GIMMS NDVI is generated from several NOAA's AVHRR sensors for a global 1/12-degree lat/lon grid. The latest version of the GIMMS NDVI dataset is named NDVI3g (third generation GIMMS NDVI from AVHRR sensors).

This dataset holds the Global Inventory Modeling and Mapping Studies-3rd Generation V1.2 (GIMMS-3G+) data for the Normalized Difference Vegetation Index (NDVI). NDVI was based on corrected and calibrated measurements from Advanced Very High Resolution Radiometer (AVHRR) data with a spatial resolution of 0.0833 degree and global coverage for 1982 to 2022. Maximum NDVI values are reported within twice monthly compositing periods (two values per month). The dataset was assembled from different AVHRR sensors and accounts for various deleterious effects, such as calibration loss, orbital drift, and volcanic eruptions. The data are provided in NetCDF format.

Brief Introduction:

The PKU GIMMS Normalized Difference Vegetation Index product (PKU GIMMS NDVI, version 1.1) provides spatiotemporally consistent global NDVI data in half-month and 1/12° from 1982 to 2022. It is created to address the major uncertainties presented in current global long-term NDVI products, i.e., the effects of NOAA satellite orbital drift and AVHRR sensor degradation.

The PKU GIMMS NDVI was generated based on biome-specific BPNN models that employed GIMMS NDVI3g product and 3.6 million high-quality global Landsat NDVI samples. It was then consolidated with the MODIS NDVI (MOD13C1) to extend the temporal coverage to 2022 via a pixel-wise Random Forests fusion method.

The PKU GIMMS NDVI exhibits overall high accuracy evaluated by Landsat NDVI samples. Besides, it efficiently eliminated the effects of satellite orbital drift and sensor degradation and presents a good temporal consistency with MODIS NDVI in terms of pixel value and global vegetation trend. It could potentially provide a more solid data basis for global change studies.

Here we provide two versions of PKU GIMMS NDVI for download, one solely based on AVHRR data (1982−2015) and the other consolidated with the MODIS NDVI (1982−2022).

Dataset Characteristics:

Spatial Coverage: 180ºW~180ºE, 63ºS~90ºN

Projection: Geographic

Spatial Resolution: 1/12 degree

Temporal Resolution: Half month

Temporal Coverage: January 1982 to December 2022

Image Dimension: Rows-2160; Columns-4320

Units: unitless

Fill Value: 65535

Data Type: uint16

Valid Range: 0-1000

Scale Factor: 0.001

File Format: TIFF(.tif)

File Size: ~8Mb each file

References:

Li, M., Cao, S., and Zhu, Z.: Spatiotemporally consistent global dataset of the GIMMS Normalized Difference Vegetation Index (PKU GIMMS NDVI) from 1982 to 2020, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2023-1, in review, 2023.

The data set is NDVI data of long time series acquired by NOAA's Advanced Very High Resolution Radiometer (AVHRR) sensor. The time range of the data set is from 1982 to 2015. In order to remove the noise in NDVI data, maximum synthesis and multi-sensor contrast correction are carried out. A NDVI image is synthesized every half month. The data set is widely used in the analysis of long-term vegetation change trend. The data set is cut out from the global data set, so as to carry out the research and analysis of the source areas of the three rivers separately. The data format of this data set is GeoTIFF with spatial resolution of 8 km and temporal resolution of 2 weeks, ranging from 1982 to 2015. Data transfer coefficient is 10000, NDVI = ND/10000.

Normalized Difference Vegetation Index-3rd generation (NDVI) using the Global Inventory Monitoring and Modeling System (GIMMS): Vegetation indices are radiometric measures of photosynthetically active radiation absorbed by chlorophyll in the green leaves of vegetation canopies and are therefore good surrogate measures of the physiologically functioning surface greenness level of a region. For 30 years, Compton J. Tucker created the NDVI time series within the framework of the Global Inventory Monitoring and Modeling System (GIMMS) project. He carefully assembled it from different Advanced Very High Resolution Radiometer (AVHRR) sensors and accounting for various deleterious effects, such as calibration loss, orbital drift, volcanic eruptions, etc. The latest version of the GIMMS NDVI data set spans the period July 1981 to December 2015 and is termed NDVI3g (third generation GIMMS NDVI from AVHRR sensors).

The NDVI data set is the latest release of the long sequence (1981-2015) normalized difference vegetation index product of NOAA Global Inventory Monitoring and Modeling System (GIMMS), version number 3g.v1. The temporal resolution of the product is twice a month, while the spatial resolution is 1/12 of a degree. The temporal coverage is from July 1981 to December 2015. This product is a shared data product and can be downloaded directly from ecocast.arc.nasa.gov. For details, please refer to https://nex.nasa.gov/nex/projects/1349/.

The characteristics of vegetation and drought for different seasons between 1982 and 2015 in Inner Mongolia were studied based on the Normalized Difference Vegetation Index (NDVI) and the Standardized Precipitation Evapotranspiration Index (SPEI). The response of vegetation to drought over various time scales for different seasons and vegetation types was investigated using the maximum Pearson correlation, allowing a discussion about the possible causes of any changes. The results indicate that the vegetation NDVI in Inner Mongolia showed an increasing trend in different seasons, with spring vegetation NDVI (April to May) having the largest significant increasing rate, followed by the growing season (April to October), autumn (September to October), and summer (June to August). Accordingly, the proportion of stations with decreasing SPEI was, in descending order, summer, growing season, autumn, and spring. Additionally, the magnitude of the SPEI decrease was greater in eastern Inner Mon...

1. Spatiotemporal patterns of vegetation growth across China over the past three decadesa. Global spatiotemporal patterns of the Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), and Gross Primary Productivity (GPP) for the 1982–2015 periodThis includes the linear trends of mean/maximum growing-season NDVI/LAI/GPP values using the Theil-Sen estimator with the non-parametric Mann-Kendall test: NDVI_Trend.tif, LAI_Trend.tif, NIRvGPP_Trend.tif, NDVI_Trend_MK.tif, LAI_Trend_MK.tif, NIRvGPP_Trend_MK.tif, NDVI_Max_Trend.tif, LAI_Max_Trend.tif, NIRvGPP_Max_Trend.tif, NDVI_Max_Trend_MK.tif, LAI_Max_Trend_MK.tif, NIRvGPP_Max_Trend_MK.tif.The third-generation Global Inventory Monitoring and Modeling System (GIMMS) Normalized Difference Vegetation Index (NDVI) data (GIMMS NDVI3g, Version 1), the consistent long-term global Leaf Area Index (LAI) product (GLOBMAP LAI,Version 3), and the long-term Gross Primary Productivity (GPP) dataset based on NIRv (GPPNIRv, Version 2) were used in this study as proxies for vegetation growth during the 1982–2015 period. The third generation of the AVHRR GIMMS NDVI3g NDVI dataset is available at https://climatedataguide.ucar.edu/climate-data/ndvi-normalized-difference-vegetation-index-3rd-generation-nasagfsc-gimms or http://poles.tpdc.ac.cn/en/data/9775f2b4-7370-4e5e-a537-3482c9a83d88. The GLOBMAP global LAI dataset is available at https://zenodo.org/record/4700264. The GPPNIRv dataset is available at https://doi.org/10.6084/m9.figshare.12981977.v2.b. Global spatiotemporal patterns of the simulated Gross Primary Productivity (GPP) derived from TRENDY-v8 ORCHIDEE, SDGVM, and VISIT models for the 1982–2015 periodThis includes the linear trends of mean/maximum growing-season simulated GPP values (the S3 simulation scenario: observed climate and CO2 and land use/land cover) using the Theil-Sen estimator with the non-parametric Mann-Kendall test: ORCHIDEE_S3_gpp_Trend.tif, SDGVM_S3_gpp_Trend.tif, VISIT_S3_gpp_Trend.tif, ORCHIDEE_S3_gpp_Trend_MK.tif, SDGVM_S3_gpp_Trend_MK.tif, VISIT_S3_gpp_Trend_MK.tif, ORCHIDEE_S3_gpp_Max_Trend.tif, SDGVM_S3_gpp_Max_Trend.tif, VISIT_S3_gpp_Max_Trend.tif, ORCHIDEE_S3_gpp_Max_Trend_MK.tif, SDGVM_S3_gpp_Max_Trend_MK.tif, VISIT_S3_gpp_Max_Trend_MK.tif.We used the monthly GPP data estimated by dynamic global vegetation models (DGVMs) from the TRENDY project (TRENDYv8, Version 8). The three DGVMs with a fine spatial resolution were selected for this study, i.e., ORCHIDEE, SDGVM, and VISIT. The monthly simulated GPP data from three TRENDYv8 DGVMs are available on request to Professor Stephen Sitch (s.a.sitch@exeter.ac.uk) and Professor Pierre Friedlingstein (p.friedlingstein@exeter.ac.uk) at https://blogs.exeter.ac.uk/trendy.c. China vegetation cover mapYou can use this "China_vegetation_cover_map.tif" as a mask to determine China’s vegetated regions.The global land use change data obtained from the HIstoric Land Dynamics Assessment+ (HILDA+) project were used to determine China’s vegetated regions. To focus our goals, we ignored the transformation between vegetated and non-vegetated regions, thereby reducing the effect of land cover change on vegetation growth. That is, we used the intersection of all the vegetation layers to ensure that each grid cell was a vegetated region from the beginning to the end for the 1982–2015 period. The global land use change data obtained from the HILDA+ project are available at https://doi.org/10.1594/PANGAEA.921846.d. The MATLAB (R2023a) code for calculating raster trends using the Theil-Sen estimator and the nonparametric Mann-Kendall testThis includes "Theil-Sen estimator.m" for calculating raster trends using the Theil-Sen estimator and "Mann-Kendall test.m" for the nonparametric Mann-Kendall test. For more details, please refer to the followings：Sen, P.K. Estimates of the regression coefficient based on Kendall’s tau. J. Am. Stat. Assoc. 1968, 63, 1379–1389.Theil, H. A rank-invariant method of linear and polynomial regression analysis. In Henri Theil’s Contributions to Economics and Econometrics. Advanced Studies in Theoretical and Applied Econometrics; Raj, B., Koerts, J., Eds.; Springer: Dordrecht, The Netherlands, 1992; Volume 23, pp. 345–381.Mann, H.B. Nonparametric tests against trend. Econometrica 1945, 13, 245–259.https://en.wikipedia.org/wiki/Theil–Sen_estimatorhttps://wikitia.com/wiki/Mann-Kendall_trend_test

GIMMS NDVI 是根据NOAA 的多个AVHRR 传感器生成的，适用于全球1/12 度经纬度网格。GIMMS NDVI 数据集的最新版本名为NDVI3g（来自 AVHRR 传感器的第三代GIMMS NDVI）。

The files named "SOSmean_Mongolia Plateau", derived from the GIMMS NDVI3g dataset (1982-2015) and the extended GIMMS NDVI (2016-2021) (i.e., NDVI for 40-year).

O NDVI do GIMMS é gerado por vários sensores AVHRR da NOAA para uma grade global de 1/12 grau de latitude/longitude. A versão mais recente do conjunto de dados NDVI do GIMMS é chamada de NDVI3g (NDVI do GIMMS de terceira geração de sensores AVHRR).

The Tibetan Plateau is a unique natural geographic unit with the highest average altitude in the world, known as the world's "Third Pole", which is extremely sensitive to global climate change and has a fragile ecological environment, and is an important ecological security barrier in China and even in Asia. Vegetation cover is an important indicator of climate change and ecological environment, and its spatial and temporal distribution patterns and trends are important indicators for assessing the regional ecological environment. In this study, based on the GIMMS NDVI3g and MOD13Q1 NDVI datasets, the monthly maximum values were synthesized by calling the Arcpy service using Python, and then the Savitzky-Golay filtering and denoising, regression analysis and 250 m resolution NDVI data were performed on the month-by-month NDVI data during the year using the GDAL and sklearn packages of the python language. The Savitzky-Golay filter and sklearn package were used to remove noise from the month-by-month NDVI data, regress the overlapping years of the two data sets, analyze the data, and extend the NDVI dataset at 250 m resolution, and finally integrate the month-by-month NDVI time series dataset at 250 m resolution for the Tibetan Plateau for the period 1981–2020. In order to ensure the accuracy and reliability of the data, this dataset is quality-controlled by various means such as quality control of the data source, consistency analysis, SG filtering, month-by-month and image-by-image fitting, and the confidence test for the data products, which ensures the good accuracy and quality of the data.This dataset can reflect the spatial and temporal changes of NDVI on the Tibetan Plateau from 1981 to 2020, and can be used to improve the spatial and temporal resolution of long time series data for the study of vegetation dynamics and spatial pattern of the Tibetan Plateau, as well as for ecological and environmental monitoring.

Spatially averaged GSVI and coefficients of variation for seven ecoregions.

A neural network algorithm was first developed between the new improved third generation Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) and best-quality Terra Moderate Resolution Imaging Spectroradiometer (MODIS) LAI products for the overlapping period 2000–2009. The trained neural network algorithm was then used to generate corresponding LAI3g and FPAR3g data sets with the following attributes: monthly temporal frequency, 0.5 degree spatial resolution and temporal span of July 1981 to December 2011.

يتم إنشاء مؤشر NDVI الخاص بنظام GIMMS من عدة أجهزة استشعار AVHRR تابعة للإدارة الوطنية للمحيطات والغلاف الجوي (NOAA) لشبكة عالمية بدرجة 1/12 من خطوط الطول والعرض. يُطلق على أحدث إصدار من مجموعة بيانات GIMMS NDVI اسم NDVI3g (الجيل الثالث من GIMMS NDVI من أجهزة استشعار AVHRR).

NDVI ของ GIMMS สร้างขึ้นจากเซ็นเซอร์ AVHRR ของ NOAA หลายตัวสำหรับกริดละติจูด/ลองจิจูด 1/12 องศาทั่วโลก ชุดข้อมูล NDVI ของ GIMMS เวอร์ชันล่าสุด มีชื่อว่า NDVI3g (NDVI ของ GIMMS รุ่นที่ 3 จากเซ็นเซอร์ AVHRR)

Chỉ số NDVI của GIMMS được tạo từ một số cảm biến AVHRR của NOAA cho lưới vĩ độ/kinh độ toàn cầu 1/12 độ. Phiên bản mới nhất của tập dữ liệu GIMMS NDVI có tên là NDVI3g (GIMMS NDVI thế hệ thứ ba từ các cảm biến AVHRR).

GIMMS NDVI, NOAA के कई AVHRR सेंसर से जनरेट किया जाता है. यह दुनिया भर के लिए 1/12 डिग्री के अक्षांश/देशांतर ग्रिड के लिए होता है. GIMMS NDVI डेटासेट के नए वर्शन का नाम NDVI3g है. यह AVHRR सेंसर से मिला, तीसरी जनरेशन का GIMMS NDVI डेटा है.

Biological practice factor(B) have an important influence on soil erosion, and they are also the basic factors for the calculation of CSLE, RUSLE and other models. This is a raster dataset of Biological practice factor on the Qinghai-Tibet Plateau for each year from 1982 to 2020, with a spatial resolution of 250m, WGS_1984 coordinate system and Albers projection (central longitude 105°E, standard parallels 25°N and 47°N). Within the Tibetan Plateau and the surrounding 1-km buffer zone, the 1982-2000 GIMMS NDVI3g data products and the 2001-2020 MODIS NDVI data products were selected for quality assessment, data optimization, and spatial fusion and other processing processes to generate a set of year-by-year, 24 half-monthly NDVI raster datasets (with a spatial resolution of 250 m). The image element dichotomy method was used to calculate the Fractional Vegetation Cover(FVC) and three-year sliding average treatment for FVC, and further combined with 8 periods of land use data (1980, 1990, 1995, 2000, 2005, 2010, 2015, 2020) and year-by-year 24 half-months of rainfall erosivity proportion data, the calculation was carried out to generate the year-by-year B-factor raster map for the period of 1982-2020, which reflect the role of vegetation cover changes on soil erosion in the past 40 years and better assess the calculation of spatial and temporal changes in soil erosion on the Qinghai-Tibet Plateau.

Vegetation coverage can effectively prevent soil erosion on the surface. The Fractional Vegetation Cover (FVC) is an important basic data for evaluating the crop management factor C (or biological measure factor B) in soil erosion prediction models. This is a raster dataset of Fractional Vegetation Cover on the Qinghai-Tibet Plateau for each year from 1982 to 2020, with a spatial resolution of 250m, WGS_1984 coordinate system and Albers projection (central longitude 105°E, standard parallels 25°N and 47°N). Within the Tibetan Plateau and the surrounding 1-km buffer zone, the 1982-2000 GIMMS NDVI3g data products and the 2001-2020 MODIS NDVI data products were selected for quality assessment, data optimization, and spatial fusion and other processing processes to generate a set of year-by-year, 24 half-monthly NDVI raster datasets. Then, the FVC was calculated using the pixel dichotomy model, and the annual mean FVC was calculated to generate the annual FVC raster data from 1982 to 2020. This data reflects the spatio-temporal changes in vegetation coverage fraction on the Qinghai-Tibet Plateau over the past 40 years. Accurate assessment of vegetation coverage fraction can improve the prediction accuracy of factors in erosion prediction models and provide strong support for soil erosion prevention and ecological restoration.