Cropland Dynamics Research Articles

Increase in per capita cropland imbalance across countries from 1985 to 2022: A threat to achieving Sustainable Development Goals

Sustainable Development Goal 2 (SDG 2, zero hunger) highlights that global hunger and food insecurity have worsened since 2015, driven in part by growing imbalance. Addressing the challenge of achieving SDG 2 in the face of rapid global population growth requires sustained attention to global and national cropland changes. Accurately quantifying the correlation between population and cropland area (i.e., SDG 2.4.1 per capita cropland) and analyzing the trends of global cropland imbalance are essential for a comprehensive understanding of SDG 2. In this study, we utilized a new global 30 m land-cover dynamic dataset (GLC_FCS30D) to analyze cropland dynamics, quantify per capita cropland and its changes across various countries and levels of development. Our results indicate that the global cropland area expanded by 0.944 million km2 from 1985 to 2022, with an average expansion rate of 2.42 × 104 km2/yr. However, the global per capita cropland area decreased from 0.347 ha in 1985 to 0.217 ha in 2022, mainly due to a higher population increase of nearly 65% in the same period. In the context of globalization, cropland expansion and per capita cropland exhibited spatial imbalances globally, particularly in developing countries. Developing countries saw an increase in total cropland area by 7.09% but a significant decrease in per capita cropland area by 37.38%. From a temporal perspective, the global imbalance has been steadily increasing with the Gini index rising from 0.895 in 1985 to 0.909 in 2022. Consequently, this study reveals an increasing imbalance across various countries, which threatens the attainment of the targets of SDG 2.

Monitoring Biophysical Variables (FVC, LAI, LCab, and CWC) and Cropland Dynamics at Field Scale Using Sentinel-2 Time Series

Biophysical variables play a crucial role in understanding phenological stages and crop dynamics, optimizing ultimate agricultural practices, and achieving sustainable crop yields. This study examined the effectiveness of the Sentinel-2 Biophysical Processor (S2BP) in accurately estimating crop dynamics descriptors, including fractional vegetation cover (FVC), leaf area index (LAI), leaf chlorophyll a and b (LCab), and canopy water content (CWC). The evaluation was conducted using estimation quality indicators (EQIs) and comprehensive ground throughout the entire growing season at the field scale. To identify soil and vegetation pixels, the spectral unmixing technique was employed. According to the EQIs, the best retrievals were obtained for FVC in around 99.9% of the 23,976 pixels that were analyzed during the growth season. For LAI, LCab, and CWC, over 60% of the examined pixels had inputs that were out-of-range. Furthermore, in over 35% of the pixels, the output values for LCab and CWC were out-of-range. The FVC, LAI, and LCab estimates agreed well with ground measurements (R2 = 0.62–0.85), whereas a discrepancy was observed for CWC estimates when compared with ground measurements (R2 = 0.51). Furthermore, the uncertainties of FVC, LAI, LCab, and CWC estimates were 0.09, 0.81 m2/m2, 60.85 µg/cm2, and 0.02 g/cm2 through comparisons to ground FVC, LAI, Cab, and CWC measurements, respectively. Considering EQIs and uncertainty metrics, the order of the estimation accuracy of the four variables was FVC > LAI > LCab > CWC. Our analysis revealed that temporal variations of FVC, LAI, and LCab were primarily driven by field-scale events like sowing date, growing period, and harvesting time, highlighting their sensitivity to agricultural practices. The robustness of S2BP results could be enhanced by implementing a pixel identification algorithm, like embedding spectral unmixing. Overall, this study provides detailed, pixel-by-pixel insights into the performance of S2BP in estimating FVC, LAI, LCab, and CWC, which are crucial for monitoring crop dynamics in precision agriculture.

Long‐Term Monitoring of the Annual Irrigated Cropland Extent in Fragmented and Heterogeneous Arid Landscapes Using Machine Learning and Landsat Imagery

AbstractUnderstanding the long‐term spatiotemporal evolution of irrigated cropland is essential for water resource management, but this knowledge remains elusive in most water‐stressed arid areas. In this study, we introduced an integrated framework for long‐term and field‐scale mapping of annual irrigated cropland in arid and semiarid regions. This framework combines the k‐means algorithm with a semiautomatically trained random forest classifier for initial classification and employs the Bayesian Updating of Land Cover algorithm for subsequent postprocessing. Taking the Heihe River basin in northwestern China as the experimental area, we generated 30‐m annual irrigated cropland maps spanning from 1990 to 2020 based on Landsat imagery and the Google Earth Engine. Comprehensive validation confirmed the reliability of this approach, with the overall accuracy of the annual maps ranging from 83% to 88.3% (mean: 86.6%). Our data set provides an unprecedentedly long‐term and fine‐scale perspective for understanding the continuous spatial and temporal dynamics of irrigated cropland in the Heihe River basin, surpassing previous studies in Central Asia and northwestern China. Notably, a rapid expansion of irrigated areas is occurring in the basin, especially in the water‐stressed midstream and downstream areas. This finding points to potential ecological risks in the foreseeable future due to water resource constraints.

A 30 m annual cropland dataset of China from 1986 to 2021

A 30 m annual cropland dataset of China from 1986 to 2021

Analyzing spatial patterns and driving factors of cropland change in China's National Protected Areas for sustainable management

Farming in protected areas frequently challenges ecological conservation goals while supporting local livelihoods. To balance protection and agriculture, a comprehensive understanding of cropland dynamics in protected areas is of paramount importance. However, studies addressing this trade-off are relatively scarce, especially considering explicit Chinese government regulations on population relocation and cropland retirement in National Protected Areas (NPAs). Our study examined the spatial and temporal pattern of cropland in NPAs and explored the covariance between cropland density and species richness. Concurrently, the driving factors of cropland development in NPAs were analyzed using Multiple Linear Regression. The results indicate that the cropland area in NPAs continued to expand, growing from 1.93 to 2.34 million hectares in 2000–2020, with a cropland density of approximately 0.4. Cropland expansion in the northern NPAs, particularly in the resource-rich Northeast (28.12 %) and the Northwest with high marginal agricultural returns (38.26 %), have encroached upon species habitats and aggravated biodiversity loss. Moreover, cities with higher cropland densities in NPAs are usually located at borders, possibly due to decentralized management. The Multiple Linear Regression results show that high cropland density is usually associated with a high population density (β = 0.156) and lower levels of rural education (β = −0.101) and income (β = −0.122). To mitigate the issue of cropland development in NPAs, it is crucial to avoid one-size-fits-all management strategies, strengthen regional legal supervision, adjust fiscal incentives, and promote eco-friendly agriculture. In the north regions, the expansion of cropland in NPAs should be strictly controlled. For the southwest, the positive role of preserving cropland in NPAs for alleviating human-nature conflict and maintaining social stability should be emphasized. This study provides research support for China's exploration of geographically suitable strategies for controlling cropland in NPAs. Moreover, the findings could serve as a reference for the governance of NPAs in other countries.

A Predictive Model for Cropland Transformation at the Regional Level: A Case Study of the Belgorod Oblast, European Russia

The problem of choosing the type of land use is now more relevant than ever. Against the backdrop of the growth of urbanized territories, the challenge is to preserve cropland, maintain the quality of soil resources, and find a balance between competing land uses. Forecasting and modeling changes in the area of cropland is a sought-after area of research against the backdrop of a growing shortage of fertile land and a threat to food security. In this study, on the example of one of the agriculturally most developed administrative regions of Russia (Belgorod Oblast), an approach to statistical modeling of agricultural land areas over the past 30 years is shown. Two approaches were used: statistical modeling of the dynamics of the total area of the study region’s cropland depending on the balance of other types of land and spatial interaction modeling of cropland in a key area. For the study region, administrative districts with positive and negative cropland dynamics were identified; the main types of land were revealed, due to which cropland is withdrawn, and a regression balance model was developed. It was revealed that the implementation of the planned regional programs to expand the development and conservation of meadow lands will reduce cropland by 3.07% or 83.2 thousand ha. On the example of one of the administrative districts with high rates of urbanization, the probability of cropland transformation into other types of land was estimated and a predictive spatial model of land use was developed. According to the forecast, about 6.2 thousand ha of cropland will turn into residential development land, and 2/3 of their area will be concentrated within 6 km from the borders of the regional capital city (Belgorod). The presented approach to forecasting the area of cropland and the threats of its reduction due to the need to replace other types of land is relevant for all agricultural regions and countries with developing urbanization processes.

Spatiotemporal Dynamics of the Cropland Area and Its Response to Increasing Regional Extreme Weather Events in the Farming-Pastoral Ecotone of Northern China during 1992–2020

The cropland area is closely related to food production. Previously, more focuses were paid on impacts of extreme events on food production, but less on cropland dynamics. This study used the piecewise linear regression, the correlation analysis, and the ridge regression to explore the spatiotemporal dynamics of cropland and its drivers in three time periods (1992–2020, 1992–2010, and 2010–2020) at the Farming-Pastoral Ecotone of Northern China (FPEN). Specifically, 13 driving factors were considered from the perspectives of extreme events, environmental conditions, socioeconomic development, urban sprawl, and ecological construction. Results showed that the cropland area increased significantly at a rate of 333.5 km2/a during 1992–2020 and was spatially clustered in the eastern FPEN. The impact extent and size of each driving factor on the cropland trend presented large spatiotemporal differences, but ecological construction had, overall, the greatest impact on cropland area changes, followed by urban sprawl. In comparison, extreme low temperature had the smallest. Since the 2010s, areas with urban sprawl being the dominant factor in cropland dynamics have increased by 41.9%, but it is still less than the ecological construction impact. Furthermore, this study found that extreme event effects on the cropland area trend evidently increased. Particularly, extreme high temperature displayed the highest increase (~99.4%). Cropland area changes dominated by extreme temperature events in 2010–2020 increased by nearly six times compared to those in 1992–2010. These results suggest that policy and planning makers should caution increasing impacts of regional extreme weather events on cropland area changes.

Improving digital mapping of soil organic matter in cropland by incorporating crop rotation

Despite human activities are key influencing factors for cropland soil organic matter (SOM), detailed characterization of human activities has always been limited in the digital mapping of SOM due to the lack of proper representations of human’s cropland use activities. Crop rotation is an essential human agricultural practice significantly affecting the spatial–temporal variations of SOM due to the periodically dynamic changes of crops. Thus, incorporating crop rotation in the digital soil mapping holds high potential for improving SOM prediction. Here, we applied time-series radar Sentinel-1 and optical Sentinel-2 to map crop rotation systems by a hierarchical rule-based method. Then we explored the effectiveness of incorporating such information in predicting SOM by implementing various combinations of predictive variables. We chose a typical multiple cropping region with various crop rotations in southern China. The model performance was evaluated by 10-fold cross-validation. Results showed significant differences in SOM among the crop rotation systems, and the single rice rotated with vegetables has the highest SOM followed by the high-diversity vegetables and long-term orchard systems. Adding crop rotation enhanced the predictability of SOM with a decrease in RMSE by 7% and an increase in R2 by 24%. Furthermore, the crop rotation systems appeared more important in the predictive models than the soil, topographic, and climatic variables. Our results demonstrated the effectiveness of including crop rotation in predicting SOM over complex agricultural landscapes. Our study indicated that human activities should be characterized more detailedly in cropland soil mapping, and that crop rotation containing information on the seasonal dynamics of cropland may be an option for such characterization.

Spatial and temporal dynamics of cropland in the Sanjiang Plain from 2014 to 2020 based on annual 30 m crop data layers

AbstractThe land cover of the Sanjiang Plain has changed dramatically since the 1950s. Although previous studies have analysed its spatiotemporal dynamics at long time intervals, a near real-time and accurate representation of the interannual evolution of cropping patterns in this region is of far-reaching importance for rationally allocating agricultural resources and ensuring food security. Based on the 30 m and 10 m land cover datasets in 2015 and 2017–2019, the current study used Landsat-8 satellite data in 2014, 2016 and 2020 to identify paddy rice and dryland crops using a decision tree classification approach and constructed the annual cropland datasets of the Sanjiang Plain from 2014 to 2020. The results show that the overall classification accuracies of crop datasets exceeded 95%, and the Kappa coefficients were higher than 0.92. The average annual accuracies of users and producers were 93% and 94% for rice fields and 97% and 95% for dryland crops, respectively. During the 7 years, the total area of paddy fields and dryland crops decreased by 5% and 8%. However, with minor positive and negative variation between years. 24.2% of paddy rice and 42% of dryland crops has been cultivated under 4 years. The centres of gravity for both crops mainly aggregated in the central counties with the migration direction and magnitude varying interannually. The current study emphasizes the importance of establishing annual high-resolution crop datasets to track the detailed spatio-temporal trajectories of cropping patterns that are essential to support sustainable cropland management and agricultural development.

An Operational Assessment Framework for Near Real-time Cropland Dynamics: Toward Sustainable Cropland Use in Mid-Spine Belt of Beautiful China

Cropland monitoring is a crucial component for a broad user community from Land Use and Land Cover Change study to food security policy making. Faced with the rich natural ecological environment and variable agricultural production conditions of Mid-Spine Belt of Beautiful China (MSBBC), this study developed a novel operational assessment framework that combined the near real-time land cover mapping platform (i.e., FROM-GLC Plus), the FAO Agricultural Stress Index System, and the land degradation monitoring method suggested by United Nations Convention to Combat Desertification for the timely monitoring of cropland extent change, cropland conditions, and cropland degradation. With integrated monitoring system, this framework can provide convenient access to high-spatiotemporal- resolution cropland maps (30 m, dekadal) and instant (near real time) cropland dynamics. According to the monitoring results, we found that the abnormally high temperatures of summer 2022 adversely affected crop health in the southwest of MSBBC. Besides, our results suggested that China’s ecological restoration projects made remarkable achievement in MSBBC. The productivity of more than 70% of cropland in MSBBC has improved, and only ~6% cropland (~3.69 × 10 4 km 2 ) has degraded since 2000, mainly distributed in cropland with steep slope, insufficient precipitation, and intensive use. Site-specific measures, such as conservation tillage, improved tillage systems, and cropland ecological projects, should be adopted for sustainable cropland use and further increase in land carrying capacity of MSBBC to achieve balanced east-west development in China.

Cropland prediction using remote sensing, ancillary data, and machine learning

Temporal and spatial environmental factors have a substantial influence on crop yields, and an accurate prediction can benefit timely decision-making in global food production. Thus for better agricultural management, the precise estimation of the croplands is helpful. Mapping the cropland dynamics with regular requirement of crops is an important prerequisite for monitoring crops, yield estimation, and crop inventories. Remote sensing and geographic information systems play a significant role in tracing and understanding environmental impacts of agriculture. The use of machine learning aids in developing a model that can give precise predictions based on the historical data. The main objective of our study is to use these machine learning algorithms to make accurate predictions about the best crop types using the spectral, temporal, and polarimetric features. A big dataset incorporating optical and polarimetric aperture radar experimental values is used to train the machine learning classifiers for predicting the right crop type in the study area. It has been observed that these features aid in accurate mapping of the cropland. Our study involves the performance comparison of the various machine learning algorithms, and it has been observed that a single-layer neural network offers prediction accuracy of ∼99.6 % for this big PoISAR dataset.

Cropland expansion, intensification, and reduction in Mato Grosso state, Brazil, between the crop years 2000/01 to 2017/18

Growing populations require increased food production through increasing yields and expanding land use. Thus, agricultural production has a dialectical relationship with natural resource conservation. South America has been the recent site of both intensification and areal expansion of crop production. The presented study investigates the dynamics of land use and land cover (LULC) in Mato Grosso state at two-year intervals for an 18-year period consisting of the 2000/01 and 2017/18 crop years. We used a stratified sampling approach and visual interpretation of satellite image time series data to obtain estimates of LULC extent and dynamics, including area of cropland expansion, intensification, and reduction. Cropland dynamics exhibited an overall increase in both extent and intensification. The cropland use area in Mato Grosso increased from 38,324 km2 (±5618 km2) in 2000/01 to 97,394 km2 (±9938 km2) in 2017/2018. For the Cerrado tropical woodland biome portion of Mato Grosso, the expansion of cropland areas was related to soy and corn prices. For the humid tropical forest biome of Amazonia, soy production and deforestation were found to not be directly correlated. The intensification in Mato Grosso was increased from 8142 km2 (±1.39%) in the period from 2008/09 to 2010/11, to 30,487 km2 (±2.56%) in the period from 2010/11 to 2012/13. Overall dynamics reflect public and private conservation policies, soy market, climate and technology factors that spur or inhibit increased production of commodity crops. The end result reflects Brazil's increasing role as a global commodity crop producer.

Анализ территориальных особенностей процесса вывода земель из сельскохозяйственного использования в Кировской области

In the last two decades in most regions of the country there has been a restoration of abandoned lands during the crisis of the 1990s. and higher yields due to a significant increase in state support for the agricultural sector and structural changes such as the emergence of agricultural holdings. As a result of modern reforms, Russia has become a leading player in the foreign food market. However, these positive developments take place against the background of a process of deepening regional differences in the productivity of the agricultural sector. The aim of the study is a comparative analysis of the dynamics of the withdrawal from circulation of sown areas in the Kirov province during the crisis of the 1990s. and post-crisis period 2000–2020. The analysis of spatio-temporal dynamics of the withdrawal from circulation and restoration of croplands was carried out by remote sensing methods for three agro-climatic zones and the main types of soils in the Kirov province. The main resource of the region is soddy-podzolic soils, which accounted for more than 77 % of the cropland in 1990 and about 70 % in 2020. The reduction in the area of cropland with this type of soil reached 90 % in the northern and 80 % in the central and southern zones, regardless of their differences in heat supply. Crisis period 1990–2000 characterized by the highest rate of withdrawal of agricultural land from circulation. In the post-crisis period, the reduction in sown areas only continued. Against this background, there is an extremely slight recovery of cropland (about 5 % of the 1990 level). There are natural differences in the restoration of sown areas in agro-climatic zones and by soil types, but they are poorly reflected in the overall negative dynamics of cropland, due to the low agro-climatic potential of the entire Kirov province.

National-Scale Cropland Mapping Based on Phenological Metrics, Environmental Covariates, and Machine Learning on Google Earth Engine

Many challenges prevail in cropland mapping over large areas, including dealing with massive volumes of datasets and computing capabilities. Accordingly, new opportunities have been opened at a breakneck pace with the launch of new satellites, the continuous improvements in data retrieval technology, and the upsurge of cloud computing solutions such as Google Earth Engine (GEE). Therefore, the present work is an attempt to automate the extraction of multi-year (2016–2020) cropland phenological metrics on GEE and use them as inputs with environmental covariates in a trained machine-learning model to generate high-resolution cropland and crop field-probabilities maps in Morocco. The comparison of our phenological retrievals against the MODIS phenology product shows very close agreement, implying that the suggested approach accurately captures crop phenology dynamics, which allows better cropland classification. The entire country is mapped using a large volume of reference samples collected and labelled with a visual interpretation of high-resolution imagery on Collect-Earth-Online, an online platform for systematically collecting geospatial data. The cropland classification product for the nominal year 2019–2020 showed an overall accuracy of 97.86% with a Kappa of 0.95. When compared to Morocco’s utilized agricultural land (SAU) areas, the cropland probabilities maps demonstrated the ability to accurately estimate sub-national SAU areas with an R-value of 0.9. Furthermore, analyzing cropland dynamics reveals a dramatic decrease in the 2019–2020 season by 2% since the 2018–2019 season and by 5% between 2016 and 2020, which is partly driven by climate conditions, but even more so by the novel coronavirus disease 2019 (COVID-19) that impacted the planting and managing of crops due to government measures taken at the national level, like complete lockdown. Such a result proves how much these methods and associated maps are critical for scientific studies and decision-making related to food security and agriculture.

The Imprint of Built-Up Land Expansion on Cropland Distribution and Productivity in Shandong Province

Grain self-sufficiency is a national food security target of China. The way that built-up land expansion impacts upon cropland loss and food provision needs to be explored in the major grain producing areas. Shandong Province is an important agricultural food production region, which is also experiencing rapidly urbanizing. Here we assessed the spatiotemporal distribution of cropland loss due to built-up land expansion and landscape dynamics of cropland during 2000–2020, by using 30 m resolution land cover data. We also analyzed the potential yield change influenced by cropland loss. The results showed that the area of built-up land expanded by 5199 km2 from 2000–2010, and 11,949 km2 from 2010–2020. Approximately 95% of the new built-up land was from cropland during the two stages, and the primary mode of built-up land expansion was the edge expansion. The patch density and the patch size of cropland kept increasing and decreasing, respectively, and the aggregation index kept decreasing from 2000 to 2020, indicating increased cropland fragmentation. The proportion of occupied cropland with potential yield greater than 7500 kg/ha was 25% and 37% during the former and the latter period. Thus, higher quality cropland was encroached in the recent period. The findings could provide meaningful implications for making sustainable land use development strategies in the study area and other similar regions.

Annual 30-m land use/land cover maps of China for 1980–2015 from the integration of AVHRR, MODIS and Landsat data using the BFAST algorithm

Annual land use land cover (LULC) change information at medium spatial resolution (i.e. at 30 m) is required in numerous subjects, such as biophysical modelling, land management and global change studies. Annual LULC information, however, is usually not available at continental or national scale due to reasons such as insufficient remote sensing data coverage or lack of computational capabilities. Here we integrate high temporal resolution and coarse spatial resolution satellite images (i.e., Moderate Resolution Imaging Spectroradiometer (MODIS) and Global Inventory Modelling and Mapping Studies (GIMMS) normalized difference vegetation index (NDVI)) with high spatial resolution datasets (China’s Land-Use/cover Datasets (CLUDs) derived from 30-meter Landsat TM/ETM+/OLI) to generate reliable annual nominal 30 m LULC maps for the whole of China between 1980 and 2015. We also test the performance of a statistical based change detection algorithm (Breaks for Additive Seasonal and Trend), originally designed for tracking forest change, in classifying all-type LULC change. As a result, a nominal 30 m annual land use/land cover datasets (CLUD-A) from 1980 to 2015 was developed for the whole China. The mapping results were assessed with a change sample dataset, a regional annual validation sample set and a three-year China sample set. Of the detected change years, 75.61% matched the exact time of conversion within ±1 year. Annual mapping results provided a detail process of urbanization, deforestation, afforestation, water and cropland dynamics over the past 36 years. The consistent characterization of land change dynamics for China can be further used in scientific research and to support land management for policy-makers.

Mapping smallholder and large-scale cropland dynamics with a flexible classification system and pixel-based composites in an emerging frontier of Mozambique

Remote sensing assessments of land use and land cover change (LULCC) are critical to improve understanding of socio-economic, institutional and ecological processes that lead to and stem from land use change. This is particularly crucial in the emerging frontiers of Southern Africa, where there is a paucity of LULCC studies relative to the humid tropics. This study focuses on Gurué District (5606 km2) of Zambezia province of Mozambique, one of many countries in the region that has experienced a recent growth in foreign investments in agriculture through large-scale land acquisitions, often resulting in land use conversions and modifications. Previous LULCC assessments covering Mozambique have focused on dynamics between natural and anthropogenic land categories, with limited efforts to distinguish the different land use agents associated with these changes, and relating this with social, economic and technological processes. In this study we built a new LULC assessment methodology that leverages the power of open remote sensing data and tools to integrate categorical and continuous training and validation data obtained from field surveys and Collect Earth software within Google Earth Engine. We then examined the suitability of five pixel-based compositing techniques for generating cloud-free Landsat images that can support analysis of land use dynamics in persistently cloudy, mosaic landscapes with more limited Landsat archives. Drawing upon the spectral and textural features of Landsat data in pixel-based composites, we classified land use over three time periods, 2006, 2012 and 2016, and characterized land use change, focusing on changes between small-scale cropland, large-scale mechanized cropland, and other land uses. This method can be upscaled and applied in many parts of Africa with similar historic image availability challenges, and similar economic contexts with great disparities between small-scale unmechanized cropland and very large-scale mechanized cropland, to explore land consolidation dynamics and agent-specific pathways of land use change.

Spatial and temporal dynamics of croplands in expanding West African cities

AbstractThe rapid expansion of cities in West Africa has implications for urban cropland. This study aimed to assess the dynamics of cropland in West African cities over time and space, to identify key drivers, and to report the effects of changing cropland on farmers and farmers’ resilience strategies. Cities studied were Accra (Ghana), Bamako (Mali), Ouagadougou (Burkina Faso), and Bamenda (Cameroon). Methodology involved multi‐temporal spatial analysis of satellite images and farmer surveys. Results showed that the share of urban land covered by cropland differed in these cities, with higher shares in Accra and Bamenda, which included rainfed cultivation. Over the past 15 years, Accra has lost large shares of its farming areas, both in the inner‐urban areas as well as in the fringe, whereas loss in Bamenda has been less substantial. In Ouagadougou and Bamako, where only irrigated sites were captured, cropland has shifted to the fringes but increased overall. Key drivers influencing the direction of change were official support of urban farming (or lack thereof), population pressure, and the availability of public open spaces that are not suitable for construction. In cities with decreasing cropland, implications included diminishing individual farm sizes, intensification of remaining sites, cessation of farming in the city, and the shift to other sites, which—apart from the physical availability of land and related resources—depends on social relations and informal rules.

Spatiotemporal dynamics of urbanization and cropland in the Nile Delta of Egypt using machine learning and satellite big data: implications for sustainable development.

The Nile Delta of Egypt is increasingly facing sustainability threats, due to a combination of nature- and human-induced changes in land cover and land use. In this paper, an analysis of big time series data from remotely sensed satellite images and the random forests classifier was undertaken to assess the spatial and temporal dynamics of urbanization and cropland in the Nile Delta between 2007 and 2017. Out of thirteen variables, five spectral indices were chosen to build 500 decision trees, with a resulting overall accuracy average of 91.9 ± 1.5%. The results revealed that the urban extent in the Nile Delta has increased, between 2007 and 2017, by 592.4 km2 (1.92%). Particularly, the results indicated that the years 2011 and 2012, which coincided the 2011 political uprising in Egypt, so-called the Arab Spring, were associated with significant land-use changes in the Nile Delta, both in rate and scale. As a result, the cropland area in the region decreased between 2010 and 2011 by 1.63% (502.21 km2). Moreover, the results showed that during the period 2012-2017, the mean annual urbanization rate in the region stood at 60 km2/year. In contrast, croplands decreased during the same period at an average annual rate of 2 km2/year. At the governorates' level, the results suggested that top agricultural producing governorates in the Nile Delta, such as Elmonoufia, Elkalubia, Elbouhyra, and Elghrbia, witnessed the highest rates of decrease in cropland areas during the period 2012-2017. Over the same period, urban areas increased the most in Elkalubia, Domiate, and Elmonoufia by 1.98%, 1.72%, and 1.34%, respectively. The f indings from this analysis are discussed along with their implications for sustainable land-use and urban planning policies.

Continued Hydrothermal and Radiative Pressure on Changed Cropland in China

Both cropland and climate change over time, but the potential effects of climate change on cropland is currently not well understood. Here, we combined temporally and spatially explicit dynamics of cropland with air temperature, precipitation, and solar radiation datasets. China’s cropland showed a clear northward-shifting trend from 1990 to 2015. The cropland decreased south of the break line at 38° N, whereas it increased from the break line to northern regions. Correspondingly, the temperature showed a significant warming trend in the early part of the study period, which slowed down in later years. During the whole study period, both precipitation and solar radiation decreased over time, showed no significant linear characteristics, and the annual fluctuations were very large. The cropland areas in China showed a displacement characteristic with the increasing temperature, precipitation, and radiation. Overall, the cropland was shifting towards the high-temperature, low-precipitation, and low-radiation areas. The cropland dynamics indicate that they are likely to face severe drought and radiation pressure. Our findings imply that more resources such as irrigation may be needed for cropland, which will undoubtedly aggravate the agricultural water use in most northern regions, and the potential impacts on food security will further emerge in the future.