data templates jeanne behnke 301-614-5326

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Global Cropland Extent (GCE) 1 km Products of

Global Food Security-support Analysis Data @ 30 m (GFSAD30) Project

http://geography.wr.usgs.gov/science/croplands/index.html

Global Cropland Extent (GCE) 1 km Products of Global Food Security-support Analysis Data @ 30

m (GFSAD30) ProjectThe overarching goal of this project is to produce consistent and unbiased estimates of global agricultural cropland areas, crop types, crop watering method, and cropping intensities using Multi-sensor, Multi-date Remote Sensing and mature cropland mapping algorithms (CMAs).

This goal will be achieved @ various resolutions:• GCE 1km Crop Dominance (aka GCE V0.0); • GCE 1km Multi-study Crop Mask (aka GCE V1.0);• GCE 250m Crop Dominance (aka GCE V2.0);• GCE 30m Crop Dominance (aka GCE V3.0);

Global Cropland Extent (GCE) 1 km Products of Global Food Security-support Analysis Data @ 30 m

(GFSAD30) ProjectTherefore, our project aims to close these gaps through a Global Cropland Area Database produced at nominal 1-km, 250 m, and 30 m using multi-sensor remote sensing, secondary data, and ground data. The five key products are:

1. Cropland extent\area,2. Irrigated versus rainfed, 3. Cropping intensities: single, double, triple, and continuous cropping; 4. Crop types with focus on 8 crops that occupy 70% of the global cropland areas; and5. Change over space and time.…….from above comes:• crop productivity (productivity per unit of land) and • water productivity (productivity per unit of water or crop per drop)

Note: at certain resolution, some products may not be feasible. For example, at 1 km, crop types is not possible at any high degree of precision and accuracies.

GCE 1km Crop Dominance (aka GCE V0.0) Products

of Global Food Security-support Analysis Data @ 30 m

(GFSAD30) Project

• Title for specific data set (ESDT) or group of datasets:– GCE 1km Crop Dominance (aka GCE V0.0) from Global Food Security Support-analysis Data @ 30 m

(GFSAD30) Project.

– Nominal Year 2000 product: Global cropland extent (GCE), Irrigation versus Rainfed, and Crop Dominance.

– Global product available as ERDAS Imagine .img file in Datum: WGS84, Projection: Geographic (Latitude, Longitude). Nominal 1 km spatial resolution.

• Brief Narrative Description:This Global Food Security-support Analysis Data (GFSAD) Project’s Global Cropland Extent Product at nominal 1km (GCE 1Km Crop Dominance). The GCE 1KM Crop Dominance provides spatial distribution of the five major global cropland types (wheat, rice, corn, barley and soybeans; which occupy 60% of all global cropland areas) at nominal 1km (GCE 1KM Crop Dominance). The map is produced by overlying the five dominant crops of the world produced by Ramankutty et al. (2008), Monfreda et al. (2008), and Portman et al. (2009) over the remote sensing derived global irrigated and rainfed cropland area map of the International Water Management Institute (IWMI; Thenkabail et al., 2009a, 2009b, 2011). GCE 1KM Crop Dominance (see Figure below) is an 8 class digital product that provides, at nominal 1 km, information on global: 1. Cropland extent\areas, 2. irrigated versus rainfed cropping, 3. Crop dominance, and 4. Cropping intensity (single, double, triple, and continuous crops).

Reference:

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.

• GCE 1km Crop Dominance (aka GCE V0.0) data is available for the entire world at nominal 1 km;

• Nominal year 2000;

• Provided in ERDAS Imagine .img format. Datum: WGS84, Projection: Geographic (Lat\long);

• Product provides: 1. Cropland extent\areas, 2. irrigated versus rainfed cropping, and 3. Crop dominance.

• The map is produced by overlying the five dominant crops of the world produced by Ramankutty et al. (2008), Monfreda et al. (2008), and Portman et al. (2009) over the remote sensing derived global irrigated and rainfed cropland area map of the International Water Management Institute (IWMI; Thenkabail et al., 2009a, 2009b, 2011);

• GFSAD30 project will ultimately produce global cropland extent (GCE) products at 250 m, and 30 m. But, this GCE 1km Crop Dominance (aka GCE V0.0) products is at coarser (~1 km) resolution;


(GFSAD30) Project

Figure 1. Figure 1. GCE 1km Crop Dominance (aka GCE V0.0). Figure here shows the USA part of the product. Classes 1 to 3 are croplands that are irrigated. Classes 4 to 7 are croplands that are rainfed. Class 8 is overwhelmingly non-croplands, but have very small fractions of croplands (refer to Thenkabail et al., 2012). Each class has some combination of croplands dominating in them. 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 overlying the five dominant crops of the world produced by Ramankutty et al. (2008), Monfreda et al. (2008), and Portman et al. (2009) over the remote sensing derived global irrigated and rainfed cropland area map of the International Water Management Institute (IWMI; Thenkabail et al., 2009a, 2009b, 2011). (Reference: Thenkabail et al., 2012].


(GFSAD30) Project

• Product Algorithm Theoretical Basis:

See http://geography.wr.usgs.gov/science/croplands/

• Science Need (justification):Monitoring global croplands (GCs) is imperative for ensuring sustainable water and food security to the people of the world in the Twenty-first Century. However, the currently available cropland products suffer from major limitations such as: (1) Absence of precise spatial location of the cropped areas; (b) Coarse resolution nature of the map products with significant uncertainties in areas, locations, and detail; (b) Uncertainties in differentiating irrigated areas from rainfed areas; (c) Absence of crop types and cropping intensities; and (e) Absence of a dedicated web\data portal for the dissemination of cropland products.


(GFSAD30) Project

• Quality Information – Known Issues:

This product emphasizes the importance of remote sensing and continued research about ways to use its assets in global agricultural cropland mapping and water use evaluation. Current cropland map products are derived from coarse resolution remotely sensed data and traditional classification methods that require substantial human involvement. We have discussed the advances and developmental needs of semi-automated and automated classification algorithms in routine, rapid, and accurate mapping of global croplands and their characteristics. Advances in global cropland mapping will require data fusion techniques from multiple satellite sensors, secondary data sources, and a large and systematic collection of in-situ information, including temporal phenologies and hyperspectral signatures. As Beddington (2010) stresses, the fundamental issues for policy makers and scientists are whether by the year 2050 over nine billion people, can be fed equitably, healthily, and sustainably and how- sound management can make water use more sustainable as a growing population moves up from poverty. In this context, the role of remote sensing is clear. There is an unequivocal need to provide a more systematic and integrated approach to global cropland mapping to support a range of initiatives, including assessments of crop productivity, helping to identify food security "hotspots" of vulnerability and resiliency, assessing the agricultural risks due to climate change and quantifying agricultural water demand.

Source: Thenkabail et al., 2012


(GFSAD30) Project

• Accuracy Information:Figure in one of the previous slides shows the spatial distribution of earth’s agricultural cropland areas generated for the five major crops (wheat, rice, corn, barley and soybeans) produced using parcel-based inventory data (Monfreda et al., 2008; Portmann et al., 2008; Ramankutty et al., 2008) overlaid with the global irrigated and rainfed cropland area map produced using remote sensing data by the International Water Management Institute (IWMI) (Thenkabail et al., 2009a,b, 2011). These five crops account for about 60 percent of worldwide cropland areas. Although there is good general agreement, the precise location of these crops is only approximate due to the coarse resolution (approx. 10 km2) and fractional representation of the crop data in each grid cell of all maps, since each pixel may contain from 1 to 100 percent of a crop. The IWMI cropland product (Thenkabail et al., 2009a, b, 2011) is at approximately one km2 resolution. Every pixel has a certain fractional percentage of a crop (typically above 50 percent). The two maps were resampled to one km2 for seamless spatial analysis.

The existing cropland datasets also differ from one another due to inherent uncertainties in establishing the precise location of croplands, the watering method (whether rainfed, fully irrigated, or with supplemental irrigation), cropping intensities, crop types and/or dominance, and their characteristics (e.g. crop or water productivity measures such as biomass, yield, or water use). Improved knowledge of the uncertainties (see Congalton and Green, 2009) in these estimates will lead to a collection of highly accurate spatial data products to support crop modeling, food security analysis, and decision making.

Source: Thenkabail et al., 2012


(GFSAD30) Project

• Intended or Appropriate Product Use:(1) The GCAD30 product line are a high value application of Remote Sensing data, creating invaluable baseline global products on irrigated and rainfed areas and its spin-offs for determining water use-food production. This would create demand for similar high resolution-high quality products in the future in order to look at change and trends and, thereby, promoting and justifying the demand for the Landsat data continuity mission (LDCM). (2) Precise estimates of global cropland areas are a must for global crop water models (GCWM; see Siebert and Döll, 2009 and Wisser et al. 2008) to help accurately determine: (a) green water use (by rainfed croplands) and (b) blue water use (by irrigated croplands), (c) food production, (d) dynamics of virtual water trade, and (e) scenario modeling. The blue water use by irrigated crops and green water use by rainfed crops can be accurately assessed, based on GCAD30 data which will provide for GCWMs needed information such as: (a) crop type, (b) precise spatial location of crops (e.g., latitude), (c) cropping intensity, (d) crop calendar, (e) watering methods (e.g., irrigation, rainfed), (f) watering source (ground water, surface water), (g) irrigation type (e.g., sprinkler, gravity). (3) GCAD30 will also significantly contribute in food security studies. For example, global food production has stagnated or decreased (e.g., drought situations, groundwater depletion, diversion of croplands to bio-fuels, urban expansion) in many countries of the world. The high resolution irrigated area maps will improve our understanding of the impacts of water exploitation on the stagnated crop yields. (4) The GCAD products will be made accessible, publicly, to anyone in the World through GFSAD30 web\data portal: http://geography.wr.usgs.gov/science/croplands/


(GFSAD30) Project

http://geography.wr.usgs.gov/science/croplands/


• Science Value:

Global cropland products are truly unique and provide new knowledge of the Planet Earth by producing pioneering and innovative Earth System Data Records (ESDRs) and Climate Data Records (CDRs). GCAD30 is targeted to make significant contribution towards this goal by producing a first of its kind new global product of very high quality using multi sensor data combination and advanced algorithms leading to new knowledge on global croplands. The project will also demonstrate the: (a) scientific advances in understanding, modeling, and mapping global cropland area database (GCAD) through advanced remote sensing and modeling, (b) methodological advances in spectral matching techniques (SMTs), and automated cropland classification algorithms (ACCA), and (c) societal benefits by providing a product critical to global food security analysis in the twenty-first Century. The proposal outputs and outcomes will contribute to Group on Earth Observations (GEO) Agriculture and Water Societal Beneficial Areas (GEO Ag. SBAs), GEO Global Agricultural Monitoring Initiative (GEO GLAM), Global Earth Observation System of Systems (GEOSS), and the Committee on Earth Observing Satellites (CEOS). The project will be make major contribution to “Big Data” thrust that the White House recently announced, which is strongly supported by the Directors of various agencies (USGS, NSF, NIH, DoE, and DOD).


(GFSAD30) Project

Global Cropland Extent GCE 1km Multi-study Crop Mask (aka GCE V1.0) Products

of Global Food Security-support Analysis Data @

30 m (GFSAD30) Project

• Title for specific data set (ESDT) or group of datasets:– GCE 1km Multi-study Crop Mask (aka GCE V1.0) from Global Food Security Support-analysis Data @

30 m (GFSAD30) Project.

– GCE 1km Multi-study Crop Mask (aka GCE V1.0) is produced for nominal Year 2000 Global cropland extent (GCE), and Irrigation versus Rainfed.

– GCE 1km Multi-study Crop Mask (aka GCE V1.0) is made available as ERDAS Imagine .img file in Datum: WGS84, Projection: Geographic (Latitude, Longitude). Nominal 1 km spatial resolution.

• Brief Narrative Description:A disaggregated five class global cropland extent map derived at nominal 1-km based on four major studies: Thenkabail et al. (2009a, 2011), Pittman et al. (2010), Yu et al. (2013), and Friedl et al. (2010). Class 1 to Class 5 are cropland classes, that are dominated by irrigated and rainfed agriculture. However, class 4 and Class 5 have ONLY minor or very minor fractions of croplands. Refer to Table 6.7c for cropland statistics of this map. Note: Irrigation major: areas irrigated by large reservoirs created by large and medium dams, barrages and even large ground water pumping. Irrigation minor: areas irrigated by small reservoirs, irrigation tanks, open wells, and other minor irrigation. However, it is very hard to draw a strict boundary between major and minor irrigation and in places there can be significant mixing. So, when major irrigated areas such as the Ganges basin, California’s central valley, Nile basin etc. are clearly distinguishable as major irrigation, in other areas major and minor irrigation may inter-mix.

Reference:

Teluguntla, P., Thenkabail, P.S., Xiong, J., Gumma, M.K., Giri, C., Milesi, C., Ozdogan, M., Congalton, R., Tilton, J., Sankey, T.R., Massey, R., Phalke, A., and Yadav, K. 2014. Global Cropland Area Database (GCAD) derived from Remote Sensing in Support of Food Security in the Twenty-first Century: Current Achievements and Future Possibilities. Chapter 7, Vol. II. Land Resources: Monitoring, Modelling, and Mapping, Remote Sensing Handbook edited by Prasad S. Thenkabail. Accepted. http://geography.wr.usgs.gov/science/croplands/docs/Global-cropland-extent-V10-teluguntla-thenkabail-xiong.pdf

http://geography.wr.usgs.gov/science/croplands/docs/Global-cropland-extent-V10-teluguntla-thenkabail-xiong.pdf



Global Cropland Extent GCE 1km Multi-study Crop Mask (aka GCE V1.0) of Global Food Security-support Analysis Data @ 30 m (GFSAD30) Project

• GCE 1km Multi-study Crop Mask (aka GCE V1.0) data is available for the entire world at nominal 1 km;

• Data comes from products derived using data from 2000-2012;

• Provided in ERDAS Imagine .img format. Datum: WGS84, Projection: Geographic (Lat\long);

• Product provides: (a) cropland extent, and (b) irrigation versus rainfed.

• Derived from 4 existing products: four major studies: Thenkabail et al. (2009a, 2011), Pittman et al. (2010), Yu et al. (2013), and Friedl et al. (2010).

• GFSAD30 project will ultimately produce global cropland extent (GCE) products at 250 m, and 30 m. But, this (GCE V1.0) products is at coarser (~1 km) resolution.

Figure 6.12c. A disaggregated five class global cropland extent map derived at nominal 1-km based on four major studies: Thenkabail et al. (2009a, 2011), Pittman et al. (2010), Yu et al. (2013), and Friedl et al. (2010). Class 1 to Class 5 are cropland classes, that are dominated by irrigated and rainfed agriculture. However, class 4 and Class 5 have ONLY minor or very minor fractions of croplands. Refer to Table 6.7c for cropland statistics of this map. Note: Irrigation major: areas irrigated by large reservoirs created by large and medium dams, barrages and even large ground water pumping. Irrigation minor: areas irrigated by small reservoirs, irrigation tanks, open wells, and other minor irrigation. However, it is very hard to draw a strict boundary between major and minor irrigation and in places there can be significant mixing. So, when major irrigated areas such as the Ganges basin, California’s central valley, Nile basin etc. are clearly distinguishable as major irrigation, in other areas major and minor irrigation may inter-mix. [Source: Teluguntla et al., 2014].


• Product Algorithm Theoretical Basis:

See http://geography.wr.usgs.gov/science/croplands/

• Science Need (justification):Monitoring global croplands (GCs) is imperative for ensuring sustainable water and food security to the people of the world in the Twenty-first Century. However, the currently available cropland products suffer from major limitations such as: (1) Absence of precise spatial location of the cropped areas; (b) Coarse resolution nature of the map products with significant uncertainties in areas, locations, and detail; (b) Uncertainties in differentiating irrigated areas from rainfed areas; (c) Absence of crop types and cropping intensities; and (e) Absence of a dedicated web\data portal for the dissemination of cropland products.


• Quality Information – Known Issues:The current state-of-the-art provides four key global cropland products derived from remote sensing, each produced by a different group. These products are derived based on four major studies: Thenkabail et al. (2009a, 2011), Pittman et al. (2010), Yu et al. (2013), and Friedl et al. (2010). The 4 products have been produced using: (a) time-series of multi-sensor data and secondary data, (b) 250 m MODIS time-series data, (c) 30 m Landsat data, and(d) a MODIS 500 m time-series derived cropland classes from a land use\land cover product has been used. These four products were synthesized, at nominal 1 km, to obtain a unified cropland mask of the world (global cropland extent version 1.0 or GCE (V1.0).

It was demonstrated from these products that the uncertainty in location of croplands in any one given product is quite high and no single product maps croplands particularly well. Therefore, a synthesis identifies where some or all of these products agree and where they disagree. This provides a starting point for the next level of more detailed cropland mapping at 250 m and 30 m. The key cropland parameters identified to be derived from remote sensing are: (1) cropland extent\areas, (2) cropping intensities, (3) watering method (irrigated versus rainfed), (4) crop type, and (5) cropland change over time and space. From these primary products one can derive crop productivity and water productivity. Such products have great importance and relevance in global food security analysis.Authors recommend the use of composite global cropland map (see one of the previous slides; 5 class map) that provides clear consensus view on of 4 major cropland studies on global: • Cropland extent location; • Cropland watering method (irrigation versus rainfed). The product (Figure in one of the previous slides; 5 class map) does not show where the crop types are or even the crop dominance. However, cropping intensity can be gathered using multi-temporal remote sensing over these cropland areas.

Source: Teluguntala et al., 2014


• Accuracy Information:Currently, the main causes of uncertainties in areas reported in various studies (Ramankutty et al., 2008 versus; Thenkabail et al., 2009a; Thenkabail et al., 2009c) can be attributed to, but not limited to: (a) reluctance of national and state agencies to furnish the census data on irrigated area and concerns of their institutional interests in sharing of water and water data; (b) reporting of large volumes of census data with inadequate statistical analysis; (c) subjectivity involved in the observation-based data collection process; (d) inadequate accounting of irrigated areas, especially minor irrigation from groundwater, in national statistics; (e) definitional issues involved in mapping using remote sensing as well as national statistics; (f) difficulties in arriving at precise estimates of area fractions (AFs) using remote sensing; (g) difficulties in separating irrigated from rainfed croplands; and (h) imagery resolution in remote sensing. Other limitations include (Thenkabail et al., 2009a, 2011): A. Absence of precise spatial location of the cropland areas for training and validation; B. Uncertainties in differentiating irrigated areas from rainfed areas;C. Absence of crop types and cropping intensities;D. Inability to generate cropland maps and statistics, routinely; andE. Absence of dedicated web\data portal for dissemination cropland products.

Source: Teluguntla et al., 2014


• Intended or Appropriate Product Use:(1) The GCAD30 product line are a high value application of Remote Sensing data, creating invaluable baseline global products on irrigated and rainfed areas and its spin-offs for determining water use-food production. This would create demand for similar high resolution-high quality products in the future in order to look at change and trends and, thereby, promoting and justifying the demand for the Landsat data continuity mission (LDCM). (2) Precise estimates of global cropland areas are a must for global crop water models (GCWM; see Siebert and Döll, 2009 and Wisser et al. 2008) to help accurately determine: (a) green water use (by rainfed croplands) and (b) blue water use (by irrigated croplands), (c) food production, (d) dynamics of virtual water trade, and (e) scenario modeling. The blue water use by irrigated crops and green water use by rainfed crops can be accurately assessed, based on GCAD30 data which will provide for GCWMs needed information such as: (a) crop type, (b) precise spatial location of crops (e.g., latitude), (c) cropping intensity, (d) crop calendar, (e) watering methods (e.g., irrigation, rainfed), (f) watering source (ground water, surface water), (g) irrigation type (e.g., sprinkler, gravity). (3) GCAD30 will also significantly contribute in food security studies. For example, global food production has stagnated or decreased (e.g., drought situations, groundwater depletion, diversion of croplands to bio-fuels, urban expansion) in many countries of the world. The high resolution irrigated area maps will improve our understanding of the impacts of water exploitation on the stagnated crop yields. (4) The GCAD products will be made accessible, publicly, to anyone in the World through GFSAD30 web\data portal: http://geography.wr.usgs.gov/science/croplands/




• Science Value:

Global cropland products are truly unique and provide new knowledge of the Planet Earth by producing pioneering and innovative Earth System Data Records (ESDRs) and Climate Data Records (CDRs). GCAD30 is targeted to make significant contribution towards this goal by producing a first of its kind new global product of very high quality using multi sensor data combination and advanced algorithms leading to new knowledge on global croplands. The project will also demonstrate the: (a) scientific advances in understanding, modeling, and mapping global cropland area database (GCAD) through advanced remote sensing and modeling, (b) methodological advances in spectral matching techniques (SMTs), and automated cropland classification algorithms (ACCA), and (c) societal benefits by providing a product critical to global food security analysis in the twenty-first Century. The proposal outputs and outcomes will contribute to Group on Earth Observations (GEO) Agriculture and Water Societal Beneficial Areas (GEO Ag. SBAs), GEO Global Agricultural Monitoring Initiative (GEO GLAM), Global Earth Observation System of Systems (GEOSS), and the Committee on Earth Observing Satellites (CEOS). The project will be make major contribution to “Big Data” thrust that the White House recently announced, which is strongly supported by the Directors of various agencies (USGS, NSF, NIH, DoE, and DOD).


data templates jeanne behnke 301-614-5326

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