250-m Normalized Difference Vegetation Index Research Articles

Early Crop Mapping Using Dynamic Ecoregion Clustering: A USA-Wide Study

Mapping target crops earlier than the harvest period is an essential task for improving agricultural productivity and decision-making. This paper presents a new method for early crop mapping for the entire conterminous USA (CONUS) land area using the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) data with a dynamic ecoregion clustering approach. Ecoregions, geographically distinct areas with unique ecological patterns and processes, provide a valuable framework for large-scale crop mapping. We conducted our dynamic ecoregion clustering by analyzing soil, climate, elevation, and slope data. This analysis facilitated the division of the cropland area within the CONUS into distinct ecoregions. Unlike static ecoregion clustering, which generates a single ecoregion map that remains unchanged over time, our dynamic ecoregion approach produces a unique ecoregion map for each year. This dynamic approach enables us to consider the year-to-year climate variations that significantly impact crop growth, enhancing the accuracy of our crop mapping process. Subsequently, a Random Forest classifier was employed to train individual models for each ecoregion. These models were trained using the time-series MODIS (Moderate Resolution Imaging Spectroradiometer) 250-m NDVI and EVI data retrieved from Google Earth Engine, covering the crop growth periods spanning from 2013 to 2017, and evaluated from 2018 to 2022. Ground truth data were sourced from the US Department of Agriculture’s (USDA) Cropland Data Layer (CDL) products. The evaluation results showed that the dynamic clustering method achieved higher accuracy than the static clustering method in early crop mapping in the entire CONUS. This study’s findings can be helpful for improving crop management and decision-making for agricultural activities by providing early and accurate crop mapping.

The association between early pregnancy exposure to green space and maternal glucolipid metabolism disorders: evaluation of the mediating role of serum 25-hydroxyvitamin D.

Green space and 25-hydroxyvitamin D (25(OH)D) can affect maternal and infant health, but limited studies have examined their effects on disorders of maternal glucolipid metabolism. We aimed to explore the interaction between green space, maternal serum 25(OH)D, and disorders of glucolipid metabolism in early pregnancy. A total of 2551 pregnant women were recruited from the Maanshan Maternal and Child Health Hospital birth cohort in China between 2020 and 2022. We calculated average residential greenness during early pregnancy using 250m normalized difference vegetation index (NDVI) from satellites. Serum biomarkers (25(OH)D, total cholesterol (TC), triglycerides (TG), high-density lipoprotein-cholesterol(HDL-C), low-density lipoprotein cholesterol (LDL-C), apolipoprotein A1(ApoA1), and apolipoprotein B(ApoB)) were measured. Associations between the factors were analyzed using multiple linear regression, mediation analysis, and stratified analysis. After adjusting for potential confounders, green space exposure associated with decreased TG (- 7.8%; 95% confidence interval (CI): - 12.8, - 2.9), TC (- 7.0%; 95% CI: - 11.4, - 2.7), and LDL-C (- 8.4%; 95% CI: - 12.9, - 3.9), ApoB (- 2.0%; 95% CI: - 3.0, - 1.0) and increased HDL-C (2.7%; 95% CI: 1.5, 3.8) and ApoA1 (5.1%; 95% CI: 3.9, 6.3) for each IQR increase in NDVI. A comparable link was found between maternal serum 25(OH)D and indicators of glucolipid metabolism (P < 0.05). In addition, mediation analysis showed that the association between green space exposure and maternal glucolipid metabolic index was mediated by serum 25(OH)D at 6.37%. In stratified analyses, a considerable association between 25(OH)D and glucolipid metabolic index (except TG) was observed only at higher green space exposures. This study confirms that high levels of green space exposure in early pregnancy and vitamin D are associated with a reduced risk of glucolipid metabolism disorders and suggests that green space may favor glucolipid metabolism by increasing vitamin D levels, particularly at high NDVI values.

Downscaling of MODIS NDVI by Using a Convolutional Neural Network-Based Model with Higher Resolution SAR Data

The normalized difference vegetation index (NDVI) is a simple but powerful indicator, that can be used to observe green live vegetation efficiently. Since its introduction in the 1970s, NDVI has been used widely for land management, food security, and physical models. For these applications, acquiring NDVI in both high spatial resolution and high temporal resolution is preferable. However, there is generally a trade-off between temporal and spatial resolution when using satellite images. To relieve this problem, a convolutional neural network (CNN) based downscaling model was proposed in this research. This model is capable of estimating 10-m high resolution NDVI from MODIS (Moderate Resolution Imaging Spectroradiometer) 250-m resolution NDVI by using Sentinel-1 10-m resolution synthetic aperture radar (SAR) data. First, this downscaling model was trained to estimate Sentinel-2 10-m resolution NDVI from a combination of upscaled 250-m resolution Sentinel-2 NDVI and 10-m resolution Sentinel-1 SAR data, by using data acquired in 2019 in the target area. Then, the generality of this model was validated by applying it to test data acquired in 2020, with the result that the model predicted the NDVI with reasonable accuracy (MAE = 0.090, ρ = 0.734 on average). Next, 250-m NDVI from MODIS data was used as input to confirm this model under conditions replicating an actual application case. Although there were mismatch in the original MODIS and Sentinel-2 NDVI data, the model predicted NDVI with acceptable accuracy (MAE = 0.108, ρ = 0.650 on average). Finally, this model was applied to predict high spatial resolution NDVI using MODIS and Sentinel-1 data acquired in target area from 1 January 2020~31 December 2020. In this experiment, double cropping of cabbage, which was not observable at the original MODIS resolution, was observed by enhanced temporal resolution of high spatial resolution NDVI images (approximately ×2.5). The proposed method enables the production of 10-m resolution NDVI data with acceptable accuracy when cloudless MODIS NDVI and Sentinel-1 SAR data is available, and can enhance the temporal resolution of high resolution 10-m NDVI data.

Recovery Rates of Wetland Vegetation Greenness in Severely Burned Ecosystems of Alaska Derived from Satellite Image Analysis

The analysis of wildfire impacts at the scale of less than a square kilometer can reveal important patterns of vegetation recovery and regrowth in freshwater Arctic and boreal regions. For this study, NASA Landsat burned area products since the year 2000, and a near 20-year record of vegetation green cover from the MODIS (Moderate Resolution Imaging Spectroradiometer) satellite sensor were combined to reconstruct the recovery rates and seasonal profiles of burned wetland ecosystems in Alaska. Region-wide breakpoint analysis results showed that significant structural change could be detected in the 250-m normalized difference vegetation index (NDVI) time series for the vast majority of wetland locations in the major Yukon river drainages of interior Alaska that had burned at high severity since the year 2001. Additional comparisons showed that wetland cover locations across Alaska that have burned at high severity subsequently recovered their green cover seasonal profiles to relatively stable pre-fire levels in less than 10 years. Negative changes in the MODIS NDVI, namely lower greenness in 2017 than pre-fire and incomplete greenness recovery, were more commonly detected in burned wetland areas after 2013. In the years prior to 2013, the NDVI change tended to be positive (higher greenness in 2017 than pre-fire) at burned wetland elevations lower than 400 m, whereas burned wetland locations at higher elevation showed relatively few positive greenness recovery changes by 2017.

A comparison of NDVI and EVI in the DisTrad model for thermal sub-pixel mapping in densely vegetated areas: a case study in Southern China

ABSTRACTThe DisTrad (Disaggregation Procedure for Radiometric Surface Temperature) model shows limited applicability for sub-pixel mapping of thermal remote-sensing images in densely vegetated areas due to the phenomenon of normalized difference vegetation index (NDVI) saturation. In this article, we compared the effect of NDVI and enhanced vegetation index (EVI) in the DisTrad model for thermal sub-pixel mapping in densely vegetated areas due to their different sensitivity in densely vegetated areas. Taking Ganzhou in Southern China as an example, we produced 250-m thermal remote-sensing images from a 1000-m image using 250-m NDVI and EVI data. After comparing with the synchronous 90-m thermal image from advanced spaceborne thermal emission and reflection radiometer, we found that the EVI can achieve a better result than NDVI in densely vegetated areas.

Spectral matching techniques (SMTs) and automated cropland classification algorithms (ACCAs) for mapping croplands of Australia using MODIS 250-m time-series (2000–2015) data

ABSTRACTMapping croplands, including fallow areas, are an important measure to determine the quantity of food that is produced, where they are produced, and when they are produced (e.g. seasonality). Furthermore, croplands are known as water guzzlers by consuming anywhere between 70% and 90% of all human water use globally. Given these facts and the increase in global population to nearly 10 billion by the year 2050, the need for routine, rapid, and automated cropland mapping year-after-year and/or season-after-season is of great importance. The overarching goal of this study was to generate standard and routine cropland products, year-after-year, over very large areas through the use of two novel methods: (a) quantitative spectral matching techniques (QSMTs) applied at continental level and (b) rule-based Automated Cropland Classification Algorithm (ACCA) with the ability to hind-cast, now-cast, and future-cast. Australia was chosen for the study given its extensive croplands, rich history of agriculture, and yet nonexistent routine yearly generated cropland products using multi-temporal remote sensing. This research produced three distinct cropland products using Moderate Resolution Imaging Spectroradiometer (MODIS) 250-m normalized difference vegetation index 16-day composite time-series data for 16 years: 2000 through 2015. The products consisted of: (1) cropland extent/areas versus cropland fallow areas, (2) irrigated versus rainfed croplands, and (3) cropping intensities: single, double, and continuous cropping. An accurate reference cropland product (RCP) for the year 2014 (RCP2014) produced using QSMT was used as a knowledge base to train and develop the ACCA algorithm that was then applied to the MODIS time-series data for the years 2000–2015. A comparison between the ACCA-derived cropland products (ACPs) for the year 2014 (ACP2014) versus RCP2014 provided an overall agreement of 89.4% (kappa = 0.814) with six classes: (a) producer’s accuracies varying between 72% and 90% and (b) user’s accuracies varying between 79% and 90%. ACPs for the individual years 2000–2013 and 2015 (ACP2000–ACP2013, ACP2015) showed very strong similarities with several other studies. The extent and vigor of the Australian croplands versus cropland fallows were accurately captured by the ACCA algorithm for the years 2000–2015, thus highlighting the value of the study in food security analysis. The ACCA algorithm and the cropland products are released through http://croplands.org/app/map and http://geography.wr.usgs.gov/science/croplands/algorithms/australia_250m.html

A comprehensive assessment of the correlations between field crop yields and commonly used MODIS products

An exploratory assessment was undertaken to determine the correlation strength and optimal timing of several commonly used Moderate Resolution Imaging Spectroradiometer (MODIS) composited imagery products against crop yields for 10 globally significant agricultural commodities. The crops analyzed included barley, canola, corn, cotton, potatoes, rice, sorghum, soybeans, sugarbeets, and wheat. The MODIS data investigated included the Normalized Difference Vegetation Index (NDVI), Fraction of Photosynthetically Active Radiation (FPAR), Leaf Area Index (LAI), and Gross Primary Production (GPP), in addition to daytime Land Surface Temperature (DLST) and nighttime LST (NLST). The imagery utilized all had 8-day time intervals, but NDVI had a 250m spatial resolution while the other products were 1000m. These MODIS datasets were also assessed from both the Terra and Aqua satellites, with their differing overpass times, to document any differences. A follow-on analysis, using the Terra 250m NDVI data as a benchmark, looked at the yield prediction utility of NDVI at two spatial scales (250m vs. 1000m), two time precisions (8-day vs. 16-day), and also assessed the Enhanced Vegetation Index (EVI, at 250m, 16-day). The analyses spanned the major farming areas of the United States (US) from the summers of 2008–2013 and used annual county-level average crop yield data from the US Department of Agriculture as a basis. All crops, except rice, showed at least some positive correlations to each of the vegetation related indices in the middle of the growing season, with NDVI performing slightly better than FPAR. LAI was somewhat less strongly correlated and GPP weak overall. Conversely, some of the crops, particularly canola, corn, and soybeans, also showed negative correlations to DLST mid-summer. NLST, however, was never correlated to crop yield, regardless of the crop or seasonal timing. Differences between the Terra and Aqua results were found to be minimal. The 1000m resolution NDVI showed somewhat poorer performance than the 250m and suggests spatial resolution is helpful but not a necessity. The 8-day versus 16-day NDVI relationships to yields were very similar other than for the temporal precision. Finally, the EVI often showed the very best performance of all the variables, all things considered.

Rice Crop Monitoring and Yield Assessment with MODIS 250m Gridded Vegetation Products: A Case Study of Sa Kaeo Province, Thailand

Abstract. Billions of people in the world depend on rice as a staple food and as an income-generating crop. Asia is the leader in rice cultivation and it is necessary to maintain an up-to-date rice-related database to ensure food security as well as economic development. This study investigates general applicability of high temporal resolution Moderate Resolution Imaging Spectroradiometer (MODIS) 250m gridded vegetation product for monitoring rice crop growth, mapping rice crop acreage and analyzing crop yield, at the province-level. The MODIS 250m Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) time series data, field data and crop calendar information were utilized in this research in Sa Kaeo Province, Thailand. The following methodology was used: (1) data pre-processing and rice plant growth analysis using Vegetation Indices (VI) (2) extraction of rice acreage and start-of-season dates from VI time series data (3) accuracy assessment, and (4) yield analysis with MODIS VI. The results show a direct relationship between rice plant height and MODIS VI. The crop calendar information and the smoothed NDVI time series with Whittaker Smoother gave high rice acreage estimation (with 86% area accuracy and 75% classification accuracy). Point level yield analysis showed that the MODIS EVI is highly correlated with rice yield and yield prediction using maximum EVI in the rice cycle predicted yield with an average prediction error 4.2%. This study shows the immense potential of MODIS gridded vegetation product for keeping an up-to-date Geographic Information System of rice cultivation.

Response of Rainfall and Vegetation to ENSO Events during 2001–2011 in Upper Wardha Watershed, Maharashtra, India

AbstractUnderstanding the variability of rainfall with El Nino Southern Oscillation (ENSO) events at the watershed level is important for water resource managers to discern the pattern of water available for agricultural planning and water and soil conservation. This study was conducted to determine the responses of rainfall and vegetation to El Nino and La Nina events occurring between 2001 and 2011 in the Upper Wardha watershed in Maharashtra, India. Daily rainfall data pertaining to 14 stations in and around the study area were analyzed in conjunction with 16-day global 250-m Normalized Difference Vegetation Index (NDVI) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor abroad the Terra (EOS AM) and Aqua (EOS PM) satellites. Results indicate correlations between ENSO events, rainfall, and vegetation. Both rainfall and vegetation show a positive anomaly during La Nina events and a negative anomaly during El Nino events, but the effects of all events are not the same. A low volum...

Exploiting the Classification Performance of Support Vector Machines with Multi-Temporal Moderate-Resolution Imaging Spectroradiometer (MODIS) Data in Areas of Agreement and Disagreement of Existing Land Cover Products

Several studies have focused in the past on global land cover (LC) datasets harmonization and inter-comparison and have found significant inconsistencies. Despite the known discrepancies between existing products derived from medium resolution satellite sensor data, little emphasis has been placed on examining these disagreements to improve the overall classification accuracy of future land cover maps. This work evaluates the classification performance of a least square support vector machine (LS-SVM) algorithm with respect to areas of agreement and disagreement between two existing land cover maps. The approach involves the use of time series of Moderate-resolution Imaging Spectroradiometer (MODIS) 250-m Normalized Difference Vegetation Index (NDVI) (16-day composites) and gridded climatic indicators. LS-SVM is trained on reference samples obtained through visual interpretation of Google Earth (GE) high resolution imagery. The core of the training process is based on repeated random splits of the training dataset to select a small set of suitable support vectors optimizing class separability. A large number of independent validation samples spread over three contrasting regions in Europe (Eastern Austria, Macedonia and Southern France) are used to calculate classification accuracies for the LS-SVM NDVI-derived LC map and for two (globally available) LC products: GLC2000 and GlobCover. The LS-SVM LC map reported an overall accuracy of 70%. Classification accuracies ranged from 71% where GlobCover and GLC2000 agreed to 68% for areas of disagreement. Results indicate that existing LC products are as accurate as the LS-SVM LC map in areas of agreement (with little margin for improvements), while classification accuracy is substantially better for the LS-SVM LC map in areas of disagreement. On average, the LS-SVM LC map was 14% and 18% more accurate compared to GlobCover and GLC2000, respectively.

Local climate determines the NDVI-based primary productivity and flooding creates heterogeneity in semi-arid floodplain ecosystem

Water is clearly the key driver of ecosystem functioning in arid and semi-arid floodplain environment. Both local rainfall and overbank flow contribute to water accessible by a plant. Variability in rainfall and hydrological regimes at seasonal and longer time scales exerts strong influences shaping ecosystem structure and function in arid and semi-arid floodplains. Yet there are few studies exploring how the interplay of climatic and hydrologic factors affects the integrity of floodplain ecosystems, especially the intermittently flooded vegetated wetlands. In fact, the importance of hydrological regimes prevailingly dominates the literature on floodplain ecology. Consequently, re-establishing and maintaining hydrological regimes has become the cornerstone underpinning most large scale floodplain conservation and restoration programs. In this study, we quantified the relative contribution of climatic and hydrologic variables to ecosystem functioning in the generalized additive modelling (GAM) framework using indicators derived from time series of MODIS 250m NDVI (Normalized Difference Vegetation Index). Results from the “best fitted” GAM models reveal that local rainfall has a closer and more important relationship with wetland mean NDVI (a surrogate of system primary productivity) although the co-variability between primary productivity and inflow is significantly positive. On the other hand, inflows play a dominant role in creating and maintaining the spatial heterogeneity represented by standard deviation of wetland NDVI while local rainfall has an opposite effect. Further, different vegetation community types exhibit distinct seasonality patterns in the NDVI-based primary productivity. These results illustrate the complexity of interactions between vegetation type, climate variations, and hydrology in floodplain wetlands. These results also demonstrate the sensitivity of floodplain wetlands to the perturbations in rainfall and temperature regimes, which would be more intense under the projected climate change.

Land use/land cover (LULC) classification with MODIS time series data and validation in the Amur River Basin

There is a need for improved and up-to-date land use/land cover (LULC) data sets over an intensively changing area in the Amur River Basin (ARB) in support of science and policy applications focused on understanding of the role and response of the LULC to environmental change issues. The main goal of this study was to map LULC in the ARB using MODIS 250-m Normalized Difference Vegetation Index (NDVI), Land Surface Vegetation Index (LSWI), and reflectance time series data for 2001 and 2007. Another goal was to test the consistency of the classification results using relatively coarse resolution MODIS imagery data in order to develop a methodology for rapid production of an up-to-date LULC data set. The results on MODIS land cover were evaluated using existing land use/cover data as derived from Landsat TM data. It was found that the MODIS 250-m NDVI data sets featured sufficient spatial, spectral and temporal resolution to detect unique multi-temporal signatures for the region’s major land cover types. It turned out that MODIS 250 NDVI time series data have high potential for large-basin land use/land cover monitoring and information updating for purposes of environmental basin research and management.

A Coupled Remote Sensing and Simplified Surface Energy Balance Approach to Estimate Actual Evapotranspiration from Irrigated Fields

Accurate crop performance monitoring and production estimation are critical fortimely assessment of the food balance of several countries in the world. Since 2001, theFamine Early Warning Systems Network (FEWS NET) has been monitoring cropperformance and relative production using satellite-derived data and simulation models inAfrica, Central America, and Afghanistan where ground-based monitoring is limitedbecause of a scarcity of weather stations. The commonly used crop monitoring models arebased on a crop water-balance algorithm with inputs from satellite-derived rainfallestimates. These models are useful to monitor rainfed agriculture, but they are ineffectivefor irrigated areas. This study focused on Afghanistan, where over 80 percent ofagricultural production comes from irrigated lands. We developed and implemented aSimplified Surface Energy Balance (SSEB) model to monitor and assess the performanceof irrigated agriculture in Afghanistan using a combination of 1-km thermal data and 250-m Normalized Difference Vegetation Index (NDVI) data, both from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. We estimated seasonal actual evapotranspiration (ETa) over a period of six years (2000-2005) for two major irrigated river basins in Afghanistan, the Kabul and the Helmand, by analyzing up to 19 cloud-free thermal and NDVI images from each year. These seasonal ETa estimates were used as relative indicators of year-to-year production magnitude differences. The temporal water- use pattern of the two irrigated basins was indicative of the cropping patterns specific to each region. Our results were comparable to field reports and to estimates based on watershed-wide crop water-balance model results. For example, both methods found that the 2003 seasonal ETa was the highest of all six years. The method also captured water management scenarios where a unique year-to-year variability was identified in addition to water-use differences between upstream and downstream basins. A major advantage of the energy-balance approach is that it can be used to quantify spatial extent of irrigated fields and their water-use dynamics without reference to source of water as opposed to a water- balance model which requires knowledge of both the magnitude and temporal distribution of rainfall and irrigation applied to fields.