Systematic bias in MODIS dust aerosol retrieval at Kanpur (AERONET), Indo-Gangetic Basin - art.no.640816

We have compared the spectral aerosol optical depth (AOD) and aerosol fine mode fraction (AFMF) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) with those of Aerosol Robotic Network (AERONET) at Kanpur (26.45N, 80.35E), northern India for the pre-monsoon season (March to June, 2001-2005). We found that MODIS systematically overestimates AOD during pre-monsoon season (known to be influenced by dust transport from north-west of India). The errors in AOD were correlated with the MODIS top-of-atmosphere apparent surface reflectance in 2.1 mu m channel (rho*(2.1)). MODIS aerosol algorithm uses p*(2.1) to derive the surface reflectance in visible channels (rho(0.47), rho(0.66)) using an empirical mid IR-visible correlation (rho(0.47) = rho(2.1)/4, rho(0.66) = rho(2.1)/2). The large uncertainty in estimating surface reflectance in visible channels (Delta rho(0.66)+/- 0.04, Delta rho(0.47)+/- 0.02) at higher values of p*(2.1) (p*(2.1) > 0.18) leads to higher aerosol contribution in the total reflected radiance at top-of atmosphere to compensate for the reduced surface reflectance in visible channels and thus leads to overestimation of AOD. This was also reflected in the very low values of AFMF during pre-monsoon whose accuracy depends on the aerosol path radiance in 0.47 and 0.66 mu m channels and aerosol models. The errors in AOD were also high in the scattering angle range 110 degrees-140 degrees, where the effect of dust non-spherity on its optical properties is significant. The direct measurements of spectral surface reflectance are required over the Indo-Gangetic basin in order to validate the mid IR-visible relationship. MODIS aerosol models should also be modified to incorporate the effect of non-spherity of dust aerosols.

Contribution of sea-salt to aerosol optical depth over the Arabian Sea derived from MODIS observations

Making use of aerosol optical depths (AOD) derived from MODIS (onboard TERRA satellite) and winds from NCEP, and the fact that sea-salt optical depth over ocean is determined primarily by sea-surface wind speed, we examine the contribution of sea-salt to the composite aerosol optical depth ( AOD) over Arabian Sea ( AS), by developing empirical models for characterizing wind-speed dependence of sea-salt optical depth. We show that at high wind speeds, sea-salt contributes 81% to the coarse mode and 42% to the composite AOD in the southern AS. In contrast to this, over the northern AS, share of sea-salt to coarse mode and composite optical depth is only 35% and 16% respectively. Comparison of the sea-salt optical depth and coarse mode optical depth ( MODIS) showed excellent agreement. The sea-salt optical depth over AS at moderate to high wind speed is comparable to the anthropogenic AOD reported for this region during winter.

Mining Land Cover Information Using Multilayer Perceptron and Decision Tree from MODIS Data

Land cover (LC) changes play a major role in global as well as at regional scale patterns of the climate and biogeochemistry of the Earth system. LC information presents critical insights in understanding of Earth surface phenomena, particularly useful when obtained synoptically from remote sensing data. However, for developing countries and those with large geographical extent, regular LC mapping is prohibitive with data from commercial sensors (high cost factor) of limited spatial coverage (low temporal resolution and band swath). In this context, free MODIS data with good spectro-temporal resolution meet the purpose. LC mapping from these data has continuously evolved with advances in classification algorithms. This paper presents a comparative study of two robust data mining techniques, the multilayer perceptron (MLP) and decision tree (DT) on different products of MODIS data corresponding to Kolar district, Karnataka, India. The MODIS classified images when compared at three different spatial scales (at district level, taluk level and pixel level) shows that MLP based classification on minimum noise fraction components on MODIS 36 bands provide the most accurate LC mapping with 86% accuracy, while DT on MODIS 36 bands principal components leads to less accurate classification (69%).

Contrained linear spectral unmixing technique for regional land cover mapping using MODIS data

Over the last few decades, there has been a significant land cover (LC) change across the globe due to the increasing demand of the burgeoning population and urban sprawl. In order to take account of the change, there is a need for accurate and up- to-date LC maps. Mapping and monitoring of LC in India is being carried out at national level using multi-temporal IRS AWiFS data. Multispectral data such as IKONOS, Landsat- TM/ETM+, IRS-1C/D LISS-III/IV, AWiFS and SPOT-5, etc. have adequate spatial resolution (~ 1m to 56m) for LC mapping to generate 1:50,000 maps. However, for developing countries and those with large geographical extent, seasonal LC mapping is prohibitive with data from commercial sensors of limited spatial coverage. Superspectral data from the MODIS sensor are freely available, have better temporal (8 day composites) and spectral information. MODIS pixels typically contain a mixture of various LC types (due to coarse spatial resolution of 250, 500 and 1000 m), especially in more fragmented landscapes. In this context, linear spectral unmixing would be useful for mapping patchy land covers, such as those that characterise much of the Indian subcontinent. This work evaluates the existing unmixing technique for LC mapping using MODIS data, using end- members that are extracted through Pixel Purity Index (PPI), Scatter plot and N-dimensional visualisation. The abundance maps were generated for agriculture, built up, forest, plantations, waste land/others and water bodies. The assessment of the results using ground truth and a LISS-III classified map shows 86% overall accuracy, suggesting the potential for broad-scale applicability of the technique with superspectral data for natural resource planning and inventory applications.

An approach to multi-temporal MODIS image analysis using image classification and segmentation

This paper discusses an approach for river mapping and flood evaluation based on multi-temporal time series analysis of satellite images utilizing pixel spectral information for image classification and region-based segmentation for extracting water-covered regions. Analysis of MODIS satellite images is applied in three stages: before flood, during flood and after flood. Water regions are extracted from the MODIS images using image classification (based on spectral information) and image segmentation (based on spatial information). Multi-temporal MODIS images from ``normal'' (non-flood) and flood time-periods are processed in two steps. In the first step, image classifiers such as Support Vector Machines (SVMs) and Artificial Neural Networks (ANNs) separate the image pixels into water and non-water groups based on their spectral features. The classified image is then segmented using spatial features of the water pixels to remove the misclassified water. From the results obtained, we evaluate the performance of the method and conclude that the use of image classification (SVM and ANN) and region-based image segmentation is an accurate and reliable approach for the extraction of water-covered regions. (c) 2012 COSPAR. Published by Elsevier Ltd. All rights reserved.

Spectral-spatial MODIS image analysis using swarm intelligence algorithms and region based segmentation for flood assessment

This paper discusses an approach for river mapping and flood evaluation based on multi-temporal time-series analysis of satellite images utilizing pixel spectral information for image clustering and region based segmentation for extracting water covered regions. MODIS satellite images are analyzed at two stages: before flood and during flood. Multi-temporal MODIS images are processed in two steps. In the first step, clustering algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are used to distinguish the water regions from the non-water based on spectral information. These algorithms are chosen since they are quite efficient in solving multi-modal optimization problems. These classified images are then segmented using spatial features of the water region to extract the river. From the results obtained, we evaluate the performance of the methods and conclude that incorporating region based image segmentation along with clustering algorithms provides accurate and reliable approach for the extraction of water covered region.

Characterization and Space-Time Downscaling of the Inundation Extent over the Inner Niger Delta Using GIEMS and MODIS Data

The objective in this work is to develop downscaling methodologies to obtain a long time record of inundation extent at high spatial resolution based on the existing low spatial resolution results of the Global Inundation Extent from Multi-Satellites (GIEMS) dataset. In semiarid regions, high-spatial-resolution a priori information can be provided by visible and infrared observations from the Moderate Resolution Imaging Spectroradiometer (MODIS). The study concentrates on the Inner Niger Delta where MODIS-derived inundation extent has been estimated at a 500-m resolution. The space-time variability is first analyzed using a principal component analysis (PCA). This is particularly effective to understand the inundation variability, interpolate in time, or fill in missing values. Two innovative methods are developed (linear regression and matrix inversion) both based on the PCA representation. These GIEMS downscaling techniques have been calibrated using the 500-m MODIS data. The downscaled fields show the expected space-time behaviors from MODIS. A 20-yr dataset of the inundation extent at 500 m is derived from this analysis for the Inner Niger Delta. The methods are very general and may be applied to many basins and to other variables than inundation, provided enough a priori high-spatial-resolution information is available. The derived high-spatial-resolution dataset will be used in the framework of the Surface Water Ocean Topography (SWOT) mission to develop and test the instrument simulator as well as to select the calibration validation sites (with high space-time inundation variability). In addition, once SWOT observations are available, the downscaled methodology will be calibrated on them in order to downscale the GIEMS datasets and to extend the SWOT benefits back in time to 1993.

Disaggregation of LST over India: comparative analysis of different vegetation indices

The non-availability of high-spatial-resolution thermal data from satellites on a consistent basis led to the development of different models for sharpening coarse-spatial-resolution thermal data. Thermal sharpening models that are based on the relationship between land-surface temperature (LST) and a vegetation index (VI) such as the normalized difference vegetation index (NDVI) or fraction vegetation cover (FVC) have gained much attention due to their simplicity, physical basis, and operational capability. However, there are hardly any studies in the literature examining comprehensively various VIs apart from NDVI and FVC, which may be better suited for thermal sharpening over agricultural and natural landscapes. The aim of this study is to compare the relative performance of five different VIs, namely NDVI, FVC, the normalized difference water index (NDWI), soil adjusted vegetation index (SAVI), and modified soil adjusted vegetation index (MSAVI), for thermal sharpening using the DisTrad thermal sharpening model over agricultural and natural landscapes in India. Multi-temporal LST data from Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Moderate Resolution Imaging Spectroradiometer (MODIS) sensors obtained over two different agro-climatic grids in India were disaggregated from 960 m to 120 m spatial resolution. The sharpened LST was compared with the reference LST estimated from the Landsat data at 120 m spatial resolution. In addition to this, MODIS LST was disaggregated from 960 m to 480 m and compared with ground measurements at five sites in India. It was found that NDVI and FVC performed better only under wet conditions, whereas under drier conditions, the performance of NDWI was superior to other indices and produced accurate results. SAVI and MSAVI always produced poorer results compared with NDVI/FVC and NDWI for wet and dry cases, respectively.

Water quality assessment and characterisation of chlorophyll-a variability related to river discharges, within the southeastern Bay of Biscay : evaluation and development of chlorophyll-a algorithms for MODIS and MERIS imagery

222 p. : il.

全球变化下中国东北样带植物多样性梯度分析及气候-NDVI-植被的关系研究

中国东北样带（NorthEast China Transect, NECT）是位于中纬度温带以降水量作为主要驱动因素的陆地样带。本文的工作以此作为研究平台，利用生态信息系统（Ecological Information System, EIS）以及Microsoft Excel 7.0软件包建立了样带的地理数据库和植物多样性数据库，包括气候数据库、植被数据库、遥感数据库和内蒙内C_4植物数据库以及样带内生态系统特征数据库。在此基础上，主要研究了以下四个方面的内容： 1. 利用Holdridge的生命地带方法对NECT内的生物群区进行了划分。 主要是确定了生物群区间过渡带的位置与宽度，并预测了在全球变化三种模式下NECT内生物群区，尤其是过渡带的变化图景。湿度升高2 ℃后，过渡带的面积都呈扩大化的趋势。森林区对于降水量的变化反应很敏感。荒漠灌丛（即荒漠草原类型）由于其水热条件处于样带内较极端类型，因而对于全球气候变化反应也比较敏感。 2. 研究了NECT内的α、β多样性以及包括生活型、水分生态型、区系地理成分等在内的植物群落特征多样性的梯度变化规律。 研究了样带内的多样性梯度，提出了在样带内存在的α多样性测度问题以及β多样性沿样带的变化规律：样带内由东到西，β多样性逐渐升高，群落内物种被替代的速率变慢;两种植被类型边界上的两个样地之间的相似程度由东到西呈上升趋势;同一类型群落之间的物种周转率比不同类型群落间的物种周转率相对要低。同时将各个环境因子与α、β多样性作了回归分析，找出样带内决定α、β多样性的主要环境因子指标。 样带内沿43.5°N一线附近植物群落的生活型共有17类，水分生态型8类，区系地理成分包括17类，以此为基础分析了群落特征沿样带的变化规律。并探讨了生活型分布的历史地理原因。 3. 对样带气候－NDVI间的关系以及植被－NDVI的关系进行了探讨。 利用来自气象卫星的遥感数据一归一化植被指数（NDVI），和数值化后的样带1:100万植被图进行叠加，找到NECT内每种植被类型对应的NDVI值。样带内共有植被类型147种，反映在NDVI变化上的植被类型有106类。其中，自然植被101种。 影响年均NDVI分布的因子主要有经度、辐射日照百分率及7月温度，与经度呈正相关，与辐射日照时数及7月温度呈负相关。回归方程如下： NDVI = -220.426 + 3.273Lon - 80.338Ratio - 1.962T_7 (R~2 = 0.9714, F = 521.52, p < 0.001) 4. 研究了NECT内的光合功能型。 主要包括内蒙古地区的C_4植物及其生态地理特性。揭标C_4植物的分类群特性、生活型、水分生态型与区系地理成分等生态学特性。C_4植物分布的科属极其集中。C_4光合型为维管植物某些分类群（科、属、种）的特性，为它们固有的遗传特性。推断C_4起源于草本的某些科属。C_4植物为喜热、耐旱的类群。世界种、泛热带种、泛地中海种C_4植物较集中。 样带内的C_3、C_4功能型及其与环境因子的相关性。样带内C_4和C_3光合型植物组成比例由东到西表现出两高两低的趋势。分布主要与年均温和降水量呈显著相关。 提出了一种新的C_3、C_4鉴别方法。即根据野外测定的光合数据建立了C_3、C_4的判别模型： f_1(x) = -1.5493 + 0.1427Pn + 0.1035Tr + 0.3768ΔT + 0.1000Gs f_2(x) = -15.6142 + 1.0542Pn - 0.2503Tr - 0.2957ΔT + 0.6491Gs 最后，综合7个GCMs模型（GFDL，GISS，LLNL，MPI，OSU，UKMOH，UKMOL）的输出结果，利用此结果和本文建立的回归模型，模拟了样带内生物多样性的窨分布格局，并预测了末来全球变化下归一化植被指数NDVI的空间分布格局的变化。

珠江流域NDVI和气象因子的变化及相关分析

对于一个特定的区域特定的时间段来说影响植被覆盖变化的主要因素是气候因素，主要包括降水、温度和光照。珠江流域地形复杂，东西狭长，气象因子差异较大，地表植被覆盖较好，研究珠江流域植被覆盖变化与气象因子之间的关系具有重要意义。 本文利用1982-1999年月平均NDVI和气象因子资料，分析了珠江流域植被和气象因子的时空分布特征，采用奇异值分解（SVD）方法和联合EOF方法研究了珠江流域NDVI和降水在年和年际尺度上的异常关系，利用相关系数、多元线性回归方法分析了NDVI与降水及其他一些气象因子的年际相关及滞后相关。 研究发现，珠江流域植被和气象因子在空间分布上具有明显的东西差异性，除温度以外均具有较好的经度方向一致性、纬度方向差异性。植被和气象因子均存在较大的季节变化和明显的年际变化。在1982-1999年期间，流域整体的NDVI在春季和秋季呈增加趋势，夏季和冬季呈下降趋势，整体呈下降趋势。降水在夏季呈明显的增加趋势，其他季节呈下降趋势，整体呈增加趋势，温度夏季呈下降趋势，其他季节呈上升趋势，整体呈升高趋势，光照在春、秋两季呈增强趋势，夏、冬两季呈减弱趋势，整体呈增强趋势。 方差分析发现，珠江流域NDVI和气象因子的年际方差均呈明显东西差异性，在季节上也有较大差异，且NDVI方差较大的季节基本会对应出现一些气象因子方差较大的现象。6-7月NDVI变化较大，降水变化也较大，说明该流域6-7月的植被有可能受当地降水的变化较大影响。早春季节NDVI变化较大，而早春的温度及光照变化也较大，说明早春的植被生长有可能受温度及光照影响较大。 SVD分析发现，NDVI和降水在年内异常上具有较好的空间一致性，在时间上具有1—2个月的滞后；年际尺度上两者异常在空间上存在明显的差异，流域东部(下游)异常为负相关，西部（上游）异常为正相关。NDVI和温度年内异常呈空间一致性，时间上滞后温度一个月，年际异常也表现为空间一致性。NDVI和光照在年内异常具有空间差异性，西北高原地区NDVI和光照年内异常反向，其他地区年内异常同向。 NDVI和各气象因子在整个区域上的年际相关分析发现，NDVI和同期降水呈负相 I 珠江流域 NDVI 和气候因子的变化及相关分析 关，值为-0.2017，NDVI和温度及短波辐射强度呈正相关，相关系数分别为：0.45和0.4，但是在不同的时间段也有很大的不同。流域NDVI和各气象因子年际相关存在明显的空间和季节差异：空间上，流域东部NDVI和降水负相关明显，和温度及太阳短波辐射正相关明显；流域西部NDVI和降水呈弱的正相关，滞后正相关明显，和温度相关不明显；季节上，NDVI在夏季和降水呈显著负相关，在春、秋季节滞后降水、成明显正相关且滞后三个月正相关最为明显。