¹ IIASA, Schlossplatz 1, A-2361 Laxenburg, Austria
² CGIAR Program on Climate Change, Agriculture and Food Security (CCAFS), ILRI, PO Box 30709, Nairobi 00100, Kenya
³ International Food Policy Research Institute, 2033 K Street, NW, Washington DC, 20006, USA
⁴ School of Nature Conservation, Beijing Forestry University, 35 Qinghua East Road, Beijing, China
⁵ Department of Geography, University of Maryland, 2181 LeFrak Hall, College Park, Maryland, 20742, USA
⁶ JRC, Via Fermi 2749, TP 266, Ispra, Italy
⁷ ILRI, PO Box 30709, Nairobi 00100, Kenya

Photo of cropland areas. Credit: dreamstime.com

This paper describes the start of a data sharing process to develop a consolidated community cropland map, which was initiated through a recent workshop on characterizing and validating global agricultural land cover. Participants from different organizations around the world were asked to contribute their various cropland maps prior to the workshop. Other data such as geo-tagged photos, in-situ data, classified satellite images and videos also were provided as part of this process. The data are now available online at agriculture.geo-wiki.org. This data sharing exercise, which has culminated in a new Sub-task on Agricultural Mapping as part of the GEO Agricultural Monitoring Task, will continue as an ongoing process and represents an effective model for how data sharing could be facilitated across the GEO community.

The Need for a Consolidated Community Cropland Product

Global land cover products provide important baseline information for resource assessments as well as inputs to a variety of land use models. Accurate estimates of cropland are crucial for determining land availability and for food security purposes, yet global land cover products do not provide consensus on the spatial distribution or total amount of cropland in production currently. For example, the global area under cropland is estimated to be between 1.22 to 1.71 billion hectares, at a 90 percent confidence level [1], which indicates a high uncertainty with a 40 percent difference between the upper and lower estimates. One source of information on croplands is the different medium- to coarse-resolution satellite-derived land cover datasets that are available, including the GLC-2000 [2], the MODIS v.5 land cover products [3] and GlobCover 2005/2009 [4] [5], which have classes for cultivated areas and mosaics of cropland and natural vegetation. Although, with the potential for being up-to-date, they were developed using different data and classification algorithms and do not have particularly high accuracy for estimation of cropland or crop types. Other global cropland products exist, some of which have been calibrated using cropland statistics, such as the M3-Cropland layer of agricultural lands for 2000 [1], the cropland probability layer from MODIS [6] and a global map of rain-fed cropland areas [7]. However, a product is needed with a minimum spatial resolution, and must be of sufficient quality to meet the needs of the food security and land use modeling communities, with an accuracy of at least 80 to 85 percent. It is thus a challenge for individuals and organizations working with these datasets to find a reliable picture of cultivation in one dataset. Africa often is the focus region for agricultural development due to consistent hunger and poverty issues, and weak local capacity results in huge data gaps in national statistics. A consistent and accurate measure of agricultural land derived from satellites could help fill these gaps partly, and provide valuable basic data for designing development programs as well as in monitoring and evaluating food security in the continent.

Home page for the Characterizing and Validating Global Land Cover Workshop with links to the presentations and final report

A Land Cover Workshop to Facilitate Data Sharing

To initiate this process of sharing data, with the aim of developing an enhanced cropland product, the Characterizing and Validating Global Agricultural Landcover workshop was held at the International Institute for Applied Systems Analysis (IIASA), from June 13-15. Data sharing was not limited to cropland maps but also included geo-tagged photos and other related in-situ data. The land cover workshop was hosted by IIASA and the CGIAR Consortium for Spatial Information, in close collaboration with the Group on Earth Observation (GEO), GOFC-GOLD and the Joint Research Centre of the European Commission (JRC). More than 70 international experts on remote sensing, land cover, land use, cropland and rangeland mapping, crop type mapping, area estimation and crowd-sourcing attended the workshop representing universities, national mapping agencies, research institutes and several international organizations including the Food and Agriculture Organization of the United Nations (FAO), the International Food Policy Research Institute (IFPRI), the International Livestock Research Institute (ILRI) and the International Crops Research Institute for the Semi-Arid-Tropics (ICRISAT). The emphasis was on improving African cropland maps, but the workshop discussions were broadened to a global scope and included participants from all major continents. Funding was provided by the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) and the JRC to allow participants from African countries and other developing nations to contribute their data and expertise during the workshop.

Screenshot of Home page for the Characterizing and Validating Global Land Cover Workshop with links to the presentations and final report

A number of key issues were raised during the two-day workshop from the plenary presentations and the breakout groups. Specific issues discussed include methods for cropland and crop-type mapping; crop area estimation; rangeland mapping, the value of crowd-sourcing for training, calibration and validation of land cover; integration of remote sensing and socio-economic data; and the availability of cropland data at the national and regional level. A key action from the workshop was to establish a Sub-Task on Agricultural Mapping under the GEO Agriculture Monitoring Task to be led by IIASA. A follow-up workshop on rangeland mapping and monitoring was also recommended in the framework of a new Sub-Task on Rangeland Productivity.

Group photo of participants who attended the Characterizing and Validating Global Land Cover Workshop

One of the first aims of the new Sub-Task on Agricultural Mapping will be to build a living, community-based consolidated cropland map. This initial product will provide the agricultural monitoring, food security and land use change communities with a better cropland product than currently exists, and will be freely available to researchers and the general public. All workshop participants agreed for their data contributions to be used in the development of an integrated cropland product. This new cropland extent map will integrate all the products contributed by workshop participants using a methodology similar to that reported in [10] at a 1-kilometer resolution. The map will also be calibrated with national and sub-national crop statistics as available. Validation will involve the wider community using crowd-sourced data and Google Earth. The first version of the map was published at the end of 2011 and is downloadable from the same site (agriculture.geo-wiki.org). Crop experts with knowledge of crop locations can use the new tools that have been developed to undertake qualitative validation with drawing tools and commenting facilities. These tools will be trialed as part of outreach activities in upcoming workshops.

The community-based consolidated cropland product will be updated when more crop information becomes available at the national and regional level. In this way, the product will become a living map, which will continue to improve with more contributions. The process of data sharing, which began with the workshop, should be seen as the start of an ongoing process that will continue through the new Sub-Task on Agricultural Mapping. The broader agricultural mapping community is encouraged to take part by providing more national and regional data on croplands, to help validate the product, and to improve our current knowledge of how much cropland there is, its location, and if data quality improves sufficiently or changes over time.

Hybrid cropland map of Africa produced by IIASA/IFPRI developed as part of (10)

The workshop was used as a vehicle to kick-start the data sharing process. A number of lessons have been learned that may be of value to those wanting to compile global datasets based on national and regional data products such as in socio-economic, geological, ecological and earth systems science in general:

• Target the right individuals: It is important to invite people who have data to contribute. Names were initially provided through the steering committee and through evolving contacts with potential participants but not all areas of Africa were covered. The benefits of sharing and contributing actively to the workshop and the community as a whole were used as arguments to persuade other organizations to contribute. This proved to be a very effective approach that gained momentum as the date of the workshop approached;

• Provide incentives: Two main types of incentives were provided to the participants. Payment or partial payment of travel expenses to attend the workshop from developing countries was offered on the conditions that the data were shared before the workshop. A second incentive was co-authorship on a scientific paper to all participants;

• Follow up after the workshop: Data contributions that were promised during the workshop were actively followed up by email along with new leads for sources of data.

The workshop was an intensive process that required more effort than a conventional workshop due to the exchange, reformatting, display and documentation of the datasets. However, the success of the workshop in terms of data sharing, networking and the initiation of an ongoing agricultural mapping process fully justifies the efforts.

If you have a cropland product you want to contribute to the consolidated community global cropland map, please contact us. The map will be registered in the GEOSS portal and become a citable product that will include your authorship details.

References

[1] N. Ramankutty, A. T. Evan, C. Monfreda, and J. A. Foley, “Farming the planet: 1. Geographic distribution of global agricultural lands in the year 2000”, Global Biogeochemical Cycles, vol. 22, GB1003, doi:10.1029/2007GB002952, 2008.

[2] E. Bartholomé and A. S. Belward, “GLC2000: A new approach to global land cover mapping from earth observation data”, International Journal of Remote Sensing, vol. 26(9), pp. 1959-1977, 2005.

[3] M. A. Friedl, D. Sulla-Menashe, B. Tan, A. Schneider, N. Ramankutty, A. Sibley and X. Huang, “MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets”, Remote Sensing of Environment, vol. 114(1), pp. 168-182, 2010.

[4] P. Bicheron, P. Defourny, C. Brockmann, L. Schouten, C. Vancutsem, M. Huc, S. Bontemps, M. Leroy, F. Achard, M. Herold, F. Ranera and O. Arino (2008). GlobCover: Products Description and Validation Report, 18, Toulouse, France. [Online]. Available: http://ionia1.esrin.esa.int/docs/GLOBCOVER_Products_Description_Validation_Report_I2.1.pdf

[5] S. Bontemps, P. Defourney, E. Van Bogaert, O. Arino, V. Kalogirou and J. R. Perez (2011) GLOBCOVER 2009: Products Description and Validation Report. [Online]. Available: http://ionia1.esrin.esa.int/docs/GLOBCOVER2009_Validation_Report_2.2.pdf.

[6] K. Pittman, M. C. Hansen, I. Becker-Reshef, P. V. Potapov, and C. O. Justice, “Estimating global cropland extent with multi-year MODIS data”, Remote Sensing, vol. 2, pp. 1844-1863, 2010.

[7] C.M. Biradar, P.S. Thenkabail, P. Noojipady, Y. Li, V. Dheeravath, H. Turral, M. Velpuri, M. K. Gumma, O. R. P. Gangalakunta, X. L. Cai, X. Xiao, M.A. Schull, R. D. Alankara, S. Gunasinghe and S. Mohideen, “A global map of rainfed cropland areas (GMRCA) at the end of the last millennium using remote sensing”, International Journal of Applied Earth Observation and Geoinformation, vol. 11, pp. 114-129, 2009.

[8] US Department of the Interior (2010). United States Launches New Global Initiative to Track Changes in Land Cover and Use. [Online]. Available: http://www.doi.gov/news/pressreleases/United-States-Launches-New-Global-Initiative-to-Track-Changes-in-Land-Cover-and-Use-Data-Sharing-Will-Assist-Land-Managers-Worldwide.cfm.

[9] European Space Agency (ESA) (2011). GMES Sentinels. [Online]. Available: http://www.esa.int/esaLP/SEM097EH1TF_LPgmes_0.html.

[10] S. Fritz, L. You, A. Bun, L. See, I. McCallum, C. Schill, C. Perger, J. Liu, M. Hansen and M. Obersteiner, “Cropland for sub-Saharan Africa: A synergistic approach using five land cover data sets”, Geophysical Research Letters, 38, L04404, doi:10.1029/2010GL046213, 2010.

[11] IIASA, “Characterizing and Validating Global Land Cover Workshop. IIASA 13-15 June 2011. Workshop Report”, http://www.iiasa.ac.at/Research/FOR/lc/IIASAWorkshopReportJun2011.pdf, 2011.

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Caren Jarmain
University of KwaZulu-Natal, South Africa, jarmainc@ukzn.ac.za

INTRODUCTION

Limited water availability and efficient water management are major challenges facing policymakers worldwide. In South Africa, water is a critical resource for which there is strong competition between the urban, industrial and agriculture sectors. The National Water Act of 1998 states that water should be used efficiently, and has to be reserved for basic human needs and for protecting aquatic eco-systems first, with agriculture having a lesser priority. However, agriculture remains of high economic importance as it contributes to food security, export, employment and livelihood. One of the major sectors in the Western Cape Province of South Africa is the grape industry that produces wine grapes, table grapes or ‘ready to eat,’ and dried fruit or raisins.

Water authorities, together with farmers and their advisors, try to maintain agricultural production while reducing water consumption. In other words, they need to increase the water productivity. Water productivity is defined as crop production divided by amount of water consumed by the crop. It is an important indicator of agricultural performance. To improve water productivity –more crop per drop– one needs to know where and how much water is consumed: the actual evapotranspiration. It is therefore necessary to assess both biomass production and the quantity of water actually consumed by crops. The water consumption is the same as the actual evapotranspiration, which combines the vapour transpiration of vegetation and the evaporation of the soil surface. Irrigation application is not an adequate measure of water consumption as it does not include rainfall and soil moisture uptake by the plant, while losses such as runoff, seepage and percolation to the ground water are not lost from the catchment and can still be used in other areas. Actual evapotranspiration is an essential component of the water balance. In combination with the potential evapotranspiration, which is the water consumption potential for a crop without water stress, one can determine the water deficit, the amount of water the crop is missing to grow optimally.

Information on actual evapotranspiration is difficult to obtain. In situ measurements are complicated, expensive and do not show the spatial variation. The evapotranspiration is often derived from the reference evapotranspiration using universally derived crop specific crop factors. Crop factors have the drawback that they are empirically and not physically based. The single crop factor is constant over time while it can highly vary over the season. Furthermore, it does not account for water stress and provides the potential, not the actual evapotranspiration. The dual crop factor was introduced to take into account water stress and changes over time, but still does not account for differences between regions, cultivars and vineyard blocks or fields. Remote sensing based energy balance algorithms are most suited for estimating crop water use at both field and regional scales. Numerous evapotranspiration models have been developed in the last three decades using visible, near infrared and thermal infrared remote sensing data. Advanced remote sensing algorithms such as the Surface Energy Balance Algorithm for Land (Bastiaanssen et al, 1998, 2005), and the Mapping EvapoTranspiration at High Resolution and Internalized Calibration METRIC (Allen et al., 2007) have been applied to provide field level data on actual evapotranspiration and water productivity worldwide.

PROJECT DESCRIPTION

In 2008, WaterWatch assessed the application of remote sensing data to optimize irrigation management of vineyards in the Western Cape Province from 2004 to 2007. Farmers were very interested in the results but the retrospective nature of this study, in which the results only became available after the season ended, limited the practical application of the data on the farm level.

Figure 1: Overview of the study areas (in black) with the water management areas (colored areas) and provincial borders (red boundaries).

The GrapeLook project is a pre-operational service to improve water productivity and optimize fertilizer use in vineyards by providing weekly updates on crop parameters using satellite technology. The service was demonstrated for vineyards in the Western Cape Province of South Africa in the 2010-2011 season. The technology, however, is applicable at most land surfaces such as deserts, shrub lands, forests and agricultural areas.

Figure 1 shows the location of the vineyards in Western Cape Province, South Africa.

Refinement of the system took place during the demonstration phase. Information relating to crop water, growth and nitrogen status was made available freely online. The service provided weekly updates from Sept. 1, 2010, to April 30, 2011, for all major table and wine grape producing areas of the Western Cape.

Figure 2: The GrapeLook website.

• Provide weekly updated semi-real time information for individual blocks/plots and farms using satellite technology. The information included parameters such as crop growth, evapotranspiration deficits and crop nitrogen status;
• Forecast soil moisture change over the five days after satellite image acquisition for participating farmers only;
• Disseminate this information through the GrapeLook website, accessible to anyone including farmers and irrigation consultants, and;
• Enable farmers, water use associations, South African authorities and other users to evaluate the benefits of the pre-operational service as a tool to optimize water use and fertilizer application.

METHODOLOGY

For GrapeLook, the information on the crop water and growth status is calculated by the Surface Energy Balance Algorithm for Land (SEBAL). SEBAL is an advanced-algorithm based on the energy balance. It requires inputs such as the Normalized Difference Vegetation Index (NDVI), albedo and surface temperature derived from earth observation, as well as meteorological data on the air temperature, relative humidity and wind speed. SEBAL determines actual and potential evapotranspiration on a pixel-by-pixel basis. Besides crop evapotranspiration, SEBAL estimates biomass production, evapotranspiration deficit and the biomass water use efficiency. In combination with yield data, it can be used to determine the water productivity.

The NDVI, albedo and surface temperature are derived from multi-spectral and thermal infrared satellite Earth observation data from the Huan Jing 1B (HJ-1B), Terra ASTER, Landsat 7ETM, Disaster Monitoring Constellation (DMC) and Fengyun sensors. The strength of the system is that it can operate by using different Earth observation satellite resources and therefore it is independent from a single source and ensures data delivery. The meteorological data in GrapeLook was derived from meteorological station measurements. The algorithm MeteoLook (Voogt, 2006) was developed at WaterWatch as a physically based regional distribution model for measured meteorological variables. It spatially interpolated weather data from meteorological stations to a raster map.

Figure 3: Overview of study areas in the catchments, indicated with 1 (Berg River), 2 (Breede River) and 3 (Olifants River).

GRAPELOOK SERVICE

The dissemination website was updated weekly during the grape season of 2010-2011. Information on crop water, nitrogen and growth status at field level was made freely available online to anyone, whether working as a farmer, farmer consultant, irrigation expert or government official. The weekly parameter layers produced by SEBAL consist of 30 meter resolution raster images, which enable detailed monitoring of the temporal and spatial variations within and between blocks. Table 1 shows the complete list of parameters made available.

Satellite imagery is the major cost in the project. Although it is possible to increase the number of updates, costs would increase considerably. The idea of the project is to develop an affordable, cost-effective tool for farmers. For this reason a weekly time interval was chosen, allowing a farmer to monitor his farm and make the required changes in his management.

Figure 4: Actual evapotranspiration in table and wine blocks for the week of Feb. 16-22, 2011.

An example on how the data can be used, is shown in Figure 4, which presents a map of the actual evapotranspiration in an area with wine and table grape vineyards. A decision maker can make a few important observations based on this map. First of all, the actual evapotranspiration of table grapes, 50-55 millimeters per week, is larger than of wine grapes, 20-40 millimeters per week. Secondly, the water consumption in a table grape block is more uniform than in a wine grape block. Wine grape blocks can have very different water consumption patterns. A farmer can use the map of actual evapotranspiration to determine the irrigation efficiency. For example, if a farmer applies 80 millimeters of irrigation water in a week, and the actual evapotranspiration is 55 millimeters per week, it means 25 millimeters are not used by the plant during that week. Or, if two blocks receive the same amount of irrigation water but the map of actual evapotranspiration shows they consume different amounts of water, it means the farmer might reduce the irrigation application in one of the two blocks. Furthermore, the map of actual evapotranspiration helps a farmer to evaluate the effect of cultivar, soil, irrigation system and schedule, and farm management on the water consumption.

Figure 5: For illustration: Block with wine grapes in Western Cape Province.

The demonstration phase, which ran from September 2010 until April 2011, proved the technology could be applied on a weekly basis and that the end-users were supportive of the service. The GrapeLook solution demonstrated the potential to efficiently monitor crop water stress, crop growth, and to support better farming practices. One can expect the GrapeLook service will help reduce labor and input costs, to increase product quality and yield, and to improve water use efficiency.

In coming years, GrapeLook will continue as FruitLook. The new name reflects the inclusion of deciduous fruit trees and the extension of the target areas. Also, the website functionality will be improved to make it more user-friendly and to improve communication with farmer consultants. The existing farmer consultants and related industries play an important role as they will inform the farmers in their network about the GrapeLook service and help the farmers translate the GrapeLook data to real farm practices. For management advice, one needs to know the specific conditions of a farm and the farmer’s objective.

REFERENCES

Allen, R.G., M. Tasumi, A. Morse, R. Trezza, J.L. Wright, W.G.M. Bastiaanssen, W. Kramber, I. Lorite and C.W. Robinson, 2007. Satellite-based energy balance for mapping evapotranspiration with internalized calibration (Metric) – applications, ASCE J. of Irrigation and Drainage Engineering 133(4): 395-406

Bastiaanssen, W.G.M., H. Pelgrum, J. Wang, Y. Ma, J. Moreno, G.J. Roerink and T. van der Wal, 1998. The Surface Energy Balance Algorithm for Land (SEBAL): Part 2 validation, J. Of Hydr. 212-213: 213-229

Bastiaanssen, W.G.M., M. Menenti, R.A. Feddes and A.A.M. Holtslag, 1998. The Surface Energy Balance Algorithm for Land (SEBAL): Part 1 formulation, J. of Hydr. 212-213: 198-212

Bastiaanssen, W.G.M, E.J.M. Noordman, H. Pelgrum, G. Davids, B.P. Thoreson, and R.G. Allen, 2005. SEBAL model with remotely sensed data to improve water-resources management under actual field conditions, J. Irrig. And Drain. Engrg. 131 (1): 85-93

Klaasse, Annemarie, Caren Jarmain, Andre Roux, Olivier Becu, and Amnon Ginati, 2011. GrapeLook: space based services to improve water use efficiency of vineyards in South Africa, 62n International Astronautical Congress, Cape Town, South Africa

Voogt, M.P. (2006) Meteolook, a physically based regional distribution model for measured meteorological variables. MSc Thesis TU Delft

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