Drought assessment and monitoring through remote sensing and GIS in western tracts of Tamil Nadu,India

Drought is an insidious hazard of nature and is considered to be the most complex but least understood of all natural hazards. Large historical datasets are required to study drought and these involve complex interrelationships between climatological and meteorological data. Rainfall is an important meteorological parameter; the amount and distribution influence the type of vegetation in a region. To analyse the changes in vegetation cover due to variation in rainfall and identify the land-use areas facing drought risk, rainfall data from 1981 to 2003 were categorized into excess, normal, deficit and drought years. The Advanced Very High Resolution Radiometer (AVHRR) sensor's composite dataset was used for analysing the temporal and interannual behaviour of surface vegetation. The various land-use classes – crop land (annual, perennial crops), scrub land, barren land, forest land, degraded pasture and grassland – were identified using satellite data for excess, normal, deficit and drought years. Normalized Difference Vegetation Indices (NDVIs) were derived from satellite data for each land-use class and the highest NDVI mean values were 0.515, 0.436 and 0.385 for the tapioca crop in excess, normal and deficit years, respectively, whereas in the drought year, the groundnut crop (0.267) showed the maximum. Grassland recorded the lowest value of NDVI in all years except for the excess year. Annual crops, such as groundnut (0.398), pulses (0.313), sorghum (0.120), tapioca (0.436) and horse gram (0.259), registered comparatively higher NDVI values than the perennial crops for the normal year. The Vegetation Condition Index (VCI) was used to estimate vegetation health and monitor drought. Among land-use classes, the maximum VCI value of 92.1% was observed in onions for the excess year, whereas groundnut witnessed the maximum values of 78.2, 64.5 and 55.2% for normal, deficit and drought years, respectively. Based on the VCI classification, all land-use classes fall into the optimal or normal vegetation category in excess and normal years, whereas in drought years most of the land-use classes fall into the drought category except for sorghum, groundnut, pulses and grasses. These crops (sorghum 39.7%, groundnut 55.2%, pulses 38.5% and grassland 38.6%) registered maximum VCI values, revealing that they were sustained under drought conditions. It is suggested that the existing crop pattern be modified in drought periods by selecting the suitable crops of sorghum, groundnut and pulses and avoiding the cultivation of onion, rice and tapioca.     

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

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

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

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

Identification of drought‐vulnerable areas using NOAA AVHRR data

Drought is a recurring phenomenon in many parts of India, bringing significant water shortages, economic losses and adverse social consequences. The western regions of India (Rajasthan and Gujarat provinces) have suffered with severe droughts several times in the past. In this study meteorological and satellite data were used for monitoring drought in the southern part of Rajasthan. Monthly rainfall data from six stations were used to derive the Standardized Precipitation Index (SPI). The Advanced Very High Resolution Radiometer (AVHRR) onboard the National Oceanic and Atmospheric Administration (NOAA) series of satellite was used for calculating brightness temperature (BT), the Normalized Difference Vegetative Index (NDVI) and the Water Supplying Vegetation Index (WSVI). BT was converted to the Vegetation Condition Index (VCI) and the Temperature Condition Index (TCI), which are useful indices for the estimation of vegetation health and drought monitoring. The analysis was carried out for a period of four years (2002–2005) and from the SPI analysis it was observed that 2002 was a drought year. On the basis of the satellite‐based indices, the study area was divided into categories of extreme, severe, moderate and slight drought and normal condition. We found that in 2002 all of the area under study was affected by drought with greater intensity, mostly classed as extreme and severe drought conditions. An analysis was carried out of the study area divided into four zones on the basis of rainfall distribution, soil characteristics, cropping patterns and other physical characteristics. This analysis revealed that zone 1 was the most drought‐prone area in all four years; zone 4 was the next area most affected by severe drought, followed by zones 2 and 3, which were less affected by drought conditions.     

A comparative study of NOAA-AVHRR derived drought indices using change vector analysis

The objective of this study was to compare the spatial occurrences of droughts, detected by remotely sensed drought-indices over the desert-steppe and desert geo-botanical zones of Mongolia. All indices were derived from reflectance and thermal data sets, obtained from the NOAA-AVHRR data between 1982 and 1999. One group of the drought-indices is based on vegetation state derived from the reflective channels. This group includes the Normalized Difference Vegetation Index (NDVI), Anomaly of Normalized Difference Vegetation Index (NDVIA), Standardized Vegetation Index (SVI), and Vegetation Condition Index (VCI). Another group, based on surface brightness temperature derived from the thermal channel of NOAA-AVHRR, includes the Temperature Condition Index (TCI). The third group is based on combination between the reflective and thermal channels includes the ratio between Land Surface Temperature (LST) and NDVI (LST/NDVI), the Vegetation Health Index (VH), and the Drought Severity Index (DSI). Change detection procedure was performed by using the Change Vector Analysis in the temporal domain. Comparison analysis among the drought-indices reveals that there is no spatial coincidence between them, even when the vegetation growing period was divided into 2-month sub-periods — beginning, middle, and end. Based on the statistical analysis, higher correlations were found among the reflective indices while lesser or no relationships were found between the thermal and combination of the thermal and reflective indices. Furthermore, no agreement was found between the spatial extent of the satellite-derived drought-indices and the meteorological-based Palmer Drought Severity Index (PDSI) and also between the traditional ground-observed drought-affected-areas (DAA) maps. It was found that the combination of satellite-derived drought-indices can identify wider drought-occurred areas rather than the PDSI and the DAA maps. In summary, this study concludes that it is difficult to point out the most reliable drought index, and that the ground observations cannot provide sufficient information for validation of satellite derived drought indices.     

Phenologically-tuned MODIS NDVI-based production anomaly estimates for Zimbabwe

For thirty years, simple crop water balance models have been used by the early warning community to monitor agricultural drought. These models estimate and accumulate actual crop evapotranspiration, evaluating environmental conditions based on crop water requirements. Unlike seasonal rainfall totals, these models take into account the phenology of the crop, emphasizing conditions during the peak grain filling phase of crop growth. In this paper we describe an analogous metric of crop performance based on time series of Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) imagery. A special temporal filter is used to screen for cloud contamination. Regional NDVI time series are then composited for cultivated areas, and adjusted temporally according to the timing of the rainy season. This adjustment standardizes the NDVI response vis-à-vis the expected phenological response of maize. A national time series index is then created by taking the cropped-area weighted average of the regional series. This national time series provides an effective summary of vegetation response in agricultural areas, and allows for the identification of NDVI green-up during grain filling. Onset-adjusted NDVI values following the grain filling period are well correlated with U.S. Department of Agriculture production figures, possess desirable linear characteristics, and perform better than more common indices such as maximum seasonal NDVI or seasonally averaged NDVI. Thus, just as appropriately calibrated crop water balance models can provide more information than seasonal rainfall totals, the appropriate agro-phenological filtering of NDVI can improve the utility and accuracy of space-based agricultural monitoring.     

Early cotton yield assessment by the use of the NOAA/AVHRR derived Vegetation Condition Index (VCI) in Greece

Satellite data can significantly contribute to agricultural monitoring. The reflected radiation, as recorded by satellite sensors, provides an indication of the type, density and condition of canopy. A widely used index for vegetation monitoring is the Normalized Difference Vegetation Index (NDVI) derived from the National Oceanic and Atmospheric Administration/Advanced Very High Resolution Radiometer (NOAA/AVHRR) data provided in high temporal resolution. An extension of the NDVI is the Vegetation Condition Index (VCI). VCI is a tool for monitoring agrometeorological conditions, providing a quantitative estimation of weather impact to vegetation. The primary objective of this paper is the quantitative assessment of the cotton yield before the end of the growing season by examining the weather effects as they are depicted by the VCI. The study area comprises several cotton producing areas in Greece. Ten-day NDVI maximum value composites (MVC) are initially utilized for the period 1982–1999. The correlation between VCI images as extracted from NDVI and the 10-day intervals during the growing season is examined to identify the critical periods associated mostly with the yield. Empirical relationships between VCI and yield are developed. The models are tested on an independent dataset. The results show that an early estimation of the cotton yield trend is feasible by the use of the VCI.     

Vegetation and temperature condition indices from NOAA AVHRR data for drought monitoring over India

The Advanced Very High Resolution Radiometer (AVHRR) onboard the National Oceanic and Atmospheric Administration (NOAA) series of satellites has been used for mapping vegetation cover and classification employing the Normalized Difference Vegetation Index (NDVI). Recently, this technique has been improved by converting NDVI with radiation measured in one of the thermal channels and converting brightness temperature into the Vegetation Condition Index (VCI) and Temperature Condition Index (TCI). These indices are being used for estimation of vegetation health and monitoring drought. The present study shows the application of vegetation and temperature condition indices for drought monitoring in India.     

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

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

ENSO drought onset prediction in northeast Brazil using NDVI

El Niño Southern Oscillation (ENSO) indices and satellite-recorded Normalized Difference Vegetation Index (NDVI) were used to construct a drought onset prediction model for northeast Brazil (NEB) using a multiple linear regression technique. Monthly NDVI and ENSO indices anomaly data for the period January 1981 to December 1993 were used to develop the model, while those of 1951 to 1998 were used to simulate the NDVI anomaly time series for model validation. Three different regression models were constructed using the NDVI anomaly as dependent variable and various ENSO indices anomalies including: Sea Surface Temperature in the Pacific Ocean area (5°N-5°S and 120°W-170°W, called Niño3.4), Southern Oscillation Index (SOI), North Atlantic Sea Surface Temperature (NATL), South Atlantic Sea Surface Temperature (SATL) and Dipole 2 (DIP2=SATL-NATL), as independent variables. Model 1 was constructed using 12-month NDVI data while Models 2 and 3 used data from only four months (September to December). The results showed that R 2 values of 0.38, 0.62 and 0.79 at a significance level of 1% were obtained for Model 1, Model 2 and Model 3 respectively. Simulated NDVI anomaly values agreed quite well with observed values for all three models but Model 3 had a better intensity estimate. The simulated dynamic evolution of the NDVI anomaly of 1951 to 1998 showed that the predicted NDVI anomalies coincided with historical ENSO induced drought events reported in the literature. It is concluded that the use of satellite-recorded NDVI instead of rainfall data improved the correlation with ENSO indices. Drought onset Model 3, based on the dataset with high anomaly values of NDVI and ENSO indices, predicted drought onset in NEB four months before its occurrence with reasonable success (68%). Combined use of ENSO indices and NDVI inferred drought may provide a better alternative to the construction of an ENSO drought onset prediction model for other regions. Further studies will be carried out to investigate the ENSO drought and flood onsets in the southeastern South America.     

Using the NDVI as auxiliary data for rapid quality assessment of rainfall estimates in Africa

Rainfall estimates derived from satellite imagery and global circulation models are frequently used for vegetation monitoring in many areas of Africa because of the shortage of observed rainfall data and the sparse network of meteorological stations. At the same time, this scarce density of rain gauge stations makes the calibration and validation of the modelled data nearly impossible. In this study we propose a methodology for a rapid quality assessment of rainfall estimates that is based on the well-known relationship between rainfall and the Normalized Difference Vegetation Index (NDVI). The results clearly confirm that the NDVI can be used as an indicator of the quality of rainfall estimates at the continental/regional scale and allow a rapid detection of major over- and underestimations of the two rainfall datasets examined for the African continent.     

Early cotton production assessment in Greece based on a combination of the drought Vegetation Condition Index (VCI) and the Bhalme and Mooley Drought Index (BMDI)

A new methodological approach is presented for quantifying the meteorological effects on cotton production during the growing season in Greece. The proposed Bhalme and Mooley Vegetation Condition Index (BMVCI) is based on the Vegetation Condition Index (VCI) extracted by National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) data. In this approach the VCI data is processed with the Bhalme and Mooley methodology to assess the accumulated meteorological effects on cotton from April to August. The resulting index is at the same scale as the Z-Index, which is the classification of the Palmer Drought Severity Index (PDSI) extensively used for drought monitoring. For this study 16 years of data are examined to illustrate that the weather development as identified from satellite data with the use of BMVCI confirm unfavourable conditions for cotton production. For the validation of BMVCI an empirical relationship between the cotton production and the BMVCI values is derived. The resultant high correlation coefficient refers to very encouraging results and confirms the usefulness of the proposed integrated methodological approach as an effective tool for early assessment of the cotton production in Greece.     

NOAA-AVHRR NDVI decomposition and subpixel classification using linear mixing in the Argentinean Pampa

Crop Normalized Difference Vegetation Index (NDVI) time profiles and crop acreage estimates were derived from the application of linear mixture modelling to Advanced Very High Resolution Radiometer (AVHRR) data over a test area in the southern part of the Pampa region, Argentina. Bands 1 and 2 from seven AVHRR scenes (June to January 1991) were combined to produce fraction images of winter crops, summer crops and pastures. A Landsat Thematic Mapper (TM) scene of the region was classified and superimposed to the AVHRR Local Area Coverage (LAC) data by means of a correlation technique. Each class signature was extracted by regressing the AVHRR response on the cover types proportions, estimated from Landsat-TM data, over sets of calibration windows. The crop NDVI profiles were hence derived from the class signatures in bands 1 and 2. These profiles appeared consistent with the cover types, but variability depending on the set of windows was noted. The assessment of the class signatures was indirectly accomplished through the subpixel classifications of the AVHRR data, performed using the different sets of class spectra. Although some discrepancies between AVHRR and Landsat–TM estimates were observed at the individual window level, the classification results compared quite well on a regional scale with Landsat–TM estimates: crop acreage was estimated to an overall accuracy ranging from 89 to 95 per cent according to the spectra used in the classification. Definitely, the proposed methodology should permit a better exploitation of the temporal resolution of AVHRR data in both the areas of yield prediction and vegetation classification. Furthermore, the perational application of such a methodology for crop monitoring will undoubtedlybe facilitated with the coming sensor systems such as the ModerateResolution Imaging Spectroradiometer (MODIS), the SPOT Vegetation Monitoring Instrument or the ‘Satelite Argentino Cientifico’ (SAC–C).     

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

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

RS、GIS、GPS在西北农业大开发中的应用前景

遥感(RS)、地理信息系统(GIS)和全球定位系统(GPS)作为三大高新技术(“3S”技术),可以 独立地,也可以相互补充地为农业生产和开发提供强大的技术支撑。它们能快速准确地获取农业生 产系统的多维信息,尤其是时间维的信息,能综合性地管理和处理属性数据和空间数据,并能为农 业生产的决策提供相应的技术服务,进而精确地指导农业生产,促进生态环境的良性发展。论述了 “3S”技术在西北地区农业开发中的应用前景,着重于土壤水分的遥感反演以及干旱和荒漠化的动 态监测。     

Remote sensing of US cornbelt areas sensitive to the El Ni@o-Southern Oscillation

A 16-year time-series (1982-1997) of monthly maximum Normalized Difference Vegetation Index (NDVI) values derived from coarse-scale Advanced Very High Resolution Radiometer (AVHRR) satellite images was investigated for its application in identifying agricultural regions sensitive to El Ni@o-Southern Oscillation (ENSO) impacts. Cumulative NDVI values over the growing season were used to test the temporal and spatial sensitivity of rainfed agricultural regions in the US cornbelt to ENSO climate teleconnections. The correlation (R) between NDVI anomalies and yield anomalies aggregated to the Agricultural Statistics Division (ASD) level was 0.59 (significant at the 99% level). Sea surface temperatures (SSTs) from the NINO3 region (between +5.0° and m 5.0° latitude and 90.0° and 180.0° west longitude in the Pacific Ocean), an indicator of the ENSO phenomenon, were significantly but weakly correlated with growing season NDVI anomalies, precipitation anomalies, and yield anomalies. Two classification schemes for El Niño, La Niña and Neutral years related to the crop growing season were applied--one based on simultaneous Pacific NINO3 SSTs (spring-summer), and one based on following winter Pacific NINO3 SSTs. The strongest differences in Vegetation Condition Index (VCI) spatial patterns among the three ENSO categories were found using the following winter SST classification. Classification of ENSO years is a key issue in analysing ENSO impacts on agriculture as represented by the NDVI, because the regularity of annual agricultural seasons is not synchronous with the quasi-regular Pacific SST cycles.     

Discrimination of irrigated and rainfed rice in a tropical agricultural system using SPOT VEGETATION NDVI and rainfall data

The classification of irrigated crops by remote sensing requires the use of time series data, since the timing, cropping intensity and duration of cropping is quite variable over the course of a year. Rice is the dominant irrigated crop in tropical and sub‐tropical Asia, where rainfall is high, but is seasonal and often uni‐modal. Existing crop classification methods for rice are not able to distinguish between rainfed and irrigated crops, leading to errors in classification and estimated irrigated area. This paper describes a technique, a ‘peak detector algorithm’, to successfully discriminate between rainfed and irrigated rice crops in Suphanburi province, Thailand. The methodology uses a three‐year time series of Satellite pour l'Observation de la Terre (SPOT) VEGETATION S10 Normalized Difference Vegetation Index (NDVI) data (10 day composites) to identify cropping intensity (number, timing and peak values). Peak NDVI is then lag‐correlated with long term average rainfall data. There is a high correlation at a 40–50 day lag, between a peak rainfall and a ‘single’ peak NDVI of rainfed rice. In irrigated areas, there are multiple peaks, and multiple correlations with low values for at least 90 days after peak rainfall. The methodology currently uses a mask to remove un‐cropped and non‐rice areas, which is derived from existing Geographical Information Systems (GIS). The method achieves a classification accuracy of 89% or better against independent groundtruth data. The procedure is designed as a second level of analysis to refine classifications using other techniques of mapping irrigated area at global and regional scales.     

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

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

Estimating spatio-temporal patterns of agricultural productivity in fragmented landscapes using AVHRR NDVI time series

The characteristics of Normalized Difference Vegetation Index (NDVI) time series can be disaggregated into a set of quantitative metrics that may be used to derive information about vegetation phenology and land cover. In this paper, we examine the patterns observed in metrics calculated for a time series of 8 years over the southwest of Western Australia—an important crop and animal production area of Australia. Four analytical approaches were used; calculation of temporal mean and standard deviation layers for selected metrics showing significant spatial variability; classification based on temporal and spatial patterns of key NDVI metrics; metrics were analyzed for eight areas typical of climatic and production systems across the agricultural zone; and relationships between total production and productivity measured by dry sheep equivalents were developed with time integrated NDVI (TINDVI). Two metrics showed clear spatial patterns; the season duration based on the smooth curve produced seven zones based on increasing length of growing season; and TINDVI provided a set of classes characterized by differences in overall magnitude of response, and differences in response in particular years. Frequency histograms of TINDVI could be grouped on the basis of a simple shape classification: tall and narrow with high, medium or low mean indicating most land is responsive agricultural cover with uniform seasonal conditions; broad and short indicating that land is of mixed cover type or seasonal conditions are not spatially uniform. TINDVI showed a relationship to agricultural productivity that is dependent on the extent to which crop or total agricultural production was directly reduced by rainfall deficiency. TINDVI proved most sensitive to crop productivity for Statistical Local Areas (SLAs) having rainfall less than 600 mm, and in years when rainfall and crop production were highly correlated. It is concluded that metrics from standardized NDVI time series could be routinely and transparently used for retrospective assessment of seasonal conditions and changes in vegetation responses and cover.     

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.