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Monitoring cropland dynamics is critical for water management and crop-yield estimation but cropland mapping is still a challenge because of its seasonality and human intervention. Both large-area mapping and change detection studies use images from certain or the same seasons; however, the uneven distribution of Landsat data within a year may affect accuracy. This study aimed to explore the accuracy and uncertainty of cropland mapping performance in different season and month by analyzing all available images in three Landsat footprints in Egypt, Ethiopia, and South Africa from 1984 to 2016. Three different combinations of training models (using yearly, seasonal, and monthly sample set) were used to compare the effect on cropland classification in different month and season through 10-fold cross-validation. There was a tendency to achieve better cropland mapping performance in the dry season and peak growth cycle in all three sites. We showed that monthly and seasonal cropland mapping accuracy was only slightly (~2%) increased using training data in corresponded month. Therefore, we recommended transfer seasonal/yearly training data to fine temporal resolution (i.e., monthly and seasonal) cropland mapping for time and labor efficiency. [ABSTRACT FROM AUTHOR]

Long-term time series of spatially explicit cropland maps are essential for global crop modelling and climate change studies. The spatial resolution and temporal continuity of global cropland maps have been improving and several global data sets are released recently. Here, we calculated country-level cropland areas from the annual land-cover (LC) maps produced by the European Space Agency Climate Change Initiative (ESA-CCI) project and from the Food and Agricultural Organization of the United Nations statistical data (FAOSTAT) from 1992 to 2014. Because these two data sets used different approaches for generating the cropland data, we further quantified the consistency/difference in cropland areas and temporal changes between both products. Using log-transformed the time-averaged country-level cropland areas, a good linear relationship was found between these two products across different countries. However, only 8% of countries (mostly Organization for Economic Co-operation and Development countries) showed cropland area difference smaller than 1% between ESA-CCI and FAOSTAT. The cropland areas (without mosaic cropland types) from ESA-CCI are lower than the areas from FAOSTAT in 26% of countries but higher in 66% of countries. The magnitude of the latter difference (i.e. higher estimates of ESA-CCI than FAOSTAT) would be further amplified if crop intensity was taken into account. In addition, opposite temporal trends of cropland areas were found between these two data sets in 41% of countries. Although there are uncertainties in ESA-CCI LC maps, resulting from remote-sensing techniques such as mixed pixels, spectral similar objects, and same subject with different spectrum, the long time series and relatively high resolution of this product help us to understand the differences between satellite-based and inventory-based data sets and thus identify the possible strategies to improve the accuracy of satellite-based LC products. [ABSTRACT FROM AUTHOR]

The potential adverse impact of international trade on the environment has received growing attention in recent years. Growing environmental pressure poses significant challenges to sustainable development, further highlighting the need for a comprehensive response to tackling the unsustainable food use driven by the soybean trade. Although a significant amount of literature on trade-related land footprints already exists, globally, there has been only a limited amount of research seeking to identify the main issues related to agriculturally driven land-use change and food trade flows that have high land-use impacts. In addition, existing research does not fully reveal the ecological significance of land footprints but instead focuses mainly on physical quantities. There have been few studies that shed any light on the underlying correlations between environmental footprints and the food trade. To address these challenges, in this study, a multi-region input-output (MRIO) model was used to study agricultural land use and changes in carbon losses related to the soybean trade along global supply chains in 2013; the bilateral flows of land and economic value between countries were also modeled. The empirical results of this research indicate that the total land footprint embodied in the global soybean trade in 2013 amounted to 16.51 Mha. Globally, China used the most land resources for the soybean trade and accounted for 9.69 Mha of this footprint. The countries where the soybean trade had the greatest impact on the land and the economy were the USA and Brazil, where 6.74 Mha and 5.76 Mha of land were used for soybeans, respectively. Most of the soybeans from these countries were exported to China. The situation on the supply side was similar as China was also ranked in the top ten countries on the supply side; however, its supply-side land footprint was sufficient to meet internal demand. To further assess the environmental impact of the international soybean trade, carbon loss values (represented by the loss of carbon sequestration capacity) were estimated for the soybean trade at a country level. The total global carbon loss and the social cost of carbon due to the soybean trade in 2013 were estimated at $93.27 billion and $15.48 billion, respectively, with Brazil, the USA, and other countries in South America having the largest figures. It was found that, following a peak in the expansion of the amount of cropland used for planting soybeans, these ecological costs had declined since 2005. Based on these results, we suggest that soybean exporting countries should focus more on improving land-use efficiency and ecological protection in order to minimize the net land footprint of soybeans. • We applied MRIO model to trace agricultural land use and environmental footprints related to global soybean trade in 2013. • The total land footprint embodied in the global soybean trade amounted to 16.51 Mha. • The carbon loss values were estimated for the soybean trade process at a country level. • Global soybean trade's carbon loss and social cost of carbon in 2013 were $93.27 billion and $15.48 billion respectively. • We suggest export countries lay more focus on ecological protection to minimize the net land footprint of soybeans. [ABSTRACT FROM AUTHOR]