FROM-GC: 30 m global cropland extent derived through multisource data integration

Yu, Le; Wang, Jie; Clinton, Nicholas; Xin, Qinchuan; Zhong, Liheng; Chen, Yanlei; Gong, Peng

doi:10.1080/17538947.2013.822574

Cited by 135 publications

(85 citation statements)

References 30 publications

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“…The method that applies fractional land use maps circumvents the threshold problem and provides reliable ε * GPP and GPP estimates (Figures 7 and 8). Even the NASS CDL data routinely produce fine-resolution land use maps on an annual basis, successful algorithms that can produce global crop-specific maps at fine resolutions remain to be developed (Yu et al, 2013;Zhong et al, 2011).…”

Section: Uncertainties Of ε * Gpp Estimates In Regional Modelingmentioning

confidence: 99%

Multi-scale evaluation of light use efficiency in MODIS gross primary productivity for croplands in the Midwestern United States

Xin

Broich

Suyker

et al. 2015

Agricultural and Forest Meteorology

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Section: Uncertainties Of ε * Gpp Estimates In Regional Modelingmentioning

confidence: 99%

Multi-scale evaluation of light use efficiency in MODIS gross primary productivity for croplands in the Midwestern United States

Xin

Broich

Suyker

et al. 2015

Agricultural and Forest Meteorology

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“…Meanwhile, as demonstrated by Table 1, particularly Requirements #4-6, the use of moderate spatial resolution data collected at a more frequent rate is a priority growth area for analyses spanning the full extent of croplands for fields of all sizes. With the Landsat archive opening, new moderate resolution missions set to launch, and computational resources growing, global scale analyses are poised to move into the moderate resolution domain [59][60][61][62][63][64], with regional to global datasets at 30 m resolution already demonstrated [65][66][67][68][69].…”

Section: Agricultural Monitoring: Spatial and Temporal Considerationsmentioning

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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“…Cropping intensity, which we define here as the number of cropping cycles per year, is an important dimension of land use that is strongly influences water demand and agricultural production [18][19][20][21], but has received relatively little attention. In areas, such as Asia, which have limited lands available for arable expansion, crop production is commonly increased by planting crops more than once a year in the same field [22][23][24][25]. As such, the gross sown area (the total area sown with crops in a year, accounting for multiple crop cycles) in many parts of Asia is frequently larger than the corresponding amount of arable land.…”

Section: Introductionmentioning

confidence: 99%

Mapping Crop Cycles in China Using MODIS-EVI Time Series

et al. 2014

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Abstract:As the Earth's population continues to grow and demand for food increases, the need for improved and timely information related to the properties and dynamics of global agricultural systems is becoming increasingly important. Global land cover maps derived from satellite data provide indispensable information regarding the geographic distribution and areal extent of global croplands. However, land use information, such as cropping intensity (defined here as the number of cropping cycles per year), is not routinely available over large areas because mapping this information from remote sensing is challenging. In this study, we present a simple but efficient algorithm for automated mapping of cropping intensity based on data from NASA's (NASA: The National Aeronautics and Space Administration) MODerate Resolution Imaging Spectroradiometer (MODIS). The proposed algorithm first applies an adaptive Savitzky-Golay filter to smooth Enhanced Vegetation Index (EVI) time series derived from MODIS surface reflectance data. It then uses an iterative moving-window methodology to identify cropping cycles from the smoothed EVI time series. Comparison of results from our algorithm with OPEN ACCESSRemote Sens. 2014, 6 2474 national survey data at both the provincial and prefectural level in China show that the algorithm provides estimates of gross sown area that agree well with inventory data. Accuracy assessment comparing visually interpreted time series with algorithm results for a random sample of agricultural areas in China indicates an overall accuracy of 91.0% for three classes defined based on the number of cycles observed in EVI time series. The algorithm therefore appears to provide a straightforward and efficient method for mapping cropping intensity from MODIS time series data.

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FROM-GC: 30 m global cropland extent derived through multisource data integration

Cited by 135 publications

References 30 publications

Multi-scale evaluation of light use efficiency in MODIS gross primary productivity for croplands in the Midwestern United States

Multi-scale evaluation of light use efficiency in MODIS gross primary productivity for croplands in the Midwestern United States

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

Mapping Crop Cycles in China Using MODIS-EVI Time Series

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