The Normalized Difference Vegetation Index is generated from the Near-IR and Red bands of each scene as (NIR - Red) / (NIR + Red), and ranges in value from -1.0 to 1.0. This product is generated from the MODIS/006/MOD09GA surface reflectance composites.

The Normalized Difference Vegetation Index is generated from the Near-IR and Red bands of each scene as (NIR - Red) / (NIR + Red), and ranges in value from -1.0 to 1.0. This product is generated from the MODIS/006/MCD43A4 surface reflectance composites.

The MOD13A2 V6.1 product provides two Vegetation Indices (VI): the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI). The NDVI is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The EVI has improved sensitivity over high biomass regions. The algorithm for this product chooses the best available pixel value from all the acquisitions from the 16-day period. The criteria used are low clouds, low view angle, and the highest NDVI/EVI value. Documentation: User's Guide Algorithm Theoretical Basis Document (ATBD) General Documentation

MSZSI: Multi-Scale Zonal Statistics [AgriClimate] Inventory -------------------------------------------------------------------------------------- MSZSI is a data extraction tool for Google Earth Engine that aggregates time-series remote sensing information to multiple administrative levels using the FAO GAUL data layers. The code at the bottom of this page (metadata) can be pasted into the Google Earth Engine JavaScript code editor and ran at https://code.earthengine.google.com/. Please refer to the associated publication: Peter, B.G., Messina, J.P., Breeze, V., Fung, C.Y., Kapoor, A. and Fan, P., 2024. Perspectives on modifiable spatiotemporal unit problems in remote sensing of agriculture: evaluating rice production in Vietnam and tools for analysis. Frontiers in Remote Sensing, 5, p.1042624. https://www.frontiersin.org/journals/remote-sensing/articles/10.3389/frsen.2024.1042624 Input options: [1] Country of interest [2] Start and end year [3] Start and end month [4] Option to mask data to a specific land-use/land-cover type [5] Land-use/land-cover type code from CGLS LULC [6] Image collection for data aggregation [7] Desired band from the image collection [8] Statistics type for the zonal aggregations [9] Statistic to use for annual aggregation [10] Scaling options [11] Export folder and label suffix Output: Two CSVs containing zonal statistics for each of the FAO GAUL administrative level boundaries Output fields: system:index, 0-ADM0_CODE, 0-ADM0_NAME, 0-ADM1_CODE, 0-ADM1_NAME, 0-ADMN_CODE, 0-ADMN_NAME, 1-AREA_PERCENT_LULC, 1-AREA_SQM_LULC, 1-AREA_SQM_ZONE, 2-X_2001, 2-X_2002, 2-X_2003, ..., 2-X_2020, .geo PREPROCESSED DATA DOWNLOAD The datasets available for download contain zonal statistics at 2 administrative levels (FAO GAUL levels 1 and 2). Select countries from Southeast Asia and Sub-Saharan Africa (Cambodia, Indonesia, Lao PDR, Myanmar, Philippines, Thailand, Vietnam, Burundi, Kenya, Malawi, Mozambique, Rwanda, Tanzania, Uganda, Zambia, Zimbabwe) are included in the current version, with plans to extend the dataset to contain global metrics. Each zip file is described below and two example NDVI tables are available for preview. Key: [source, data, units, temporal range, aggregation, masking, zonal statistic, notes] Currently available: MSZSI-V2_V-NDVI-MEAN.tar: [NASA-MODIS, NDVI, index, 2001–2020, annual mean, agriculture, mean, n/a] MSZSI-V2_T-LST-DAY-MEAN.tar: [NASA-MODIS, LST Day, °C, 2001–2020, annual mean, agriculture, mean, n/a] MSZSI-V2_T-LST-NIGHT-MEAN.tar: [NASA-MODIS, LST Night, °C, 2001–2020, annual mean, agriculture, mean, n/a] MSZSI-V2_R-PRECIP-SUM.tar: [UCSB-CHG-CHIRPS, Precipitation, mm, 2001–2020, annual sum, agriculture, mean, n/a] MSZSI-V2_S-BDENS-MEAN.tar: [OpenLandMap, Bulk density, g/cm3, static, n/a, agriculture, mean, at depths 0-10-30-60-100-200] MSZSI-V2_S-ORGC-MEAN.tar: [OpenLandMap, Organic carbon, g/kg, static, n/a, agriculture, mean, at depths 0-10-30-60-100-200] MSZSI-V2_S-PH-MEAN.tar: [OpenLandMap, pH in H2O, pH, static, n/a, agriculture, mean, at depths 0-10-30-60-100-200] MSZSI-V2_S-WATER-MEAN.tar: [OpenLandMap, Soil water, % at 33kPa, static, n/a, agriculture, mean, at depths 0-10-30-60-100-200] MSZSI-V2_S-SAND-MEAN.tar: [OpenLandMap, Sand, %, static, n/a, agriculture, mean, at depths 0-10-30-60-100-200] MSZSI-V2_S-SILT-MEAN.tar: [OpenLandMap, Silt, %, static, n/a, agriculture, mean, at depths 0-10-30-60-100-200] MSZSI-V2_S-CLAY-MEAN.tar: [OpenLandMap, Clay, %, static, n/a, agriculture, mean, at depths 0-10-30-60-100-200] MSZSI-V2_E-ELEV-MEAN.tar: [MERIT, [elevation, slope, flowacc, HAND], [m, degrees, km2, m], static, n/a, agriculture, mean, n/a] Coming soon MSZSI-V2_C-STAX-MEAN.tar: [OpenLandMap, Soil taxonomy, category, static, n/a, agriculture, area sum, n/a] MSZSI-V2_C-LULC-MEAN.tar: [CGLS-LC100-V3, LULC, category, 2015–2019, mode, none, area sum, n/a] Data sources: https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD13Q1 https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD11A2 https://developers.google.com/earth-engine/datasets/catalog/UCSB-CHG_CHIRPS_PENTAD https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_SOL_SOL_BULKDENS-FINEEARTH_USDA-4A1H_M_v02 https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_SOL_SOL_ORGANIC-CARBON_USDA-6A1C_M_v02 https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_SOL_SOL_PH-H2O_USDA-4C1A2A_M_v02 https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_SOL_SOL_WATERCONTENT-33KPA_USDA-4B1C_M_v01 https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_SOL_SOL_CLAY-WFRACTION_USDA-3A1A1A_M_v02 https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_SOL_SOL_SAND-WFRACTION_USDA-3A1A1A_M_v02 https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_SOL_SOL_GRTGROUP_USDA-SOILTAX_C_v01...

Overview

A geospatial dataset of point geometries with a land use / land cover label and several remote-sensing derived predictor variables that can be used to train and test a land use / land cover classifier.

This dataset was generated with support from a Climate Change AI Innovation Grant and the Australian Centre for International Agricultural Research.

Each of the point geometries was assigned one of the following class labels:

1. water
2. mangrove
3. bare soil / rock
4. urban / impervious
5. cropland / agriculture
6. grassland
7. shrubland
8. trees

The class property associated with each POINT feature stores the point's class label.

Class definitions

The cropland / agriculture class is defined as any location where agricultural activities associated with cropping or livestock management were visible in high-resolution images. Land that is recently fallow, but where evidence of cropping or grazing activities is present, would be labelled as cropland. Grassland is defined as any low vegetation (e.g. below knee height) without a bush, shrub, or woody structure. Scrubland is defined as any vegetation that is below head height, does not form a closed canopy, and has a clearly visible bush, shrub, or woody structure. Trees are defined as any vegetation greater than head height forming a clear canopy.

Methods

Image interpretation and labelling points with a land cover class was undertaken within a custom Google Earth Engine application. Within a region of interest, a year’s worth of Sentinel-2 images was clustered into 15 classes using a k-means algorithm. A stratified random sample of points was generated for manual labelling using clusters as strata. Ground truth datasets ere generated in the Ba, Magodro, Rewa, Sigatoka, RakiRaki, Sigatoka, Suva, Suva (urban), Lautoka (urban), Noco, Vuya, Nadi, and Labasa regions.

To support image interpretation and labelling a point’s land cover using high-resolution images (Google satellite basemaps), ancillary datasets were used (e.g. Planet and Sentinel-2 images) in conjunction with field verification.

Two quality-checks were applied to the labelled land cover points. First, each point was manually screened and quality checked to ensure consistency in class labels. Second, using Planet NICFI basemaps and Sentinel-2 RGB composites 2019, 2020, and 2021, each of the labelled land cover points was screened for a change in land cover event occurring at any point during those three years. If a change in land cover was observed, the point was dropped from the dataset.

For each labelled point in 2019, 2020, and 2021 features were extracted comprising annual median cloud free spectral reflectance across Sentinel-2 wavebands, monthly NDVI composites, and annual median NDVI, NDBI, NDWI, and GCVI bands and elevation, slope, and aspect bands.

This resulted in a dataset of 13,914 labelled points across three years: 2019, 2020, and 2021. The difference in the number of points across years is due to cloud cover preventing features being generated in some years

Feature definitions:

• B_* - median annual cloud free spectral reflectance for Sentinel-2 wavebands
• ndvi - median annual cloud free NDVI computed from Sentinel-2
• gcvi - median annual cloud free GCVI computed from Sentinel-2
• ndwi - median annual cloud free NDWI computed from Sentinel-2
• ndbi - median annual cloud free NDBI computed from Sentinel-2
• ndvi_* - median monthly cloud free NDVI computed from Sentinel-2
• elevation - elevation computed from SRTM
• aspect - aspect computed from SRTM
• slope - slope computed from SRTM