GFSAD1000: Cropland Extent 1km Crop Dominance, Global Food-Support Analysis Data

USGS/GFSAD1000_V0
Dataset Availability
2000-01-01T00:00:00 - 2001-01-01T00:00:00
Dataset Provider
Earth Engine Snippet
ee.Image("USGS/GFSAD1000_V0")
Tags
landcover cropland crop gfsad usgs

Description

The GFSAD is a NASA-funded project to provide high-resolution global cropland data and their water use that contributes towards global food security in the twenty-first century. The GFSAD products are derived through multi-sensor remote sensing data (e.g., Landsat, MODIS, AVHRR), secondary data, and field-plot data and aims at documenting cropland dynamics.

At a nominal 1km scale, V0.0 provides the spatial distribution of the five major global cropland types (wheat, rice, corn, barley, and soybeans) which occupy 60% of all global cropland areas. The map is produced by overlaying these crops over the remote sensing derived global irrigated and rainfed cropland area map of the International Water Management Institute. V0.0 an 8-class product that provides information on global: cropland extent, crop dominance, irrigated versus rainfed cropping, and cropping intensity (single, double, triple, and continuous crops).

Bands

Resolution
1000 meters

Bands

Name Min Max Description
landcover 0 9

Crop dominance class descriptions

landcover Class Table

Value Color Description
0 black Non-croplands
1 white Irrigated: wheat and rice
2 green Irrigated mixed crops 1: Wheat, rice, barley, soybeans
3 yellow Irrigated mixed crops 2: wheat, rice, cotton, orchards
4 brown Rainfed: wheat, rice, soybeans, sugarcane, corn, cassava
5 orange Rainfed: wheat, barley
6 02be11 Rainfed: corn, soybens
7 015e08 Rainfed mixed crops: wheat, corn, rice, barley, soybeans
8 02a50f Fractions of mixed crops: wheat, maize, rice, barley, soybeans
9 purple Other classes

Terms of Use

Terms of Use

Most U.S. Geological Survey (USGS) information resides in the public domain and may be used without restriction. Additional information on Acknowledging or Crediting USGS as Information Source is available.

Citations

Citations:
  • Thenkabail P.S., Knox J.W., Ozdogan, M., Gumma, M.K., Congalton, R.G., Wu, Z., Milesi, C., Finkral, A., Marshall, M., Mariotto, I., You, S. Giri, C. and Nagler, P. 2012. Assessing future risks to agricultural productivity, water resources and food security: how can remote sensing help?. Photogrammetric Engineering and Remote Sensing, August 2012 Special Issue on Global Croplands: Highlight Article. 78(8): 773-782.

Explore in Earth Engine

var dataset = ee.Image('USGS/GFSAD1000_V0');
var cropDominance = dataset.select('landcover');
var cropDominanceVis = {
  min: 0.0,
  max: 9.0,
  palette: [
    'black', 'white', 'green', 'yellow', 'brown', 'orange', '02be11', '015e08',
    '02a50f', 'purple'
  ],
};
Map.setCenter(-17.22, 13.72, 2);
Map.addLayer(cropDominance, cropDominanceVis, 'Crop Dominance');