crop acreage in North Korea using time-series normalized difference vegetation index (NDVI) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) data. We proposed a method to identify crop type based on NDVI phenology features using data collected in other areas with similar agri-environmental conditions to mitigate the shortage of ground truth data. Eventually the classification map (MODIScrop) was assessed using the Food and Agriculture Organization (FAO) statistical data and high-resolution crop classification maps derived from one Landsat scene (LScrop). The Pareto boundary method was used to assess the accuracy and crop distribution of the MODIScrop maps. Results showed that acreage derived from the MODIScrop maps was generally consistent with that reported in the FAO data (a relative error <4.1% for rice and <6.1% for maize, and <9.0% for soybean except for in 2004, 2008, and 2009) and the maps derived from the LScrop (a relative error about 5% in 2013, and 7% in 2008 and 2014). The classification accuracy reached 74.4%, 69.8%, and 73.1% of the areas covered by the Landsat images in 2008, 2013, and 2014, respectively. This indicates that features derived from NDVI profiles were able to characterize major crops, and the approaches developed in this study are feasible for crop mapping and acreage estimation in regions with limited ground truth data. 相似文献
17.
以地形地貌特征复杂、观测站点分布稀疏不均匀的四川省为研究区,引入地形因子（坡度和坡向）和植被指数,采用顾及空间关系非平稳性的（混合）地理加权回归克里格模型（（mixed）geographically weighted regression Kriging,（m）GWRK）进行月尺度平均气温插值方法及精度分析研究。针对不同季节和不同地区,将（m）GWRK插值结果与基于全局回归的回归克里格（regression Kriging,RK）插值结果进行对比。结果表明,RK、GWRK、mGWRK回归关系的决定系数R2分别为0.795、0.922、0.911,均方根误差分别为0.83℃、0.64℃、0.55℃,表明GWRK、mGWRK对目标变量的解释能力以及插值精度都优于RK;GWRK、mGWRK相对于RK对月平均气温插值的改进具有季节与地区差异,冬半年的改进大于夏半年,在地形地貌变化大的地区改进大于地形地貌变化小的地区。 相似文献
18.
Fusing MODIS with Landsat 8 data to downscale weekly normalized difference vegetation index estimates for central Great Basin rangelands,USA 总被引：1，自引：0，他引：1
Data fused from distinct but complementary satellite sensors mitigate tradeoffs that researchers make when selecting between spatial and temporal resolutions of remotely sensed data. We integrated data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor aboard the Terra satellite and the Operational Land Imager sensor aboard the Landsat 8 satellite into four regression-tree models and applied those data to a mapping application. This application produced downscaled maps that utilize the 30-m spatial resolution of Landsat in conjunction with daily acquisitions of MODIS normalized difference vegetation index (NDVI) that are composited and temporally smoothed. We produced four weekly, atmospherically corrected, and nearly cloud-free, downscaled 30-m synthetic MODIS NDVI predictions (maps) built from these models. Model results were strong with R2 values ranging from 0.74 to 0.85. The correlation coefficients (r ≥ 0.89) were strong for all predictions when compared to corresponding original MODIS NDVI data. Downscaled products incorporated into independently developed sagebrush ecosystem models yielded mixed results. The visual quality of the downscaled 30-m synthetic MODIS NDVI predictions were remarkable when compared to the original 250-m MODIS NDVI. These 30-m maps improve knowledge of dynamic rangeland seasonal processes in the central Great Basin, United States, and provide land managers improved resource maps. 相似文献
19.
空间连续的气象要素是区域环境、生态系统模拟的重要参数,空间插值是实现气象观测站点数据空间化的主要方法。由于不同区域气候要素时空分布的复杂性,研究适用于特定区域的空间化方法具有重要意义。本文以湖南省为研究区,分析长江中游亚热带丘陵区气象要素的空间化方法。基于湖南省及其毗邻地区的46个气象站点14年(2000—2013年)的气象观测数据(月降水量、月均温)和DEM数据,对普通克里金法(OK)、反距离加权法(IDW)、样条法(SPLINE)和基于DEM的多元线性回归插值法(MLR)进行比较分析研究。研究结果表明:1)月降水量和月均温的MRE和RMISE值排序分别为:SPLINEIDWMLROK和SPLINOKIDWMLR;年平均降水量和年均温插值的MRE和RMISE值排序分别为:SPLINEIDWOKMLR和SPLINOKIDWMLR;2)MLR法不仅插值误差小,而且能从细节上反映出气象变化的空间变异性;3)对多变量进行数据探索分析表明,MLR插值法中,经度、坡度与年降水量空间分布存在空间的一致性;纬度、高程和坡度对年均温的空间分布具有很强的解释力。 相似文献
20.
一种高时空分辨率NDVI数据集构建方法-STAVFM 总被引：1，自引：1，他引：0
ETM NDVI可以用来在30m的尺度上开展植被的监测,然而在Landsat卫星16天的重访周期和云污染等因素的影响下,常常会在相当长的一段时间内无法获取有效的ETM NDVI数据,给这一尺度下的植被动态监测带来了一定困难。相比之下,MODIS虽然在空间上只有250m分辨率的NDVI产品,却可以每天进行相同区域的监测。针对ETM空间分辨率高和MODIS时间分辨率高的特点,本研究选择实验区,基于对STARFM方法的改进,构建不同时空分辨率NDVI的时空融合模型-STAVFM,使用该模型对ETM NDVI与MODIS NDVI融合,构建了高时空分辨率NDVI数据集。研究结果表明,通过MODIS NDVI时间变化信息与ETM NDVI空间差异信息的有机结合,实现缺失高空间分辨率NDVI的有效预测（3景预测NDVI与实际NDVI的相关系数分别达到了0.82、0.90和0.91）,从而构建高时空分辨率NDVI数据集。所构建的高时空分辨率NDVI数据集在时间上保留了高时间分辨率数据的时间变化趋势,空间上又反映了高空间分辨率数据的空间细节差异。 相似文献