The effect of land-cover change on vegetation greenness-based satellite agricultural drought indicators: a case study in the southwest climate division of Indiana,USA

During the last decade, the use of the normalized difference vegetation index (NDVI) for drought monitoring applications has drawn many criticisms, mainly because a number of drivers such as land-cover/land-use change, pest infestation, and flooding may depress the NDVI, further causing false drought identification. In this study, the impacts of land-cover change on the NDVI-derived satellite drought indicator, the vegetation condition index (VCI), are presented. It was found that the VCI is sensitive to changes in land cover, especially deforestation, the land cover changes from evergreen and deciduous forests to other land-cover classes. However, because the scale of land-cover changes was very small across the study area, only trivial drought alerts were observed in the VCI-based drought maps during non-drought years. Because drought is a large-scale climate event, it is reasonable to neglect these alerts. Besides, when the VCI was averaged to climate division scale, the results obtained through the VCI method were in good agreement with those acquired by the meteorological data-based drought indices such as the Palmer drought severity index and standardized precipitation index.     

Monitoring regional drought using the Vegetation Condition Index

NDVI (Normalized Difference Vegetation Index) images generated from NOAA AVHRR GVI data were recently used to monitor large scale drought patterns and their climatic impact on vegetation. The purpose of this study is to use the Vegetation Condition Index (VCI) to further separate regional NDVI variation from geographical contributions in order to assess regional drought impacts. Weekly NDVI data for the period of July 1985 to June 1992 were used to produce NDVI and VCI images for the South American continent. NDVI data were smoothed with a median filtering technique for each year. Drought areas were delineated with certain threshold values of the NDVI and VCI. Drought patterns delineated by the NDVI and VCI agreed quite well with rainfall anomalies observed from rainfall maps of Brazil. NDVI values reflected the different geographical conditions quite well. Seasonal and interannual comparisons of drought areas delineated by the VCI provided a useful tool to analyse temporal and spatial evolution of regional drought as well as to estimate crop production qualitatively. It is suggested that VCI data besides NDVI may be used to construct a large scale crop yield prediction model.     

Analysis of multi-temporal SPOT NDVI images for small-scale land-use mapping

Land-use information is required for a number of purposes such as to address food security issues, to ensure the sustainable use of natural resources and to support decisions regarding food trade and crop insurance. Suitable land-use maps often either do not exist or are not readily available. This article presents a novel method to compile spatial and temporal land-use data sets using multi-temporal remote sensing in combination with existing data sources. Satellite Pour l'Observation de la Terre (SPOT)-Vegetation 10-day composite normalized difference vegetation index (NDVI) images (1998–2002) at 1km² resolution for a part of the Nizamabad district, Andhra Pradesh, India, were linked with available crop calendars and information about cropping patterns. The NDVI images were used to stratify the study area into map units represented by 11 distinct NDVI classes. These were then related to an existing land-cover map compiled from high resolution Indian Remote Sensing (IRS)-images (Liss-III on IRS-1C), reported crop areas by sub-district and practised crop calendar information. This resulted in an improved map containing baseline information on both land cover and land use. It is concluded that each defined NDVI class represents a varying but distinct mix of land-cover classes and that the existing land-cover map consists of too many detailed ‘year-specific’ features. Four groups of the NDVI classes present in agricultural areas match well with four categories of practised crop calendars. Differences within a group of NDVI classes reveal area specific variations in cropping intensities. The remaining groups of NDVI classes represent other land-cover complexes. The method illustrated in this article has the potential to be incorporated into remote sensing and Geographical Information System (GIS)-based drought monitoring systems.     

Land-use land-cover classification analysis of Giba catchment using hyper temporal MODIS NDVI satellite images

Landsat-based land-use land-cover (LULC) mapping studies were previously conducted in Giba catchment, comprising an area of 4019 km². No attempt has been done to map LULC of this catchment through the analysis of Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) time-series data. This article is aimed to see whether time-series MODIS NDVI data set is applicable for LULC mapping of Giba catchment or not. MODIS NDVI data sets of the year 2010 were used for classification analysis. The original data were subjected to MODIS Reproduction Tool and stacking. The re-projected and stacked images were filtered using Harmonic Analysis of Time-Series filtering algorism to remove the effects of cloud and other noises. The MODIS NDVI data sets (16-day maximum value composite) were classified using the ISODATA clustering algorithm available under ERDAS IMAGINE software. A series of unsupervised classification runs were carried out with a pre-defined number of classes (5–24). From this classification, the optimal numbers of classes were determined to be eight after checking for average divergence analysis. The classification result became eight LULC classes namely: bare land, grass land, irrigated land, cultivated land, area closure, shrub land, bush land, and forest land with an overall accuracy of 87.7%. It was therefore concluded that MODIS NDVI time-series image is applicable for mapping large watersheds.     

Research on Crop Classification Method based on Sentinel-2 Time Series Combined Vegetation Index

Time series is a widely used phenological research method. A new time series vegetation indices which takes full advantage of the red edge information of Sentinel 2 data were used for crop classification to improve the classification accuracy. The NDVI, EVI, and red edge NDVI were combined to construct a time series vegetation index image. Then, four different algorithms (support vector machine, random forest, CART decision tree and maximum likelihood) were used to classify four crops, three forest grasses, bare land, and water bodies. Among the original classification results, the random forest with the highest overall accuracy is 87.92%, and the maximum likelihood with the lowest overall accuracy is 80.07%. In the classification details, the boundaries of random forest and support vector machine are the clearest. Among the four classification results, the classification accuracy of crops is higher than other land types, just smaller than water body. The error mainly comes from the mixture of three forests. It indicates that the time series combined vegetation index is feasible and accurate for crop classification.     

基于哨兵2时间序列组合植被指数的作物分类研究

时间序列是一种常用的物候研究方法。为充分利用哨兵2数据在红边波段的丰富信息,本文利用多种植被指数组合成时间序列进行作物分类。将NDVI、EVI、红边NDVI三种植被指数进行组合,构建时序植被指数图像,然后使用支持向量机、随机森林、CART决策树和最大似然4种不同的算法对四种作物、三种林草、裸露地表、水体进行分类。原始分类结果中,总体精度最高的随机森林为87.92%,最低的最大似然为80.07%,在分类细节上,随机森林和支持向量机的边界最清晰,4种分类结果中,农作物的分类精度均高于其他地类,仅次于水体的精度,误差主要来自三种林草的混分,表明时间序列组合植被指数用于农作物分类是可行的。     

The application of time-series MODIS NDVI profiles for the acquisition of crop information across Afghanistan

We investigated and developed a prototype crop information system integrating 250 m Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data with other available remotely sensed imagery, field data, and knowledge as part of a wider project monitoring opium and cereal crops. NDVI profiles exhibited large geographical variations in timing, height, shape, and number of peaks, with characteristics determined by underlying crop mixes, growth cycles, and agricultural practices. MODIS pixels were typically bigger than the field sizes, but profiles were indicators of crop phenology as the growth stages of the main first-cycle crops (opium poppy and cereals) were in phase. Profiles were used to investigate crop rotations, areas of newly exploited agriculture, localized variation in land management, and environmental factors such as water availability and disease. Near-real-time tracking of the current years’ profile provided forecasts of crop growth stages, early warning of drought, and mapping of affected areas. Derived data products and bulletins provided timely crop information to the UK Government and other international stakeholders to assist the development of counter-narcotic policy, plan activity, and measure progress. Results show the potential for transferring these techniques to other agricultural systems.     

Large-area crop mapping using time-series MODIS 250 m NDVI data: An assessment for the U.S. Central Great Plains

Improved and up-to-date land use/land cover (LULC) data sets that classify specific crop types and associated land use practices are needed over intensively cropped regions such as the U.S. Central Great Plains, to support science and policy applications focused on understanding the role and response of the agricultural sector to environmental change issues. The Moderate Resolution Imaging Spectroradiometer (MODIS) holds considerable promise for detailed, large-area crop-related LULC mapping in this region given its global coverage, unique combination of spatial, spectral, and temporal resolutions, and the cost-free status of its data. The objective of this research was to evaluate the applicability of time-series MODIS 250 m normalized difference vegetation index (NDVI) data for large-area crop-related LULC mapping over the U.S. Central Great Plains. A hierarchical crop mapping protocol, which applied a decision tree classifier to multi-temporal NDVI data collected over the growing season, was tested for the state of Kansas. The hierarchical classification approach produced a series of four crop-related LULC maps that progressively classified: 1) crop/non-crop, 2) general crop types (alfalfa, summer crops, winter wheat, and fallow), 3) specific summer crop types (corn, sorghum, and soybeans), and 4) irrigated/non-irrigated crops. A series of quantitative and qualitative assessments were made at the state and sub-state levels to evaluate the overall map quality and highlight areas of misclassification for each map.The series of MODIS NDVI-derived crop maps generally had classification accuracies greater than 80%. Overall accuracies ranged from 94% for the general crop map to 84% for the summer crop map. The state-level crop patterns classified in the maps were consistent with the general cropping patterns across Kansas. The classified crop areas were usually within 1-5% of the USDA reported crop area for most classes. Sub-state comparisons found the areal discrepancies for most classes to be relatively minor throughout the state. In eastern Kansas, some small cropland areas could not be resolved at MODIS' 250 m resolution and led to an underclassification of cropland in the crop/non-crop map, which was propagated to the subsequent crop classifications. Notable regional areal differences in crop area were also found for a few selected crop classes and locations that were related to climate factors (i.e., omission of marginal, dryland cropped areas and the underclassification of irrigated crops in western Kansas), localized precipitation patterns (overclassification of irrigated crops in northeast Kansas), and specific cropping practices (double cropping in southeast Kansas).     

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.     

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.     

Spatial and temporal responses of different crop‐growing environments to agricultural drought: a study in Haryana state,India using NOAA AVHRR data

Spatial and temporal responses to agricultural drought of different districts with different crop‐growing environments were assessed using National Oceanic & Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR)‐derived monthly time composite Normalized Difference Vegetation Index (NDVI) images of a drought year (2002) and a normal year (2004) in Haryana state, one of the most prolific agricultural states of India. The seasonal NDVI profiles derived from NOAA AVHRR data, despite coarse spatial resolution, successfully captured the response of vulnerable districts to drought events. The greenness (NDVI) in mid‐season and at the end of the season of drought and normal years was compared. Districts having less irrigation support due to insufficient canal supplies and poor quality of groundwater had very high NDVI deviation from normal, signifying the impact of severe drought conditions in terms of reduced/delayed sown area, poor germination etc. in the year 2002. The districts with high irrigation support (surface water plus good quality groundwater) have either higher NDVI or insignificant deviation from a normal year and are not influenced by meteorological drought. Thus, quality of groundwater in different districts is a key factor to determine the vulnerability and sensitivity of the district to meteorological drought events in the study area state. The results of the study are relevant for vulnerability mapping and drought hazard zonation in the state to aid in‐season and long‐term management of droughts.     

Influence of plant pigments on spectral reflectance of maize,groundnut and soybean grown in semi-arid environments

Abstract. Spectral reflectance of leaves is influenced primarily by plant pigments, chlorophyll and carotenoids. Such reflectance can be used to study the changes in chlorophyll content and nitrogen status and in turn measures the amount of biomass accumulation. A field experiment was laid out at the Research Farm of ANGR Agricultural University, Hyderabad. The reflectance observations were taken using a hand-held ground radiometer at an interval of 15 days beginning from 30 days after sowing (DAS) until harvest of the crops. The plant pigments were determined simultaneously using DMSO (dimethyl sulphoxide) method in the laboratory. The experimental results revealed the influence of plant pigments on spectral reflectance of maize, groundnut and soybean. It was observed that there was an increase in chlorophyll- a, chlorophyll- b, total chlorophyll and carotenoid content up to flowering and thereafter chlorophyll- a content declined at a faster rate than chlorophyll- b towards physiological maturity. With the increase in concentrations of chlorophyll and carotenoids, there was a decline in spectral reflectance of the blue band (450-520 nm) and the red band (620-680 nm). Whereas, NIR (near-infrared) reflectance in the case of soybean and groundnut was found to be higher than that of maize by 11% and 2%, respectively. This was attributed to canopy cover of soybean and groundnut crops, where the soil was fully covered with vegetation. In case of maize, due to wider spacing, the soil exposure is greater, which results in low reflectance values of the NIR band. Normalized Difference Vegetation Index (NDVI) is linearly related to total chlorophyll content and the growth stages of a crop up to flowering. The NDVI differs significantly during the peak vegetative growth period among the three crop types. The study revealed that the significant differences in reflectance of maize, groundnut and soybean in the red and NIR bands were influenced by concentrations of chlorophyll- a, chlorophyll- b and carotenoids, which indicates the photosynthetic behaviour of the crops.     

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.     

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.     

A comparison of MODIS 250-m EVI and NDVI data for crop mapping: a case study for southwest Kansas

Multi-temporal vegetation index (VI) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) are becoming widely used for large-area crop classification. Most crop-mapping studies have applied enhanced vegetation index (EVI) data from MODIS instead of the more traditional normalized difference vegetation index (NDVI) data because of atmospheric and background corrections incorporated into EVI's calculation and the index's sensitivity over high biomass areas. However, the actual differences in the classification results using EVI versus NDVI have not been thoroughly explored. This study evaluated time-series MODIS 250-m EVI and NDVI for crop-related land use/land cover (LULC) classification in the US Central Great Plains. EVI- and NDVI-derived maps classifying general crop types, summer crop types and irrigated/non-irrigated crops were produced for southwest Kansas. Qualitative and quantitative assessments were conducted to determine the thematic accuracy of the maps and summarize their classification differences. For the three crop maps, MODIS EVI and NDVI data produced equivalent classification results. High thematic accuracies were achieved with both indices (generally ranging from 85% to 90%) and classified cropping patterns were consistent with those reported for the study area (> 0.95 correlation between the classified and USDA-reported crop areas). Differences in thematic accuracy (< 3% difference), spatially depicted patterns (> 90% pixel-level thematic agreement) and classified crop areas between the series of EVI- and NDVI-derived maps were negligible. Most thematic disagreements were restricted to single pixels or small clumps of pixels in transitional areas between cover types. Analysis of MODIS composite period usage in the classification models also revealed that both VIs performed equally well when periods from a specific growing season phase (green, peak or senescence) were heavily utilized to generate a specific crop map.     

Analysis of time-series MODIS 250 m vegetation index data for crop classification in the U.S. Central Great Plains

The global environmental change research community requires improved and up-to-date land use/land cover (LULC) datasets at regional to global scales to support a variety of science and policy applications. Considerable strides have been made to improve large-area LULC datasets, but little emphasis has been placed on thematically detailed crop mapping, despite the considerable influence of management activities in the cropland sector on various environmental processes and the economy. Time-series MODIS 250 m Vegetation Index (VI) datasets hold considerable promise for large-area crop mapping in an agriculturally intensive region such as the U.S. Central Great Plains, given their global coverage, intermediate spatial resolution, high temporal resolution (16-day composite period), and cost-free status. However, the specific spectral-temporal information contained in these data has yet to be thoroughly explored and their applicability for large-area crop-related LULC classification is relatively unknown. The objective of this research was to investigate the general applicability of the time-series MODIS 250 m Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI) datasets for crop-related LULC classification in this region. A combination of graphical and statistical analyses were performed on a 12-month time-series of MODIS EVI and NDVI data from more than 2000 cropped field sites across the U.S. state of Kansas. Both MODIS VI datasets were found to have sufficient spatial, spectral, and temporal resolutions to detect unique multi-temporal signatures for each of the region's major crop types (alfalfa, corn, sorghum, soybeans, and winter wheat) and management practices (double crop, fallow, and irrigation). Each crop's multi-temporal VI signature was consistent with its general phenological characteristics and most crop classes were spectrally separable at some point during the growing season. Regional intra-class VI signature variations were found for some crops across Kansas that reflected the state's climate and planting time differences. The multi-temporal EVI and NDVI data tracked similar seasonal responses for all crops and were highly correlated across the growing season. However, differences between EVI and NDVI responses were most pronounced during the senescence phase of the growing season.     

在植物生长周期内县级区域植被指数的时空演变特征分析

采用１９９１至１９９２年晴空时的ＮＯＡＡ卫星ＡＶＨＲＲ资料，计算甘肃省河东地区６０个县（市）作物和牧草生长周期内标准化差植被指数（ＮＤＶＩ）的平均值和标准差，并逐县绘制其时间演变曲线和直方图。选取以农作物、草地和森林草地混合为主的三类县作对比分析，研究县级区域植被指数时空变化与作物和牧草生育期的关系。分析１９９１至１９９２年度冬小麦生长周期内遇到严重干旱的情况，为干旱监测、估产和区分土地使用类型选择最佳时相提供依据     

Environmental monitoring and crop forecasting in the Sahel through the use of NOAA NDVI data. A case study: Niger 1986–89

Abstract

Several investigations have shown that NOAA NDVI data accumulated during a rainy season can be related to total rainfall or final primary productivity in the Sahel. However, serious problems can arise when looking for quantitative relations to monitor and forecast crop yield from NDVI values. Geographical variability can affect such relations, while the use of data taken from a whole season is impractical for forecasting. The present paper proposes a complete methodology of NDVI data processing which only utilizes NOAA AVHRR scenes from the first part of successive rainy seasons. A series of basic corrections are first applied to the original data to obtain reliable NDVI maximum value composites at the middle of the rainy seasons considered. Next, the variability in land resources is accounted for by means of a standardization process which normalizes the mean NDVI levels of some areas on the relevant multi-temporal averages and standard deviations. In this way, good estimates of the actual condition of vegetation can be obtained in relation to the local seasonal trend

The methodology was applied to the Sahelian sub-departments of Niger with data from four years (1986–1989). The most interesting result achieved concerns the estimation of final grain (millet and sorghum) yield for the sub-departments by the end of July with a mean error of about 0·08 T ha ^?1. This timely evaluation could be of great utility in the context of an efficient drought early warning system.