MODIS Vegetation Index Products Research Articles

Monitoring vegetation dynamics using multi-temporal Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) images of Tamil Nadu

Vegetation indices serve as an essential tool in monitoring variations in vegetation. The vegetation indices used often, viz., normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) were computed from MODIS vegetation index products. The present study aimed to monitor vegetation's seasonal dynamics by using time series NDVI and EVI indices in Tamil Nadu from 2011 to 2021. Two products characterize the global range of vegetation states and processes more effectively. The data sources were processed and the values of NDVI and EVI were extracted using ArcGIS software. There was a significant difference in vegetation intensity and status of vegetation over time, with NDVI having a larger value than EVI, indicating that biomass intensity varies over time in Tamil Nadu. Among the land cover classes, the deciduous forest showed the highest mean values for NDVI (0.83) and EVI (0.38), followed by cropland mean values of NDVI (0.71) and EVI (0.31) and the lowest NDVI (0.68) and EVI (0.29) was recorded in the scrubland. The study demonstrated that vegetation indices extracted from MODIS offered valuable information on vegetation status and condition at a short temporal time period.

A dataset of monthly VCI and TCI drought indices at a resolution of 1km in the Yellow River Basin (2003–2021)

The Yellow River Basin is located in the arid, semi-arid climate zone and semi-humid climate zones. According to the statistics of the Second National Comprehensive Water Resources Planning, its annual average water resources total 71.94 billion m3, accounting for 2.5% of the country's total water resources. However, it is the most severely affected by drought among the major river basins in China, and ecological protection faces severe challenges. With the global climate change, the drought problem in the Yellow River Basin has attracted more and more attention. Based on MODIS vegetation index products (MYD13A2, V6) and surface temperature radiation products (MYD11A2, V6). The dataset is obtained by approaches including projection conversion, cloud removal, mosaic clipping, time series interpolation and SG filter smoothing and other preprocessing. Using the calculation method proposed by Kogan in 1995, the VCI and TCI indices were calculated according to the corresponding MODIS products, respectively, and the monthly 1km resolution VCI and TCI drought index datasets in the Yellow River Basin from 2003 to 2021 were generated. Through data sharing, it is expected to provide important data support for drought monitoring and research in the Yellow River Basin under the background of global change.

Influence of Varying Solar Zenith Angles on Land Surface Phenology Derived from Vegetation Indices: A Case Study in the Harvard Forest

Vegetation indices are widely used to derive land surface phenology (LSP). However, due to inconsistent illumination geometries, reflectance varies with solar zenith angles (SZA), which in turn affects the vegetation indices, and thus the derived LSP. To examine the SZA effect on LSP, the MODIS bidirectional reflectance distribution function (BRDF) product and a BRDF model were employed to derive LSPs under several constant SZAs (i.e., 0°, 15°, 30°, 45°, and 60°) in the Harvard Forest, Massachusetts, USA. The LSPs derived under varying SZAs from the MODIS nadir BRDF-adjusted reflectance (NBAR) and MODIS vegetation index products were used as baselines. The results show that with increasing SZA, NDVI increases but EVI decreases. The magnitude of SZA-induced NDVI/EVI changes suggests that EVI is more sensitive to varying SZAs than NDVI. NDVI and EVI are comparable in deriving the start of season (SOS), but EVI is more accurate when deriving the end of season (EOS). Specifically, NDVI/EVI-derived SOSs are relatively close to those derived from ground measurements, with an absolute mean difference of 8.01 days for NDVI-derived SOSs and 9.07 days for EVI-derived SOSs over ten years. However, a considerable lag exists for EOSs derived from vegetation indices, especially from the NDVI time series, with an absolute mean difference of 14.67 days relative to that derived from ground measurements. The SOSs derived from NDVI time series are generally earlier, while those from EVI time series are delayed. In contrast, the EOSs derived from NDVI time series are delayed; those derived from the simulated EVI time series under a fixed illumination geometry are also delayed, but those derived from the products with varying illumination geometries (i.e., MODIS NBAR product and MODIS vegetation index product) are advanced. LSPs derived from varying illumination geometries could lead to a difference spanning from a few days to a month in this case study, which highlights the importance of normalizing the illumination geometry when deriving LSP from NDVI/EVI time series.

Rapid Extraction of Regional-scale Agricultural Disasters by the Standardized Monitoring Model Based on Google Earth Engine

Remote sensing has been used as an important tool for disaster monitoring and disaster scope extraction, especially for the analysis of spatial and temporal disaster patterns of large-scale and long-duration series. Google Earth Engine provides the possibility of quickly extracting the disaster range over a large area. Based on the Google Earth Engine cloud platform, this study used MODIS vegetation index products with 250-m spatial resolution synthesized over 16 days from the period 2005–2019 to develop a rapid and effective method for monitoring disasters across a wide spatiotemporal range. Three types of disaster monitoring and scope extraction models are proposed: the normalized difference vegetation index (NDVI) median time standardization model (RNDVI_TM(i)), the NDVI median phenology standardization model (RNDVI_AM(i)(j)), and the NDVI median spatiotemporal standardization model (RNDVI_ZM(i)(j)). The optimal disaster extraction threshold for each model in different time phases was determined using Otsu’s method, and the extraction results were verified by medium-resolution images and ground-measured data of the same or quasi-same period. Finally, the disaster scope of cultivated land in Heilongjiang Province from 2010–2019 was extracted, and the spatial and temporal patterns of the disasters were analyzed based on meteorological data. This analysis revealed that the three aforementioned models exhibited high disaster monitoring and range extraction capabilities, with verification accuracies of 97.46%, 96.90%, and 96.67% for RNDVI_TM(i), RNDVI_AM(i), and (j)RNDVI_ZM(i)(j), respectively. The spatial and temporal disaster distributions were found to be consistent with the disasters of the insured plots and the meteorological data across the entire province. Moreover, different monitoring and extraction methods were used for different disasters, among which wind hazard and insect disasters often required a delay of 16 days prior to observation. Each model also displayed various sensitivities and was applicable to different disasters. Compared with other techniques, the proposed method is fast and easy to implement. This new approach can be applied to numerous types of disaster monitoring as well as large-scale agricultural disaster monitoring and can easily be applied to other research areas. This study presents a novel method for large-scale agricultural disaster monitoring.

An operational deforestation mapping system using MODIS data and spatial context analysis

The Brazilian Amazon has the world's highest absolute rate of forest loss, currently averaging nearly 2 million hectares per year. In this study, we present a near‐real‐time automated deforestation mapping system for the Brazilian Amazon based on the analysis of spatial context information and MODIS Vegetation Index products and implemented on an ArcGIS 9.0 platform. This system, already validated and operational, was developed as part of the “Integrated Warning Deforestation System for the Amazon” (SIAD), an initiative of the Brazilian government within the scope of the Amazon Protection System (SIPAM), which also comprises: (1) a spatial information module, aimed at the assessment of the causes and impacts of the deforested areas; (2) a prediction module, indicative of deforestation trends; and (3) a data and information gateway based on map server technology.