Comparison of Global Land Cover Datasets for Cropland Monitoring

Pérez-Hoyos, Ana; Rembold, Felix; Herve, Kerdiles; Gallego, Javier

doi:10.3390/rs9111118

Cited by 140 publications

(96 citation statements)

References 48 publications

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“…to derive PFT maps for use in global land surface modeling in this study). As Pérez-Hoyos et al (2017) and our study show, the agreement (or lack thereof) is country-dependent, further implying that more consistent definitions of LC classes are required and/or regional LC satellite mapping algorithms (or cross-walking table, see below) are needed. Cropland mapping issues, including those discussed in Pérez-Hoyos et al (2017) are being addressed in upcoming versions of the ESA CCI maps.…”

Section: Differences In Total Area Of Forest Cropland and Grasslandmentioning

confidence: 99%

“…As Pérez-Hoyos et al (2017) and our study show, the agreement (or lack thereof) is country-dependent, further implying that more consistent definitions of LC classes are required and/or regional LC satellite mapping algorithms (or cross-walking table, see below) are needed. 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%

“…They also showed that spatial resolution is a key driver of product suitability for agriculture monitoring (Pérez-Hoyos et al, 2017). However, the specific framework of their study is the suitability for agriculture monitoring for early warning and they focus on a limited number of countries selected to be "with high risk of food insecurity" (Pérez-Hoyos et al, 2017). The issues of cropland area from the ESA CCI LC maps discussed in their study is fully justified for a study addressing the challenge of cropland monitoring, but it does not allow generalizing the conclusion to all domains (e.g.…”

Section: Differences In Total Area Of Forest Cropland and Grasslandmentioning

confidence: 99%

Section: Differences In Total Area Of Forest Cropland and Grasslandmentioning

confidence: 99%

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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

221

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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Section: Differences In Total Area Of Forest Cropland and Grasslandmentioning

confidence: 99%

Section: Differences In Total Area Of Forest Cropland and Grasslandmentioning

confidence: 99%

Section: Differences In Total Area Of Forest Cropland and Grasslandmentioning

confidence: 99%

Section: Differences In Total Area Of Forest Cropland and Grasslandmentioning

confidence: 99%

See 2 more Smart Citations

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

221

119

View full text Add to dashboard Cite

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“…Nevertheless, FAO defines smallholder farms as farms with agricultural areas of <2 ha. Reports on the percentage of smallholder farms in total agricultural areas varie considerably, for example, 12% in Lowder et al (2016) & Gallego, 2017). If such layers appear in the near future, the potential users of the field size data set could repeat our approach to estimate area proportions of field sizes.…”

Section: Discussionmentioning

confidence: 99%

Estimating the global distribution of field size using crowdsourcing

Lesiv

Bayas

See

et al. 2018

Global Change Biology

130

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There is an increasing evidence that smallholder farms contribute substantially to food production globally, yet spatially explicit data on agricultural field sizes are currently lacking. Automated field size delineation using remote sensing or the estimation of average farm size at subnational level using census data are two approaches that have been used. However, both have limitations, for example, automatic field size delineation using remote sensing has not yet been implemented at a global scale while the spatial resolution is very coarse when using census data. This paper demonstrates a unique approach to quantifying and mapping agricultural field size globally using crowdsourcing. A campaign was run in June 2017, where participants were asked to visually interpret very high resolution satellite imagery from Google Maps and Bing using the Geo‐Wiki application. During the campaign, participants collected field size data for 130 K unique locations around the globe. Using this sample, we have produced the most accurate global field size map to date and estimated the percentage of different field sizes, ranging from very small to very large, in agricultural areas at global, continental, and national levels. The results show that smallholder farms occupy up to 40% of agricultural areas globally, which means that, potentially, there are many more smallholder farms in comparison with the two different current global estimates of 12% and 24%. The global field size map and the crowdsourced data set are openly available and can be used for integrated assessment modeling, comparative studies of agricultural dynamics across different contexts, for training and validation of remote sensing field size delineation, and potential contributions to the Sustainable Development Goal of Ending hunger, achieve food security and improved nutrition and promote sustainable agriculture.

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