Dynamic Drought Monitoring in Guangxi Using Revised Temperature Vegetation Dryness Index

Moderate resolution imaging spectroradiometer （MODIS） data are very suitable for vast extent, long term and dynamic drought monitoring for its high temporal resolution, high spectral resolution and moderate spatial resolution. The composite Enhanced Vegetation Index （EVI） and composite land surface temperature （Ts） obtained from MODIS data MOD11A2 and MOD13A2 were used to construct the EVI-Ts space. And Temperature Vegetation Dryness Index （TVDI） was calculated to evaluate the agriculture drought in Guangxi province, China in October of 2006. The results showed that the drought area in Guangxi was evidently increasing and continuously deteriorating from the middle of September to the middle of November. The TVDI, coming from the EVI-Ts space, could effectively indicate the spatial distribution and temporal evolution of drought, so that it could provide a strong technical support for the forecasting agricultural drought in south China.

An improved temperature vegetation dryness index(iTVDI) and its applicability to drought monitoring

Using Moderate Resolution Imaging Spectroradiometer(MODIS) data from the dry season during 2010–2012 over the whole Yunnan Province, an improved temperature vegetation dryness index(iTVDI), in which a parabolic dry-edge equation replaces the traditional linear dry-edge equation, was developed, to reveal the regional drought regime in the dry season. After calculating the correlation coefficient, root-mean-square error, and standard deviation between the iTVDI and observed topsoil moisture at 10 and 20 cm for seven sites, the effectiveness of the new index in depicting topsoil moisture conditions was verified. The drought area indicated by iTVDI mapping was then compared with the drought-affected area reported by the local government. The results indicated that the iTVDI can monitor drought more accurately than the traditional TVDI during the dry season in Yunnan Province. Using iTVDI facilitates drought warning and irrigation scheduling, and the expectation is that this new index can be broadly applied in other areas.

Retrieving Soil Moisture in Hebei by Using Temperature Vegetation Dryness Index

The temperature-vegetation index space coupled with information of surface temperature and vegetation, is an important method to realize soil moisture estimation and agricultural drought monitoring. In order to estimate the soil moisture in the study area, we collected soil relative humidity of Agricultural meteorological station and downloaded Moderate Resolution Imaging Spectrometer (MODIS) image data. Then, the temperature vegetation dryness index was calculated based on the MODIS Normalized difference vegetation index (NDVI) and land surface temperature (LST). A correlation analysis of TVDI and soil relative humidity at depth of 10 cm was carried out and an empirical model of moisture estimation was established. Finally, another set of data was used to validate the accuracy of model. The results show that the TVDI method can be used to achieve the soil moisture in the study area. The empirical model has certain universality in the study area, and obtains a high accuracy of soil moisture estimation with an R2 of 0.374 and RMSE of 11.73%.

RS-based Analysis of the Correlation between Brightness Temperature and Vegetation Cover—A Case Study of Neijiang City,Sichuan Province of China

Landsat TM data(June 23,1988,May 6,2007) and Landsat ETM+data(May 10,2000) of Neijiang City,Sichuan Province was taken as the data source,brightness temperature of the study area was obtained by using TM/ETM+thermal infrared wave,and also normalized difference vegetation index(NDVI) was calculated.NDVI of the study area on June 23,1988,May 6,2007,and May 10,2000 was respectively obtained by using Band Math,the least square fitting was adopted to simulate the correlation between surface temperature and vegetation cover.Moreover,linear regression analysis of the correlation between vegetation cover and NDVI was carried out in Excel.The results showed that(a) most of the constructed area has a low NDVI value because there are large areas of hard surface such as buildings and roads,but less vegetation cover;(b) the quarters with better vegetation cover have higher NDVI values;the Tuojiang River has a negative NDVI value;rural areas have better vegetation cover and higher NDVI values.Brightness temperature and vegetation cover has distinct negative correlation,specifically,the higher the vegetation cover is,the lower the surface temperature is,and vice versa.

Spatio-Temporal Variability in Land Surface Temperature and Its Relationship with Vegetation Types over Ibadan, South-Western Nigeria

Ibadan has experienced a rapid urbanization over the past few decades due to heavy influx of people from different parts of the country as a result of improved economy of the region. This development induced a great change in land use and land cover over the region which has become a major environmental concern recently. This study assessed Land Surface Temperature (LST) and its spatio-temporal relationship with land cover type over Ibadan. Land use/Land cover dynamics were assessed using index maps generated from Landsat Satellite data (TM, ETM+ and OLI) of Ibadan. The corrected thermal Infrared bands of the Landsat data were used to retrieve LST. The results revealed a notable increase in built-up areas from 5.64% of the total land cover area in 1984 to 14.05% in 2014. This change has caused increase in surface temperature of Ibadan from 3.56?C to 8.54?C between 1984 and 2014 respectively. The study recorded a continuous decrease in the vegetal part of Ibadan (from 43.28% in 1984 to 14.76 in 2014) which could be attributed to anthropogenic activities as the vegetated land area lost was been converted to other form of land use. The change was found to be positively correlated to the surface temperature intensity over the region with correlation coefficient, r value of 0.9251, 0.8256 and 0.7017 in 1984, 2000 and 2014 respectively. It is recommended that Policies should be considered for planting trees, new guidelines for urban landscape design and construction.

Applying the Global Disturbance Index for Detecting Vegetation Changes in Lao Tropical Forests

Land cover change is a major challenge for many developing countries. Spatiotemporal information on this change is essential for monitoring global terrestrial ecosystem carbon, climate and biosphere exchange, and land use management. A combination of LST and the EVI indices in the global disturbance index (DI) has been proven to be useful for detecting and monitoring of changes in land covers at continental scales. However, this model has not been adequately applied or assessed in tropical regions. We aimed to demonstrate and evaluate the DI algorithm used to detect spatial change in land covers in Lao tropical forests. We used the land surface temperature and enhanced vegetation index of the Moderate Resolution Imaging Spectroradiometer time-series products from 2006-2012. We used two dates Google EarthTM images in 2006 and 2012 as ground truth data for accuracy assessment of the model. This research demonstrated that the DI was capable of detecting vegetation changes during seven-year periods with high overall accuracy;however, it showed low accuracy in detecting vegetation decrease.

Intra-annual Vegetation Change Characteristics in the NDVI-Ts Space:Application to Farming-Pastoral Zone in North China

Land use/land cover change (LUCC) mapping and analysis using multi-temporal normalize difference vegetation index (NDVI) data have been well documented. Recent empirical studies have documented that many new methods with high accuracy of retrieved land surface temperature ( Ts) have been developed. Thus, the combination of land surface temperature and NDVI has the greatest potential to improve the surface vegetation dynamic monitoring. In this study, the following objectives are pursued to: (1) introduce the practical method to produce the Ts, NDVI and Ts/NDVI based on remotely sensed data; (2) investigate the different retrieved result of vegetation cover information from NDVI, Ts and Ts/NDVI data sets, and analyze the intra-annual time trajectories of different vegetation cover categories in the NDVI- Ts space for farming-pastoral zone in North China, and (3) quantitative analysis the difference in using NDVI, Ts and Ts/NDVI data sets to express information based on the indices (information entropy and averaged information grads), and evaluate the relative role of Ts/NDVI data set in the discrimination of different vegetation cover categories through comparison to traditional NDVI data set.

Soil temperature estimation at different depths,using remotely-sensed data

Soil temperatures at different depths down the soil profile are important agro-meteorological indicators which are necessary for ecological modeling and precision agricultural activities. In this paper, using time series of soil temperature(ST) measured at different depths(0, 5, 10, 20, and 40 cm) at agro-meteorological stations in northern China as reference data, ST was estimated from land surface temperature(LST) and normalized difference vegetation index(NDVI) derived from AQUA/TERRA MODIS data, and solar declination(Ds) in univariate and multivariate linear regression models. Results showed that when daytime LST is used as predictor, the coefficient of determination(R^2) values decrease from the 0 cm layer to the 40 cm layer. Additionally, with the use of nighttime LST as predictor, the R^2 values were relatively higher at 5, 10 and 15 cm depths than those at 0, 20 and 40 cm depths. It is further observed that the multiple linear regression models for soil temperature estimation outperform the univariate linear regression models based on the root mean squared errors(RMSEs) and R^2. These results have demonstrated the potential of MODIS data in tandem with the Ds parameter for soil temperature estimation at the upper layers of the soil profile where plant roots grow in. To the best of our knowledge, this is the first attempt at the synergistic use of LST, NDVI and Ds for soil temperature estimation at different depths of the upper layers of the soil profile, representing a significant contribution to soil remote sensing.

Impact of land cover change on land surface temperature: A case study of Spiti Valley

Land surface temperature(LST) is the skin temperature of the earth surface. LST depends on the amount of sunlight received by any geographical area. Apart from sun light, LST is also affected by the land cover, which leads to change in land surface temperature. Impact of land cover change(LCC) on LST has been assessed using Landsat TM5, Landsat 8 TIRS/OLI and Digital Elevation Model(ASTER) for Spiti Valley, Himachal Pradesh, India. In the present study, Spiti valley was divided into three altitudinal zones to check the pattern of changing land cover along different altitudes and LST was calculated for all the four land cover categories extracted from remote sensing data for the years of 1990 and 2015. Matrix table was used as a technique to evaluate the land cover change between two different years. Matrix table shows that as a whole, about 2,151,647 ha(30%) area of Spiti valley experienced change in land cover in the last 25 years. The result also shows vegetation and water bodies increased by 107,560.2 ha(605.87%) and 45 ha(0.98%), respectively. Snow cover and barren land decreased by 19,016.5 ha(23.92%) and 88,589(14.14%), during the study period. A significant increase has been noticed in vegetation amongst all land cover types. Minimum, maximum and mean LST for three altitudinal zones have been calculated. The mean LST recorded was 11℃ in 1990 but it rose by 2℃ and reached to 13℃ in 2015. Changes in LST were obtained for each land cover categories. The mean temperature of different land cover types was calculated by averaging value of all pixels of a given land cover types. The mean LST of vegetation, barren land, snow cover and water body increased by 6℃, 9℃, 1℃, and 7℃, respectively. Further, relationships between LST, Normalized Difference Snow Index(NDSI), and Normalised Difference Vegetation Index(NDVI) were established using Linear Regression.

Changes in Global Vegetation Distribution and Carbon Fluxes in Response to Global Warming:Simulated Results from IAP-DGVM in CAS-ESM2

Terrestrial ecosystems are an important part of Earth systems,and they are undergoing remarkable changes in response to global warming.This study investigates the response of the terrestrial vegetation distribution and carbon fluxes to global warming by using the new dynamic global vegetation model in the second version of the Chinese Academy of Sciences(CAS)Earth System Model(CAS-ESM2).We conducted two sets of simulations,a present-day simulation and a future simulation,which were forced by the present-day climate during 1981-2000 and the future climate during 2081-2100,respectively,as derived from RCP8.5 outputs in CMIP5.CO_(2)concentration is kept constant in all simulations to isolate CO_(2)-fertilization effects.The results show an overall increase in vegetation coverage in response to global warming,which is the net result of the greening in the mid-high latitudes and the browning in the tropics.The results also show an enhancement in carbon fluxes in response to global warming,including gross primary productivity,net primary productivity,and autotrophic respiration.We found that the changes in vegetation coverage were significantly correlated with changes in surface air temperature,reflecting the dominant role of temperature,while the changes in carbon fluxes were caused by the combined effects of leaf area index,temperature,and precipitation.This study applies the CAS-ESM2 to investigate the response of terrestrial ecosystems to climate warming.Even though the interpretation of the results is limited by isolating CO_(2)-fertilization effects,this application is still beneficial for adding to our understanding of vegetation processes and to further improve upon model parameterizations.

Spatiotemporal variation in vegetation net primary productivity and its relationship with meteorological factors in the Tarim River Basin of China from 2001 to 2020 based on the Google Earth Engine

Vegetation growth status is an important indicator of ecological security.The Tarim River Basin is located in the inland arid region of Northwest China and has a highly fragile ecological environment.Assessing the vegetation net primary productivity(NPP)of the Tarim River Basin can provide insights into the vegetation growth variations in the region.Therefore,based on the Google Earth Engine(GEE)cloud platform,we studied the spatiotemporal variation of vegetation NPP in the Tarim River Basin(except for the eastern Gobi and Kumutag deserts)from 2001 to 2020 and analyzed the correlations between vegetation NPP and meteorological factors(air temperature and precipitation)using the Sen slope estimation method,coefficient of variation,and rescaled range analysis method.In terms of temporal characteristics,vegetation NPP in the Tarim River Basin showed an overall fluctuating upward trend from 2001 to 2020,with the smallest value of 118.99 g C/(m2•a)in 2001 and the largest value of 155.07 g C/(m2•a)in 2017.Regarding the spatial characteristics,vegetation NPP in the Tarim River Basin showed a downward trend from northwest to southeast along the outer edge of the study area.The annual average value of vegetation NPP was 133.35 g C/(m2•a),and the area with annual average vegetation NPP values greater than 100.00 g C/(m2•a)was 82,638.75 km2,accounting for 57.76%of the basin.The future trend of vegetation NPP was dominated by anti-continuity characteristic;the percentage of the area with anti-continuity characteristic was 63.57%.The area with a significant positive correlation between vegetation NPP and air temperature accounted for 53.74%of the regions that passed the significance test,while the area with a significant positive correlation between vegetation NPP and precipitation occupied 98.68%of the regions that passed the significance test.Hence,the effect of precipitation on vegetation NPP was greater than that of air temperature.The results of this study improve the understanding on the spatiotemporal variation of vegetation NPP in the Tarim River Basin and the impact of meteorological factors on vegetation NPP.

Analysis of Climate Variability and Relation to Vegetation in Garamba National Park from 1990-2020

Climatic factors impact vegetation. Our study was to examine and analyze the climate variability and relationship to vegetation in Garamba National Park of the Democratic Republic of the Congo over the past 30 years (1990 to 2020), then to relate the climatic variables. Mann Kendall’s non parametric test, ANOVA, and p-value tests are used to analyze existing trends and relationships between vegetation cover, climatic factors, land surface temperature (LST) and normalized difference in temperature Vegetation index (NDVI), Enhanced vegetation index (EVI) in Garamba national park which is of particular importance for the network of protected areas of the Democratic republic of Congo because its position at the northern limit of the savanna-forest mosaics gives it a unique biodiversity. The southern part of the park is dominated by grassy shrub savannas. The results showed that: 1) In Garamba, the monthly correlation coefficient of Kendall and Pearsan between temperature and precipitation are negative respectively 0.763 and −0.876 (p-value < 0.00001). 2) Annually during the three decades in Garamba, the correlation between precipitation and NDVI is significant 0.416 (Kendall) and 0.496 (Pearsan);the same between precipitation and EVI 0.291 (Kendall) and 0.496 (Pearsan) while LST and precipitation are negatively correlated (p-value < 0.00001).

Estimation of Land Surface Temperature from Landsat-8 OLI Thermal Infrared Satellite Data. A Comparative Analysis of Two Cities in Ghana

This study employs Landsat-8 Operational Land Imager (OLI) thermal infrared satellite data to compare land surface temperature of two cities in Ghana: Accra and Kumasi. These cities have human populations above 2 million and the corresponding anthropogenic impact on their environments significantly. Images were acquired with minimum cloud cover (<10%) from both dry and rainy seasons between December to August. Image preprocessing and rectification using ArcGIS 10.8 software were used. The shapefiles of Accra and Kumasi were used to extract from the full scenes to subset the study area. Thermal band data numbers were converted to Top of Atmospheric Spectral Radiance using radiance rescaling factors. To determine the density of green on a patch of land, normalized difference vegetation index (NDVI) was calculated by using red and near-infrared bands i.e. Band 4 and Band 5. Land surface emissivity (LSE) was also calculated to determine the efficiency of transmitting thermal energy across the surface into the atmosphere. Results of the study show variation of temperatures between different locations in two urban areas. The study found Accra to have experienced higher and lower dry season and wet season temperatures, respectively. The temperature ranges corresponding to the dry and wet seasons were found to be 21.0985oC to 46.1314oC, and, 18.3437oC to 30.9693oC respectively. Results of Kumasi also show a higher range of temperatures from 32.6986oC to 19.1077oC during the dry season. In the wet season, temperatures ranged from 26.4142oC to -0.898728oC. Among the reasons for the cities of Accra and Kumasi recorded higher than corresponding rural areas’ values can be attributed to the urban heat islands’ phenomenon.

Land Characterization Analysis of Surface Temperature of Semi-Arid Mountainous City Abha, Saudi Arabia Using Remote Sensing and GIS

This knowledge of land surface temperature and its spatial variations within a city environment is of prime importance to the study of urban climate and human-environment interactions. Few studies have examined the influence of land use and terrain on the surface temperature effects of semi-arid mountainous urban areas. This study investigates the urban environment characterization and its effects on surface temperature using remote sensing. The methodologies adapted for this study are geometric and radiometric corrections of satellite data, extraction of land use/land cover and digital elevation model, estimation of vegetation density using Normalized Difference Vegetation Index (NDVI), and estimation of surface temperature and emissivity using temperature emissivity separation (TES) algorithm. Finally geospatial model and statistical techniques are used for assessing the overall impact of urban environmental characterization on urban climate of semi-arid region of Abha, Kingdom of Saudi Arabia. Herein, results reveal that the spatial distribution of surface temperature was affected by land use/land cover (LULC) and topography. The high dense built-up and commercial/industrial areas display higher surface temperature in comparison with surrounding lands. There is gradual decrease of LULC classes’ surface temperature with the increase in altitude. The cooling effect towards the surrounding urban built-up area is found increasing at the hill located vegetated area, the downward slope and valley terrain inside the recreational park. Therefore the spatial variation in surface temperature also reflected the effects of topography on LULC classes. Suitable mountainous land use utilization would help to expand the cooling effect. In the future, the outcomes of this study could be used to build environmentally sustainable urban planning suitable to semi-arid regions and to create practices that consider the local weather environment in urban planning.

Evaluating Vegetation Health Condition Using MODIS Data in the Three Gorges Area, China

The satellite-based vegetation condition index(VCI) and temperature condition index(TCI) have been used extensively for drought detection and tracking, the assessment of weather impacts on vegetation and evaluation of the health and productivity of vegetation. In this study, in order to detect and monitor the growth condition of vegetation, we have collected data on vegetation indices and land surface temperature derived from MODIS(2001-2012) and defined a vegetation health index(VHI) based on VCI and TCI for assessing vegetation health condition in the Three Gorges Area, China(TGA). The results of the study show that temporal and spatial characteristics of vegetation health condition can be detected, tracked and mapped by the VHI index. In most parts of the TGA, the vegetation health condition showed an overall increasing trend during the study period, especially in Wulong, Fengdu, Shizhu and other regions located in the midstream sections of the Three Gorges Reservoir. In addition, the four studied vegetation types all showed clear increasing trends during the study period. The increasing trend in the vegetation health condition shows a strong positive correlation with topographical slope and altitude(below 500 m). Over the seasons, this trend is strongest in autumn, followed by spring. However, the correlations between vegetation health condition and climatic factors are more frequently significant in summer and winter than in autumn and spring. The vegetation health condition has been low in 2006 and 2011. This finding is consistent with the extreme weather conditions in those two years. However, only in the summer is vegetation health condition significantly correlated with three climatic factors in most of the study area. This result implies that vegetation growth may show a lagged response to climatic factors and may also be affected by human activities, including agricultural activities, industrial activities and other economic activities.

TVDI与土壤湿度关系的多时间尺度分析与旱情监测

[目的]揭示不同时间尺度的TVDI与土壤湿度的关联关系,确定反映土壤湿度的最佳时间尺度,以更为准确地获取土壤湿度信息,精准监测旱情。[方法]以3种不同时间尺度(8 d,16 d和月)的遥感地表温度、反射率数据以及山东省31个地面观测站土壤湿度数据为基础,分析不同时间尺度下植被指数-温度空间特征,建立不同时段不同时间尺度温度植被干旱指数(TVDI)的热边和冷边函数,研究TVDI与土壤湿度之间的关联性随时间尺度的变化规律,确定了区域土壤墒情监测的时间尺度,反演了区域土壤湿度,监测旱情。[结果]植被指数-温度二维空间形状呈三角形,但随时间发生改变;二维空间中植被指数与最大、最小温度之间有明显的线性关系,湿边并非理想的与坐标轴平行的直线;TVDI与土壤湿度线性相关,但两者之间的紧密程度随时间尺度而变,8 d尺度的TVDI与土壤湿度有更高的相关系数;山东省冬小麦旱情与降水高度一致。[结论]短时间尺度的TVDI更适于区域的旱情监测,尽管人工灌溉有效降低了对降水的需求,但降水的多寡仍是影响区域小麦旱情的主要因素。

雄安新区植被指数变化及其与气候条件的关系

建设雄安新区是国家大事、千年大计。系统分析雄安新区植被指数变化及其与气候条件的关系,可以为雄安新区未来规划、生态修复和重建等提供科学依据。利用雄安新区及其所辖三县2001—2020年月、季、年不同时间尺度植被指数与气温、降水数据,通过算术平均法、最小二乘法和Pearson相关分析法,分析了雄安新区植被指数的变化规律及其与气温、降水等气候条件的相关关系。结果表明:2001—2020年,雄安新区植被指数总体呈上升趋势,且年代际变化特征明显,前10年平均植被指数相对较小但年际波动大,后10年平均植被指数较大但年际波动相对较小。雄安新区植被指数除夏季有下降趋势外,其余三个季节均有上升趋势,其中春季上升趋势相对最为明显。雄安新区植被指数与年平均气温呈正相关,但区域性差异明显,其中雄县相关性较差,安新相关性较好。春季和秋、冬季,植被指数与气温均呈正相关,其中春季相关系数相对最大;夏季平均气温与植被指数呈负相关,但与秋季植被指数呈正相关。雄安新区植被指数与年降水量有明显的正相关关系,其中夏季降水与夏季、秋季和冬季植被指数均呈较明显的正相关,影响的滞后效应可达半年以上,但春季和秋、冬季降水量与植被指数的相关系数相对较小。

基于温度植被干旱指数(TVDI)的甘肃省农业干旱监测方法研究

改进温度植被干旱指数(Temperature Vegetation Dryness Index,TVDI)并明确TVDI的农业干旱等级阈值,对提高TVDI指数监测农业干旱能力有重要意义。利用近19 a的MODIS(Moderate Resolu⁃tion Imaging Spectro-radiometer,MODIS)遥感数据,基于单时次和多时次方法构建NDVI(Normalized Difference Vegetation Index,NDVI)-LST(Land Surface Temperature,LST)、EVI(Enhanced Vegetation In⁃dex,EVI)-LST、RVI(Ratio Vegetation Index,RVI)-LST、SAVI(Soil-Adjusted Vegetation Index,SAVI)-LST等几种特征空间,讨论TVDI计算方法,分析TVDI在甘肃省农业干旱监测中的适用性,并明确甘肃省夏季TVDI农业干旱分级标准。结果表明:(1)基于多时次方法构建的SAVI-LST特征空间TVDI更适合甘肃省农业干旱监测,其对土壤相对湿度(Relative Soil Moisture,RSM)拟合的均方根误差(Root Mean Squared Error,RMSE)和平均绝对误差(Mean Absolute Error,MAE)比NDVI-LST特征空间TVDI对RSM拟合的RMSE和MAE下降1%~5%;(2)TVDI适用于夏季甘肃省半干旱区、半湿润区、湿润区等非干旱区浅层10、20 cm土壤深度的农业干旱监测,RMSE和MAE约15.6%和12.6%,拟合误差湿润区<半湿润区<半干旱区;(3)利用TVDI与RSM线性关系确定的TVDI农业干旱等级更有利于提高TVDI监测农业干旱的准确性。

三种遥感干旱监测指数在黄土高原东部的适用性研究

研究不同遥感干旱监测方法在黄土高原东部山西省的适用性,对于提高地形复杂区域农业气象服务水平和防灾减灾能力具有重要意义。本文利用风云气象卫星数据,结合气象站观测资料,通过本地化修正作物水分胁迫指数(Crop Water Stress Index,CWSI)中地形和天气参数,对比分析2022年5月6—12日降雨过程前后及整个作物生长期植被供水指数(Vegetation Supply Water Index,VSWI)、温度植被指数(Temperature Vegetation Dryness Index,TVDI)和CWSI 3种遥感干旱监测指数在山西省的监测效果。结果表明:一次降雨过程前后,CWSI、TVDI、VSWI均表现出与实测土壤墒情同样的变化趋势,但当旱情严重时,TVDI、VSWI易低估旱情等级,反之,则易高估旱情等级,而CWSI均接近于实测土壤墒情,并能够实时反映降雨对土壤墒情的影响;整个作物生长期,CWSI均与实测墒情相吻合,TVDI只在春季时与实测墒情接近,对于植被覆盖较好的夏、秋季则效果较差,VSWI则不适用于地形复杂区域干旱监测。此外,前期累积遥感干旱监测结果比当天监测结果更接近于地表浅层土壤墒情。总之,修正后CWSI能够有效反映山西省遥感干旱状况,为黄土高原复杂地形区遥感干旱监测提供了新的尝试。

基于时空数据融合的塔吉克斯坦中高时空分辨率NDVI数据集(2010-2020)

归一化植被指数(Normalized Difference Vegetation Index,NDVI)是研究植被最常用的遥感指数之一。NDVI长时间序列数据对于植被变化研究有着重要的意义。然而由于传感器的限制,遥感数据的时间分辨率与空间分辨率不能兼顾,因此在目前广泛使用的NDVI数据产品中,高时空分辨率的数据还较为缺乏。本产品基于Cubist模型对MODIS数据与Landsat及哨兵等遥感数据进行时空数据融合,得到了塔吉克斯坦2010–2020年中高时空分辨率Landsat-MODIS融合数据,以及2020年中高时空分辨率Sentinel-MODIS融合数据。为保证数据的准确性和可靠性,本数据集从数据源的质控,模型训练优化,以及模型独立验证三个方面对数据产品进行质量控制,且取得了较好的验证效果。本数据集可反映塔吉克斯坦2010–2020年NDVI时空变化情况,可为该地区植被变化分析、生态环境监测等提供长时间序列数据支撑。