融合遥感先验信息的叶面积指数反演

借助植被辐射传输模型,利用遥感观测数据估算LAI是一种较为可靠和稳健的反演方法。然而,地表的复杂性、遥感观测的有限性以及自相关性导致遥感数据包含的信息量不足,不能完全支持LAI等地表参数的估算,易造成“病态”反演。在遥感反演过程中引入先验知识能够有效地解决该问题。研究基于遥感数据提取LAI先验信息,并将其用于代价函数的构建,利用PROSAIL辐射传输模型和遗传算法,分别在500 m和250 m尺度反演LAI。将高空间分辨率LAI分别升尺度到500 m和250 m,验证对应尺度LAI结果,评价引入先验信息对于提高LAI反演精度的作用。研究表明,引入先验信息有助于提高不同分辨率下LAI反演精度,且先验信息的质量一定程度上也影响着LAI反演结果。与未加入先验信息的LAI反演结果相比,以MODIS LAI产品作为先验信息反演的500 m尺度LAI结果精度R²由0.55提高至0.65,RMSE由1.29下降至0.38。在250 m尺度,以500 m LAI反演结果作为先验信息反演的叶面积指数,其精度优于以MODIS LAI产品为先验知识的估算结果,验证精度R²增加了0.08,RMSE减少了0.18。研究使用的先验信息主要来自遥感数据本身,没有地面实测数据的参与,在此基础上发展的多分辨率LAI反演方法具有估算大区域尺度LAI的应用潜力。     

融合遥感先验信息的叶面积指数反演

借助植被辐射传输模型,利用遥感观测数据估算LAI是一种较为可靠和稳健的反演方法。然而,地表的复杂性、遥感观测的有限性以及自相关性导致遥感数据包含的信息量不足,不能完全支持LAI等地表参数的估算,易造成"病态"反演。在遥感反演过程中引入先验知识能够有效地解决该问题。研究基于遥感数据提取LAI先验信息,并将其用于代价函数的构建,利用PROSAIL辐射传输模型和遗传算法,分别在500 m和250 m尺度反演LAI。将高空间分辨率LAI分别升尺度到500 m和250 m,验证对应尺度LAI结果,评价引入先验信息对于提高LAI反演精度的作用。研究表明,引入先验信息有助于提高不同分辨率下LAI反演精度,且先验信息的质量一定程度上也影响着LAI反演结果。与未加入先验信息的LAI反演结果相比,以MODIS LAI产品作为先验信息反演的500 m尺度LAI结果精度R2由0.55提高至0.65,RMSE由1.29下降至0.38。在250 m尺度,以500 m LAI反演结果作为先验信息反演的叶面积指数,其精度优于以MODIS LAI产品为先验知识的估算结果,验证精度R2增加了0.08,RMSE减少了0.18。研究使用的先验信息主要来自遥感数据本身,没有地面实测数据的参与,在此基础上发展的多分辨率LAI反演方法具有估算大区域尺度LAI的应用潜力。     

融合遥感先验信息的叶面积指数反演

借助植被辐射传输模型，利用遥感观测数据估算LAI是一种较为可靠和稳健的反演方法。然而，地表的复杂性、遥感观测的有限性以及自相关性导致遥感数据包含的信息量不足，不能完全支持LAI等地表参数的估算，易造成“病态”反演。在遥感反演过程中引入先验知识能够有效地解决该问题。研究基于遥感数据提取LAI先验信息，并将其用于代价函数的构建，利用PROSAIL辐射传输模型和遗传算法，分别在500 m和250 m尺度反演LAI。将高空间分辨率LAI分别升尺度到500 m和250 m，验证对应尺度LAI结果，评价引入先验信息对于提高LAI反演精度的作用。研究表明，引入先验信息有助于提高不同分辨率下LAI反演精度，且先验信息的质量一定程度上也影响着LAI反演结果。与未加入先验信息的LAI反演结果相比，以MODIS LAI产品作为先验信息反演的500 m尺度LAI结果精度R²由0.55提高至0.65，RMSE由1.29下降至0.38。在250 m尺度，以500 m LAI反演结果作为先验信息反演的叶面积指数，其精度优于以MODIS LAI产品为先验知识的估算结果，验证精度R²增加了0.08，RMSE减少了0.18。研究使用的先验信息主要来自遥感数据本身，没有地面实测数据的参与，在此基础上发展的多分辨率LAI反演方法具有估算大区域尺度LAI的应用潜力。     

FY-3/VIRR及MERSI与EOS/MODIS植被指数比较与差异原因分析

为了论证FY-3/VIRR、FY-3/MERSI及EOS/MODIS影像之间协同反演的可行性,这些数据生成植被指数间的比较来说明。选取2012年覆盖黑河上游附近的多对同日过境的晴空数据,利用统计方法对比分析了FY-3/VIRR、FY-3/MERSI与EOS/MODIS的归一化植被指数。结果表明:FY-3/VIRR和FY-3/MERSI的NDVI与EOS/MODIS存在显著的相关性,相关系数均超过0.99;但3种数据所生成的NDVI也存在显著的差异。对引起NDVI差异的原因进行了详细分析,发现光谱响应函数、大气水汽、辐射定标、观测角度等都会对植被指数产生一定的影响;这些分析可以为以后多源遥感数据协同反演提供参考。     

基于时序定量遥感的冬小麦长势监测与估产研究

遥感技术是高效、客观监测农作物生长状态的重要手段,对农业生产管理具有重要意义。以安徽龙亢农场为研究区,收集了中高分辨率多源卫星遥感数据并进行了定量化处理,构建了冬小麦叶绿素密度、叶面积指数的遥感反演模型,生产了长时序冬小麦植被参数卫星遥感产品。通过监测冬小麦叶绿素密度、叶面积指数的时序变化规律,分析了不同品种冬小麦的长势情况,发现高产量小麦在越冬期长势显著优于低产量小麦。在此基础上,构建了基于归一化植被指数（NDVI）的冬小麦估产模型,结果表明：利用小麦抽穗期和乳熟期的累计NDVI值可以实现产量的精确估算,据此绘制了龙亢农场2017年冬小麦产量遥感估算地图,产量分布与实际种植情况吻合良好。实现了基于时序卫星定量遥感数据的冬小麦长势监测和产量预测,为区域范围内农作物长势监测提供了一种有效途径。     

基于时序定量遥感的冬小麦长势监测与估产研究

遥感技术是高效、客观监测农作物生长状态的重要手段,对农业生产管理具有重要意义。以安徽龙亢农场为研究区,收集了中高分辨率多源卫星遥感数据并进行了定量化处理,构建了冬小麦叶绿素密度、叶面积指数的遥感反演模型,生产了长时序冬小麦植被参数卫星遥感产品。通过监测冬小麦叶绿素密度、叶面积指数的时序变化规律,分析了不同品种冬小麦的长势情况,发现高产量小麦在越冬期长势显著优于低产量小麦。在此基础上,构建了基于归一化植被指数(NDVI)的冬小麦估产模型,结果表明:利用小麦抽穗期和乳熟期的累计NDVI值可以实现产量的精确估算,据此绘制了龙亢农场2017年冬小麦产量遥感估算地图,产量分布与实际种植情况吻合良好。实现了基于时序卫星定量遥感数据的冬小麦长势监测和产量预测,为区域范围内农作物长势监测提供了一种有效途径。     

利用MODIS数据估算西藏高原地表植被覆盖度

根据2004年9月13日至14日在西藏高原中部地面观测的植被覆盖度和同期接收的EOS/MODIS数据,分别建立了250m分辨率归一化植被指数(NDVI)、土壤调节植被指数(SAVI)与地面观测的植被覆盖度之间的相关关系,并以西藏高原中部和整个西藏高原作为两个试验区,选择典型植被类型,验证了Carlson和Ripley植被覆盖度算法的精度。结果表明,地面观测的植被覆盖度与植被指数之间呈线性关系。其中,地面观测值与NDVI的相关系数R2=0.90;与SAVI的相关系数为R2=0.89;Carlson和Ripley算法适合于中等植被覆盖度的草地植被。     

基于时序定量遥感的冬小麦长势监测与估产研究

遥感技术是高效、客观监测农作物生长状态的重要手段，对农业生产管理具有重要意义。以安徽龙亢农场为研究区，收集了中高分辨率多源卫星遥感数据并进行了定量化处理，构建了冬小麦叶绿素密度、叶面积指数的遥感反演模型，生产了长时序冬小麦植被参数卫星遥感产品。通过监测冬小麦叶绿素密度、叶面积指数的时序变化规律，分析了不同品种冬小麦的长势情况，发现高产量小麦在越冬期长势显著优于低产量小麦。在此基础上，构建了基于归一化植被指数（NDVI）的冬小麦估产模型，结果表明：利用小麦抽穗期和乳熟期的累计NDVI值可以实现产量的精确估算，据此绘制了龙亢农场2017年冬小麦产量遥感估算地图，产量分布与实际种植情况吻合良好。实现了基于时序卫星定量遥感数据的冬小麦长势监测和产量预测，为区域范围内农作物长势监测提供了一种有效途径。     

利用随机森林和地理加权回归的黄河源GIMMS3gNDVI降尺度方法

针对现有AVHRR、SPOTVGT、MODIS产品难以构建长时序、高时空分辨率NDVI数据集的问题,提出了利用随机森林和地理加权回归模型对GIMMS_3g NDVI进行降尺度的方法。基于“关系尺度不变”假设,从不同空间分辨率和数据源角度将其空间分辨率从8 km提高至250 m,并利用MODIS数据进行精度评价。结果表明：降尺度数据的空间分辨率有较大提升,能真实反映源区内NDVI空间分布特征;降尺度数据与MODIS数据具有较好的一致性,除500 m分辨率下的RF降尺度外,其他降尺度结果的绝对误差≤0.1的比例达到70%;东南部高植被覆盖区的降尺度效果要优于西北部;RF模型在体现局部细节纹理特征方面更具优势;解释变量的不同组合会影响降尺度的精度;降尺度模型尺度的改变对RF模型降尺度结果影响较大。     

基于ETM和图像融合的优势植被冠层叶面积指数和消光系数的遥感反演

叶面积指数和消光系数是表征植被群体冠层结构及光能利用的地球表层下垫面参量,国内外对叶面积指数的遥感反演有较多的研究与应用,但对消光系数的遥感反演尚不多见。我国南方少见单一大面积的均匀植被分布。为更好地匹配叶面积指数和光合有效辐射(用于估算消光系数)的实测数据,反映植被混交和疏密不均的状态,以Landsat ETM作为遥感信息源,通过HSV、Brovey和Gram-Schmidt(GS)3种图像融合方法的比较,选取效果最佳的图像融合方法,将ETM融合成空间分辨率为15 m的多光谱数据。以鄱阳湖源头梅江流域为研究区,在实测优势植被叶面积指数和光合有效辐射的基础上,利用植被指数法经验公式法反演流域的叶面积指数,并根据Beer-Lambert定律,建立了流域优势植被冠层消光系数的反演模型。在此基础上,反演了流域植被冠层叶面积指数和消光系数的空间分布,为SWAT植物生长模式的修正提供输入数据基础。     

基于MERSI盘锦湿地植被动态监测及其与MODIS数据对比分析

利用2009年5~9月期间11个时相的FY3/MERSI、EOS/MODIS卫星遥感影像资料,依托土地利用数据,提取122个湿地样点的植被指数,对盘锦湿地植被长势进行动态监测,结果表明:盘锦湿地植被在生长的前期和后期平均每天以1%~1.5%的速率发生变化,2009年8月9日前后,植被指数达到最大值,MERSI-NDVI为0.66,MODIS-NDVI为0.64。同时对MERSI-NDVI与MODIS-NDVI 2009年时间序列数据对比分析,发现二者的相关系数非常高R0.9,监测数据值接近,为我国自主研发的中分辨率卫星监测湿地植被长势的长序列植被指数构建提供了初步的数据基础。     

Estimation of wheat production by the integration of MODIS and ground data

This paper presents a methodology capable of combining Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) imagery and ancillary data to estimate durum wheat production in Tuscany (Central Italy). First, the phenological stages of winter wheat are simulated by the use of an agro-meteorological model (Syrius 4.1). Next, MODIS NDVI images at 250 m spatial resolution are utilized to identify fields likely grown with winter wheat. The multitemporal NDVI profiles of these fields are then integrated with Syrius 4.1 outputs through a previously developed procedure in order to simulate wheat above-ground biomass and harvest index. This allows the computation of wheat yield, which, combined with relevant cropped area, provides provincial wheat production estimates. The methodology is tested using ground and MODIS data taken over four Tuscany provinces where winter wheat is widely cultivated. The accuracy of all estimated variables (wheat cropped area, yield and production) is finally evaluated against provincial statistical data. The results of this experiment indicate that the accuracy of wheat cropped area estimation and yield simulation is variable, but interannual production variations are reproduced well for all provinces.     

On the relationship of NDVI with leaf area index in a deciduous forest site

Numerous studies have reported on the relationship between the normalized difference vegetation index (NDVI) and leaf area index (LAI), but the seasonal and annual variability of this relationship has been less explored. This paper reports a study of the NDVI-LAI relationship through the years from 1996 to 2001 at a deciduous forest site. Six years of LAI patterns from the forest were estimated using a radiative transfer model with input of above and below canopy measurements of global radiation, while NDVI data sets were retrieved from composite NDVI time series of various remote sensing sources, namely NOAA Advanced Very High Resolution Radiometer (AVHRR; 1996, 1997, 1998 and 2000), SPOT VEGETATION (1998-2001), and Terra MODIS (2001). Composite NDVI was first used to remove the residual noise based on an adjusted Fourier transform and to obtain the NDVI time-series for each day during each year.The results suggest that the NDVI-LAI relationship can vary both seasonally and inter-annually in tune with the variations in phenological development of the trees and in response to temporal variations of environmental conditions. Strong linear relationships are obtained during the leaf production and leaf senescence periods for all years, but the relationship is poor during periods of maximum LAI, apparently due to the saturation of NDVI at high values of LAI. The NDVI-LAI relationship was found to be poor (R² varied from 0.39 to 0.46 for different sources of NDVI) when all the data were pooled across the years, apparently due to different leaf area development patterns in the different years. The relationship is also affected by background NDVI, but this could be minimized by applying relative NDVI.Comparisons between AVHRR and VEGETATION NDVI revealed that these two had good linear relationships (R²=0.74 for 1998 and 0.63 for 2000). However, VEGETATION NDVI data series had some unreasonably high values during beginning and end of each year period, which must be discarded before adjusted Fourier transform processing. MODIS NDVI had values greater than 0.62 through the entire year in 2001, however, MODIS NDVI still showed an “M-shaped” pattern as observed for VEGETATION NDVI in 2001. MODIS enhanced vegetation index (EVI) was the only index that exhibited a poor linear relationship with LAI during the leaf senescence period in year 2001. The results suggest that a relationship established between the LAI and NDVI in a particular year may not be applicable in other years, so attention must be paid to the temporal scale when applying NDVI-LAI relationships.     

Applicability of leaf area index products for boreal regions of Sweden

Leaf area index (LAI) of boreal ecosystems was estimated with optical instruments at the Laxemar and the Forsmark investigation areas in Sweden. The aim was to study relationships between LAI and normalized difference vegetation index (NDVI), and to evaluate the applicability of the moderate resolution imaging spectroradiometer (MODIS) LAI product for small boreal regions. Relationships between optically-estimated LAI and NDVI were significant for different forest types in Laxemar and for Forsmark, effective LAI was correlated to the NDVI for all sites. NDVI-estimated LAI was used for evaluating accuracy of the MODIS LAI product and the comparison showed no correlation in Forsmark, whereas they were correlated in Laxemar. MODIS LAI was, on average, 2.28 higher than NDVI-based LAI, and it showed larger scatter. Scale issues were the main explanation for the high MODIS LAI, since heterogeneous landscapes with open areas were seen as forest in the large pixels of the MODIS LAI product.     

Book reviews

Proximal and remote sensing measurements were used to calculate different vegetation indices that were applied as predictors of gross primary production (GPP), total ecosystem respiration (TER), net ecosystem production (NEP) and leaf area index (LAI). Reflectance data and carbon fluxes were collected during the 2005 growing season at a mountain grassland site in the Italian Alps. Significant relationships were found between GPP, TER, NEP, LAI and the most commonly used spectral vegetation indices, the Normalized Difference Vegetation Index (NDVI) and Green‐NDVI. Saturation of the spectral indices was evident in the estimation of both biophysical and ecophysiological parameters. Among the different indices, Green‐NDVI was less affected by saturation on both a spatial and a temporal basis. Therefore, the use of an additional green‐band sensor for spectral measurements at eddy covariance grassland sites is recommended. Concerning the bandwidth for the calculation of the indices, the highest predictive capacities among the sensor simulations included in the analysis were those of the Moderate Resolution Imaging Spectroradiometer (MODIS) and the high‐resolution hyperspectral instrument Hyperion, indicating the advantage of narrow bands for the prediction of plant parameters. Further analyses are, however, required to investigate the relationships between NEP, GPP and vegetation indices retrieved from satellite platforms, using the bands available on MODIS and Hyperion sensors.     

基于ISODATA法的冬小麦产量分级监测预报

以江苏省姜堰市为例,进行了基于TM卫星遥感技术和小麦估产模型的冬小麦产量监测研究。在利用GPS实地采样调查和建立解译标志的基础上,通过影像校正、采用优化的ISODATA分类方法,结合人机交互式判读解译等操作,将样点的作物信息数据贯穿到整个校验分类过程中,信息解译精度在90%以上。利用分类提取的冬小麦数据,反演叶面积指数、生物量信息等,结合冬小麦估产模型,计算单点产量信息,经过线性转换,对整个区域的冬小麦产量进行监测预报,并制作了冬小麦产量分级专题图。     

Earth observation of vegetation status in the Sahelian and Sudanian West Africa: comparison of Terra MODIS and NOAA AVHRR satellite data

The first year of Moderate Resolution Imaging Spectroradiometer (MODIS) data are compared with National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) data for derivation of biophysical variables in Senegal, West Africa. The dynamic range of the two MODIS vegetation indices (VIs)—the continuity vegetation index (CVI) and the enhanced vegetation index (EVI)—is generally much larger than for the NOAA AVHRR normalized difference vegetation index (NDVI) data, indicating the importance of the change in near-infrared wavelength configuration from the NOAA AVHRR sensor to the MODIS sensor. Senegal is characterized by a pronounced gradient in the vegetation density covering a range of agro-climatic zones from arid to humid and it is found that the MODIS CVI values saturate for high VI values while the EVI demonstrates improved sensitivity for high biomass. Compared to NOAA AVHRR the MODIS VIs generally correlate better to the MODIS fraction of absorbed photosynthetically active radiation (fAPAR) absorbed by vegetation canopies and the leaf area index (LAI; the one-sided green leaf area per unit ground area). CVI is found to correlate better to both fAPAR and LAI than is the case for EVI because of the larger dynamic range of the CVI data. This suggests that the problem of background contamination on VIs from soil is not as severe in Senegal as has been found in other semi-arid African areas.     

基于多源数据的禹城农田生态系统冬小麦生育期识别方法比较研究

多源数据与其技术方法逐渐被应用于植被物候的研究当中,但基于多源数据物候识别方法间的差异性比较及定量化评估工作还有待加强。以山东禹城农田生态系统为例,探讨了基于多源数据,NDVI、EVI、数字相机图片、碳通量数据(NEE)以及人工实测数据获取的冬小麦主要生育日期的结果进行差异比较及定量化评估。结果表明:①通过碳通量数据获取的主要生育日期的计算结果与人工实测结果最接近,各阶段差异均<3 d;通过数字相机图片获取的结果仅次于通过碳通量数据获取的结果,而通过遥感数据NDVI、EVI获取的结果与人工实测结果差距最大;②通过NDVI、EVI两种数据获取的冬小麦主要生育期结果具有极显著的相关性,最高达到R²=0.857(P<0.001);③基于多源数据获取的冬小麦主要生育期的计算结果,均显示出禹城站冬小麦返青期提前,蜡熟期推迟,生长季长度变长的年际变化特征。     

基于MODIS时间序列数据的作物季相信息提取

基于MODIS NDVI时间序列数据对浙北平原单季稻区进行作物季相一致性分析。对NDVI时间序列数据进行离散傅立叶变换去除噪声,再利用土地利用现状图提取耕地区的NDVI影像图,根据时间序列曲线的最大值研究作物的季相。结果表明:水稻生长期对NDVI时间序列曲线的响应和季相一致性均较小麦和油菜好;8 d合成的数据较16 d合成的数据可以更详细地反映作物季相信息。研究证实了MODIS NDVI时间序列曲线对区域作物季相分析的意义。     

FY3A/MERSI地表温度反演

MERSI是我国第二代极轨气象卫星上的重要传感器,可获取高空间分辨率和高时间分辨率的对地观测影像。为使Jimènez-Mu珘nozSobrino算法更适用于FY3A/MERSI传感器通道特性,更新了大气函数的估算系数,并引入观测角度因子,以获取更为精确像元间更为平滑的地表温度。用MODTRAN4模拟验证该算法精度,得引入角度因子后反演精度显著提升,所有角度下平均误差为-0.6±2.2K。用实测的敦煌戈壁地表温度和MODIS地表温度产品评价MERSI反演结果,显示MERSI地表温度的空间分布准确,结果精度也较高。与实测温度对比,平均误差为1.74K,均方根误差小于1.9K。研究区域与MODIS地表温度间差异平均为2.6307K。虽然会受云检测精度和观测亮温偏高的影响,由MERSI反演的高精度地表温度在相关科研和业务方面仍然具有极好的应用前景。