A Unified Cropland Layer at 250 m for Global Agriculture Monitoring

Waldner, François; Fritz, Steffen; Gregorio, A. Di; Плотников, Д.Е.; Bartalev, Sergey; Kussul, Nataliia; Gong, Peng; Thenkabail, Prasad S.; Hazeu, G.W.; Klein, Igor; Löw, Fabian; Miettinen, Jukka; Dadhwal, V. K.; Lamarche, Céline; Bontemps, Sophie; Defourny, Pierre

doi:10.3390/data1010003

Cited by 55 publications

(42 citation statements)

References 43 publications

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“…Within the area that can be allocated a preference is given to cropland locations from the unified cropland layer map (Waldner et al, 2016). This is done in order to make use of the high-quality empirical data that are available and to correct for the characteristic that the ESA cropland land cover types are most likely a mix of cropland and pasture lands in many regions.…”

Section: Cropland and Grazing Land Statisticsmentioning

confidence: 99%

See 1 more Smart Citation

Anthropogenic land use estimates for the Holocene – HYDE 3.2

Goldewijk

Beusen

Doelman

et al. 2017

Earth Syst. Sci. Data

757

624

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Abstract. This paper presents an update and extension of HYDE, the History Database of the Global Environment (HYDE version 3.2). HYDE is an internally consistent combination of historical population estimates and allocation algorithms with time-dependent weighting maps for land use. Categories include cropland, with new distinctions for irrigated and rain-fed crops (other than rice) and irrigated and rain-fed rice. Grazing lands are also provided, divided into more intensively used pasture and less intensively used rangeland, and further specified with respect to conversion of natural vegetation to facilitate global change modellers. Population is represented by maps of total, urban, rural population, population density and built-up area. The period covered is 10 000 before Common Era (BCE) to 2015 Common Era (CE). All data can be downloaded from https://doi.org/10.17026/dans-25g-gez3.We estimate that global population increased from 4.4 million people (we also estimate a lower range < 0.01 and an upper range of 8.9 million) in 10 000 BCE to 7.257 billion in 2015 CE, resulting in a global population density increase from 0.03 persons (or capita, in short cap) km −2 (range 0-0.07) to almost 56 cap km −2 respectively. The urban built-up area evolved from almost zero to roughly 58 Mha in 2015 CE, still only less than 0.5 % of the total land surface of the globe.Cropland occupied approximately less than 1 % of the global land area (13 037 Mha, excluding Antarctica) for a long time period until 1 CE, quite similar to the grazing land area. In the following centuries the share of global cropland slowly grew to 2.2 % in 1700 CE (ca. 293 Mha, uncertainty range 220-367 Mha), 4.4 % in 1850 CE (578 Mha, range 522-637 Mha) and 12.2 % in 2015 CE (ca. 1591 Mha, range 1572-1604 Mha). Cropland can be further divided into rain-fed and irrigated land, and these categories can be further separated into rice and non-rice. Rain-fed croplands were much more common, with 2.2 % in 1700 CE (289 Mha, range 217-361 Mha), 4.2 % (549 Mha, range 496-606 Mha) in 1850 CE and 10.1 % (1316 Mha, range 1298-1325 Mha) in 2015 CE, while irrigated croplands used less than 0.05 % (4.3 Mha, range 3.1-5.5 Mha), 0.2 % (28 Mha, range 25-31 Mha) and 2.1 % (277 Mha, range 273-278 Mha) in 1700, 1850 and 2015 CE, respectively. We estimate the irrigated rice area (paddy) to be 0.1 % (13 Mha, range 9-16 Mha) in 1700 CE, 0.2 % (28 Mha, range 26-31 Mha) in 1850 CE and 0.9 % (118 Mha, range 117-120 Mha) in 2015 CE.The estimates for land used for grazing are much more uncertain. We estimate that the share of grazing land grew from 5.1 % in 1700 CE (667 Mha, range 507-820 Mha) to 9.6 % in 1850 CE (1192 Mha, range 1068-1304 Mha) and 24.9 % in 2015 CE (3241 Mha, range 3211-3270 Mha). To aid the modelling community we have divided land used for grazing into more intensively used pasture, less intensively used converted rangeland and less or unmanaged natural unconverted rangeland. Pasture occupied 1.1 % in 1700 CE (145 Mha, range 79-175 Mha), 1.9 % in 1850 CE (253 Mha...

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Section: Cropland and Grazing Land Statisticsmentioning

confidence: 99%

“…-the use of additional data (cropland layer map of Waldner et al, 2016; irrigation data of Siebert, 2008, andSiebert et al, 2015, MIRCA data set) and rice data from You et al, 2014);…”

Section: Introductionmentioning

confidence: 99%

Anthropogenic land use estimates for the Holocene – HYDE 3.2

Goldewijk

Beusen

Doelman

et al. 2017

Earth Syst. Sci. Data

757

624

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“…Cropland mapping issues, including those discussed in Pérez-Hoyos et al (2017) are being addressed in upcoming versions of the ESA CCI maps. Additionally, Waldner et al (2016) have produced a product that aims to combine the "fittest" LC maps on the country level into a unified 250 m resolution cropland product, but again this is dependent upon a specific definition (the JECAM, Joint Experiment of Crop Assessment and Monitoring, cropland definition for the purposes of cropland monitoring).…”

Section: Differences In Total Area Of Forest Cropland and Grasslandmentioning

confidence: 99%

Gross and net land cover changes in the main plant functional types derived from the annual ESA CCI land cover maps (1992–2015)

MacBean

Ciais

et al. 2018

Earth Syst. Sci. Data

Self Cite

220

119

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Abstract. Land-use and land-cover change (LULCC) impacts local energy and water balance and contributes on global scale to a net carbon emission to the atmosphere. The newly released annual ESA CCI (climate change initiative) land cover maps provide continuous land cover changes at 300 m resolution from 1992 to 2015, and can be used in land surface models (LSMs) to simulate LULCC effects on carbon stocks and on surface energy budgets. Here we investigate the absolute areas and gross and net changes in different plant functional types (PFTs) derived from ESA CCI products. The results are compared with other datasets. Global areas of forest, cropland and grassland PFTs from ESA are 30.4, 19.3 and 35.7 million km 2 in the year 2000. The global forest area is lower than that from LUH2v2h (Hurtt et al., 2011), Hansen et al. (2013 or Houghton and Nassikas (2017) while cropland area is higher than LUH2v2h (Hurtt et al., 2011), in which cropland area is from HYDE 3.2 (Klein Goldewijk et al., 2016). Gross forest loss and gain during 1992-2015 are 1.5 and 0.9 million km 2 respectively, resulting in a net forest loss of 0.6 million km 2 , mainly occurring in South and Central America. The magnitudes of gross changes in forest, cropland and grassland PFTs in the ESA CCI are smaller than those in other datasets. The magnitude of global net cropland gain for the whole period is consistent with HYDE 3.2 (Klein Goldewijk et al., 2016), but most of the increases happened before 2004 in ESA and after 2007 in HYDE 3.2. Brazil, Bolivia and Indonesia are the countries with the largest net forest loss from 1992 to 2015, and the decreased areas are generally consistent with those from Hansen et al. (2013) based on Landsat 30 m resolution images. Despite discrepancies compared to other datasets, and uncertainties in converting into PFTs, the new ESA CCI products provide the first detailed long-term time series of land-cover change and can be implemented in LSMs to characterize recent carbon dynamics, and in climate models to simulate land-cover change feedbacks on climate. The annual ESA CCI land cover products can be downloaded from

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“…To provide accurate validation it is worth to compare the product with some more precise country level cropland map which have higher resolution. Globland30 [36] and Unified Cropland layer [37] as one of the alternative for the analysis in next study.…”

Section: Comparison Of Cropland Area Estimates From Cal With the Fao mentioning

confidence: 99%

Development of Global Cropland Agreement Level Analysis by Integrating Pixel Similarity of Recent Global Land Cover Datasets

Sakti¹,

Takeuchi²,

Wikantika³

2017

JEP

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Global cropland monitoring is important when considering tactical strategies for achieving food sustainability. Different global land cover (GLC) datasets providing cropland information have already been published and they are used in many applications. The different data input methods, classification techniques, class definitions and production years among the different GLC datasets make them all independently useful sources of information. This study attempted to produce a cropland agreement level (CAL) analysis based on the integration of several cropland datasets to more accurately estimate cropland area distribution. Estimating cropland area and how it has changed on a national level was done by converting the level of cropland agreement into percentages with an existing cropland fraction map. A pre-analysis showed that the four GLC datasets used in the 2005 and 2010 groups had similar year input data acquisitions. Therefore, we placed these four datasets (GlobCover, MODIS LC, GLCNMO and ESACCI LC) into 2005 and 2010 year-groups and selected them to process dataset integration through a CRISP approach. The results of this process proposed four agreement levels for this CAL analysis, and the model correlation was converted into percentage values. The cropland estimate results from the CAL analysis were observed along with FAO data statistics and showed the highest accuracy, with a 0.70 and 0.71 regression value for 2005 and 2010 respectively. In the cropland area change analysis, this CAL change analysis had the highest level of accuracy when describing the total size of cropland area change from 2005 and 2010 when compared to other individual original GLC datasets.

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A Unified Cropland Layer at 250 m for Global Agriculture Monitoring

Cited by 55 publications

References 43 publications

Anthropogenic land use estimates for the Holocene – HYDE 3.2

Anthropogenic land use estimates for the Holocene – HYDE 3.2

Gross and net land cover changes in the main plant functional types derived from the annual ESA CCI land cover maps (1992–2015)

Development of Global Cropland Agreement Level Analysis by Integrating Pixel Similarity of Recent Global Land Cover Datasets

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