Response of spectral vegetation indices to soil moisture in grasslands and shrublands

The relationships between satellite-derived vegetation indices (VIs) and soil moisture are complicated because of the time lag of the vegetation response to soil moisture. In this study, we used a distributed lag regression model to evaluate the lag responses of VIs to soil moisture for grasslands and shrublands at Soil Climate Analysis Network sites in the central and western United States. We examined the relationships between Moderate Resolution Imaging Spectroradiometer (MODIS)-derived VIs and soil moisture measurements. The Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) showed significant lag responses to soil moisture. The lag length varies from 8 to 56 days for NDVI and from 16 to 56 days for NDWI. However, the lag response of NDVI and NDWI to soil moisture varied among the sites. Our study suggests that the lag effect needs to be taken into consideration when the VIs are used to estimate soil moisture.     

AVHRR derived phenological change in the Sahel and Soudan, Africa, 1982-2005

The Sahel region of Africa has experienced a decrease in rainfall from the early 1960s to mid 1990s. Recent studies have detected an increased in NDVI amplitude and growing season integrated NDVI for the region since 1982. However, these studies have not examined how plant phenology has changed. Phenology examines life cycle events such as bud burst and leaf senescence. Using the software TIMESAT to estimate phenological parameters from the GIMMS AVHRR NDVI dataset, we have found significant positive trends for the length of the growing and end of the growing season for the Soudan and Guinean regions, but significant trends in the Sahel could not be detected. The geographical extent of these trends contrasts with the more northern extent of positive trends of NDVI amplitude and growing season integrated NDVI. Results suggest two types of “greening” trends associated with rainfall change since the drought in the early 1980s.     

Synergism between NOAA-AVHRR and Meteosat data for studying vegetation development in semi-arid West Africa

Abstract

Rainfall estimates, based on cold cloud duration estimated from Meteosat data, are compared with vegetation development depicted by data of the normalized difference vegetation index (NDVI) from the National Oceanic and Atmospheric Administration's (NOAA) advanced very high resolution radiometer (AVHRR) for part of the Sahel. Decadal data from the 1985 and 1986 growing seasons are examined to determine the synergism of the datasets for rangeland monitoring. There is a general correspondence between the two datasets with a marked lag between rainfall and NDVI of between 10 and 20 days. This time lag is particularly noticeable at the beginning of the rainy season and in the more northern areas where rainfall is the limiting factor for growth. Principal component analysis was used to examine deviations from the general relationship between rainfall and the NDVI. Areas of low NDVI values for a given input of rainfall were identified: at a regional scale, they give an indication of areas of low production potential and possible degradation of ecosystems. This study demonstrates in a preliminary way the synergism of such datasets derived from satellite--borne sensors with coarse spatial resolution, which may provide new information for the improved management of the Sahelian grasslands.     

Mapping of Sahelian vegetation parameters from ERS scatterometer data with an evolution strategies algorithm

The West African Sahel rainfall regime is known for its spatio-temporal variability at different scales which has a strong impact on vegetation development. This study presents results of the combined use of a simple water balance model, a radiative transfer model and ERS scatterometer data to produce map of vegetation biomass and thus vegetation cover at a spatial resolution of 25 km. The backscattering coefficient measured by spaceborne wind scatterometers over Sahel shows a marked seasonality linked to the drastic changes of both soil and vegetation dielectric properties associated to the alternating dry and wet seasons. For lack of a direct observation, METEOSAT rainfall estimates are used to calculate temporal series of soil moisture with the help of a water balance model. This a priori information is used as input of the radiative transfer model that simulates the interaction between the radar wave and the surface components (soil and vegetation). Then, an inversion algorithm is applied to retrieve vegetation aerial mass from the ERS scatterometer data. Because of the nonlinear feature of the inverse problem to be solved, the inversion is performed using a global stochastic nonlinear inversion method. A good agreement is obtained between the inverse solutions and independent field measurements with mean and standard deviation of −54 and 130 kg of dry matter by hectare (kg DM/ha), respectively. The algorithm is then applied to a 350,000 km² area including the Malian Gourma and Seno region and a Sahelian part of Burkina Faso during two contrasted seasons (1999 and 2000). At the considered resolution, the obtained herbaceous mass maps show a global qualitative consistency (r²=0.71) with NDVI images acquired by the VEGETATION instrument.     

GAC NDVI images: Relationship to rainfall and potential evaporation in the grazing lands of The Gourma (northern Sahel) and in the croplands of the Niger-Nigeria border (southern Sahel)

The relationship between Normalized Difference Vegetation Index (NDVI) and rainfall is fairly well established for growing-season rainfall between 250 and 500mm. In desert border zones such as the African Sahel, with rainfall significantly lower than 250mmy-1, this relationship becomes unpredictable. In this paper, we first explore some causes of this unpredictability for The Gourma, an area in the northern Sahel, and find that both the previous year's and current year's rainfall, as well as potential evaporation, play roles in determining seasonal NDVI. We next examine the NDVI-rainfall relationship in an area of the southern Sahel on the Niger-Nigeria border with rainfall fluctuating between approximately 200 and 500mmy-1, and confirm its robustness.     

Land Surface Water Index (LSWI) response to rainfall and NDVI using the MODIS Vegetation Index product

For more than 20 years the Normalized Difference Vegetation Index (NDVI) has been widely used to monitor vegetation stress. It takes advantage of the differential reflection of green vegetation in the visible and near-infrared (NIR) portions of the spectrum and provides information on the vegetation condition. The Land Surface Water Index (LSWI) uses the shortwave infrared (SWIR) and the NIR regions of the electromagnetic spectrum. There is strong light absorption by liquid water in the SWIR, and the LSWI is known to be sensitive to the total amount of liquid water in vegetation and its soil background. In this study we investigated the LSWI characteristics relative to conventional NDVI-based drought assessment, particularly in the early crop season. The area chosen for the study was the state of Andhra Pradesh located in the Indian peninsular. The Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Index (VI) product from the Aqua satellite was used in the study. The analysis was carried out for the years 2002 (deficit year) and 2005 (normal year) using the NDVI from the MODIS VI product and deriving the LSWI using the NIR and SWIR reflectance available with the MODIS VI product. The response of LSWI to rainfall, observed in the rate of increase in LSWI in the subsequent fortnights, shows that this index could be used to monitor the increase in soil and vegetation liquid water content, especially during the early part of the season. The relationship between the cumulative rainfall and the current fortnight LSWI is stronger in the low rainfall region (<500 mm), while the one-fortnight lagged LSWI had a stronger relationship in the high rainfall region (>500 mm). The relationship between LSWI and the cumulative rainfall for the entire state was mixed in 2002 and 2005. The strength of the relationship was weak in the high rainfall region. When LSWI was regressed directly with NDVI for three LSWI ranges, it was observed that the NDVI with the one-fortnight lag had a strong relationship with the LSWI in most of the categories.     

Impact of climate variability on NDVI over the Indian region during 1981–2010

The normalized difference vegetation index (NDVI), derived from the Advanced Very High Resolution Radiometer (AVHRR) (1981–2000), and Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua data (2000–2010) are analysed to examine their spatio-temporal variability over the Indian region. Climatic factors are well known to be associated with vegetation. Therefore, an attempt has also been made in this study to examine the impact of climate variability on NDVI over the Indian region. The average spatio-temporal patterns of NDVI suggest that the variability in NDVI is well associated with climatic factors such as rainfall and temperature. During hot weather, the all-India NDVI shows the lowest values; the values increase from the onset of the summer monsoon season (June–September) onwards over the Indian region. The NDVI attains its peak value in the month of October. The composite annual cycles of NDVI during drought and flood years also show similar features. During drought years, there is a decrease in all-India NDVI for all months. Opposite features are seen during flood years, with a substantial increase in all-India NDVI from the month of October onwards compared to normal years. This clearly indicates the impact of heavy summer monsoon rainfall over the country on NDVI during winter (October–December) and suggests that soil moisture gained by flood conditions helps the NDVI to increase. In contrast, drought conditions show an immediate effect on NDVI but the incremental changes are of smaller magnitude. Spatial patterns also show similar features, with negative anomalies in NDVI over large parts of the country during drought years and positive anomalies during flood years. There exist year-to-year variations in NDVI depending on the performance of the monsoon. NDVI is positively correlated with rainfall during the southwest (June–September) and northeast (October–December) monsoons over a large part of the country. Also, there exists strong lag correlation between summer monsoon rainfall of the current year and NDVI of the next year, indicating that an increase (decrease) in rainfall during the rainy seasons is favourable (unfavourable) for vegetation during the winter (January and February) and the pre-monsoon season (March–May) of the following year. Thus, the analysis shows significant impact of inter-annual variability of climate on the NDVI over the Indian region. Strong lag correlations between rainfall and NDVI indicate the potential for estimating NDVI over India by the regression method.     

Analysis of teleconnections between AVHRR-based sea surface temperature and vegetation productivity in the semi-arid Sahel

Vegetation productivity across the Sahel is known to be affected by a variety of global sea surface temperature (SST) patterns. Often climate indices are used to relate Sahelian vegetation variability to large-scale ocean-atmosphere phenomena. However, previous research findings reporting on the Sahelian vegetation response to climate indices have been inconsistent and contradictory, which could partly be caused by the variations in spatial extent/definitions of climate indices and size of the region studied. The aim of this study was to analyze the linkage between climate indices, pixel-wise spatio-temporal patterns of global sea surface temperature and the Sahelian vegetation dynamics for 1982-2007. We stratified the Sahel into five subregions to account for the longitudinal variability in rainfall. We found significant correlations between climate indices and the Normalized Difference Vegetation Index (NDVI) in the Sahel, however with different magnitudes in terms of strength for the western, central and eastern Sahel. Also the correlations based on NDVI and global SST anomalies revealed the same East-West gradient, with a stronger association for the western than the eastern Sahel. Warmer than average SSTs throughout the Mediterranean basin seem to be associated with enhanced greenness over the central Sahel whereas colder than average SSTs in the Pacific and warmer than average SSTs in the eastern Atlantic were related to increased greenness in the most western Sahel. Accordingly, we achieved high correlations for SSTs of oceanic basins which are geographically associated to the climate indices yet by far not always these patterns were coherent. The detected SST-NDVI patterns could provide the basis to develop new means for improved forecasts in particular of the western Sahelian vegetation productivity.     

Relations between AVHRR NDVI and ecoclimatic parameters in China

Based on monthly AVHRR NDVI data and weather records collected from 160 stations throughout China for 10 years, the relationships between NDVI and two ecoclimatic parameters (growing degree-days (GDD) and rainfall) were analysed. The results indicate that a significant correlation exists between NDVI and the two ecoclimatic parameters; the NDVI/GDD correlation was stronger than the NDVI/rainfall correlation. The NDVI/rainfall correlation coefficient exhibits a clear structure in terms of spatial distribution. Further, the results indicate that for natural vegetation, the NDVI/rainfall correlation coefficient increases in order from evergreen forest, to deciduous forest, to shrubs and desert vegetation, to steppe and savanna. The correlation coefficients associated with cultural vegetation type depend on a number of factors including annual rainfall, seasonal variation in precipitation, type and intensity of irrigation practice and other environmental factors.     

典型资源型城市的植被覆盖变化及驱动力分析

干旱半干旱区植被变化研究较多,然而很少关注资源型城市社会经济对植被变化影响。基于2000~2020年鄂尔多斯市MOD13Q1数据、降雨和温度等气候数据、原煤产量等11个社会经济指标,结合GIS技术和线性回归法等统计学方法,对植被覆盖时空变化及其影响因素进行了研究。结果表明：①21年间鄂尔多斯的NDVI值介于0.233~0.395,呈波动性增长趋势,增长速率为0.059/10 a;下辖的8个区县的NDVI值也呈波动性增长趋势,但各地区存在差异。②鄂尔多斯植被呈东北高,西南低的分布特征,低植被区面积5.35万km²,占整个鄂尔多斯面积的61.58%,高植被区面积仅0.20万km²;植被改善区面积远远大于植被退化区面积,改善区占整个鄂尔多斯面积的52.19%,植被退化区仅占3.69%。③NDVI值与降雨量表现为极显著性正相关,相关系数为0.794（P<0.01）;NDVI变化与当月累计降雨量的相关系数较大,与1个月前温度的相关系数较大。④NDVI变化与11种社会经济指标均表现为极显著正相关,相关性为0.728~0.796（P<0.01）。鄂尔多斯植被恢复效果较好,降雨量和温度是影响植被生长的主要因素,NDVI变化对降雨量的响应无明显滞后性,对温度的响应存在一个月的滞后期,社会经济发展对植被覆盖的积极作用大于消极影响。     

The impact of seasonal rainfall variability on NDVI in the Tunga and Bhadra river basins,Karnataka, India

Rainfall is the major climatic factor that affects the growth and distribution of natural vegetation at a regional scale. The high space–time variability of rainfall in the Tunga and Bhadra river basins caused by the high-elevation Western Ghats mountains forces changes in the seasonal distribution of local vegetation. Understanding the relationship between vegetation greenness and rainfall is a key feature in managing the vegetation of the river basin. For this, we have analysed a 7-year (2005–2011) time series of the Moderate Resolution Imaging Spectroradiometer normalized difference vegetation index (NDVI) and Tropical Rainfall Measuring Mission 3B42 rainfall data. The results show that rainfall exerts seasonal control on vegetation greenness. A significant negative correlation was observed for the monsoon season and a favourable positive association for the rest of the seasons. We found a maximum amount of vegetation greenness in the post-monsoon season (October–December). The availability of enough soil moisture from the southwest monsoon season along with suitable climatic conditions triggers an increased greenness amount during the post-monsoon months. We also investigated seasonal and monthly correlations of monsoon rainfall with the NDVI of its subsequent months. The results suggest that monsoon rainfall is a key factor that sustains the long-term greenness in the river basins.     

An assessment of AVHRR/NDVI-ecoclimatological relations in Nebraska,U.S.A.

Research designed to better define relations between 1-km multitemporal AVHRR-derived NDVI data and selected climatological parameters, soil hydrological properties and land cover characteristics is summarized. Bi-weekly maximum value composite NDVI data and concurrently measured meteorological data acquired in 1990 and 1991 for Nebraska were utilized to study relations between NDVI and accumulated growing degree days,soil temperature, precipitation and potential evapotranspiration. Temporal change in NDVI was found to be closely linked with the temperature regime. NDVI-precipitation and NDVI-potential evapotranspiration relations exhibited time lags, although the length of lag varied with land cover type, precipitation, and soil hydrologic properties. NDVI response to precipitation was stronger in natural grasslands and grassland/wet meadows than in areas of irrigated cropland and mixed crop/ grass. NDVI-climate relations were strongest where vegetation was developed on soils with low root zone available water capacity and high permeability. Relations derived by using NDVI values over 3 pixel by 3 pixel windows showed little difference from those using single 1 km pixel. This may reflect both the relatively homogeneous land cover characteristics of the study area and the effect of off-nadir viewing geometry on AVHRR data acquisition.     

Spatial patterns of the NDVI–rainfall relationship at the seasonal and interannual time scales in an African savanna

Climate change is predicted to affect both the mean annual rainfall and its seasonal distribution over the African continent. Understanding their respective influences on primary production, an ecosystem's key feature, is therefore a major challenge for rangeland ecologists. We have investigated the change in intra‐ and interannual Normalized Difference Vegetation Index (NDVI) in relation to rainfall in Hwange National Park, Zimbabwe. Two distinct NDVI time series were built using NOAA/AVHRR data for the period 1982–2002. Long‐term monthly means described the change in seasonal NDVI, whereas annually integrated NDVI related to year‐to‐year fluctuations. The rainfall–NDVI relationship was stronger along the seasonal course [with a lag of 1 month, Kendall's tau (τ) = 0.879] than when studied interannually (τ = 0.476). Principal component analysis (PCA) demonstrated that spatial patterns of the NDVI fluctuations differed when studied interannually or during the seasonal course. Field features such as topography or vegetation composition influenced seasonal NDVI values whereas only rainfall distribution played a role at the interannual time scale. Our results show that rainfall controls on primary production and their mitigation differ between time scales, and these findings bring insights on the future response of savannas to climate change.     

Estimating relations between AVHRR NDVI and rainfall in East Africa at 10-day and monthly time scales

Temporal relations between AVHRR NDVI and rainfall data over East Africa at 10-day and monthly time scales have been analysed using distributed lag models. On average, only 10 per cent of the variation in 10-day NDVI values could be explained by concurrent and preceding rainfall. Corresponding values for monthly data was 36 per cent. If it is assumed that rainfall data can be used as an indicator of vegetation development the study indicates that AVHRR NDVI may have limitations for temporal vegetation monitoring in these environments.     

Assessment of rainfall and NDVI anomalies in Spain (1989–1999) using distributed lag models

In this study a link was established between anomalies in climatic and Advanced Very High Resolution Radiometer (AVHRR)/Normalized Difference Vegetation Index (NDVI) data in Spain for the period from 1989 to 1999 on a monthly and annual basis using multivariate distributed lag (DL) models and generalized least‐square (GLS) parameter estimation. In most areas significant time‐delayed correlation between anomalies of monthly rainfall and NDVI data was confined to an interval of 1 month. Locally higher lag orders of up to 3 months were found. By contrast, relationships between surface temperature and the NDVI were insignificant in the multivariate context at most locations. The multiple correlation coefficients of the DL models achieved 0.6 in the maximum. Regions characterized by the most significant NDVI–rainfall correlations include the southern forelands of the Pyrenees in Catal?na, rainfed agricultural areas in Extremadura, Andalusia, and the western parts of Castilla y Leon. Average ratios of rainfall to potential evapotranspiration (PET) in the sensitive areas ranged between 0.5 and 2, with annual rainfall amounts less than 700 mm. For each land‐cover class a linear discriminant analysis (LDA) was carried out to assess the environmental factors that might explain the differences in the NDVI–rainfall relationships. The highest discriminant coefficients and factor loadings were recorded for those factors that recurrently trigger water deficit in the sensitive regions, such as low total annual rainfall, large seasonal rainfall variability, high average PET and surface temperature. On the annual basis the lagged correlation of the NDVI and rainfall data was confined to natural vegetation (grassland and scrubland) areas in western Spain. This region suffered from a severe drought in the early 1990s, after which biomass production lagged several years behind improved rainfall conditions. The approach presented is useful for assessing the influence of climatic variables on the pattern of temporal anomalies in the NDVI or related vegetation parameters.     

Vegetation responses to precipitation and temperature: a spatiotemporal analysis of ecoregions in the Colorado River Basin

Predicting vegetation response to precipitation and temperature anomalies, particularly during droughts, is of great importance in semi-arid regions, because ecosystem and hydrologic processes depend on vegetation conditions. This article studies vegetation responses to precipitation and temperature in 10 ecological regions within the semi-arid Colorado River Basin (CRB). The Normalized Difference Vegetation Index (NDVI) from Global Inventory Modeling and Mapping Studies (GIMMS) database and the Standardized Precipitation Index (SPI) and temperature series from Parameter-Elevation Regressions on Independent Slope Models (PRISM) database were jointly evaluated for the period 1986–2006, using Multichannel Singular Spectrum Analysis (MSSA) to determine common oscillations and significant lags in vegetation response to seasonal and annual precipitation and temperature. Results show high correlations between lagged SPI series and standardized NDVI: from 1-month lag in the warm deserts (Sonora, Chihuahua and Mojave) to two months in the Temperate Sierras and Semi-Arid Highlands and three months in the Colorado and Arizona/New Mexico Plateaus and the Western Cordillera. Temperature anomalies are negatively correlated to NDVI in the lower CRB and positively correlated in the upper CRB. Notably, we see a basin-wide response to SPI anomalies, and consequently, the identified latitudinal and altitudinal lags between SPI and NDVI will allow an early, basin-wide assessment of lagged vegetation responses to precipitation along the CRB ecoregions.     

Monitoring desertification and land degradation over sub-Saharan Africa

A desertification monitoring system is developed that uses four indicators derived using continental-scale remotely sensed data: vegetation cover, rain use efficiency (RUE), surface run-off and soil erosion. These indicators were calculated on a dekadal time step for 1996. Vegetation cover was estimated using the Normalized Difference Vegetation Index (NDVI). The estimation of RUE also employed NDVI and, in addition, rainfall derived from Meteosat cold cloud duration data. Surface run-off was modelled using the Soil Conservation Service (SCS) model parametrized using the rainfall estimates, vegetation cover, land cover, and digital soil maps. Soil erosion, one of the most indicative parameters of the desertification process, was estimated using a model parametrized by overland flow, vegetation cover, the digital soil maps and a digital elevation model (DEM). The four indicators were then combined to highlight the areas with the greatest degradation susceptibility. The system has potential for near-real time monitoring and application of the methodology to the remote sensing data archives would allow both spatial and temporal trends in degradation to be determined.     

Global analysis of empirical relations between annual climate and seasonality of NDVI

This paper describes the use of satellite data to calibrate a new climate vegetation greenness relation for global change studies. We examined statistical relations between annual climate indexes (temperature, precipitation, and surface radiation) and seasonal attributes of the AVHRR Normalized Difference Vegetation Index (NDVI) time series for the mid-1980s in order to refine our understanding of intra-annual patterns and global controls on natural vegetation dynamics. Multiple linear regression results using global 1 gridded data sets suggest that three climate indexes: degree days (growing/chilling), annual precipitation total, and an annual moisture index together can account to 70-80% of the geographical variation in the NDVI seasonal extremes (maximum and minimum values) for the calibration year 1984. Inclusion of the same annual climate index values from the previous year explains no substantial additional portion of the global scale variation in NDVI seasonal extremes. The monthly timing of NDVI extremes is closely associated with seasonal patterns in maximum and minimum temperature and rainfall, with lag times of 1 to 2 months. We separated well-drained areas from 1 grid cells mapped as greater than 25% inundated coverage for estimation of both the magnitude and timing of seasonal NDVI maximum values. Predicted monthly NDVI, derived from our climate-based regression equations and Fourier smoothing algorithms, shows good agreement with observed NDVI for several different years at a series of ecosystem test locations from around the globe. Regions in which NDVI seasonal extremes are not accurately predicted are mainly high latitude zones, mixed and disturbed vegetation types, and other remote locations where climate station data are sparse.     

Analysis of the dynamics of African vegetation using the normalized difference vegetation index

Images at a resolution of 8?km are currently being generated for the whole of Africa, displaying the normalized difference vegetation index (NDVI). These images have undergone a process of temporal compositing to reduce the effects of cloud cover and atmospheric variation. When the NDVI is plotted against time, different cover types are shown to have characteristic profiles corresponding closely with their phenology. The resultant pattern of NDVI values displayed on the images is analysed in terms of the cover types present and local variations in rainfall. Comparison between images for 1983 and 1984 overall showed considerable similarities, but significant differences were observed in the northward extent of the greening wave in the Sahel, the greening up of the Kalahari Desert and East African communities. It is concluded that vegetation monitoring using NDVI images needs to be associated with scene stratification according to cover type     

Use of AVHRR-derived surface temperatures in evaluating a land–atmosphere model

The vertical transports of heat and moisture at the Earth's surface provide a critical linkage between meteorological, hydrological and ecological processes. Unfortunately, estimates of energy and water fluxes by land surface–atmosphere models are difficult to validate. This is due to the fine spatial and temporal resolution of the models, as well as a lack of widespread observations of these fluxes and other components of the surface energy budget. However, modelled fluxes of energy and water are often based largely on accurate determination of the modelled soil surface temperature. Land surface temperatures (LSTs) derived from the Advanced Very High Resolution Radiometer (AVHRR) onboard polar-orbiting satellites can be determined over large spatial extents a few times per day. They provide a means by which modelled surface temperatures, and thus modelled fluxes, can be evaluated. In this paper, two versions of the Simulator for Hydrology and Energy Exchange at the Land Surface (SHEELS) model are evaluated using AVHRR-derived LSTs. Model vegetation cover is held constant in the first version, whereas Normalized Difference Vegetation Index (NDVI) data are used to temporally modify vegetation cover in the second version. Inclusion of the NDVI data is found to decrease the bias between modelled and satellite-derived LSTs by 0.5?K. However, even with this improvement in vegetation parametrization, bias remains large (3.7?K).