考虑下垫面类型的干旱指数比较研究

干旱作为常见的自然灾害,在世界各地发生的频率日渐增加,已对经济发展、农业生产和人类生活等方面产生了严重影响。但是干旱的类型较多,包括气象干旱、土壤干旱、水文干旱、农田干旱等,无法用单个干旱指数对不同类型的干旱进行监测。按照干旱发生类型,利用气象干旱指数(Standardized Precipitation Index SPI)、土壤水分干旱指数(Soil Moisture Index, SMI)和蒸发压力干旱指数(Evaporative Stress Index,ESI)对美国的旱情进行监测。研究结果表明:不同干旱指数之间呈显著相关,相关系数R在0.7以上。ESI整体监测精度较高,它能够真实反映地表水分盈亏状况,同时与遥感数据结合,可以实现从田块到全球不同尺度干旱实时监测。不同植被类型覆盖下垫面对不同类型干旱响应存在较大差异,草地下垫面对不同类型的干旱响应较为一致,但是随着地上生物量的增加,不同干旱指数监测结果之间差异逐渐增大。因此,在干旱监测时需要考虑植被的结构特征,植被与气候之间的相互作用,才能具体分析不同下垫面的受灾情况,进一步考虑更适合的方法以及干旱指数监测不同下垫面的干旱情况。     

考虑下垫面类型的干旱指数比较研究

干旱作为常见的自然灾害，在世界各地发生的频率日渐增加，已对经济发展、农业生产和人类生活等方面产生了严重影响。但是干旱的类型较多，包括气象干旱、土壤干旱、水文干旱、农田干旱等，无法用单个干旱指数对不同类型的干旱进行监测。按照干旱发生类型，利用气象干旱指数（Standardized Precipitation Index SPI）、土壤水分干旱指数（Soil Moisture Index, SMI）和蒸发压力干旱指数（Evaporative Stress Index, ESI）对美国的旱情进行监测。研究结果表明：不同干旱指数之间呈显著相关，相关系数R在0.7以上。ESI整体监测精度较高，它能够真实反映地表水分盈亏状况，同时与遥感数据结合，可以实现从田块到全球不同尺度干旱实时监测。不同植被类型覆盖下垫面对不同类型干旱响应存在较大差异，草地下垫面对不同类型的干旱响应较为一致，但是随着地上生物量的增加，不同干旱指数监测结果之间差异逐渐增大。因此，在干旱监测时需要考虑植被的结构特征，植被与气候之间的相互作用，才能具体分析不同下垫面的受灾情况，进一步考虑更适合的方法以及干旱指数监测不同下垫面的干旱情况。     

考虑下垫面类型的干旱指数比较研究

干旱作为常见的自然灾害，在世界各地发生的频率日渐增加，已对经济发展、农业生产和人类生活等方面产生了严重影响。但是干旱的类型较多，包括气象干旱、土壤干旱、水文干旱、农田干旱等，无法用单个干旱指数对不同类型的干旱进行监测。按照干旱发生类型，利用气象干旱指数（Standardized Precipitation Index SPI）、土壤水分干旱指数（Soil Moisture Index, SMI）和蒸发压力干旱指数（Evaporative Stress Index, ESI）对美国的旱情进行监测。研究结果表明：不同干旱指数之间呈显著相关，相关系数R在0.7以上。ESI整体监测精度较高，它能够真实反映地表水分盈亏状况，同时与遥感数据结合，可以实现从田块到全球不同尺度干旱实时监测。不同植被类型覆盖下垫面对不同类型干旱响应存在较大差异，草地下垫面对不同类型的干旱响应较为一致，但是随着地上生物量的增加，不同干旱指数监测结果之间差异逐渐增大。因此，在干旱监测时需要考虑植被的结构特征，植被与气候之间的相互作用，才能具体分析不同下垫面的受灾情况，进一步考虑更适合的方法以及干旱指数监测不同下垫面的干旱情况。     

MODIS-TVDI指数监测新疆干旱动态

针对近年频发的干旱情况不能准确及时监测评估的问题,该文以新疆为研究区域,基于温度植被干旱指数方法,利用2007年到2012年3月~8月MODIS合成产品数据获取归一化植被指数和陆地地表温度,构建LST-NDVI特征空间,得到全区的温度植被干旱指数和旱情等级空间分布图,分析了新疆干旱变化趋势,验证了温度植被干旱指数和降水因子的关系。结果表明:2007年~2012年新疆的干旱面积逐年趋于平稳,空间上表现为南疆旱情高于北疆,春季旱情高于夏季,降水量是影响温度植被干旱指数的重要因子。该研究为政府部门对新疆旱情严重地区治理提供了有效数据保证。     

基于温度植被干旱指数的印度和巴基斯坦干旱监测

针对印度和巴基斯坦近年干旱频发的问题,该文使用温度植被干旱指数对印巴地区2009~2014年干季(3~5月)实现遥感干旱监测,利用多年同期MODIS卫星数据构建印巴地区归一化植被指数-陆地表面温度的特征空间,拟合特征空间中的干、湿边方程,进一步反演温度植被干旱指数,对该区土地利用和地形作了统计与分析,对温度植被干旱指数划分等级,并利用印巴气象站点的实测降水量以及标准降水指数进行验证。结果表明:1)从干旱等级面积统计来看,印巴地区干季主要以中旱为主,其他等级面积所占比例较小;2)从土地利用类型来看,全区土地覆盖良好,温度植被干旱指数作为印巴地区旱情评价指标具有一定的合理性;3)从气象站点数据来看,归一化植被指数-陆地表面温度特征空间反演的温度植被干旱指数与降水具有密切相关性。     

苏丹遥感干旱指数及其适用性

针对苏丹地区利用遥感手段进行旱情监测的研究相对缺乏这一问题,该文利用MODIS归一化植被指数和地表温度计算植被条件指数、温度植被干旱指数和归一化植被供水指数,利用AMSR-E土壤湿度数据与3种干旱指数进行相关性分析,选取与土壤湿度相关性最好的干旱指数作为干旱监测的指标,对苏丹典型干湿年份的干旱进行监测。定量分析与实验结果表明:归一化植被供水指数与土壤湿度相关性最高,且与降水量存在滞后关系,3种典型植被覆盖类型下归一化植被供水指数的滞后期均为1个月;苏丹干旱主要发生在北部的撒哈拉沙漠及其边缘地区,且干旱分布受季节变化影响显著,其中春季和冬季是干旱发生的高峰期。     

基于表层水分含量指数(SWCI)的土壤干旱遥感监测

土壤湿度和植被生长状况是干旱最重要和最直接的指标,对植被和土壤光谱特征的解译是进行旱情程度判断的重要因子。近期,基于水的光谱反射特性,提出的地表含水量指数(SWCI) 模型能较好地反映地表的含水量值及其变化,可用于大范围的快速的浅层土壤墒情遥感监测。通过与NDVI对比分析发现, 在对浅层(0~50 cm)土壤水分进行监测时,SWCI 比NDVI 更为敏感,这有助于在实时干旱动态监测中更好地采用不同的指数以提高监测精度。     

基于层次分析法的加权VTCI和小麦产量分析

针对干旱对农业生产的影响,选取关中平原冬小麦时间序列的条件植被温度指数(VTCI)遥感干旱监测结果,采用层次分析法确定了冬小麦不同生育期旱情对产量影响的权重系数,计算加权VTCI,并应用一元线性回归分析了加权VTCI指数与县域尺度单产统计数据间的相关关系。通过对关中地区5市2000年~2007年主要生育期的VTCI和单产分析,表明关中大部分地区加权VTCI和单产有着较好的线性相关关系,同时验证了用VTCI监测关中的旱情是可行的。     

遥感干旱指数在洛川苹果干旱监测中的适用性分析

目前对苹果干旱研究较少且主要运用站点数据,对空间信息表征有限,遥感干旱指数可用于大范围干旱时空动态监测,但在苹果干旱监测中的适用性还有待研究。基于2014~2018年MODIS反射率、地表温度以及地表覆被数据,结合土壤湿度数据和野外调查资料,分析洛川苹果区温度植被干旱指数（TVDI）、归一化植被水分指数（NDWI）、植被供水指数（VSWI）与10 cm深度土壤湿度（SM）的一致性,探索遥感干旱指标对土壤干湿状况表征能力,并进一步研究遥感干旱指标对干旱响应敏感时段。结果表明：①由增强型植被指数（EVI）计算的VSWI与SM的时空一致性最好,其在2014、2017年表现出的干旱特征与实际旱情相符;②VSWI（EVI）和TVDI（EVI）与SM的相关性分别高于VSWI（NDVI）和TVDI（NDVI）与SM的相关性,使用EVI能提高VSWI和TVDI对干旱的表征能力;③TVDI、NDWI、VSWI对SM存在不同时间的反应滞后,滞后3时相（24 d）的VSWI（EVI）与SM的相关性最高,而NDWI对SM滞后时间短,对干旱响应较及时,结合VSWI（EVI）和NDWI可能更有利于监测苹果干旱;④在不同苹果生育期,遥感指标对土壤湿度敏感性不同,VSWI在不同生育期敏感性差异最明显：新梢旺长期（5、6月）对土壤湿度敏感性高于萌芽开花期、果实膨大期、成熟期;该结果符合洛川县苹果不同生育期需水规律和洛川降水、干旱发生特征。研究结果可为遥感监测苹果干旱提供参考依据。     

垂直干旱指数在高寒农区春旱监测中的应用研究

青海省东部农业区“十年九旱”,“春旱年年有”,对农业生产的影响非常严重,但该地区至今缺乏有效的春季干旱遥感监测方法。使用环境减灾卫星CCD数据提取青海省东部农业区农业气象观测站的垂直干旱指数(PDI),拟合其与不同深度土壤水分的关系模型,各模型的无偏相关系数均在0.7以上;其中PDI与0~20 cm土壤相对湿度关系模型(y=-489.00x+188.78)的拟合效果最好(无偏相关系数为0.7985)。该模型反演的湟源农业气象观测站固定观测地段的土壤水分时间变化序列与人工测量值的时间变化序列,在趋势变化上较为一致。2013年西宁农区的春季干旱监测中,该模型监测结果显示:发生干旱的地区主要出现在大通河谷地和湟水谷地,湟源农区的土壤旱情在整个西宁农区的土壤旱情发展中最为严重,监测结果与实际旱情分布地区一致。     

Assessing the sensitivity of MODIS to monitor drought in high biomass ecosystems

Drought monitoring is important to analyse the influence of rainfall deficiency patterns on bushfire behaviour. Remote sensing provides tools for spatially explicit monitoring of drought across large areas. The objective of this study was to assess the performance of MODIS-based reflectance spectral indices to monitor drought across forest and woodland vegetation types in the fire prone Sydney Basin Bioregion, NSW, Australia. A time series of eight spectral indices were created from 2000 to 2009 to monitor inter-annual changes in drought and were compared to the Standardized Precipitation Index (SPI), a precipitation deficit/surplus indicator. A pixel-to-weather station paired correlation approach was used to assess the relationship between SPI and the MODIS-based spectral indices at different time scales. Results show that the Normalised Difference Infrared Index—band 6 (NDIIb6) provided the most suitable indicator of drought for the high biomass vegetation types considered. The NDIIb6 had the highest sensitivity to drought intensity and was highly correlated with SPI at all time scales analysed (i.e., 1, 3 and 6-month SPI) suggesting that variations in precipitation patterns have a stronger influence on vegetation water content than vegetation greenness properties. Spatial similarities were also found between patterns of NDIIb6-based drought maps and SPI values distribution. NDIIb6 outperformed the spectral index currently in use for operational drought monitoring systems in the region (Normalised Difference Vegetation Index, NDVI) and its implementation in existing drought-monitoring systems is recommended.     

Analysis of drought events for the semi-arid central plains of Iran with satellite and meteorological based indicators

The aim of this article is to study the spatial and temporal pattern of drought events in the Northeastern fringes of the Central Plateau of Iran using remote sensing and in situ meteorological data sets. Drought recognition is based on the analysis of the Standardized Precipitation Index (SPI) derived from meteorological variables such as rainfall, and indices derived from the Normalized Difference Vegetation Index (NDVI) obtained from the Advanced Very High Resolution Radiometer (AVHRR). The latter includes the Vegetation Condition Index (VCI), Land Surface Temperature (LST), thye Temperature Condition Index (TCI), Land Surface Moisture (LSM) and the Vegetation Health Index (VHI). Analysis is confined to the spring season from 1998 to 2004, inclusive. Results show that indices derived from the thermal bands have a higher sensitivity to drought conditions than indices derived from visible bands in this area. Indices derived from reflective bands such as NDVI and VCI seem to be better correlated to meteorological parameters than thermal band-derived indices like TCI. Indices that are calculated from both reflective and thermal bands such as LSM and VHI do not seem to be a reliable measure of drought conditions in this region.     

A new agricultural drought monitoring index combining MODIS NDWI and day–night land surface temperatures: a case study in China

The vegetation health index (VHI) is a widely utilized remote-sensing-based index for monitoring agricultural drought on the regional or global scale. However, the validity of VHI as a drought detection tool relies on the assumption that the normalized difference vegetation index (NDVI) and land-surface temperature (T_s) at a given pixel will vary inversely over time. This assumption may introduce large uncertainties in VHI for drought monitoring over areas with complex landforms, such as China. In order to monitor agricultural drought over the whole of China, a new drought detection index is suggested in this article, termed the vegetation drought index (VDI). VDI is developed from the classical VHI by substituting NDVI and T_s with the normalized difference water index (NDWI) and day–night T_s difference (?T_s), respectively. Terra Moderate Resolution Imaging Spectroradiometer (MODIS) MOD11C3 and MOD13C2 products from 2001 to 2011, monthly precipitation data from 1970 to 2010, and yearly winter wheat yield data from 2000 to 2012 were utilized to evaluate VDI. Results indicated that (1) many areas in China show a positive correlation between NDVI and T_s, especially in the cold season, whereas most areas have a negative correlation between NDWI and ?T_s; (2) VDI has a significant linear correlation with VHI in areas and periods where the NDVI–T_s correlation and NDWI–?T_s correlation are both negative; (3) VDI presents a significant correlation with 3 and 6 month standardized precipitation indices, which is comparable to VHI; and (4) VDI has a significant correlation with normalized crop yield, and is better than VHI. As an example, the extreme drought event over southwestern China from winter 2009 to spring 2010 was successfully explored by VDI. It is concluded that the new index, VDI, has the potential to monitor agricultural drought over the whole of China, including areas and periods where the NDVI–T_s correlation is non-negative.     

Monitoring agricultural drought for arid and humid regions using multi-sensor remote sensing data

While existing remote sensing-based drought indices have characterized drought conditions in arid regions successfully, their use in humid regions is limited. We propose a new remote sensing-based drought index, the Scaled Drought Condition Index (SDCI), for agricultural drought monitoring in both arid and humid regions using multi-sensor data. This index combines the land surface temperature (LST) data and the Normalized Difference Vegetation Index (NDVI) data from Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, and precipitation data from Tropical Rainfall Measuring Mission (TRMM) satellite. Each variable was scaled from 0 to 1 to discriminate the effect of drought from normal conditions, and then combined with the selected weights. When tested against in-situ Palmer Drought Severity Index (PDSI), Palmer's Z-Index (Z-Index), 3-month Standardized Precipitation Index (SPI), and 6-month SPI data during a ten-year (2000-2009) period, SDCI performed better than existing indices such as NDVI and Vegetation Health Index (VHI) in the arid region of Arizona and New Mexico as well as in the humid region of North Carolina and South Carolina. The year-to-year changes and spatial distributions of SDCI over both arid and humid regions generally agreed to the changes documented by the United States Drought Monitor (USDM) maps.     

Assessing drought probability for agricultural areas in Africa with coarse resolution remote sensing imagery

Drought is one of the most frequent climate-related disasters occurring across large portions of the African continent, often with devastating consequences for the food security of agricultural households. This study proposes a novel method for calculating the empirical probability of having a significant proportion of the total agricultural area affected by drought at sub-national level. First, we used the per-pixel Vegetation Health Index (VHI) from the Advanced Very High Resolution Radiometer (AVHRR) averaged over the crop season as main drought indicator. A phenological model based on NDVI was employed for defining the start of season (SOS) and end of the grain filling stage (GFS) dates. Second, the per-pixel average VHI was aggregated for agricultural areas at sub-national level in order to obtain a drought intensity indicator. Seasonal VHI averaging according to the phenological model proved to be a valid drought indicator for the African continent, and is highly correlated with the drought events recorded during the period (1981-2009). The final results express the empirical probability of drought occurrence over both the temporal and the spatial domain, representing a promising tool for future drought monitoring.     

Assessing vegetation response to drought in the northern Great Plains using vegetation and drought indices

The Normalized Difference Vegetation Index (NDVI) derived from the Advanced Very High Resolution Radiometer (AVHRR) has been widely used to monitor moisture-related vegetation condition. The relationship between vegetation vigor and moisture availability, however, is complex and has not been adequately studied with satellite sensor data. To better understand this relationship, an analysis was conducted on time series of monthly NDVI (1989-2000) during the growing season in the north and central U.S. Great Plains. The NDVI was correlated to the Standardized Precipitation Index (SPI), a multiple-time scale meteorological-drought index based on precipitation. The 3-month SPI was found to have the best correlation with the NDVI, indicating lag and cumulative effects of precipitation on vegetation, but the correlation between NDVI and SPI varies significantly between months. The highest correlations occurred during the middle of the growing season, and lower correlations were noted at the beginning and end of the growing season in most of the area. A regression model with seasonal dummy variables reveals that the relationship between the NDVI and SPI is significant in both grasslands and croplands, if this seasonal effect is taken into account. Spatially, the best NDVI-SPI relationship occurred in areas with low soil water-holding capacity. Our most important finding is that NDVI is an effective indicator of vegetation-moisture condition, but seasonal timing should be taken into consideration when monitoring drought with the NDVI.     

Research on Using End-member Abundance to Estimate Summer Maize Planting Area

Multispectral satellite remote sensing data of low or moderate spatial resolution are widely used in large range crop planting area extraction.For those areas with complex structure，when the low or moderate spatial resolution remote sensing data sources is used to extract the planting area of target crop，mixed pixel is the main obstacle factor to restrict the area extracting precision.Extracting it on sub\|pixel scale could overcome the restriction of low or moderate spatial resolution and develop the extraction precision.However，the extraction method of target crop planting area on sub\|pixel scale now usually directly use the end\|member abundance to instead the percentage of planting area.Therefore it may cause some errors.On the basis of previous researches，taking Hebi City，Henan Province as the study area，which located in Huang\|Huai\|Hai plain，has the largest summer maize planting area and the complex planting structure.Taking FY3/MERSI data as the main information source.Using the method of spectral matched adaptive best end\|member combination of pixel unmixing to extract the summer maize end\|member abundances.Making regression modeling in various equation forms between summer maize end\|member abundances in pixel and the percentage of planting area.Then select the optimal regression equation form to build regression model，and estimate the actual summer maize ground planting area.Summing up the correlation coefficient when the model was building，significance test and the RMS errors condition of sample point verification.Then choose the cubic model to estimate the planting area of summer maize in the study area.It is proved by remote sensing estimation that the area precision of summer maize planting area is 97.1%，the position precision is 82.5%.     

Vegetation,water and thermal stress index for study of drought in Nepal and central northeastern India

Drought is the degradation of land in arid, semi-arid and dry sub-humid regions caused primarily by human activity and climatic variations. The present study is the first attempt to identify and monitor drought using a vegetation index, a vegetation-water index and land surface temperature (LST) data for Nepal and central northeastern India. We propose a Vegetation Water Temperature Condition Index (VWTCI) for monitoring drought on a regional scale. The VWTCI includes the Normalized Difference Water Index (NDWI), which measures the water status in vegetation, the Normalized Difference Vegetation Index (NDVI) and LST data. To validate the approach, the VWTCI was compared with the Vegetation Temperature Condition Index (VTCI) and Tropical Rainfall Measuring Mission (TRMM) 3B31 Precipitation Radar (PR) data. The study revealed a gradual increase in the extent of drought in the central part of the study area from 2000 to 2004. Certain constant drought areas were also identified and the results indicate that these areas are spreading slowly towards the northeast into the central part of the study area. Comparison of the drought areas also shows a decrease in rainfall in June and July from 2000 to 2004.