基于MODIS的区域动态干旱监测方法

以辽宁省2009年夏季的干旱情况为例,利用多时相、多光谱的MODIS数据信息,结合地面实测气象信息与水文信息对旱情监测进行了研究,根据具体区域的实际情况,动态选择遥感指数及模型参数,实现了利用遥感数据对大范围区域的动态干旱监测。     

MODIS短波红外水分胁迫指数及其在农业干旱监测中的适用性分析

植被土壤水分状态的微小变化能引起短波红外光谱反射率的巨大变化。利用MODIS第6波段和第7波段构建短波红外光谱特征空间,依据不同土地利用类型分析不同地物在光谱特征空间中的分布规律,提出MODIS短波红外水分胁迫指数MSIWSI。利用实测20 cm土壤相对湿度验证MSIWSI、EVI以及MPDI与实测数据相关性关系并对比分析不同指数敏感性,利用不同物候期春小麦土壤墒情分析MSIWSI指数适用性。研究结果表明:与其他指数相比,MSIWSI模型与实测土壤湿度的相关性更高;MSIWSI能够反映不同物候期春小麦土壤水分变化趋势,相关性都达到极显著性水平。     

遥感干旱指数在金沙江流域干旱监测中的适用性研究

利用2015—2020年金沙江流域MODIS数据和流域内29个气象站1991—2020年月降水、气温资料,研究不同遥感干旱指数在金沙江流域的适用性,这些指数包括温度状态指数(temperature condition index, TCI)、温度植被干旱指数(temperature vegetation dryness index, TVDI)、植被状态指数(vegetation condition index, VCI)、植被供水指数(vegetation supply water index, VSWI)和标准化降水蒸散指数(standardized precipitation evapotranspiration index, SPEI)。结果表明：TCI与TVDI、VSWI与TCI、VSWI与TVDI、VSWI与VCI各月的相关性均较为显著。TVDI与SPEI和TCI与SPEI全年相关性较好。SPEI与VSWI相关性在1月、10月较低,其余月份均相关性较好。SPEI与VCI在1—3月相关性较低,其余月份均相关性较好。根据4种遥感干旱指数与SPEI的相关性分析,建议金沙江上游地区...     

MODIS干旱监测模型各参数权值分析

以湖北省2003年6、7月份的数据作为试验数据,MODIS干旱监测模型采用最大连续有云天数、最大 连续无云天数、无云百分比和昼夜温差,并分析各参数的显著性和独立性以及对干旱监测的贡献程度,从而确定 各参数在模型中的影响权。结果验证了各参数的显著性和独立性,在没有地面数据验证的情况下,可认为其在 模型中近似等权;利用最大连续有云天数、最大连续无云天数、无云百分比和昼夜温差就能很好地监测旱情。     

阜新地区干旱监测研究

选定温度植被干旱指数法建立阜新地区干旱监测模型。通过参数的确定,得到温度植被干旱指数,再通过阜新地区的气象站点地面实测土壤含水量数据,建立温度植被干旱指数-土壤含水量(TVDI-SWC)经验模型。通过回归分析以及2007年预测分析的实验数据表明,TVDI-SWC模型适用于阜新地区早春的干旱监测,可以使用该方法来实现对阜新地区的整体旱情状况快速,准确的评估。     

阜新地区干旱监测研究

选定温度植被干旱指数法建立阜新地区干旱监测模型。通过参数的确定,得到温度植被干旱指数,再通过阜新地区的气象站点地面实测土壤含水量数据,建立温度植被干旱指数-土壤含水量（ TVDI-SWC ）经验模型。通过回归分析以及2007年预测分析的实验数据表明, TVDI-SWC模型适用于阜新地区早春的干旱监测,可以使用该方法来实现对阜新地区的整体旱情状况快速,准确的评估。     

条件植被温度指数及其在干旱监测中的应用

应用NOAA -AVHRR数据,在用条件植被指数、条件温度指数和距平植被指数进行年度间相对干旱程度监测的基础上,提出了条件植被温度指数的概念,它适用于监测某一特定年内某一时期(如旬)区域级的相对干旱程度。条件植被温度指数的定义既考虑了区域内归一化植被指数的变化,又考虑了在归一化植被指数值相同条件下土地表面温度的变化。陕西省关中平原地区2000年3月下旬干旱的监测结果表明,条件植被温度指数能较好地监测该区域的相对干旱程度,并可用于研究干旱程度的空间变化特征,对干旱的监测结果与用土壤热惯量模型反演的土壤表层含水量的结果基本吻合。     

基于MODIS的华北地区2008～2017年干旱遥感监测研究

干旱是全球范围内影响最广的自然灾害,它不仅威胁着人类赖以生存的自然环境,而且还严重影响着人类社会、经济的可持续发展.华北地区作为我国重要的农业经济区,出现连年干旱情况,是我国最大的干旱区.分析了MODIS干旱指数对干旱响应的敏感性,依据2008～2017年气象降水资料,初步估算了旱情发展过程和空间分布范围旱情发展过程和...     

基于MODIS图像的植被叶面水反演与农业干旱监测

研究了应用MODIS数据,采用与植被物种无关的GVMI指数模型反演出植被水含量指标EWT,并以气象局发布的实际温度降雨数据和旱灾报告验证了植被水含量与旱灾的相关性。     

MODIS数据在黄河凌汛监测中的应用

对MODIS数据的特性及其在黄河凌汛监测中的应用情况进行了探讨和总结。试验结果表明,MODIS数据在冰雪检测方面具有很大的潜力。     

MODIS observations of water color of the largest 10 lakes in China between 2000 and 2012

Forel-Ule (FU) index of water color is an important parameter in traditional water quality investigations. We retrieved the FU index of the largest 10 lakes in China during 2000-2012 from MODerate-resolution Imaging Spectroradiometer surface reflectance product (MOD09) images. Since FU index is an optical parameter, it can be derived from optical remote sensing data by direct formulas, which is invariant with region and season. Based on validation by in situ measured reflectance data, the FU index products are reliable, with average relative error of 7.7%. FU index can be used to roughly assess water clarity: the clearer a water body is, and the bluer it is in color, the smaller its FU index is. FU index can also be used to roughly classify trophic state into three classes: oligotrophic, mesotrophic, and eutrophic. We analyzed the spatial, interannual, and seasonal variations of the FU index and its implications for water clarity and trophic state, and the findings are mostly consistent with the results from related literature. All in all, it might be a feasible way to roughly assess inland water quality by FU index in large region and over long time period.     

利用MODIS数据计算陆地植被指数VIUPD

介绍了利用MODIS卫星数据计算VIUPD的步骤,将计算结果与NDVI和EVI进行了比较,验证了本文方法的正确性。     

Comparison of three remotely sensed drought indices for assessing the impact of drought on winter wheat yield

ABSTRACT

Agricultural drought threatens food security. Numerous remote-sensing drought indices have been developed, but their different principles, assumptions and physical quantities make it necessary to compare their suitability for drought monitoring over large areas. Here, we analyzed the performance of three typical remote sensing-based drought indices for monitoring agricultural drought in two major agricultural production regions in Shaanxi and Henan provinces, northern China (predominantly rain-fed and irrigated agriculture, respectively): vegetation health index (VHI), temperature vegetation dryness index (TVDI) and drought severity index (DSI). We compared the agreement between these indices and the standardized precipitation index (SPI), soil moisture, winter wheat yield and National Meteorological Drought Monitoring (NMDM) maps. On average, DSI outperformed the other indices, with stronger correlations with SPI and soil moisture. DSI also corresponded better with soil moisture and NMDM maps. The jointing and grain-filling stages of winter wheat are more sensitive to water stress, indicating that winter wheat required more water during these stages. Moreover, the correlations between the drought indices and SPI, soil moisture, and winter wheat yield were generally stronger in Shaanxi province than in Henan province, suggesting that remote-sensing drought indices provide more accurate predictions of the impacts of drought in predominantly rain-fed agricultural areas.     

Combination of multi-sensor remote sensing data for drought monitoring over Southwest China

Drought is one of the most frequent climate-related disasters occurring in Southwest China, where the occurrence of drought is complex because of the varied landforms, climates and vegetation types. To monitor the comprehensive information of drought from meteorological to vegetation aspects, this paper intended to propose the optimized meteorological drought index (OMDI) and the optimized vegetation drought index (OVDI) from multi-source satellite data to monitor drought in three bio-climate regions of Southwest China. The OMDI and OVDI were integrated with parameters such as precipitation, temperature, soil moisture and vegetation information, which were derived from Tropical Rainfall Measuring Mission (TRMM), Moderate Resolution Imaging Spectroradiometer Land Surface Temperature (MODIS LST), AMSR-E Soil Moisture (AMSR-E SM), the soil moisture product of China Land Soil Moisture Assimilation System (CLSMAS), and MODIS Normalized Difference Vegetation Index (MODIS NDVI), respectively. Different sources of satellite data for one parameter were compared with in situ drought indices in order to select the best data source to derive the OMDI and OVDI. The Constrained Optimization method was adopted to determine the optimal weights of each satellite-based index generating combined drought indices. The result showed that the highest positive correlation and lowest root mean square error (RMSE) between the OMDI and 1-month standardized precipitation evapotranspiration index (SPEI-1) was found in three regions of Southwest China, suggesting that the OMDI was a good index in monitoring meteorological drought; in contrast, the OVDI was best correlated to 3-month SPEI (SPEI-3), and had similar trend with soil relative water content (RWC) in temporal scale, suggesting it a potential indicator of agricultural drought. The spatial patterns of OMDI and OVDI along with the comparisons of SPEI-1 and SPEI-3 for different months in one year or one month in different years showed significantly varied drought locations and areas, demonstrating regional and seasonal fluctuations, and suggesting that drought in Southwest China should be monitored in seasonal and regional level, and more fine distinctions of seasons and regions need to be considered in the future studies of this area.     

FY-3A/MERSI数据在中国北方干旱监测中的应用

中国新一代极轨气象卫星风云3号A星(简称FY-3A)上搭载的中分辨率光谱成像仪(MERSI)具有5个250m分辨率通道,进一步加强了对地表精细地物的观测能力,为大面积干旱监测提供了新的遥感数据源。利用FY-3A/MERSI传感器250m分辨率数据和垂直干旱指数(perpendicular drought index,PDI)对2009年夏季内蒙古自治区中东部干旱进行了监测。结合气象部门的降水、温度和气象干旱资料对比分析表明,基于250m分辨率的时序FY-3A/MERSI卫星资料反演的PDI干旱指数能够客观地反映研究区旱情的空间分布和动态发展过程。利用实地观测的土壤水分数据验证了PDI指数和土壤水分之间的关系。从验证结果看:PDI指数和对应实地观测的10cm、20cm土壤持水百分含量之间有较好的负相关关系,其中PDI和20cm深度处的土壤持水百分含量之间的相关关系更为稳定,R2达到0.69以上。该研究表明利用中国新型、自主的FY-3A卫星资料进行干旱监测业务是可行的。     

利用MODIS BRDF估算中国东北森林背景反射率——以大兴安岭加格达奇地区为例

基于MODIS二向反射分布函数(BRDF)模型参数产品数据,利用4-scale模型建立查找表,以中国东北大兴安岭加格达奇地区为研究区,反演森林背景反射率,并分析不同森林类型二向反射与背景反射率特性及其季节变化。研究结果表明:(1)研究区针叶林和混交林二向反射特征较为相似,夏季阔叶林在红光波段的二向反射率值均低于针叶林和混交林,而在近红外波段则相反;不同森林类型二向反射率均存在明显的季节变化,其中阔叶林二向反射率季节变化最为明显;(2)研究区夏季森林背景反射率在红光波段较低,均在0.1以下,近红外波段背景反射率普遍高于0.3,且空间差异较大;(3)不同森林类型的背景反射率季节变化趋势大致相同,但变化幅度存在差异:阔叶林的背景反射率值季节差异最大,尤其在近红外波段。     

植被指数对旱灾的响应研究—以中国西南地区2009年—2010年特大干旱为例

基于中国西南地区5个省(市)2001年—2010年期间由中分辨率成像光谱仪MODIS影像资料反演得到的归一化植被指数NDVI产品数据和区内气象站点的连续观测资料,提取了研究区内各气象站点印迹区的NDVI值,计算了降水距平百分率Pa和D指数(降水量与潜在蒸散量之差)这两种气象干旱指数。依据全国植被类型图(2000年版),对研究区内的主要植被类型在季节时间尺度上开展了这两种气象干旱指数与距平NDVI的相关性分析。研究结果表明:距平NDVI对D指数的最大响应滞后约一个月,在此尺度上表现出明显的线性相关性,所选取的6个季度的相关系数均接近或大于0.7,显著性水平小于0.01;对干旱敏感的植被类型如旱地和草地等,表现出更显著的相关性,其相关系数分别达到了0.83和0.71(平均);在干旱季节,D指数与距平NDVI表现出较为一致的空间分异规律,而Pa指数仅在旱情比较严重的情况下或对干旱比较敏感的植被类型区与距平NDVI表现出一致性分布。     

Decreased vegetation growth in response to summer drought in Central Asia from 2000 to 2012

Climate change scenarios predict that Central Asia may experience an increase in the frequency and magnitude of temperature and precipitation extremes by the end of the 21st century, but the response regularity of different types of vegetation to climate extremes is uncertain. Based on remote-sensed vegetation index and in-situ meteorological data for the period of 2000–2012, we examined the diverse responses of vegetation to climate mean/extremes and differentiated climatic and anthropogenic influence on the vegetation in Central Asia. Our results showed that extensive vegetation degradation was related to summer water deficit as a result of the combined effect of decreased precipitation and increased potential evapotranspiration. Water was a primary climatic driver for vegetation changes regionally, and human-induced changes in vegetation confined mainly to local areas. Responses of vegetation to water stress varied in different vegetation types. Grasslands were most responsive to water deficit followed by forests and desert vegetation. Climate extremes caused significant vegetation changes, and different vegetation types had diverse responses to climate extremes. Grasslands represented a symmetric response to wet and dry periods. Desert vegetation was more responsive during wet years than in dry years. Forests responded more strongly to dry than to wet years due to a severe drought occurred in 2008. This study has important implications for predicting how vegetation ecosystems in drylands respond to climate mean/extremes under future scenarios of climate change.     

Evaluating land cover changes in Eastern and Southern Africa from 2000 to 2010 using validated Landsat and MODIS data

In this study, we assessed land cover land use (LCLU) changes and their potential environmental drivers (i.e., precipitation, temperature) in five countries in Eastern & Southern (E&S) Africa (Rwanda, Botswana, Tanzania, Malawi and Namibia) between 2000 and 2010. Landsat-derived LCLU products developed by the Regional Centre for Mapping of Resources for Development (RCMRD) through the SERVIR (Spanish for “to serve”) program, a joint initiative of NASA and USAID, and NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) data were used to evaluate and quantify the LCLU changes in these five countries. Given that the original development of the MODIS land cover type standard products included limited training sites in Africa, we performed a two-level verification/validation of the MODIS land cover product in these five countries. Precipitation data from CHIRPS dataset were used to evaluate and quantify the precipitation changes in these countries and see if it was a significant driver behind some of these LCLU changes. MODIS Land Surface Temperature (LST) data were also used to see if temperature was a main driver too.Our validation analysis revealed that the overall accuracies of the regional MODIS LCLU product for this African region alone were lower than that of the global MODIS LCLU product overall accuracy (63–66% vs. 75%). However, for countries with uniform or homogenous land cover, the overall accuracy was much higher than the global accuracy and as high as 87% and 78% for Botswana and Namibia, respectively. In addition, the wetland and grassland classes had the highest user’s accuracies in most of the countries (89%–99%), which are the ones with the highest number of MODIS land cover classification algorithm training sites.Our LCLU change analysis revealed that Botswana’s most significant changes were the net reforestation, net grass loss and net wetland expansion. For Rwanda, although there have been significant forest, grass and crop expansions in some areas, there also have been significant forest, grass and crop loss in other areas that resulted in very minimal net changes. As for Tanzania, its most significant changes were the net deforestation and net crop expansion. Malawi’s most significant changes were the net deforestation, net crop expansion, net grass expansion and net wetland loss. Finally, Namibia’s most significant changes were the net deforestation and net grass expansion.The only noticeable environmental driver was in Malawi, which had a significant net wetland loss and could be due to the fact that it was the only country that had a reduction in total precipitation between the periods when the LCLU maps were developed. Not only that, but Malawi also happened to have a slight increase in temperature, which would cause more evaporation and net decrease in wetlands if the precipitation didn’t increase as was the case in that country. In addition, within our studied countries, forestland expansion and loss as well as crop expansion and loss were happening in the same country almost equally in some cases. All of that implies that non-environmental factors, such as socioeconomics and governmental policies, could have been the main drivers of these LCLU changes in many of these countries in E&S Africa. It will be important to further study in the future the detailed effects of such drivers on these LCLU changes in this part of the world.