Vegetation greenness modeling in response to climate change for Northeast Thailand

In Northeast Thailand, the climate change has resulted in erratic rainfall and tem- perature patterns. The region has experienced both periods of drought and seasonal floods with the increasing severity. This study investigated the seasonal variation of vegetation greenness based on the Normalized Difference Vegetation Index （NDVI） in major land cover types in the region. An assessment of the relationship between climate patterns and vegeta- tion conditions observed from NDVI was made. NDVI data were collected from year 2001 to 2009 using multi-temporal Terra MODIS Vegetation Indices Product （MOD13Q1）. NDVI pro- files were developed to measure vegetation dynamics and variation according to land cover types. Meteorological information, i.e. rainfall and temperature, for a 30 year time span from 1980 to 2009 was analyzed for their patterns. Furthermore, the data taken from the period of 2001-2009, were digitally encoded into GIS database and the spatial patterns of monthly rainfall and temperature maps were generated based on kriging technique. The results showed a decreasing trend in NDVI values for both deciduous and evergreen forests. The highest productivity and biomass were observed in dry evergreen forests and the lowest in paddy fields. Temperature was found to be increasing slightly from 1980 to 2009 while no significant trends in rainfall amounts were observed. In dry evergreen forest, NDVI was not correlated with rainfall but was significant negatively correlated with temperature. These re- sults indicated that the overall productivity in dry evergreen forest was affected by increasing temperatures. A vegetation greenness model was developed from correlations between NDVI and meteorological data using linear regression. The model could be used to observe the change in vegetation greenness and dynamics affected by temperature and rainfall.     

基于遥感和地面数据的景观尺度叶面积指数和生物量的模拟

The method for simulating the temporal and spatial distribution patterns of leaf area index (LAI) and biomass at landscape scale using remote sensing images and surface data was discussed in this paper,The procedure was:(1) annual maximum normalized difference vegetation index (NDVI) over the landscape was calculated from TM images;(2) the relationship model between NDVI and LAI was built and annual maximum LAI over the landscape was simulated;(3) the relationship models between LAI and biomass were built and annual branch ,stem ,root and maximum leaf biomass over the landscape were simulated;(4) spatial distribution patterns of leaf biomass and LAI in different periods all the year round were obtained.The simulation was based on spatial analysis module GRID in ArcoInfo software ,The method is laso a kind of scaling method from patch scale to landscape scale ,A case study of Changbai Mountain Nature Reserve was dissertated ,Aalysis and primary validation were carried out to the simulated LAI and biomass for the major vegetation types in the Changbai Mountain in 1995.     

30年来呼伦贝尔地区草地植被对气候变化的响应(英文)

Global warming has led to significant vegetation changes especially in the past 20 years. Hulun Buir Grassland in Inner Mongolia, one of the world’s three prairies, is undergoing a process of prominent warming and drying. It is essential to investigate the effects of climatic change (temperature and precipitation) on vegetation dynamics for a better understanding of climatic change. NDVI (Normalized Difference Vegetation Index), reflecting characteristics of plant growth, vegetation coverage and biomass, is used as an indicator to monitor vegetation changes. GIMMS NDVI from 1981 to 2006 and MODIS NDVI from 2000 to 2009 were adopted and integrated in this study to extract the time series characteristics of vegetation changes in Hulun Buir Grassland. The responses of vegetation coverage to climatic change on the yearly, seasonal and monthly scales were analyzed combined with temperature and precipitation data of seven meteorological sites. In the past 30 years, vegetation coverage was more correlated with climatic factors, and the correlations were dependent on the time scales. On an inter-annual scale, vegetation change was better correlated with precipitation, suggesting that rainfall was the main factor for driving vegetation changes. On a seasonal-interannual scale, correlations between vegetation coverage change and climatic factors showed that the sensitivity of vegetation growth to the aqueous and thermal condition changes was different in different seasons. The sensitivity of vegetation growth to temperature in summers was higher than in the other seasons, while its sensitivity to rainfall in both summers and autumns was higher, especially in summers. On a monthly-interannual scale, correlations between vegetation coverage change and climatic factors during growth seasons showed that the response of vegetation changes to temperature in both April and May was stronger. This indicates that the temperature effect occurs in the early stage of vegetation growth. Correlations between vegetation growth and precipitation of the month before the current month, were better from May to August, showing a hysteresis response of vegetation growth to rainfall. Grasses get green and begin to grow in April, and the impacts of temperature on grass growth are obvious. The increase of NDVI in April may be due to climatic warming that leads to an advanced growth season. In summary, relationships between monthly-interannual variations of vegetation coverage and climatic factors represent the temporal rhythm controls of temperature and precipitation on grass growth largely.     

Forest phenological patterns of Northeas China inferred from MODIS data

The role of remote sensing in phenological studies is increasingly regarded as a key to understand large area seasonal phenomena. This paper describes the application of Moderate Resolution Imaging Spectroradiometer (MODIS) time series data for forest phenological patterns. The forest phenological phase of Northeast China (NE China) and its spatial characteristics were inferred using 1-km 10-day MODIS normalized difference vegetation index (NDVI) datasets of 2002. The threshold-based method was used to estimate three key forest phenological variables, which are the start of growing season (SOS), the end of growing season (EOS) and growing season length (GSL).Then the spatial patterns of forest phenological variables of NE China were mapped and analyzed. The derived phenological variables were validated by the field observed data from published papers in the same study area. Results indicate that forest phenological phase from MODIS data is comparable with the observed data. As the derived forest phenological pattern is related to forest type distribution, it is helpful to discriminate between forest types.     

Sensitivity and areal differentiation of vegetation responses to hydrothermal dynamics on the northern and southern slopes of the Qinling Mountains in Shaanxi province

The Qinling Mountains, located at the junction of warm temperate and subtropical zones, serve as the boundary between north and south China. Exploring the sensitivity of the response of vegetation there to hydrothermal dynamics elucidates the dynamics and mechanisms of the main vegetation types in the context of changes in temperature and moisture. Importance should be attached to changes in vegetation in different climate zones. To reveal the sensitivity and areal differentiation of vegetation responses to hydrothermal dynamics, the spatio-temporal variation characteristics of the normalized vegetation index(NDVI) and the standardized precipitation evapotranspiration index(SPEI) on the northern and southern slopes of the Qinling Mountains from 2000 to 2018 are explored using the meteorological data of 32 meteorological stations and the MODIS NDVI datasets. The results show that: 1) The overall vegetation coverage of the Qinling Mountains improved significantly from 2000 to 2018. The NDVI rise rate and area ratio on the southern slope were higher than those on the northern slope, and the vegetation on the southern slope improved more than that on the northern slope. The Qinling Mountains showed an insignificant humidification trend. The humidification rate and humidification area of the northern slope were greater than those on the southern slope. 2) Vegetation on the northern slope of the Qinling Mountains was more sensitive to hydrothermal dynamics than that on the southern slope. Vegetation was most sensitive to hydrothermal dynamics from March to June on the northern slope, and from March to May(spring) on the southern slope. The vegetation on the northern and southern slopes was mainly affected by hydrothermal dynamics on a scale of 3–7 months, responding weakly to hydrothermal dynamics on a scale of 11–12 months. 3) Some 90.34% of NDVI and SPEI was positively correlated in the Qinling Mountains. Spring humidification in most parts of the study area promoted the growth of vegetation all the year round. The sensitivity of vegetation responses to hydrothermal dynamics with increasing altitude increased first and then decreased. Elevations of 800 to 1200 m were the most sensitive range for vegetation response to hydrothermal dynamics. The sensitivity of the vegetation response at elevations of 1200–3000 m decreased with increasing altitude. As regards to vegetation type, grass was most sensitive to hydrothermal dynamics on both the northern and southern slopes of the Qinling Mountains; but most other vegetation types on the northern slope were more sensitive to hydrothermal dynamics than those on the southern slope.     

Vegetation water content retrieval using MERIS and AATSR data over the Loess Plateau

Vegetation water content is an important indicator of the degree of stress experienced by plants. This paper explored the potential of using the remote sensing data of MERIS (Medium Resolution Imaging Spectrometer) and AATSR (Advanced Along-Track Scanning Radiometer) collected during the Loess Plateau land surface process field Experiment 2005 (LOPEX05) to map and monitor vegetation water content for corn canopies. By comparing with the daily ground observation to validate the satellite reflectance data, we established relationships between the vegetation water content and satellite remotely sensed indices. The two indices studied were the NDVI (Normalized Different Vegetation Index) from MERIS and the NDWI (Normalized Different Water Index) from AATSR. We used the daily ground observation to demonstrate that the NDVI was saturated during the study period while the NDWI continued to reflect the changes in VWC. We found that NDWI, based on near infrared channel (0.855-0.875μm) and short wave infrared wavelength channel (1.58-1.64μm), is suggested to be more suitable and robust approach for retrieval of vegetation water content. The proposed method was validated with experimental field data with biases that are 1.0314 kg/m2 and 0.9413 kg/m2 respectively. Therefore the NDWI was recommended to retrieval the vegetation water content.     

利用遥感数据探测气候变化对罗马尼亚西南部森林生态系统的影响(英文)

Nowadays, Southwestern Romania faces a large-scale aridization of the climate, revealed by the rise of temperatures and the decline of the amount of precipitations, with negative effects visible, among others, in the desiccation of forest vegetation. The present study means to identify the changes that occurred, quality-wise, in the past two decades(1990–2011) in forest vegetation in Southwestern Romania, and to establish the link between those changes and extant thermal stress in the region, whose particular features are high average annual and seasonal temperatures. In order to capture the evolution in time of climate aridization, a first step consisted in using climate data, the temperature and precipitation parameters from three weather stations; these parameters were analyzed both individually and as aridity indexes(De Martonne and UNEP). In order to quantify the changes in forest vegetation, NDVI indexes were used and analyzed, starting off from Landsat satellite images, acquired at three distinct moments in time, 1990, 2000 and 2011. In order to identify the link between the changes of NDVI index values and regional thermal stress, a yardstick of climate changes, statistical correlations were established between the peak values of average annual temperatures, represented in space, and negative changes in the NDVI index, as revealed by the change-detection analysis. The results obtained indicated there is an obvious(statistically significant) connection between thermal stress and the desiccation(degradation) of forest species in the analyzed area, with false acacia(Robinia Pseudoacacia) the main species to be impacted.     

Response of revegetation to climate change with meso-and micro-scale remote sensing in an arid desert of China

The revegetation protection system(VPS)on the edge of the Tengger Desert can be referred to as a successful model of sand control technology in China and even the world,and there has been a substantial amount of research on revegetation stability.However,it is unclear how meso-and micro-scale revegetation activity has responded to climatic change over the past decades.To evaluate the relative influence of climatic variables on revegetation activities in a restored desert ecosystem,we analysed the trend of revegetation change from 2002 to 2015 using a satellite-derived normalized difference vegetation index(NDVI)dataset.The time series of the NDVI data were decomposed into trend,seasonal,and random components using a segmented regression method.The results of the segmented regression model indicate a changing trend in the NDVI in the VPS,changing from a decrease(?7×10?3/month)before 2005 to an increase(0.3×10?3/month)after 2005.We found that precipitation was the most important climatic factor influencing the growing season NDVI(P<0.05),while vegetation growth sensitivity to water and heat varied significantly in different seasons.In the case of precipitation reduction and warming in the study area,the NDVI of the VPS could still maintain an overall slow upward trend(0.04×10?3/month),indicating that the ecosystem is sustainable.Our findings suggest that the VPS has been successful in maintaining stability and sustainability under current climate change conditions and that it is possible to introduce the VPS in similar areas as a template for resistance to sand and drought hazards.     

青藏高原植被覆盖变化与降水关系

The temporal and spatial changes of NDVI on the Tibetan Plateau, as well as the relationship between NDVI and precipitation, were discussed in this paper, by using 8-km resolution multi-temporal NOAA AVHRR-NDVI data from 1982 to 1999. Monthly maximum NDVI and monthly rainfall were used to analyze the seasonal changes, and annual maximum NDVI, annual effective precipitation and growing season precipitation (from April to August) were used to discuss the interannual changes. The dynamic change of NDVI and the corre- lation coefficients between NDVI and rainfall were computed for each pixel. The results are as follows: (1) The NDVI reached the peak in growing season (from July to September) on the Tibetan Plateau. In the northern and western parts of the plateau, the growing season was very short (about two or three months); but in the southern, vegetation grew almost all the year round. The correlation of monthly maximum NDVI and monthly rainfall varied in different areas. It was weak in the western, northern and southern parts, but strong in the central and eastern parts. (2) The spatial distribution of NDVI interannual dynamic change was different too. The increase areas were mainly distributed in southern Tibet montane shrub-steppe zone, western part of western Sichuan-eastern Tibet montane coniferous forest zone, western part of northern slopes of Kunlun montane desert zone and southeastern part of southern slopes of Himalaya montane evergreen broad-leaved forest zone; the decrease areas were mainly distributed in the Qaidam montane desert zone, the western and northern parts of eastern Qinghai-Qilian montane steppe zone, southern Qinghai high cold meadow steppe zone and Ngari montane desert-steppe and desert zone. The spatial distribution of correlation coeffi- cient between annual effective rainfall and annual maximum NDVI was similar to the growing season rainfall and annual maximum NDVI, and there was good relationship between NDVI and rainfall in the meadow and grassland with medium vegetation cover, and the effect of rainfall on vegetation was small in the forest and desert area.     

Shrubs proliferated within a six-year exclosure in a temperate grassland—Spatiotemporal relationships between vegetation and soil variables

Overgrazing has been considered one of the maj or causes that trigger shrub encroachment of grassland. Proliferation of shrubs in grassland is recognized as an important indicator of grassland degradation and desertification. In China, various conservation measures, including enclosures to reduce livestock grazing, have been taken to reverse the trend of grassland desertification, yet shrubs have been reported to increase in the grasslands over the past decades. In late 2007, we set up a 400-m-by-50-m exclosure in a long-term overgrazed temperate grassland in Inner Mongolia, with the ob- jective to quantify the spatiotemporal relationship between vegetation dynamics, soil variables, and grazing exclusion. Soil moisture was continuously monitored within the exclosure, and cover and aboveground biomass of the shrubs were measured inside the exclosure in 2007, 2009, 2010, 2012, and 2013, and outside the exclosure in 2012 and 2013. We found the average shrub cover and biomass significantly increased in the six years by 103 % and 120%, respectively. The result supported the hypothesis that releasing grazing pressure following long-term overgrazing tends to trigger shrub invasion into grassland. Our results, limited to a single gradient, suggest that any conservation measures with quick release of overgrazing pressure by enclosure or other similar means might do just the opposite to accelerate shrub en- croachment in grassland. The changes in vegetation cover and biomass were regressed on the temporal average of the soil moisture content by means of the generalized least square technique to quantify the effect of the spatial autocor- relation. The result indicates that the grass cover and biomass significantly increased with the top, but decreased with the bottom layer soil moisture. The shrub cover and biomass, on the other hand, decreased with the top, but increased with bottom soil moisture, although the regression coefficients for the shrubs were not statistically significant. Hence this study supports the two-layered soil model which assumes grasses and shrubs use belowground resources in dif- ferent depths.     

Modeling Aboveground Biomass Using MODIS Images and Climatic Data in Grasslands on the Tibetan Plateau

Accurate quantification of aboveground biomass of grasslands in alpine regions plays an important role in accurate quantification of global carbon cycling. The monthly normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), mean air temperature (Ta), ≥5℃ accumulated air temperature (AccT), total precipitation (TP), and the ratio of TP to AccT (TP/AccT) were used to model aboveground biomass (AGB) in grasslands on the Tibetan Plateau. Three stepwise multiple regression methods, including stepwise multiple regression of AGB with NDVI and EVI, stepwise multiple regression of AGB with Ta, AccT, TP and TP/AccT, and stepwise multiple regression of AGB with NDVI, EVI, Ta, AccT, TP and TP/AccT were compared. The mean absolute error (MAE) and root mean squared error (RMSE) values between estimated AGB by the NDVI and measured AGB were 31.05 g m^-2 and 44.12 g m^-2, and 95.43 g m^-2 and 131.58 g m^-2 in the meadow and steppe, respectively. The MAE and RMSE values between estimated AGB by the AccT and measured AGB were 33.61g m^-2 and 48.04 g m^-2 in the steppe, respectively. The MAE and RMSE values between estimated AGB by the vegetation index and climatic data and measured AGB were 28.09 g m^-2 and 42.71 g m^-2, and 35.86 g m^-2 and 47.94 g m^-2, in the meadow and steppe, respectively. The study finds that a combination of vegetation index and climatic data can improve the accuracy of estimates of AGB that are arrived at using the vegetation index or climatic data. The accuracy of estimates varied depending on the type of grassland.     

The Effect of Higher Warming on Vegetation Indices and Biomass Production is Dampened by Greater Drying in an Alpine Meadow on the Northern Tibetan Plateau

In order to understand whether or not the response of vegetation indices and biomass production to warming varies with warming magnitude, an experiment of field warming at two magnitudes was conducted in an alpine meadow on the northern Tibetan Plateau beginning in late June, 2013. The normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI) and soil adjusted vegetation index (SAVI) data were obtained using a Tetracam Agricultural Digital Camera in 2013-2014. The gross primary production (GPP) and aboveground plant biomass (AGB) were modeled using the surface measured NDVI and climatic data during the growing seasons (i.e. June-September) in 2013-2014. Both low and high warming significantly increased air temperature by 1.54 and 4.00°C, respectively, and significantly increased vapor pressure deficit by 0.13 and 0.31 kPa, respectively, in 2013-2014. There were no significant differences of GNDVI, AGB and ANPP among the three warming treatments. The high warming significantly reduced average NDVI by 23.3% (-0.06), while the low warming did not affect average NDVI. The low and high warming significantly decreased average SAVI by 19.0% (-0.04) and 27.4% (-0.05), respectively, and average GPP by 24.2% (i.e. 0.21 g C m^-2 d^-1) and 44.0% (i.e. 0.39 g C m^-2 d^-1), respectively. However, the differences of the average NDVI, SAVI, and GPP between low and high warming were negligible. Our findings suggest that a greater drying may dampen the effect of a higher warming on vegetation indices and biomass production in alpine meadow on the northern Tibetan Plateau.     

Seasonal and inter-annual relationships between vegetation and climate in central New Mexico, USA

Linear correlations between seasonal and inter-annual measures of meteorological variables and normalized difference vegetation index (NDVI) are calculated at six nearby yet distinct vegetation communities in semi-arid New Mexico, USA Monsoon season (June–September) precipitation shows considerable positive correlation with NDVI values from the contemporaneous summer, following spring, and following summer. Non-monsoon precipitation (October–May), temperature, and wind display both positive and negative correlations with NDVI values. These meteorological variables influence NDVI variability at different seasons and time lags. Thus vegetation responds to short-term climate variability in complex ways and serves as a source of memory for the climate system.     

基于NDVI与偏最小二乘回归的荒漠化地区植被覆盖度高光谱遥感估测

以星载高光谱影像Hyperion为数据源,系统比较了NDVI与偏最小二乘回归（PLS）估测荒漠化地区植被覆盖度的能力,模型的建立（n=46）与独立检验所用样本（n=10）均为地面实测数据。研究结果表明,基于星载高光谱数据的NDVI与PLS模型可以有效地估测荒漠化地区植被覆盖度。相比于宽波段NDVI（RMSEP=10.5618）及基于803.3/671.02 nm计算的标准高光谱NDVI（RMSEP=8.3863）,选择特定高光谱波段（823.65/701.55 nm）构建的NDVI预测植被覆盖度的误差明显较低（RMSEP=6.5189）。基于高光谱所有波段原始反射率、一阶导数及包络线去除光谱的PLS回归模型表现,要明显优于仅利用两个波段信息的NDVI,其中基于原始反射率的PLS回归模型表现最佳,RMSEP为4.4998,约为因变量平均值的23%。     

Can vegetation productivity be derived from greenness in a semi-arid environment? Evidence from ground-based measurements

Trends of biomass production and land processes in the Sahel have been widely studied since the droughts of 1970s. Satellite data have been an important source of information because of limited in situ data. Previous studies relied on the assumed existence of a relationship between vegetation productivity and the NDVI, in particular the annually integrated NDVI (iNDVI). This study examines this assumption and its limitations, based on in situ time series measurements of biomass, species composition, NDVI and soil moisture at the Dahra test site in northern Senegal. It is shown that, there are large differences between the NDVI – vegetation productivity relationships, and these differences can be linked to species composition. There is moderate correlation between NDVI and above-ground net primary productivity (ANPP) at the peak season (r² = 0.39). In particular, the species Zornia glochidiata is characterized by high peak NDVI and low ANPP, compared to other common species such as Cenchrus biflorus and Aristida adscensionis. It is concluded that spatial and temporal variations in species dominance is likely to add noise to the relationship between NDVI and biomass. However, the seasonal cyclic fraction of the NDVI – “small seasonal integral” – reduces such noise.     

Aboveground biomass in Tibetan grasslands

This study investigated spatial patterns and environmental controls of aboveground biomass (AGB) in alpine grasslands on the Tibetan Plateau by integrating AGB data collected from 135 sites during 2001–2004 and concurrent enhanced vegetation index derived from MODIS data sets. The AGB was estimated at 68.8 g m^?2, with a larger value (90.8 g m^?2) in alpine meadow than in alpine steppe (50.1 g m^?2). It increased with growing season precipitation (GSP), but did not show a significant overall trend with growing season temperature (GST) although it was negatively correlated with GST at dry environments (<200 mm of GSP). Soil texture also influenced AGB, but the effect was coupled with precipitation; increased silt content caused a decrease of AGB at small GSP, and generated a meaningful increase under humid conditions. The correlation between AGB and sand content indicated an opposite trend with that between AGB and silt content. An analysis of general linear model depicted that precipitation, temperature, and soil texture together explained 54.2% of total variance in AGB. Our results suggest that moisture availability is a critical control of plant production, but temperature and soil texture also affect vegetation growth in high-altitude regions.     

TVDI用于干旱区农业旱情监测的适宜性

基于地表温度/植被指数(T_s/VI)特征空间建立的温度植被干旱指数(TVDI)受诸多因素的影响,其中一个重要的影响因素是植被指数,该指数在高、低植被覆盖时的敏感性不同,从而导致TVDI对旱情监测的准确度不同.针对这一问题,以新疆塔里木盆地北缘渭干河-库车河三角洲绿洲为研究区,选择2011年4月、8月两景TM影像,利用归一化植被指数(NDVI)和比值植被指数(RVI)分别建立T_s/VI特征空间,线性拟合特征空间的上、下边界,计算得到两种温度植被干旱指数(TVDI-NDVI、TVDI-RVI).用TVDI与同期野外实测的土壤含水量数据进行回归分析.结果表明:(1)植被指数、地表温度、土壤水分之间有显著互动关系,以不同植被指数计算得到的两种TVDI与表层土壤水分相关性较好,均能够反映区域土壤干旱状况;(2)由于植被指数对植被探测的敏感性,在4月低植被覆盖时,TVDI-NDVI与表层土壤水分的相关性较高,为0.4299,8月高植被覆盖时,TVDI-RVI与表层土壤水分的相关性较高,达到0.5791;(3)在低植被覆盖区域,NDVI较RVI敏感,而在高植被覆盖区域,RVI敏感性较高.RVI适用于高植被覆盖时反演土壤湿度,NDVI则更适用于中、低植被覆盖时.     

基于因子分析方法的中国植被NDVI与气候关系研究

利用1982~2000年NDVI数据和气象台站资料,对我国几种植被型组和气候的相关关系进行了研究。首先利用NDVI结合植被类型图将我国植被划分为9种植被型组;然后利用因子分析方法进行了气候指标的选择并采用相对湿度、平均最高温度和平均风速作为本研究的气候因子;最后对7种植被型组NDVI值同相应季节及其前三个季节的气候指标进行了相关分析。结果表明,利用因子分析方法选择的气候指标可以较好地进行植被气候关系分析;在我国温度条件比水分条件更明显地影响植被的生长,水分条件较其他气候因素对植被生长表现了更明显的滞后效应;而平均风速则对我国荒漠植被生长有较大的影响。     

太行山区遥感卫星反演降雨产品降尺度研究

高时空分辨率降雨数据的获取对陆地水循环研究至关重要。遥感卫星反演降水产品虽然能有效再现降雨的空间格局,但存在空间分辨率较低的问题。以植被指数NDVI(Normalized Difference Vegetation Index)和海拔高度为自变量,通过构建太行山区GPM降水(Global Precipitation Measurement Mission)的时滞地理加权回归模型,得到了2014—2016年研究区1 km分辨率GPM降水数据。研究结果表明：利用植被指数和海拔高度构建的时滞地理加权回归模型能够有效地对太行山月尺度GPM降雨数据进行尺度下延,在提高GPM数据空间分辨率的同时保留了原始数据的观测精度。考虑NDVI的时滞性提高了地理加权回归模型的降尺度效果,相对于多元线性回归模型和不考虑NDVI时滞效应的地理加权回归模型,时滞地理加权回归模型的降尺度结果与站点实测数据的确定性系数更高,RMSE更低。冬季降雨与第二年春季植被NDVI的关系较为密切,虽然采用第二年春季的NDVI作为解释变量构建降尺度模型能有效地提高冬季降雨的降尺度效果,但基于植被指数和海拔高度构建的时滞地理加权回归模型更加适用于植被生长季GPM降雨数据的降尺度研究。