The Need for Improved Maps of Global Cropland

Fritz, Steffen; You, Liangzhi; Justice, Chris; Becker‐Reshef, Inbal; Bydekerke, Lieven; Cumani, Renato; Defourny, Pierre; Erb, Karl‐Heinz; Foley, J. A.; Gilliams, Sven; Gong, Peng; Hansen, M.; Hertel, Thomas W.; Herold, Martin; Herrero, Mario; Francois, Kayitakire; Latham, J.; Léo, Oberdan; McCallum, Ian; Obersteiner, Michael; Ramankutty, Navin; Rocha, Jansle Vieira; Tang, Huajun; Thornton, Philip K.; Vancutsem, Christelle; Velde, Marijn van der; Wood, Stan; Woodcock, Curtis E.

doi:10.1002/2013eo030006

Cited by 75 publications

(53 citation statements)

References 4 publications

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“…The first step in defining EO requirements for global agricultural monitoring is identifying the areas that require monitoring, namely the locations of global croplands [71]. To this end, Fritz et al (2015) [1] have developed a "best-available" cropland mask that indicates the probability that any 0.0083° (~1 km at the Equator) cell contains cropland based on a suite of existing land cover and cropland masks.…”

Section: Input Datasets: Where To Image?mentioning

confidence: 99%

A Framework for Defining Spatially Explicit Earth Observation Requirements for a Global Agricultural Monitoring Initiative (GEOGLAM)

2015

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Global agricultural monitoring utilizes a variety of Earth observations (EO) data spanning different spectral, spatial, and temporal resolutions in order to gather information on crop area, type, condition, calendar, and yield, among other applications. Categorical requirements for space-based monitoring of major agricultural production areas have been articulated based on best practices established by the Group on Earth Observation's (GEO) Global Agricultural Monitoring Community (GEOGLAM) of Practice, in collaboration with the Committee on Earth Observation Satellites (CEOS). We present a method to transform generalized requirements for agricultural monitoring in the context of GEOGLAM into spatially explicit (0.05°) Earth observation (EO) requirements for multiple resolutions of data. This is accomplished through the synthesis of the necessary remote sensing-based datasets concerning where (crop mask, when (growing calendar, and how frequently imagery is required (considering cloud cover impact throughout the agricultural growing season. Beyond this provision of the framework and tools necessary to articulate these requirements, investigated in depth is the requirement for reasonably clear moderate spatial resolution (10-100 m) optical data within 8 days over global within-season croplands of all sizes, a data type prioritized by GEOGLAM and CEOS. Four definitions of "reasonably clear" are investigated: 70%, 80%, 90%, or 95% clear. The revisit frequency required (RFR) for a reasonably clear view varies greatly both geographically and throughout the growing season, as well as with the threshold of acceptable clarity. The global average RFR for a 70% clear view within 8 days is 3.9-4.8 days (depending on the month), 3.0-4.1 days for 80% clear, OPEN ACCESSRemote Sens. 2015, 7 1462 2.2-3.3 days for 90% clear, and 1.7-2.6 days for 95% clear. While some areas/times of year require only a single revisit (RFR = 8 days) to meet their reasonably clear requirement, generally the RFR, regardless of clarity threshold, is below to greatly below the 8 day mark, highlighting the need for moderate resolution optical satellite systems or constellations with revisit capabilities more frequent than 8 days. This analysis is providing crucial input for data acquisition planning for agricultural monitoring in the context of GEOGLAM.

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Section: Input Datasets: Where To Image?mentioning

confidence: 99%

A Framework for Defining Spatially Explicit Earth Observation Requirements for a Global Agricultural Monitoring Initiative (GEOGLAM)

2015

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“…In contrast, the κ-values of GLC2000, GlobCover2005 and MODIS2010 revealed low agreement, which is most likely due to the disagreement in these global land cover products as outlined in e.g. FRITZ et al (2011) andFRITZ et al (2013b). Our preliminary results give us confidence that crowdsourcing is an appropriate tool for gathering up-to-date land cover information of areas that are, for example, not easily accessible or restricted.…”

Section: Discussionmentioning

confidence: 56%

“…Based on our analysis, we are confident that more than 50% of the points were classified correctly. When comparing our dataset with a recently developed hybrid cropland map (FRITZ et al 2013b), we revealed moderate agreement. In contrast, the κ-values of GLC2000, GlobCover2005 and MODIS2010 revealed low agreement, which is most likely due to the disagreement in these global land cover products as outlined in e.g.…”

Section: Discussionmentioning

confidence: 62%

“…We performed an accuracy assessment to compare our crowdsourced cropland information with a recently developed global hybrid cropland map (hereafter: hybrid map, FRITZ et al 2013b). The hybrid map was considered as ground truth or reference data (Producer), while the crowdsourced dataset represented the classification data (User).…”

Section: Accuracy Assessment Of Croplandmentioning

confidence: 99%

“…However, none of the main global land cover products is able to capture cropland extents at a sufficient accuracy (FRITZ et al 2013b, FRITZ et al 2011. When these products are compared, large areas of spatial disagreement are apparent.…”

Section: Introductionmentioning

confidence: 99%

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Accurate geospatial cropland information is one of several critical input parameters needed for the assessment of food security and for achieving sustainable development. However, current land cover products are either not accurate enough or lacking in many food insecure countries. Here we demonstrate how we can use the Geo-Wiki crowdsourcing tool and student volunteers to generate a cropland map of Myanmar, and validate a global hybrid cropland map. The preliminary results clearly indicate that the method could be applied in a similar manner to other countries.

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Balancing water scarcity and quality for sustainable irrigated agriculture

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The challenge of meeting the projected doubling of global demand for food by 2050 is monumental. It is further exacerbated by the limited prospects for land expansion and rapidly dwindling water resources. A promising strategy for increasing crop yields per unit land requires the expansion of irrigated agriculture and the harnessing of water sources previously considered ''marginal'' (saline, treated effluent, and desalinated water). Such an expansion, however, must carefully consider potential long-term risks on soil hydroecological functioning. The study provides critical analyses of use of marginal water and management approaches to map out potential risks. Long-term application of treated effluent (TE) for irrigation has shown adverse impacts on soil transport properties, and introduces certain health risks due to the persistent exposure of soil biota to anthropogenic compounds (e.g., promoting antibiotic resistance). The availability of desalinated water (DS) for irrigation expands management options and improves yields while reducing irrigation amounts and salt loading into the soil. Quantitative models are used to delineate trends associated with long-term use of TE and DS considering agricultural, hydrological, and environmental aspects. The primary challenges to the sustainability of agroecosystems lies with the hazards of saline and sodic conditions, and the unintended consequences on soil hydroecological functioning. Multidisciplinary approaches that combine new scientific knowhow with legislative, economic, and societal tools are required to ensure safe and sustainable use of water resources of different qualities. The new scientific knowhow should provide quantitative models for integrating key biophysical processes with ecological interactions at appropriate spatial and temporal scales.

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The Need for Improved Maps of Global Cropland

Cited by 75 publications

References 4 publications

A Framework for Defining Spatially Explicit Earth Observation Requirements for a Global Agricultural Monitoring Initiative (GEOGLAM)

A Framework for Defining Spatially Explicit Earth Observation Requirements for a Global Agricultural Monitoring Initiative (GEOGLAM)

Using Student Volunteers to Crowdsource Land Cover Information

Balancing water scarcity and quality for sustainable irrigated agriculture

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