{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T19:13:11Z","timestamp":1773688391476,"version":"3.50.1"},"reference-count":101,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2015,1,29]],"date-time":"2015-01-29T00:00:00Z","timestamp":1422489600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000104","name":"National Aeronautics and Space Administration","doi-asserted-by":"publisher","award":["NNX11AL56H"],"award-info":[{"award-number":["NNX11AL56H"]}],"id":[{"id":"10.13039\/100000104","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000104","name":"National Aeronautics and Space Administration","doi-asserted-by":"publisher","award":["NNX11AQ79G"],"award-info":[{"award-number":["NNX11AQ79G"]}],"id":[{"id":"10.13039\/100000104","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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\u2019s (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\u00b0) 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\u2013100 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 \u201creasonably clear\u201d 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\u20134.8 days (depending on the month), 3.0\u20134.1 days for 80% clear, 2.2\u20133.3 days for 90% clear, and 1.7\u20132.6 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.<\/jats:p>","DOI":"10.3390\/rs70201461","type":"journal-article","created":{"date-parts":[[2015,1,29]],"date-time":"2015-01-29T10:10:02Z","timestamp":1422526202000},"page":"1461-1481","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":127,"title":["A Framework for Defining Spatially Explicit Earth  Observation Requirements for a Global Agricultural Monitoring Initiative (GEOGLAM)"],"prefix":"10.3390","volume":"7","author":[{"given":"Alyssa","family":"Whitcraft","sequence":"first","affiliation":[{"name":"Department of Geographical Sciences, University of Maryland, 4321 Hartwick Rd. Suite 410,  College Park, MD 20742, USA"}]},{"given":"Inbal","family":"Becker-Reshef","sequence":"additional","affiliation":[{"name":"Department of Geographical Sciences, University of Maryland, 4321 Hartwick Rd. Suite 410,  College Park, MD 20742, USA"}]},{"given":"Christopher","family":"Justice","sequence":"additional","affiliation":[{"name":"Department of Geographical Sciences, University of Maryland, 4321 Hartwick Rd. Suite 410,  College Park, MD 20742, USA"}]}],"member":"1968","published-online":{"date-parts":[[2015,1,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Fritz, S., See, L., McCallum, I., You, L., Bun, A., Moltchanova, E., Duerauer, M., Albrecht, F., Schill, C., and Perger, C. (2015). Mapping global cropland and field size. Glob. Change Biol.","DOI":"10.1111\/gcb.12838"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Whitcraft, A.K., Becker-Reshef, I., and Justice, C.O. (2014). Agricultural growing season calendars derived from MODIS surface reflectance. Int. J. Digit. 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