Km2 Of Cropland Research Articles

A Cropland Disturbance Monitoring Method Based on Probabilistic Trajectories

Acquiring the spatiotemporal patterns of cropland disturbance is of great significance for regional sustainable agricultural development and environmental protection. However, effective monitoring of cropland disturbances remains a challenge owing to the complexity of the terrain landscape and the reliability of the training samples. This study integrated automatic training sample generation, random forest classification, and the LandTrendr time-series segmentation algorithm to propose an efficient and reliable medium-resolution cropland disturbance monitoring scheme. Taking the Amur state of Russia in the Amur river basin, a transboundary region between Russia and China in east Asia with rich agriculture resources as research area, this approach was conducted on the Google Earth Engine cloud-computing platform using extensive remote-sensing image data. A high-confidence sample dataset was then created and a random forest classification algorithm was applied to generate the cropland classification probabilities. LandTrendr time-series segmentation was performed on the interannual cropland classification probabilities. Finally, the identification, spatial mapping, and analysis of cropland disturbances in Amur state were completed. Further cross-validation comparisons of the accuracy assessment and spatiotemporal distribution details demonstrated the high accuracy of the dataset, and the results indicated the applicability of the method. The study revealed that 2815.52 km2 of cropland was disturbed between 1990 and 2021, primarily focusing on the southern edge of the Amur state. The most significant disturbance occurred in 1991, affecting 1431.48 km2 and accounting for 50.84% of the total disturbed area. On average, 87.98 km2 of croplands are disturbed annually. Additionally, 2495.4 km2 of cropland was identified as having been disturbed at least once during the past 32 years, representing 83% of the total disturbed area. This study introduced a novel approach for identifying cropland disturbance information from long time-series probabilistic images. This methodology can also be extended to monitor the spatial and temporal dynamics of other land disturbances caused by natural and human activities.

The Decline in Summer Fallow in the Northern Great Plains Cooled Near‐Surface Climate but had Minimal Impacts on Precipitation

AbstractLand management can moderate or intensify the impacts of a warming atmosphere. Since the early 1980s, nearly 116,000 km2 of cropland that was once held in fallow during the summer is now planted in the northern North American Great Plains. To simulate the impacts of this substantial land cover change on regional climate processes, convection‐permitting model experiments using the Weather Research and Forecasting model were performed to simulate modern and historical amounts of summer fallow. The control simulation was extensively validated using multiple observational data products as well as eddy covariance tower observations. Results of these simulations show that the transition from summer fallow to modern land cover led to ∼1.5°C cooler temperatures and decreased vapor pressure deficit by ∼0.15 kPa during the growing season across the study region, which is consistent with observed cooling trends. The cooler and wetter land surface with vegetation led to a shallower planetary boundary layer and lower lifted condensation level, creating conditions more conducive to convective cloud formation and precipitation. Our model simulations however show little widespread evidence of land surface changes effects on precipitation. The observed precipitation increase in this region is more likely related to increased moisture transport by way of the Great Plains Low Level Jet as revealed by the ERA5 reanalysis. Our results demonstrate that land cover change is consistent with observed regional cooling in the northern North American Great Plains but changes in precipitation cannot be explained by land management alone.

Returning Cropland to Grassland as a Potential Method for Increasing Carbon Storage in Dry-Hot Valley Areas

A key aspect of mitigating global climate warming is enhancing the carbon storage capacity of terrestrial ecosystems. China’s Grain for Green Program (GFGP) is the largest ecological restoration project in the world, which is closely associated with land use change. A systematic assessment of the GFGP’s impact on regional carbon storage is of great significance for promoting regional development and maintaining ecosystem stability. Therefore, this study selects a typical dry-hot valley area—Yanjin County—as the study area, which serves as an ecological protection barrier in Southwest China. We employed the InVEST model and Geo-detector model based on land use data from three periods (2000, 2014, and 2019), combined with static overlay analysis methods, in order to evaluate the impact of the implementation of GFGP on the spatial and temporal distribution of carbon storage. We also explored the driving factors of the spatial differentiation of carbon storage. The results indicate that, since the implementation of the GFGP, a total of 180.03 km2 of cropland has been converted to forestland, increasing the forest cover rate from 81.83% to 83.37%. The project has contributed 5.88 × 105 t to regional carbon storage, effectively offsetting carbon emissions caused by human activities such as urban expansion while also promoting the growth of regional carbon storage. The implementation of the GFGP has led to changes in three types of land use. Among them, converting cropland to forestland (3262 t/km2) is the most effective carbon sequestration method, and converting cropland to grassland (2530 t/km2) has shown great potential in carbon sequestration. Additionally, the study found that elevation (0.038–0.059) is the main factor affecting the spatial differentiation of carbon storage, and the interaction between elevation and other factors can effectively enhance the carbon sequestration capacity of regional ecosystems. Overall, the GFGP not only plays a significant role in combating climate warming but also makes an important contribution to improving the stability and sustainability of regional ecosystems.

Spatial planning constraints will mitigate the fragmentation trajectory of natural and semi-natural landscapes: a case of Lushan City, China

ContextThe biodiversity faces an underlying threat from landscape fragmentation resulting from rapid urbanization. Examining the future trajectory of landscape fragmentation is imperative to understanding the impact of current spatial planning constraints on natural and semi-natural habitat preservation, ecosystem services, and sustainability.MethodsWe employed a Patch-generating Land Use Simulation (PLUS) model to simulate and predict the land use and landscape pattern changes in Lushan City under two distinct scenarios: “Planning Constraints (PC)” and “Natural Development (ND)”. We then identified an appropriate landscape fragmentation index (LFI) that effectively captured the fragmentation essence. To determine the optimal scale, we adopted an experimental approach using both the moving window (MW) method and the semi-variance function. By constructing a LFI spatiotemporal sequence and conducting trend analysis, we identified the potential fragmentation areas (PFA) with significant fragmentation tendencies.ResultsThe spatial planning constraints will (1) prevent the encroachment of construction land into 2.14 km2 of cropland, 0.21 km2 of forest, and 0.13 km2 of grassland; (2) shift the highly fragmented area from the northeastern portion of Lushan to the planned area defined by the development boundary; (3) mitigate and decelerate the trend of landscape fragmentation in natural and semi-natural landscapes, decrease PFA by 7.74 km2 and preserve 15.61 km2 of natural landscapes. (4) still leave 29.42% of forest and 22.82% of grassland at risk of fragmentation.ConclusionsSpatial planning constraints will effectively control the potential fragmentation in natural and semi-natural landscapes by changing the spatial distribution of LFI and PFA. This control mechanism will greatly exclude the anthropogenic impact and ensure the conservation of habitats. The habitats remaining within PFA should be focused in future eco-management optimization.

Spatio-temporal dynamics of land use transitions associated with human activities over Eurasian Steppe: Evidence from improved residual analysis

We presented a framework to evaluate the land use transformations over the Eurasian Steppe (EUS) driven by human activities from 2000 to 2020. Framework involves three main components: (1) evaluate the spatial-temporal dynamics of land use transitions by utilizing the land change modeler (LCM) and remote sensing data; (2) quantifying the individual contributions of climate change and human activities using improved residual trend analysis (IRTA) and pixel-based partial correlation coefficient (PCC); and (3) quantifying the contributions of land use transitions to Leaf Area Index Intensity (LAII) by using the linear regression. Research findings indicate an increase in cropland (+1.17 % = 104,217 km2) over EUS, while a − 0.80 % reduction over Uzbekistan and − 0.16 % over Tajikistan. From 2000 to 2020 a slight increase in grassland was observed over the EUS region by 0.05 %. The detailed findings confirm an increase (0.24 % = 21,248.62 km2) of grassland over the 1st half (2000−2010) and a decrease (−0.19 % = −16,490.50 km2) in the 2nd period (2011−2020), with a notable decline over Kazakhstan (−0.54 % = 13,690 km2), Tajikistan (−0.18 % = 1483 km2), and Volgograd (−0.79 % = 4346 km2). Area of surface water bodies has declined with an alarming rate over Kazakhstan (−0.40 % = 10,261 km2) and Uzbekistan (−2.22 % = 8943 km2). Additionally, dominant contributions of human activities to induced LULC transitions were observed over the Chinese region, Mongolia, Uzbekistan, and Volgograd regions, with approximately 87 %, 83 %, 92 %, and 47 %, respectively, causing effective transitions to 12,997 km2 of cropland, 24,645 km2 of grassland, 16,763 km2 of sparse vegetation in China, and 12,731.2 km2 to grassland and 15,356.1 km2 to sparse vegetation in Mongolia. Kazakhstan had mixed climate-human impact with human-driven transitions of 48,568 km2 of bare land to sparse vegetation, 27,741 km2 to grassland, and 49,789 km2 to cropland on the eastern sides. Southern regions near Uzbekistan had climatic dominancy, and 8472 km2 of water bodies turned into bare soil. LAII shows an increasing trend rate of 0.63 year−1, particularly over human-dominant regions. This study can guide knowledge of oscillations and reduce adverse impacts on ecosystems and their supply services.

Combining LSTM and PLUS Models to Predict Future Urban Land Use and Land Cover Change: A Case in Dongying City, China

Land use is a process that turns a piece of land’s natural ecosystem into an artificial one. The mix of plant and man-made covers on the Earth’s surface is known as land cover. Land use is the primary external force behind change in land cover, and land cover has an impact on how land use is carried out, resulting in a synergistic interaction between the two at the Earth’s surface. In China’s Shandong Peninsula city cluster, Dongying is a significant coastal port city. It serves as the administrative hub for the Yellow River Delta and is situated in Shandong Province, China’s northeast. The changes in its urban land use and land cover in the future are crucial to understanding. This research suggests a prediction approach that combines a patch-generation land use simulation (PLUS) model and long-term short-term memory (LSTM) deep learning algorithm to increase the accuracy of predictions of future land use and land cover. The effectiveness of the new method is demonstrated by the fact that the average inaccuracy of simulating any sort of land use in 2020 is around 5.34%. From 2020 to 2030, 361.41 km2 of construction land is converted to cropland, and 424.11 km2 of cropland is converted to water. The conversion areas between water and unused land and cropland are 211.47 km2 and 148.42 km2, respectively. The area of construction land and cropland will decrease by 8.38% and 3.64%, respectively, while the area of unused land, water, and grassland will increase by 5.53%, 2.44%, and 0.78%, respectively.

Sustainability Consequences of Making Land Change Decisions Based on Current Climatology in the Brazilian Cerrados

Brazil is one of the largest suppliers of commodities in the world, partly due to the agricultural expansion in the Brazilian savannas (also known as Cerrado) that began in the 1970s. However, as areas with better soil and climate for agriculture become scarce, farmers have been advancing to the ecotone between the savanna and xeric shrubland, where precipitation is less reliable for rainfed agriculture. The expected increase in temperature will lead to extended drought periods, with negative consequences for surface and groundwater resources. This study explores the hazards associated with making land-use decisions based on current climatology in regions where projected increases in temperature and reductions in water availability are anticipated to pose significant challenges to rainfed agriculture in the Brazilian Cerrado biome. We modeled future farmland expansion and how that matches with future climate change predictions (2016–2046). According to our estimates, at least 129 thousand km2 of cropland and 418 thousand km2 of pastures will be added in places with projected higher annual temperatures ranging from 26–30 °C. This is equivalent to ~60% of the current agricultural areas, and a novel agro-climatology will emerge for the Cerrado biome. Therefore, we discuss the agro-environmental policies that are pushing and pulling farmland expansion in the Cerrado. For instance, payments for environmental services could support the conservation of native vegetation on private land in regions with the highest temperature increases and deforestation risks. Moreover, in areas with expected reduced water yields, such as in the western Cerrado, the protection of riparian vegetation and strict regulation of water use could mitigate future risks to agriculture.

Using Landsat and MODIS satellite collections to examine extent, timing, and potential impacts of surface water inundation in California croplands

The state of California, United States of America produces many crop products that are both utilized domestically and exported throughout the world. With nearly 39,000 km2 of croplands, monitoring unintentional and intentional surface water inundation is important for water resource management and flood hazard readiness. We examine inundation dynamics in California croplands from 2003 to 2020 by intersecting monthly surface water maps (n = 216 months) derived using two satellite remote sensing platforms (Landsat and Moderate Resolution Imaging Spectroradiometer [MODIS]) with a high-quality cropland map generated by the California Department of Water Resources. Surface water maps were produced using the Dynamic Surface Water Extent model, in which satellite image pixels are classified into different levels of detection confidence. Our analysis focused on calculating monthly and annual occurrence of “high confidence” water for each satellite collection across eight cropland types and 58 counties. Results indicate that 49.9% (MODIS) to 56.4% (Landsat) of croplands were inundated at least once during the 18-year timespan. Rice crops, due to their unique need of consistent surface water and dominance as a crop type in CA, had the highest proportion of and mean annual inundation area, while citrus crops had the lowest. Mean monthly inundation patterns in most croplands followed California's precipitation patterns with high inundation during the winter and spring rainy season. At the county level, croplands in the southern Central Valley typically had high occurrences of inundation in conjunction with large crop areas. Exposure and sensitivity of inundation for three crop types (citrus, deciduous, and vineyards) that are typically less associated with intentional inundation were geographically variable, but overall were generally highest in counties in the southern Central Valley, California's primary agricultural region. Flood and precipitation related crop insurance claims indicated that rice had the highest mean indemnity payout for any month with damages typically occurring in March and April. Insurance claims were also high in deciduous fruit and nut crops, which had peak damages in February. A comparison between inundation results and insurance claims suggests that the inundation mapped by our process coincides with claim activity. These data elucidate water inundation patterns across the state that can serve to inform farmers, insurers, decision makers, resource managers, and flood mitigation professionals.

Crop insurance model to consolidate academia-industry cooperation: a case study over Assam, India

Agriculture is one of the deciding factors of Indian economy, contributing almost 17% of the total GDP. Every year, crops are lost due to natural disasters. This academic research may provide a solution for a long-standing problem in the industry. Crop insurance is one of the most effective ways to not only compensate loss, but also to increase poor farmers’ resilience. Remote sensing has huge potential in the crop insurance market; it can be exploited for vulnerability mapping, damage assessment, risk mapping, and various other aspects. The purpose of this study was to present a method for evaluating crop vulnerability over an area using remote sensing and Geographic Information System (GIS), followed by an assessment of crops damaged due to flood. For application purposes, a crop risk map was prepared from a GIS model for the determination of crop insurance parameters. The study area selected (i.e., the Morigaon and Nagaon districts of Assam) is very much flood-prone. The districts have almost 50% agricultural land of the total land cover, thus making the crops very vulnerable to recurrent flooding. For this study, assessment of damage to crops due to flood was performed for a full year, followed by crop risk map generation from the GIS model. The results revealed that 345 km2 of land was inundated by flood in August 2016. Due to the flooding, 1435.08 km2 of agricultural land bearing crops was damaged at different levels. The crop risk map depicts 103.33 km2 of cropland at high risk due to flood.

The changing patterns of cropland conversion to built-up land in China from 1987 to 2010

Over the past few decades, built-up land in China has increasingly expanded with rapid urbanization, industrialization and rural settlements construction. The expansions encroached upon a large amount of cropland, placing great challenges on national food security. Although the impacts of urban expansion on cropland have been intensively illustrated, few attentions have been paid to differentiating the effects of growing urban areas, rural settlements, and industrial/transportation land. To fill this gap and offer comprehensive implications on framing policies for cropland protection, this study investigates and compares the spatio- temporal patterns of cropland conversion to urban areas, rural settlements, and industrial/ transportation land from 1987 to 2010, based on land use maps interpreted from remote sensing imagery. Five indicators were developed to analyze the impacts of built-up land expansion on cropland in China. We find that 42,822 km2 of cropland were converted into built-up land in China, accounting for 43.8% of total cropland loss during 1987–2010. Urban growth showed a greater impact on cropland loss than the expansion of rural settlements and the expansion of industrial/transportation land after 2000. The contribution of rural settlement expansion decreased; however, rural settlement saw the highest percentage of traditional cropland loss which is generally in high quality. The contribution of industrial/transportation land expansion increased dramatically and was mainly distributed in major food production regions. These changes were closely related to the economic restructuring, urban-rural transformation and government policies in China. Future cropland conservation should focus on not only finding a reasonable urbanization mode, but also solving the “hollowing village” problem and balancing the industrial transformations.

Remote Sensing and GIS Support to Identify Potential Areas for Wetland Restoration from Cropland: A Case Study in the West Songnen Plain, Northeast China

Wetland restoration is important to responding to climate change and ensuring ecological security. In terms of the serious wetland loss and limited wetland restoration in China, there is a need to investigate approaches to identifying potential areas for wetland restoration from cropland, in order to support making spatial decisions at a regional scale. Here, we provide an example of integrating remote sensing (RS) and geographical information systems (GIS) effectively to identify where and how many croplands could be converted into wetlands in the West Songnen Plain (WSNP). The map of potential areas for wetland restoration from croplands generated in this study is expected to help decision makers to implement wetland restoration in the WSNP. Besides the widely highlighted hydrological, topographical, and landscape features, four indicators, namely, flooded area, time under cultivation, human disturbance, and wetland conservation level, were selected to identify the potential areas for wetland restoration—with different priorities—from croplands. Satellite observation revealed that a total of 2753.3 km2 of wetlands have been cultivated into croplands for grain production from 1990 to 2015 in the WSNP. It is estimated that 8882.1 km2 of croplands are suitable for conversion to wetlands, of which 3706 km2 (29.4%) are with high priority, and 44.5% are from dry farmlands. A total of 3284.7 km2 of paddy fields are identified to be potential areas for wetland restoration, of which 1119.6 km2 are high priority, and another 2165.1 km2 are medium priority.

Monitoring Rice Agriculture across Myanmar Using Time Series Sentinel-1 Assisted by Landsat-8 and PALSAR-2

Assessment and monitoring of rice agriculture over large areas has been limited by cloud cover, optical sensor spatial and temporal resolutions, and lack of systematic or open access radar. Dense time series of open access Sentinel-1 C-band data at moderate spatial resolution offers new opportunities for monitoring agriculture. This is especially pertinent in South and Southeast Asia where rice is critical to food security and mostly grown during the rainy seasons when high cloud cover is present. In this research application, time series Sentinel-1A Interferometric Wide images (632) were utilized to map rice extent, crop calendar, inundation, and cropping intensity across Myanmar. An updated (2015) land use land cover map fusing Sentinel-1, Landsat-8 OLI, and PALSAR-2 were integrated and classified using a randomforest algorithm. Time series phenological analyses of the dense Sentinel-1 data were then executed to assess rice information across all of Myanmar. The broad land use land cover map identified 186,701 km2 of cropland across Myanmar with mean out-of-sample kappa of over 90%. A phenological time series analysis refined the cropland class to create a rice mask by extrapolating unique indicators tied to the rice life cycle (dynamic range, inundation, growth stages) from the dense time series Sentinel-1 to map rice paddy characteristics in an automated approach. Analyses show that the harvested rice area was 6,652,111 ha with general (R2 = 0.78) agreement with government census statistics. The outcomes show strong ability to assess and monitor rice production at moderate scales over a large cloud-prone region. In countries such as Myanmar with large populations and governments dependent upon rice production, more robust and transparent monitoring and assessment tools can help support better decision making. These results indicate that systematic and open access Synthetic Aperture Radar (SAR) can help scale information required by food security initiatives and Monitoring, Reporting, and Verification programs.

Pennar (Somasila) to Cauvery (Grand Anicut) Inter Basin Water Transfer Impact Assessment on Land Use/Land Cover Environment

As a part of the National Water Development Authority (NWDA) proposal, the linking between Pennar and Cauvery is put forth with a single purpose of conserving water to the maximum extent possible. The present study covers with land use/land cover (LU/LC) along the alignment study area 17215.68 sq·km. All the details of these features have been studied using IRS-P6, LISSIII data to analyze the effect of land use and land cover. The land use and land cover data are classified into 9 categories such as crop land, current fallow, forest, plantations, built-up land, water bodies, scrub land, sandy area and others. The total area going to be capsized is 17215.68 sq·km out of which 10105.96 sq·km is proposed command area. The 244 villages have to be rehabilitated due to this canal whereas about 4597 villages will enjoy the fruits of this canal in the form of drinking water, ground water recharge and as an additional source of irrigation as well. The study indicated current fallow land of 5340.14 km2 and 6307.98 km2 of cropland can be brought under cultivation which is more than what NWDA estimated land that can be benefitted.

Monitoring peri-urbanization in the greater Ho Chi Minh City metropolitan area

Across the globe, urban areas are rapidly expanding, and now the majority of the world's population lives in cities. Peri-urbanization, a specific form of urbanization characterized by rapid and fragmented growth, is also increasing, especially in developing countries. By 2030, it is anticipated that peri-urban areas in East Asia will expand by 200 million people, or 40% of total projected urban population growth, creating in these areas one of the largest and most significant land cover changes in the region. Characterizing and understanding the peri-urbanization process is critical, as these transition zones have a wide range of impacts across multiple scales, including local effects on farmer livelihoods, regional impacts to economic development, fragmented governance, as well as detrimental environmental impacts such as increased air and groundwater pollution, loss of native vegetation, and decreases in biodiversity. Yet the dynamic and piecemeal nature of peri-urbanization presents challenges for monitoring this unique process. This research combines commune-level Vietnamese census information, dense time stacks of Landsat satellite data (1990–2012), and several spatial measures to quantify urbanization and peri-urbanization in the greater Ho Chi Minh City metropolitan area from 1990 to 2012. The results indicate that 660.2 km2 of cropland was converted to urban uses (a near quintupling of urban land), while 3.5 million persons moved into the region, bringing the total population to nearly 12 million by 2012. The analysis also highlights the rapid, unplanned nature of peri-urban development: approximately one-third of new urban expansion occurs in areas >40 km from the core, with nearly 50% of population expansion occurring in communes classified as peri-urban. This pattern of growth is likely linked to policies meant to spur foreign investment, and we expect that these regions will continue to attract much, if not most, of the foreign direct investment (FDI) flowing to the region, since most large manufacturing enterprises now locate in peri-urban areas.

The impacts of climate change on river flood risk at the global scale

This paper presents an assessment of the implications of climate change for global river flood risk. It is based on the estimation of flood frequency relationships at a grid resolution of 0.5 × 0.5°, using a global hydrological model with climate scenarios derived from 21 climate models, together with projections of future population. Four indicators of the flood hazard are calculated; change in the magnitude and return period of flood peaks, flood-prone population and cropland exposed to substantial change in flood frequency, and a generalised measure of regional flood risk based on combining frequency curves with generic flood damage functions. Under one climate model, emissions and socioeconomic scenario (HadCM3 and SRES A1b), in 2050 the current 100-year flood would occur at least twice as frequently across 40 % of the globe, approximately 450 million flood-prone people and 430 thousand km2 of flood-prone cropland would be exposed to a doubling of flood frequency, and global flood risk would increase by approximately 187 % over the risk in 2050 in the absence of climate change. There is strong regional variability (most adverse impacts would be in Asia), and considerable variability between climate models. In 2050, the range in increased exposure across 21 climate models under SRES A1b is 31–450 million people and 59 to 430 thousand km2 of cropland, and the change in risk varies between −9 and +376 %. The paper presents impacts by region, and also presents relationships between change in global mean surface temperature and impacts on the global flood hazard. There are a number of caveats with the analysis; it is based on one global hydrological model only, the climate scenarios are constructed using pattern-scaling, and the precise impacts are sensitive to some of the assumptions in the definition and application.

Effects of the “Conversion of Cropland to Forest and Grassland Program” on the Water Budget of the Jinghe River Catchment in China

In 1999 China adopted the "Conversion of Cropland to Forest and Grassland Program" (CCFGP), a nationwide ecological recovery program, to minimize wide-scale soil erosion and vegetation degradation in China, as well as to improve water budgeting results. In the 10 yr since implementation, the CCFGP has resulted in the recovery and reforestation of >100,000 km of cropland and bare land, though the quantitative effect of this program on catchment water budget is not entirely clear. Therefore, we used the Soil and Water Assessment Tool to evaluate and quantify the effects of the CCFGP on the water budget of the Jinghe River catchment, a tributary of the Yellow River covering the central region of the Loess Plateau. Our results indicated that precipitation had dropped by 12.0% from the 1970s (611.6 mm) to the 2000s (538 mm) and that there was a corresponding 25.2% decrease in humidity index from 0.48 to 0.36. Before the CCFGP's implementation, forest and grassland had been decreasing, while bare land, cropland, and shrub land had been increasing. After the implementation of the CCFGP, the opposite trend was observed. Moreover, streamflow increased by about 15 and 20% for the upstream and middle stream subbasins, respectively, while soil water content also showed an obvious increase. Over the same period, evapotranspiration decreased by 5.2 and 13.5 mm and runoff decreased by 37.5 and 38.6% in the two subbasins. The same trends were obtained in the downstream subbasin, where changes were even greater. As a result of the reduced runoff and evapotranspiration, utilization of water resources was more efficient and ecological environment was improved under the CCFGP policy. Our results indicate the CCFGP resulted in a favorable ecological impact and should therefore be maintained.

Farming the planet: 1. Geographic distribution of global agricultural lands in the year 2000

Agricultural activities have dramatically altered our planet's land surface. To understand the extent and spatial distribution of these changes, we have developed a new global data set of croplands and pastures circa 2000 by combining agricultural inventory data and satellite‐derived land cover data. The agricultural inventory data, with much greater spatial detail than previously available, is used to train a land cover classification data set obtained by merging two different satellite‐derived products (Boston University's MODIS‐derived land cover product and the GLC2000 data set). Our data are presented at 5 min (∼10 km) spatial resolution in longitude by longitude, have greater accuracy than previously available, and for the first time include statistical confidence intervals on the estimates. According to the data, there were 15.0 (90% confidence range of 12.2–17.1) million km2 of cropland (12% of the Earth's ice‐free land surface) and 28.0 (90% confidence range of 23.6–30.0) million km2 of pasture (22%) in the year 2000.

Mapping Single‐, Double‐, and Triple‐crop Agriculture in China at 0.5° × 0.5° by Combining County‐scale Census Data with a Remote Sensing‐derived Land Cover Map

Assessments of agriculture at the national scale require the best possible information on the distribution of cropland and the management of that cropland. Official cropland statistics for China contain much information on the distribution of crop types, but are known to underestimate total cropland area and are generally at coarse spatial resolution. Remote sensing products can provide moderate to fine spatial resolution estimates of cropland locationand extent, but usually supply little information on crop type or management. We combined 1990 county‐scale agricultural census statistics on total cropland area and sown area of 17 major crops with a fine‐resolution land‐cover map derived from 1995‐96 optical remote sensing (Landsat Thematic Mapper) data to generate 0.5° resolution maps of the distribution of 47 different single‐ and multi‐crop rotations in mainland China. We estimated that, of 1.3‐million km2 of cropland, approximately 60% was single‐cropped, 30% was double‐cropped, and 10% was triple‐cropped. Total sown or planted area was 2.0‐million km2.