Global rain-fed, irrigated, and paddy croplands: A new high resolution map derived from remote sensing, crop inventories and climate data

By J. Salmon, Mark A. Friedl, Steve Frolking, D. Wisser, E. Douglas. International Journal of Applied Earth Observation and Geoinformation, 2015

304 citations recorded by the source corpus.

Subfields: Ecology, Ecology, Evolution, Behavior and Systematics, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/d2d581ced8bc5c542d17d532f8bc456baf03aa98

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In Ecology

In Ecology, Evolution, Behavior and Systematics

In Global and Planetary Change

Citations in the corpus

Nominal 30-m Cropland Extent Map of Continental Africa by Integrating Pixel-Based and Object-Based Algorithms Using Sentinel-2 and Landsat-8 Data on Google Earth Engine (2017, methodology): “The authors would like to thank following persons for their support: Felix T. Portman and Stefan Siebert for providing statistics of MIRCA2000 data; Peng Gong for sharing of FROMGLC Validation Dataset (Zhao et al., 2014); Ryutaro Tateishi for sharing of CEReS Gaia validation data (Tateishi et al., 2”
Mapping cropland-use intensity across Europe using MODIS NDVI time series (2016, background): “Cluster 1 characterized moderately intensive rain-fed cropping and included some irrigated areas in southern Europe along rivers and reservoirs (e.g., Ebro-basin in northern Spain, Po Valley in northern Italy, Black Sea area) (Siebert et al 2006, Salmon et al 2015).”
Automated cropland mapping of continental Africa using Google Earth Engine cloud computing (2017, background): “, 2006) especially in the extent of the cultivated areas and their spatial distribution (Fritz et al., 2011a; Salmon et al., 2015; Teluguntla et al., 2015a,b; Thenkabail and Wu, 2012; Waldner et al., 2015) as a result of fragmented and heterogeneous rural landscapes (Lobell and Asner, 2004) and low ”
A 30-m landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud computing platform (2018, unknown): “…High-Re-solution Radiometer (AVHRR) and the Moderate-Resolution Imaging Spectroradiometer (MODIS) data (Biradar et al., 2009; Kumar et al., 2018; Thenkabail et al., 2009, 2012; Pittman et al., 2010; Portmann et al., 2010; Siebert and Döll, 2010; Salmon et al., 2015; Waldner et al., 2015, 2016).”
Assessment of an Operational System for Crop Type Map Production Using High Temporal and Spatial Resolution Satellite Optical Imagery (2015, background): “For instance, global irrigation maps [15] have been produced using a mix of remote sensing imagery, climate data and national surveys.”