Rainfall and vegetation monitoring in the Savanna Zone of the Democratic Republic of Sudan using the NOAA Advanced Very High Resolution Radiometer

NOAA-6 and NOAA-7 Advanced Very High Resolution Radiometer (AVHRR) global-area coverage (4?km ground resolution) data were obtained at three-day intervals throughout each of the four-month periods covering the 1980, 1983 and 1984 growing seasons, between latitudes 10° and 22° North in the Democratic Republic of Sudan. Daily rainfall data for twelve meteorological stations spanning the Savanna Zone were analysed. Rainfall in Sudan during 1980 was below normal, but in 1983 and 1984 there were moderate and severe droughts. The satellite data were used to calculate normalized difference vegetation index (NDVI) values from the visible and near-infrared bands of the satellite data. These were processed into ten-day composite data sets using the AVHRR thermal-infrared channel as a cloud screen and a temporal compositing procedure that reduces cloud contamination and selects viewing angles closest to nadir.

The ten-day composite NDVI values and the integrals of NDVI for each growing season were found to be closely correlated with rainfall. The constants of regressions between NDVI and rainfall were lower in 1983 and 1984 than in 1980, which suggests there was reduced water-use efficiency by the rangeland vegetation in drought years. It was found that July and August NDVI values were closely related to the integrated NDVI values; hence early- and mid-season NDVI data could be used to predict annual primary production. Images showing the geographical distribution of values of NDVI prepared for the three years clearly illustrate the effects of the 1983 and 1984 droughts, compared with the higher rainfall of 1980. The precision of the relationship between rainfall and the vegetation indices for the meteorological stations encourages the view that NOAA AVHRR GAC composite NDVI values can be used to monitor effective rainfall in the Savanna Zone of the Democratic Republic of Sudan     

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.     

Vegetation spatial variability and its effect on vegetation indices

Abstract

Landsat MSS data were used to simulate low resolution satellite data, such as NOAA AVHRR, to quantify the fractional vegetation cover within a pixel and relate the fractional cover to the normalized difference vegetation index (NDVI) and the simple ratio (SR). The MSS data were converted to radiances from which the NDVI and SR values for the simulated pixels were determined. Each simulated pixel was divided into clusters using an unsupervised classification programme. Spatial and spectral analysis provided a means of combining clusters representing similar surface characteristics into vegetated and non-vegetated areas. Analysis showed an average error of 12·7 per cent in determining these areas. NDVI values less than 0·3 represented fractional vegetated areas of 5 per cent or less, while a value of 0·7 or higher represented fractional vegetated areas greater than 80 per cent. Regression analysis showed a strong linear relation between fractional vegetation area and the NDVI and SR values; correlation values were 0·89 and 0·95 respectively. The range of NDVI values calculated from the MSS data agrees well with field studies.     

Estimating soil moisture from 6·6 GHz dual polarization,and/or satellite derived vegetation index

Eight and a half years (January 1979 to August 1987) of Scanning Multichannel Microwave Radiometer (SMMR) data taken at a frequency of 6·6 GHz for both day and night observations at both polarizations were processed, documented and used to study the relationship between brightness temperature (T_B) and antecedent precipitation index (API) in a wide range of vegetation index (normalized difference vegetation index (NDVI) varies from 0·2 to 0·6) in the mid-west and southern United States. In general, this study validates the model structure for soil wetness developed by Choudhury and Golus. For NDVI greater than 0·45 the resultant microwave signal is substantially affected by the vegetation. The night-time observations by both polarizations gave a better correlation between T_B and API. The horizontal polarization is more sensitive to vegetation. For the least and greatest vegetated areas, nighttime observations by vertical polarization showed less scatter in the T_B versus API relation. A non-linear model was developed for soil wetness using horizontal and vertical plarization and their difference. The estimate of error for this model is better than previous models, and can be used to obtain six levels of soil moisture.     

Constraints on using AVHRR composite index imagery to study patterns of vegetation cover in boreal forests

A wide range of techniques are being developed to map vegetation cover types using multi-date imagery from the Advanced Very High Resolution Radiometer. To date, these techniques do not account for severe constraints which exist for the world's boreal forest. Using composite AVHRR imagery collected over Alaska, we demonstrate how several factors influence the time-series normalized vegetation difference index (NDVI) signatures developed for the boreal forests in this region, including the effects of: (1) clouds and atmospheric haze; (2) climate variations on plant phenology; (3) fire on forest succession; and (4) forest stand patch size with respect to system resolution. Based on the analysis of AVHRR composite data from Alaska, the results of this study show: (1) clouds and haze have distinct effects on the intra-seasonal NDVI signature; (2) there are significant interseasonal variations in NDVI signatures caused by variations in the length of the growing season as well as variations in precipitation and moisture during the growing season; (3) disturbances affect large areas in interior Alaska and forest succession after fire results in significant variations in the inter-seasonal NDVI signatures; and (4) much of the landscape in interior Alaska consists of heterogeneous patches of forest which are much smaller than the resolution cell size of the AVHRR sensor, resulting in significant sub-pixel mixing. Based on these findings, the overall conclusion of this study is scientists using AVHRR to map land cover types in boreal regions must develop approaches which account for these sources of variation.     

Evaluation of AVHRR PAL and GIMMS 10‐day composite NDVI time series products using SPOT‐4 vegetation data for the African continent

Global 8 km resolution AVHRR (advanced very high resolution radiometer) NDVI (normalized difference vegetation index) 10‐day composite data sets have been used for numerous local to global scale vegetation time series studies during recent years. AVHRR Pathfinder (PAL) NDVI was available from 1981 until 2001, and the new AVHRR GIMMS NDVI was available from 1981 to the present time. A number of aspects potentially introduce noise in the NDVI data set due to the AVHRR sensor design and data processing. NDVI from SPOT‐4 VGT data is considered an improvement over AVHRR, and for this reason it is important to examine how and if the differences in sensor design and processing influence continental scale NDVI composite products. In this study, the quality of these AVHRR NDVI time series are evaluated by the continental scale 1 km resolution SPOT‐4 vegetation (VGT) 10‐day composite (S10) NDVI data. Three years of AVHRR PAL (1998–2000) and seven years of GIMMS (1998–2004) have been compared to 8 km resampled SPOT‐4 VGT (1998–2004) data. The dynamic range of SPOT‐4 VGT NDVI tends to be higher than the AVHRR PAL NDVI, whereas there is an exact match between AVHRR GIMMS NDVI and SPOT‐4 VGT NDVI. Ortho‐regression analysis on annually integrated values of AVHRR PAL/GIMMS and SPOT‐4 VGT on a continental scale reveals high correlations amongst the AVHRR and the SPOT data set, with lowest RMSE (root mean square error) on the GIMMS/SPOT‐4 VGT compared to the PAL/SPOT‐4 VGT.

Analyses on decade data likewise show that a linear relation exists between Spot‐4 VGT NDVI and the two AVHRR composite products; GIMMS explaining most of the Spot‐4 VGT NDVI variance compared to PAL. These results show that the AVHRR GIMMS NDVI is more consistent with Spot‐4 VGT NDVI compared to AVHRR PAL versus Spot‐4 VGT NDVI (in terms of RMSE and dynamic range) and can therefore be considered the more accurate long time AVHRR data record. Analyses performed on monthly maximum composites and decade composite data, however, reveal intra‐annual variations in the correlation between SPOT‐4 VGT and the two AVHRR data sets, which are attributed to different cloud masking algorithms. The SPOT‐4 VGT cloud‐screening algorithm is insufficient, thereby suppressing the rainy season NDVI.     

Vegetation cover changes and their relationship to climate variation in the source region of the Yellow River,China, 1990–2000

The change history of vegetation cover and its relations to growing season precipitation (GSP) and average growing season temperature (AGST) in the source region of the Yellow River (SRYR) during 1990–2000 was retrieved based on the 1 km Advanced Very High‐Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) data and meteorological records. The results show an overall warming and drying trend of the climate and a common degradation tendency of the ecosystem, with a greening trend in higher rugged regions. The pixel‐by‐pixel correlations between NDVI and climate factors indicate that a decrease in GSP mainly affects ecosystems with low precipitation and worse vegetation condition, and superimposes on the effects of increasing AGST which further deteriorate the climate background of these ecosystems. However, the positive correlations between AGST and NDVI in some higher/rugged regions suggest that the raising temperature can ameliorate vegetation growth conditions in these areas. Comparison and combination of the results of three change detection algorithms, i.e. post‐classification comparison (PCC), principal components analysis (PCA) and a newly developed multi‐temporal image difference (MTID) method, show that the integration of different methods can give a more comprehensive understanding of vegetation changes than any single method.     

On the relationship between monthly mean and maximum-value composite normalized vegetation indices

Abstract

A relationship between the maximum-value composite and monthly mean normalized difference vegetation index (NDVI) is derived statistically using data over the U.S. Great Plains during 1986. The monthly mean NDVI is obtained using a simple nine-day compositing technique based on the specifics of the scan patterns of the NOAA-9 Advanced Very High Resolution Radiometer (AVHRR). The results indicate that these two quantities are closely related over grassland and forest during the growing season. It is suggested that in such areas a monthly mean NDVI can be roughly approximated by 80 per cent of the monthly maximum NDVI, the latter being a standard satellite data product. The derived relationship was validated using data for the growing season of 1987.     

Assessing the biomass dynamics of Andean bofedal and totora high-protein wetland grasses from NOAA/AVHRR

Biomass measurements of totora and bofedal Andean wetland grasses in the Bolivian Northern Altiplano were correlated over a growing season to vegetation indices derived from 1-km visible and near-infrared bands of the advanced very high resolution radiometer (AVHRR) instrument flown on the NOAA-14 polar-orbiting meteorological satellite. This article discusses the potential and limits of these indices for the assessment of the spatial and temporal variation of biomass and of the fraction of the photosynthetic active radiation absorbed by these herbaceous native forages growing in water-saturated environments. Bidirectional reflectance distribution function (BRDF) normalization was also investigated based on simple kernel-driven models. BRDF normalized difference vegetation index (NDVI) performed the best for both totorales and bofedales vegetation associations, followed by the uncorrected maximum-value composite NDVI. BRDF normalized NDVI was shown to be sensitive to the green leaf or photosynthetically active biomass.Estimation of biomass production after Kumar and Monteith (1982) was used to determine the efficiency of solar energy conversion into biomass (ε_b) for the main phenological periods, corresponding to the rainy and dry seasons. Two approaches were investigated for the biomass production estimation: the first one is based on monthly field biomass measurements; the second one is based on estimates from the regression computed previously using Roujean's BRDF normalized NDVI. The values found for these efficiencies for the rainy season agree with those of the literature for grasslands of temperate regions. For the dry season, more accurate information on totora and bofedal senescence and on animal consumption is required to get a reasonable efficiency value. This is not surprising, as other workers have reported biomass estimation with remotely sensed data to be most relevant to the growing season.     

Response of seasonal vegetation development to climatic variations in eastern central Asia

Meteorological records show that central Asia has experienced one of the strongest warming signals in the world over the last 30 years. The objective of this study was to examine the seasonal vegetation response to the recent climatic variation on the Mongolian steppes, the third largest grassland in the world. The onset date of green-up for central Asia was estimated using time-series analysis of advanced very high resolution radiometer (AVHRR) normalized difference vegetation index (NDVI) biweekly composite data collected between January 1982 and December 1991. Monthly precipitation and mean temperature data (1982-1990) were acquired from 19 meteorological stations throughout the grasslands of the eastern Mongolian steppes in China. Our results showed that while the taiga forest north of the Mongolian steppes (>50°N) experienced an earlier onset of green-up during the study period, a later onset was observed at the eastern and northern edges of the Gobi Desert (40°N-50°N). Responses of different vegetation types to climatic variability appeared to vary with vegetation characteristics and spring soil moisture availability of specific sites. Plant stress caused by drought was the most significant contributor to later vegetation green-up as observed from satellite imagery over the desert steppe. Areas with greater seasonal soil moisture greened up earlier in the growing season. Our results suggested that water budget limitations determine the pattern of vegetation responses to atmospheric warming.     

NDVI from low altitude aircraft and composited NOAA AVHRR data for scaling Arctic ecosystem fluxes

The relationship between normalized difference vegetation index (NDVI) patterns obtained from high spatial resolution aircraft and low spatial resolution satellite data (Advanced Very High Resolution Radiometer (AVHRR)) was investigated with the intent of using multilevel data to scale carbon flux models in Arctic tundra ecosystems. Despite variable illumination conditions during the aircraft missions and maximum value compositing of the AVHRR data, the difference between 3?km average aircraft and AVHRR NDVI values was generally constant along each flight transect. However, the magnitude of the offset differed between flight dates and small lakes had a greater effect on area averaged aircraft NDVI values than on the satellite values. A cloud index was calculated using incident solar radiation measured by the aircraft and this index was used to identify periods when the aircraft NDVI values may have been biased by cloud cover. Removal of NDVI values based on a cloud index threshold did not appear to be justified given the marginal improvement in the relationship between the two NDVI datasets. If the systematic difference between AVHRR and aircraft NDVI values can be determined, then the scaling of carbon flux models based on the NDVI should be a viable approach in Arctic ecosystems.     

On the relationship of NDVI with leaf area index in a deciduous forest site

Numerous studies have reported on the relationship between the normalized difference vegetation index (NDVI) and leaf area index (LAI), but the seasonal and annual variability of this relationship has been less explored. This paper reports a study of the NDVI-LAI relationship through the years from 1996 to 2001 at a deciduous forest site. Six years of LAI patterns from the forest were estimated using a radiative transfer model with input of above and below canopy measurements of global radiation, while NDVI data sets were retrieved from composite NDVI time series of various remote sensing sources, namely NOAA Advanced Very High Resolution Radiometer (AVHRR; 1996, 1997, 1998 and 2000), SPOT VEGETATION (1998-2001), and Terra MODIS (2001). Composite NDVI was first used to remove the residual noise based on an adjusted Fourier transform and to obtain the NDVI time-series for each day during each year.The results suggest that the NDVI-LAI relationship can vary both seasonally and inter-annually in tune with the variations in phenological development of the trees and in response to temporal variations of environmental conditions. Strong linear relationships are obtained during the leaf production and leaf senescence periods for all years, but the relationship is poor during periods of maximum LAI, apparently due to the saturation of NDVI at high values of LAI. The NDVI-LAI relationship was found to be poor (R² varied from 0.39 to 0.46 for different sources of NDVI) when all the data were pooled across the years, apparently due to different leaf area development patterns in the different years. The relationship is also affected by background NDVI, but this could be minimized by applying relative NDVI.Comparisons between AVHRR and VEGETATION NDVI revealed that these two had good linear relationships (R²=0.74 for 1998 and 0.63 for 2000). However, VEGETATION NDVI data series had some unreasonably high values during beginning and end of each year period, which must be discarded before adjusted Fourier transform processing. MODIS NDVI had values greater than 0.62 through the entire year in 2001, however, MODIS NDVI still showed an “M-shaped” pattern as observed for VEGETATION NDVI in 2001. MODIS enhanced vegetation index (EVI) was the only index that exhibited a poor linear relationship with LAI during the leaf senescence period in year 2001. The results suggest that a relationship established between the LAI and NDVI in a particular year may not be applicable in other years, so attention must be paid to the temporal scale when applying NDVI-LAI relationships.     

Attribution of divergent northern vegetation growth responses to lengthening non-frozen seasons using satellite optical-NIR and microwave remote sensing

The non-frozen (NF) season duration strongly influences the northern carbon cycle where frozen (FR) temperatures are a major constraint to biological processes. The landscape freeze-thaw (FT) signal from satellite microwave remote sensing provides a surrogate measure of FR temperature constraints to ecosystem productivity, trace gas exchange, and surface water mobility. We analysed a new global satellite data record of daily landscape FT dynamics derived from temporal classification of overlapping SMMR and SSM/I 37 GHz frequency brightness temperatures (T_b). The FT record was used to quantify regional patterns, annual variability, and trends in the NF season over northern (≥45°N) vegetated land areas. The ecological significance of these changes was evaluated against satellite normalized difference vegetation index (NDVI) anomalies, estimated moisture and temperature constraints to productivity determined from meteorological reanalysis, and atmospheric CO₂ records. The FT record shows a lengthening (2.4 days decade^?1; p < 0.005) mean annual NF season trend (1979–2010) for the high northern latitudes that is 26% larger than the Northern Hemisphere trend. The NDVI summer growth response to these changes is spatially complex and coincides with local dominance of cold temperature or moisture constraints to productivity. Longer NF seasons are predominantly enhancing productivity in cold temperature-constrained areas, whereas these effects are reduced or reversed in more moisture-constrained areas. Longer NF seasons also increase the atmospheric CO₂ seasonal amplitude by enhancing both regional carbon uptake and emissions. We find that cold temperature constraints to northern growing seasons are relaxing, whereas potential benefits for productivity and carbon sink activity are becoming more dependent on the terrestrial water balance and supply of plant-available moisture needed to meet additional water use demands under a warming climate.     

Temporal and spatial dynamics of phenology along the North–South Transect of Northeast Asia

Vegetation phenology is sensitive to climate change and, as such, is often regarded as an indicator of climate change. It is a common practice to extract vegetation phenological indicators based on satellite remote sensing data. In this study, we used the Advanced Very High Resolution Radiometer (AVHRR) Global Inventory Modeling and Mapping Study (GIMMS) Third-Generation normalized difference vegetation index (NDVI_3G) to investigate temporal and spatial changes in phenology in Northeast Asia. Based on the maximum rate of change in the NDVI and dynamic threshold, we used the Asymmetric Gaussian model, Double Logistic method, and Savitzky-Golay filter to extract the start of the growing season (SOS), the end of the growing season (EOS), and the length of the growing season (LOS), respectively, along the North–South Transect of Northeast Asia (NSTNEA) from 1982 to 2014. We then compared the differences in SOS, EOS, and LOS and considered their spatio-temporal dynamics and relationship with temperature. The results show that the Asymmetric Gaussian model has the highest stability among the three methods. Dynamic thresholds corresponding to the maximum change rate of NDVI were mainly between 0.5 and 0.6. From 1982 to 2014, the SOS in the NSTNEA region occurred approximately 0.19 days earlier each year; the trends in EOS and LOS were not significant. In general, temperature and latitude have a strong linear relationship, both of which significantly impact vegetation phenology in the NSTNEA region. In addition, elevation also significantly impacts on vegetation phenology in the NSTNEA region.     

Monitoring the phenology of Tunisian grazing lands

Abstract

Normalized difference vegetation index (NDVI) data obtained from the Advanced Very High Resolution Radiometer (AVHRR) on board NOAA-9 have been analysed to assess their utility for monitoring the vegetation of Tunisian grazing lands. Preliminary analysis shows that the NDVI provides a sensitive indicator of monthly variations in biomass which correlate with spatial and temporal changes in growing conditions. Investigations suggest that the percentage contribution of the soil background to total recorded reflectance, provides an important limiting factor to the sensitivity of the NDVI, creating a threshold beyond which the accuracy of this index becomes less reliable.     

Temporal responses of NDVI to precipitation and temperature in the central Great Plains,USA

Normalized Difference Vegetation Index (NDVI) is generally recognized as a good indicator of terrestrial vegetation productivity. Understanding climatic influences, in particular precipitation and temperature, on NDVI enables prediction of productivity changes under different climatic scenarios. We examined temporal responses of remotely sensed NDVI to precipitation and temperature during a nine-year period (1989-97) in Kansas. Biweekly (every two weeks) and monthly precipitation data were derived from 410 weather stations and biweekly temperature data were derived from 17 weather stations inside and around the borders of Kansas. Biweekly and monthly climate maps were derived by interpolation. Biweekly growing season (March-October) NDVI values for Kansas were calculated using National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) NDVI images. Average growing season NDVI values were highly correlated with precipitation received during the current growing season and seven preceding months (15-month duration); biweekly NDVI values were correlated with precipitation received during 2-4 preceding biweekly periods; and response time of NDVI to a major precipitation event was typical 1-2 biweekly periods (2-4 weeks). Temperature was positively correlated with NDVI early and late in the growing season, and there was a weak negative correlation between temperature and NDVI in the mid growing season. Precipitation has the primary influence on NDVI and, by inference, on productivity. The relationship between precipitation and NDVI is strong and predictable when viewed at the appropriate spatial scale.     

A statistical analysis of the relationship between climatic factors and the Normalized Difference Vegetation Index in China

Climate change has a large impact on vegetation dynamics. A series of statistical analyses were employed to demonstrate the relationship between Advanced Very High Resolution Radiometer (AVHRR) Global Inventory Modelling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI) data with an 8?×?8 km resolution and meteorological data, during the period 1982–2005. Rainfall has a great impact on vegetation with varying time lags. The sensitivity of NDVI to the threshold of accumulated temperature varies regionally. To identify a ‘best factor’ for each meteorological station simple and partial correlation analyses were carried out. Multiple correlation analysis was used to validate the association between the two climatic factors and monthly maximum NDVI (MNDVI). This study led to the conclusion that good correlations between MNDVI and two climatic factors are prevalent in China. It also indicated that the ‘best factors’ for some regions identified by partial correlation analysis are better than those selected by simple correlation analysis. The partial correlation coefficients of MNDVI and each climate factor were calculated to describe the singular influence of each meteorological variable. The results indicated that the impact of other variables on vegetation should be considered in the ‘best factor’ selection for one climatic variable. Temperature has a significant positive influence on vegetation growth in China. Precipitation is the most important climatic factor that closely correlates with MNDVI, particularly in arid and semi-arid environments. However, in some wet regions, precipitation is not a limiting factor on vegetation growth. A trend analysis was carried out to study climate change and its impacts on vegetation. The annual accumulated temperature had an increasing trend in China during 1982–2005. Temperature increases had different influences on vegetation dynamics in different parts of China. The results coincided with those of the multiple and partial correlation analysis.     

TVDI在冬小麦春季干旱监测中的应用

应用冬小麦春季生长期的NOAA/AVHRR资料,反演归一化植被指数（NDVI）、土壤调整植被指数（SAVI）和下垫面温度（T_s）,分析了植被指数和下垫面温度空间特征,采用温度植被旱情指数（TVDI）,研究了河北省2005年3~5月的冬小麦旱情状况。结果表明：基于SAVI的温度植被旱情指数与土壤表层相对湿度的相关性好于基于NDVI的温度植被旱情指数。通过与气象站土壤水分观测资料进行相关性分析,表明温度植被旱情指数与10 cm土壤相对湿度关系最好,20 cm次之,50 cm较差。因此,基于SAVI的温度植被旱情指数更适于监测冬小麦春季的旱情。     

Interannual covariability between actual evapotranspiration and PAL and GIMMS NDVIs of northern Asia

This study examined the covariability between interannual changes in the normalized difference vegetation index (NDVI) and actual evapotranspiration (ET). To reduce possible uncertainty in the NDVI time series, two NDVI datasets derived from Pathfinder AVHRR Land (PAL) data and the Global Inventory Monitoring and Modeling Studies (GIMMS) group were used. Analyses were conducted using data over northern Asia from 1982 to 2000. Interannual changes over 19 years in the PAL-NDVI and GIMMS-NDVI were compared with interannual changes in ET estimated from model-assimilated atmospheric data and gridded precipitation data. For both NDVI datasets, the annual maximum correlation with ET occurred in June, which is the beginning of the vegetation growing season. The PAL and GIMMS datasets showed a significant, positive correlation between interannual changes in the NDVI and ET over most of the vegetated land area in June. These results suggest that interannual changes in vegetation activity predominantly control interannual changes in ET in June. Based on analyses of interannual changes in temperature, precipitation, and the NDVI in June, the study area can be roughly divided into two regions, the warmth-dominated northernmost region and the wetness-dominated southern region, indicating that interannual changes in vegetation and the resultant interannual changes in ET are controlled by warmth and wetness in these two regions, respectively.     

Quantifying characteristic growth dynamics in a semi-arid grassland ecosystem by predicting short-term NDVI phenology from daily rainfall: a simple four parameter coupled-reservoir model

Predicting impacts on phenology of the magnitude and seasonal timing of rainfall pulses in water-limited grassland ecosystems concerns ecologists, climate scientists, hydrologists, and a variety of stakeholders. This report describes a simple, effective procedure to emulate the seasonal response of grassland biomass, represented by the satellite-based normalized difference vegetation index (NDVI), to daily rainfall. The application is a straightforward adaptation of a staged linear reservoir that simulates the pulse-like entry of rainwater into the soil and its redistribution as soil moisture, the uptake of water by plant roots, short-term biomass development, followed by the subsequent transpiration of water through foliage. The algorithm precludes the need for detailed, site specific information on soil moisture dynamics, plant species, and the local hydroclimate, while providing a direct link between discrete rainfall events and consequential biomass responses throughout the growing season. We applied the algorithm using rainfall data from the Central Plains Experimental Range to predict vegetation growth dynamics in the semi-arid shortgrass steppe of North America. The mean annual rainfall is 342 mm, which is strongly bifurcated into a dominantly ‘wet’ season, where during the three wettest months (May, June and July) the mean monthly rainfall is approximately 55 mm month^?1; and a ‘dry’ season, where during the three driest months (December, January and February), the mean monthly rainfall is approximately 7 mm month^?1. NDVI data from the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD13Q1 16 day, 250 m × 250 m product were used as a proxy for grassland phenology for the period-of-record 2000–2013. Allowing for temporal changes in basic parameters of the response function over the growing season, the predicted response of the model tracks the observed NDVI metric with correlation coefficients exceeding 0.92. A two-stage series reservoir is preferred, whereby the characteristic time for transfer of a rainfall event to the peak response of NDVI decreases from 24 days (early growing season) to 12 days (late growing season), while the efficiency of a given volume of rainfall to produce a correspondingly similar amount of aboveground biomass decreases by a factor of 40% from April to October. Behaviours of the characteristic time of greenup and loss of rainfall efficiency with progression of the growing season are consistent with physiological traits of cool-season C₃ grasses versus warm-season C₄ grasses, and with prior research suggesting that early season production by C₃ grasses is more responsive to a given amount of precipitation than mid-summer growth of C₄ shortgrasses. Our model explains >90% of seasonal biomass dynamics. We ascribe a systematic underprediction of observed early season greenup following drought years to a lagged or ‘legacy’ effect, as soil inorganic nitrogen, accumulated during drought, becomes available for future plant uptake.