Estimating chlorophyll content from hyperspectral vegetation indices: Modeling and validation

Leaf chlorophyll content, a good indicator of photosynthesis activity, mutations, stress and nutritional state, is of special significance to precision agriculture. Recent studies have demonstrated the feasibility of retrieval of chlorophyll content from hyperspectral vegetation indices composed by the reflectance of specific bands. In this paper, a set of vegetation indices belonged to three classes (normalized difference vegetation index (NDVI), modified simple ratio (MSR) index and the modified chlorophyll absorption ratio index (MCARI, TCARI) and the integrated forms (MCARI/OSAVI and TCARI/OSAVI)) were tested using the PROSPECT and SAIL models to explore their potentials in chlorophyll content estimation. Different bands combinations were also used to derive the modified vegetation indices. In the sensitivity study, four new formed indices (MSR[705,750], MCARI[705,750], TCARI/OSAVI[705,750] and MCARI/OSAVI[705,750]) were proved to have better linearity with chlorophyll content and resistant to leaf area index (LAI) variations by taking into account the effect of quick saturation at 670 nm with relatively low chlorophyll content. Validation study was also conducted at canopy scale using the ground truth data in the growth duration of winter wheat (chlorophyll content and reflectance data). The results showed that the integrated indices TCARI/OSAVI[705,750] and MCARI/OSAVI[705,750] are most appropriate for chlorophyll estimation with high correlation coefficients R² of 0.8808 and 0.9406, respectively, because more disturbances such as shadow, soil reflectance and nonphotosynthetic materials are taken into account. The high correlation between the vegetation indices obtained in the developmental stages of wheat and Hyperion data (R² of 0.6798 and 0.7618 for TCARI/OSAVI[705,750] and MCARI/OSAVI[705,750], respectively) indicated that these two integrated index can be used in practice to estimate the chlorophylls of different types of corns.     

Remote estimation of gross primary production in wheat using chlorophyll-related vegetation indices

A number of recent studies have focused on estimating gross primary production (GPP) using vegetation indices (VIs). In this paper, GPP is retrieved as a product of incident light use efficiency (LUE), defined as GPP/PAR, and the photosynthetically active radiation (PAR). As a good correlation is found between canopy chlorophyll content and incident LUE for six types of wheat canopy (R² = 0.87, n = 24), indices aimed for chlorophyll assessment can be used as an indicator of incident LUE and the product of chlorophyll indices and PAR will be a proxy of GPP. In a field experiment, we investigated four canopy chlorophyll content related indices (Red edge Normalized Difference Vegetation Index [Red Edge NDVI], modified Chlorophyll Absorption Ratio Index [MCARI₇₁₀], Red Edge Chlorophyll Index [CI_{red edge}] and the MERIS Terrestrial Chlorophyll Index [MTCI]) for GPP estimation during the growth cycle of wheat. These indices are validated for leaf and canopy chlorophyll estimation with ground truth data of canopy chlorophyll content. With ground truth data, a strong correlation is observed for canopy chlorophyll estimation with correlation coefficients R² of 0.79, 0.84, 0.85 and 0.87 for Red Edge NDVI, MCARI₇₁₀, CI_{red edge} and MTCI, respectively (n = 24). As evidence of the existence of a relationship between canopy chlorophyll and GPP/PAR, these indices are shown to be a good proxy of GPP/PAR with R² ranging from 0.70 for Red Edge NDVI and 0.75 for MTCI (n = 240). Remote estimation of GPP from canopy chlorophyll content × PAR is proved to be relatively successful (R² of 0.47, 0.53, 0.65 and 0.66 for Red edge NDVI, MCARI₇₁₀, CI_{red edge} and MTCI respectively, n = 240). These results open up a new possibility to estimate GPP and should inspire new models for remote sensing of GPP.     

玉米全氮含量高光谱遥感估算模型研究

该文对不同品种玉米测定了其室内光谱反射率及其对应的全氮含量,采用相关性分析以及单变量线性与非线性拟合分析技术,对全氮含量与原始光谱反射率、光谱反射率一阶微分、一些高光谱特征参数(如红边波长、红边位置以及红边面积等)以及由一阶微分光谱所构建的一些比值植被指数和归一化植被指数之间的关系进行了分析,结果表明：全氮含量与原始光谱在716 nm处具有最大相关系数(r=-0.847),呈极显著负相关,并且基于此波长所构建的对数关系估算模型明显优于线性模型;与光谱反射率一阶微分值在759 nm处具有最大相关系数(r=0.944),呈极显著正相关,并且基于此波长所构建的线性和非线性模型的拟合效果接近;对于所选取的3类高光谱特征变量,全氮含量除了与黄边位置(λy)以及由红边面积和黄边面积所构建的比值植被指数和归一化植被指数的相关性较弱之外,与其余变量均呈极显著相关关系,说明由这些变量对玉米全氮含量进行估算具有可行性;对所建立的各类方程进行精度检验,最终筛选确定由759 nm处的光谱反射率一阶微分值所构建的指数模型作为对玉米全N含量的预测模型最为理想。     

基于植被初级生产力的农用地理论和可实现产能核算研究

为解决传统基于农用地分等成果计算农用地理论产能和可实现产能时遇到的问题,本文将植被初级生产力(NPP)测算应用于农用地产能核算。以湖北省武汉市为研究区,运用CASA模型测算研究区农用地NPP,通过样点调查分别建立NPP与农用地理论单产和可实现单产的线性回归模型,在此基础上进行农用地理论产能和可实现产能核算。通过核算得到研究区75个乡镇的农用地理论产能及单产和可实现产能及单产,理论和可实现产能较大的乡镇主要分布在研究区北部及东南部地区,理论和可实现单产较大的乡镇则主要分布在研究区西部和东北地区,而靠近主城区周边乡镇的理论和可实现产能及单产均较低。将核算结果与传统方法计算得到的农用地产能核算成果进行对比分析,发现两种方法的核算成果在乡镇理论和可实现产能分布上非常接近,具有较高一致性;但在乡镇理论单产和可实现单产分布上并不完全一致。研究结果表明基于NPP测算的农用地产能核算方法是可行的,该方法避免了传统核算方法资料收集繁琐,主观性强等不足,且不以农用地分等定级成果为基础,可实现农用地产能快速核算。     

植被碳同化估测与遥感信息的关系研究

简析了估测植被碳同化遥感-光合机理模型的重要性,阐述了遥感信息反演的参数与植被碳同化的关系,其中包括归一化差值光谱植被指数(NDVI)、叶面积指数(LAI)、吸收光合有效辐射(APAR)、热红外辐射(TIR)等与植被碳同化之间关系,为建立全遥感-光合机理碳同化模型,应用遥感信息估测植被碳同化提供依据。     

土壤质地对玉米不同生理指标水分有效性的影响

为确定土壤质地对玉米不同生理指标水分有效性的影响,该文利用3种土壤（重壤土、中壤土和砂壤土）的盆栽控水试验和1种土壤（重壤土）的田间小区控水试验,研究了玉米不同生理指标随相对土壤含水率（土壤含水率占田间持水率的比）的动态变化。结果表明：3种土壤中各生理指标相对值在相对土壤含水率降低到土壤水分阈值之前保持不变,低于此阈值时随相对含水率的进一步降低而线性降低,且均可用分段函数来拟合（R2=0.824～0.999）。土壤水分有效性大小排序为：砂壤土＞中壤土＞重壤土,而且瞬时生理指标的土壤水分阈值低于日变化和整个试验阶段的累积指标。因此土壤质地和不同生理指标的时间尺度都会影响玉米生理指标对土壤水分有效性的响应。     

基于植被降水利用效率的荒漠化遥感评价方法

 在考虑荒漠化评价的空间尺度和基准以及潜在评价指标的尺度特征的基础上,以国内外对土地荒漠化过程中植被降水利用效率(RUE)变化的研究为依据,提出一种以植被的降水利用效率为主要评价指标、适合于荒漠化遥感监测应用的荒漠化评价方法。该方法可很大程度上克服以植被指标为主的评价方法受降水波动影响大的缺点。这种方法重视不同荒漠化类型的共性特征,适合在多种类型荒漠化遥感评价中应用。     

基于水稻背景特性的植被指数参数修正研究

植被指数广泛应用在各种植被遥感监测中,但不同土壤背景会对基于植被指数的遥感监测精度产生影响,特别是以水为背景的水稻遥感监测。该研究旨在对各种背景调节植被指数的参数进行修正,以便更适合以水土混合物为背景的水稻参数估算。首先通过不同生育期水稻的冠层光谱构建在不同参数条件下的背景调节植被指数（WDVI、SAVI、SAVI2、TSAVI）,然后以多种方程形式拟合以不同参数构建的各个植被指数与水稻叶面积指数LAI的关系,最后通过比较各拟合方程的决定系数（R2）得到各植被指数修正后的合适参数。结果表明：在使用植被指数估算水稻LAI时,其参数都需要修正。对WDVI,其修正后的参数α＝1.44;对SAVI,其修正后的参数L＝0.08;对SAVI2,其修正后的参数θ＝0.02;而对TSAVI,其修正后的参数a＝0.5,b＝0.02,X =0.02。另外,在各种拟合方程形式中,以指数和幂函数的拟合效果最佳。在以WDVI、SAVI、SAVI2和TSAVI为自变量,以LAI为因变量的各种估算模型中,TSAVI对LAI具有较高的估算精度,SAVI和SAVI2次之,WDVI最差。总之,在进行水稻遥感监测时,对植被指数的参数进行修正有利于提高监测精度。     

Using digital repeat photography and eddy covariance data to model grassland phenology and photosynthetic CO2 uptake

The continuous and automated monitoring of canopy phenology is of increasing scientific interest for the multiple implications of vegetation dynamics on ecosystem carbon and energy fluxes. For this purpose we evaluated the applicability of digital camera imagery for monitoring and modeling phenology and physiology of a subalpine grassland over the 2009 and 2010 growing seasons.We tested the relationships between color indices (i.e. the algebraic combinations of RGB brightness levels) tracking canopy greenness extracted from repeated digital images against field measurements of green and total biomass, leaf area index (LAI), greenness visual estimation, vegetation indices computed from continuous spectroradiometric measurements and CO₂ fluxes observed with the eddy covariance technique. A strong relationship was found between canopy greenness and (i) structural parameters (i.e., LAI) and (ii) canopy photosynthesis (i.e. Gross Primary Production; GPP). Color indices were also well correlated with vegetation indices typically used for monitoring landscape phenology from satellite, suggesting that digital repeat photography provides high-quality ground data for evaluation of satellite phenology products.We demonstrate that by using canopy greenness we can refine phenological models (Growing Season Index, GSI) by describing canopy development and considering the role of ecological factors (e.g., snow, temperature and photoperiod) controlling grassland phenology. Moreover, we show that canopy greenness combined with radiation use efficiency (RUE) obtained from spectral indices related to photochemistry (i.e., scaled Photochemical Reflectance Index) or meteorology (i.e., MOD17 RUE) can be used to predict daily GPP.Building on previous work that has demonstrated that seasonal variation in the structure and function of plant canopies can be quantified using digital camera imagery, we have highlighted the potential use of these data for the development and parameterization of phenological and RUE models, and thus point toward an extension of the proposed methodologies to the dataset collected within PhenoCam Network.     

Environmental controls on the spatial variability of savanna productivity in the Northern Territory, Australia

Gross Primary Productivity (GPP) is a critical measure of the health and sustainability of natural ecosystems. Understanding the magnitude, spatial patterns and processes of GPP will underpin predictions of the impact of climate change on the carbon cycle. In Australia, savannas account for one third of the terrestrial carbon stores and therefore, estimating the magnitude of savanna GPP and studying the spatial relationship between GPP and environmental determinants at the regional scale is essential in understanding ecosystem responses to increasing atmospheric CO₂ concentrations and climate change. In this study we employed an integrated approach combining in situ measurements, eddy covariance based flux tower data and remote sensing techniques to examine the role of environmental drivers in controlling the spatial variation in GPP of savannas in the Northern Territory (NT), Australia. We used field based light use efficiency (LUE), regional specific meteorology and Moderate Resolution Imaging Spectro-radiometer (MODIS) based fraction of absorbed Photosynthetically Active Radiation (fPAR) data to estimate GPP. The estimated GPP agreed quite well (only a 6% error) with GPP estimated from flux tower at the Howard Springs site. The spatial pattern of GPP along the Northern Australian Tropical Transect (NATT) was calculated and showed a strong gradient in GPP from the coast (12.50°S where rainfall was 1622 mm year⁻¹) to inland (17.73°S where mean rainfall was 643 mm year⁻¹) with a decrease of 77%. A decreasing trend in GPP with rainfall is noticed especially at the dry end of the transect studied. However, in the wet end and middle part of the transect (e.g. dominated by different Eucalyptus species), the response of GPP to changes in rainfall is reduced. This finding suggests that the influence of rainfall on various Eucalyptus species may be dampened by biotic factors. Our results suggest that future changes in precipitation driven by climate change may affect the future distribution and dynamics of GPP in northern Australia.     

基于冠层光谱植被指数的冬小麦作物系数估算

目前针对局地气候条件下某一作物类型的作物系数及其年际变化已开展了较多分析,但适于区域尺度运用的作物系数估算方法的研究还比较缺乏,这是将FAO 56作物系数法成功应用于区域作物实际蒸散量估算的关键环节。该文基于2008-2009和2009-2010年度2个冬小麦生长季的大田试验数据,研究了作物系数（Kc）、基本作物系数（Kcb）与8种常用冠层光谱植被指数（VIs）的相关关系以及水分和氮素胁迫对其的影响,分析了基于VIs估算作物Kc、Kcb的可行性,并对其估算精度进行了验证。结果表明,高氮水平下Kcb较大而土壤蒸发系数（Ke）较小,低氮水平下Kcb较小而Ke较大,不同施氮水平下Kc无明显规律性差异。冬小麦Kc与VIs相关性较弱（决定系数R2＝0.094～0.150,p<0.01,n＝195）,而Kcb与VIs则具有很强的相关性（决定系数R2＝0.511～0.685, p<0.01,n＝195）;施氮水平不影响 Kcb-VIs 关系,而不足以使冠层光谱出现明显表征的水分胁迫可使 Kcb-VIs相关关系减弱。利用VIs估算的冬小麦实际生长条件下的Kcb值与FAO 56确定的Kcb值均具有很好的线性回归关系（R2＝0.765～0.864,n＝150）,其中增强型植被指数（EVI）的估算精度最好。但在不足以使冠层光谱出现明显表征的水分胁迫条件下,利用该法可能会产生较大误差,还需要结合其他途径获取的水分胁迫信息来准确确定。     

Environmental controls on photosynthetic production and ecosystem respiration in semi-arid grasslands of Mongolia

The Mongolian steppe zone comprises a major part of East Asian grasslands. The objective of this study was to separately evaluate the quantitative dependencies of gross primary production (GPP) and ecosystem respiration (R_eco) on the environmental variables of temperature, moisture, radiation, and plant biomass in a semi-arid grassland ecosystem. We determined GPP and R_eco using transparent and opaque closed chambers in a grassland dominated by Poaceae species in central Mongolia during five periods: July 2004, May 2005, July 2005, September 2005, and June 2006. Values of GPP were linearly related to live aboveground biomass (AGB) enclosed by the chamber. The amount of GPP per unit ground area differed among the study periods, whereas GPP normalized by the amount of AGB did not differ significantly among the periods, suggesting that plant production per unit green biomass did not depend on the phenological stage. GPP/AGB fit well a rectangular hyperbolic light–response curve for all the study periods. When the air and soil were dry, considerable reduction in GPP was observed. The GPP/AGB ratio was also expressed as individual functions of air temperature, vapor pressure deficit, and volumetric soil water content. R_eco was exponentially related to the soil temperature and the relationship was modified by soil moisture. The amount of R_eco and its temperature sensitivity (Q₁₀) declined with decreasing soil moisture. Sharp increases of R_eco after rainfall events were observed. The values of R_eco, even including the rain-induced pulses, were expressed well as a bivariate function of soil temperature and soil moisture near the soil surface.     

秸秆还田与氮肥施用对夏玉米物质生产的影响研究

试验研究秸秆还田与N肥施用对夏玉米干物质生产的影响结果表明,秸秆还田与N肥施用对夏玉米干物质生产有促进作用,且在一定程度上延缓夏玉米叶片衰老,延长叶片功能期,并促进夏玉米干物质由茎秆向籽粒的转运。     

基于无人机遥感植被指数优选的田块尺度冬小麦估产

田块尺度作物快捷精准估产对规模化农业经营管理具有重要意义。因此,急需选取最优植被指数和最佳无人机遥感作业时期,建立冬小麦无人机遥感估产模型,获取及时、快速、低成本的无人机遥感估产方法。该文以山东省滨州市典型规模化农田为研究对象,利用固定翼无人机遥感平台对冬小麦进行多期遥感观测与估产。基于2016年冬小麦返青拔节期、抽穗灌浆期和成熟期的无人机遥感影像数据集,采用最小二乘法,构建了基于不同植被指数与冬小麦实测产量的9种线性模型,并结合作物实测产量进行模型评价。多时相多种类植被指数的优选分析结果显示,抽穗灌浆期估产模型R~2最高,RMSE最低(n=34)。其中,模型R~2达到0.70的植被指数共6个,从高到低依次为EVI2、MSAVI2、SAVI、MTVI1、MSR和OSAVI;RMSE由低到高依次为EVI2、MSAVI2、SAVI、MTVI1、MSR和OSAVI。另外,该文进一步评价农田土壤像元对无人机遥感估产的影响,经过阈值滤波法处理后,返青拔节期估产模型的R~2(n=34)从约0.20提升至0.30以上,RMSE和MRE下降;抽穗灌浆期模型的RMSE降低,R~2(n=34)有所提升但不显著。综上所述,最佳无人机飞行作业时期为冬小麦抽穗灌浆期,最优植被指数为EVI2,土壤像元的滤除对抽穗灌浆期无人机遥感估产模型的影响不显著。因此,优化后的基于植被指数的无人机遥感估产模型,可以快速有效诊断和评估作物长势和产量,为规模化农业种植经营提供一种快捷高效的低空管理工具。     

北京郊区植被覆盖变化动态遥感监测——以怀柔区为例

植被覆盖度是一个十分重要的生态学参数,对于全球变化和监测研究具有重要意义。本文基于像元二分模型,利用归一化植被指数NDVI,对怀柔区1992年和2004年植被覆盖度进行了监测,并对十几年来的植被覆盖变化情况进行了统计分析。结果表明,全区植被覆盖整体呈上升趋势,但存在区域间的不平衡现象。     

太行山区不同植被条件下土壤水分动态变化特征研究

为了解太行山区主要植被类型的土壤水分状况,选取太行山区4种典型植被——小麦/玉米(农作物)、黄背草(草本)、荆条(灌木)和核桃(乔木),利用大型称重式蒸渗仪,监测了4种植被2018年和2019年生长季(4—10月)的土壤水分、深层渗漏和蒸散发要素,并利用数理统计方法分析了土壤含水量的变化特征。结果表明,2018年小麦/玉米、黄背草、荆条和核桃地平均土壤含水量分别为0.30 cm~3·cm~(-3)、0.35 cm~3·cm~(-3)、0.32 cm~3·cm~(-3)和0.36cm~3·cm~(-3),而2019年平均土壤含水量分别为0.28cm~3·cm~(-3)、0.26cm~3·cm~(-3)、0.23cm~3·cm~(-3)和0.31 cm~3·cm~(-3),相比于2017年生长季末(11月)土壤含水量,2018年小麦/玉米、黄背草、荆条和核桃地土壤含水量下降幅度分别为0.05cm~3·cm~(-3)、0.04cm~3·cm~(-3)、0.09cm~3·cm~(-3)和0.05cm~3·cm~(-3),2019年下降幅度分别为0.07 cm~3·cm~(-3)、0.13 cm~3·cm~(-3)、0.18 cm~3·cm~(-3)和0.10 cm~3·cm~(-3),荆条地土壤水分的下降幅度最大,小麦/玉米地下降幅度最小。在垂直方向上, 4种植被土壤水分变化趋势一致,随着土层深度的增加土壤含水量逐渐降低,但对土壤水分的获取深度有所差异。土壤水分获取深度小麦/玉米主要为45～100cm,黄背草和核桃主要为100～150 cm,荆条主要为150～180cm。形成这一结果的原因可能是根系分布的差异,小麦/玉米和黄背草为禾本科植物,根系分布在浅层土壤,虽然核桃为乔木,但其为浅根植物,而荆条在主根周围分布有丰富的侧根,且主根延伸到土层深部,能够获取深层土壤水分。这表明荆条是该地区主要耗水物种,在太行山区绿化植被的选取和生态系统的管理和修复中,应尽量减少荆条的覆盖面积。     

夏玉米叶片全氮含量高光谱遥感估算模型研究

在5种不同施氮量和2种夏玉米品种处理下,分别在玉米拔节期、大喇叭口期、抽雄期、吐丝期、乳熟期测定了玉米冠层高光谱反射率及其对应叶片的全氮含量。选取了470、550、620和720 nm 4个代表性光谱波段,分品种对叶片全氮含量与原始光谱反射率、光谱反射率一阶微分以及部分高光谱特征参数（基于光谱位置、面积、植被指数的特征参量）分别进行线性回归和非线性回归拟合。在每个生育时期,选择决定系数和F值最高的模型3个,并分别用第二年测定的光谱和全氮含量数据分别对两个品种进行均方根差和相对误差的验证,选择均方根差和相对误差较小的拟合模型。结果表明:在拔节期、大喇叭口期、抽雄期、吐丝期和乳熟期,玉米叶片全氮含量最佳拟合光谱参量分别为R720、DR720、SDb、DR550和DR550,玉米叶片全氮含量最佳高光谱遥感估算模型依次为:Y=5.129e-2.317x、Y=3.421－10.010x－477802.331x3、Y=4.070－2.304x－52.177x2、Y=－0.468－0.528lnx和Y=－2.390－0.793lnx。     

小尺度地区植被管理系统的构建——以东莞林科园为例

介绍了组件式GIS技术,在VB环境下嵌入MapObjects组件,开发植被信息管理系统,实现对与植被相关的土壤、水资源和基础地理等信息进行综合管理,实现通用的GIS功能,极大地提高小尺度地区植被管理的效率。     

Evaluation of soil erosion protective cover by crop residues using vegetation indices and spectral mixture analysis of multispectral and hyperspectral data

Crop residues are efficient in reducing erosion and surface water runoff on agricultural soils. Evaluating the crop residue cover fraction and its spatial distribution is important to scientists involved in the modelling of soil erosion and surface runoff, and also to authorities wishing to assess soil conservation adoption by farmers. This study focuses on the evaluation of four remote sensing techniques to estimate the cover fraction of cereal crop residues (i.e., wheat and corn) from multispectral and hyperspectral measurements. These are the Soil Adjusted Corn Residue Index (SACRI), the Crop Residue Index Multiband (CRIM), the Normalized Difference Index (NDI) and the spectral mixture analysis technique (SMA). Field campaigns that were carried out by the FLOODGEN project in Sainte-Angèle-de-Monnoir, Québec, Canada and in the Pays-de-Caux located in the Normandy region of France, allowed us to gather digital photographs, spectra and other measurements to determine the actual ground cover fraction. A linear regression analysis between results derived from Landsat-5 TM simulated field spectra and the actual ground cover fractions showed best results for the CRIM on the Ste-Angèle-de-Monnoir study site (R² = 0.96), and equally good results for the Pays-de-Caux study site (R² = 0.94). Results were not as good when SMA was applied to the same Landsat-5 TM simulated field spectra with R² values of 0.70 and 0.68 for both sites, respectively. However, results improved significantly when SMA was applied to the hyperspectral data in which case the R² values increased to 0.92 for the Sainte-Angèle-de-Monnoir site and 0.89 for the Pays-de-Caux study site. Results obtained with the NDI and SACRI from both simulated TM and hyperspectral field spectra were not conclusive.     

MODIS植被指数的多传感器比较

Vegetation indices （Ⅵ） are one of the standard science products available from the Moderate Resolution Imaging Spectroradiometer （MODIS）. Validation of MODIS-Ⅵ products was an important prerequisite to using these variables for global modeling. In this study, validation of the MODIS-Ⅵ products including single-day MODIS, level 2 （gridded） daily MODIS surface reflectance （MOD09）, 16-day composited MODIS （MOD13） was performed utilizing multisensor data from MODIS, Thematic Mapper （TM）, and field radiometer, for a rice-planting region in southern China. The validation approach involved scaling up independent fine-grained datasets, including ground measurement and high spatial resolution imagery, to the coarser MODIS spatial resolutions. The 16-day composited MODIS reflectance and Ⅵ matched well with the ground measurement reflectance and Ⅵ. The Ⅵ of TM and MODIS were lower than the ground Ⅵ. The results demonstrated the accuracy, reliability, and utility of the MODIS-Ⅵ products for the study region.