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.

A global reference data set on cropland was collected through a crowdsourcing campaign using the Geo-Wiki crowdsourcing tool. The campaign lasted three weeks, with over 80 participants from around the world reviewing almost 36,000 sample units, focussing on cropland identification. For quality assessment purposes, two additional data sets are provided. The first is a control set of 1,793 sample locations validated by students trained in satellite image interpretation. This data set was used to assess the quality of the crowd as the campaign progressed. The second data set contains 60 expert validations for additional evaluation of the quality of the contributions. All data sets are split into two parts: the first part shows all areas classified as cropland and the second part shows cropland average per location and user. After further processing, the data presented here might be suitable to validate and compare medium and high resolution cropland maps generated using remote sensing. These could also be used to train classification algorithms for developing new maps of land cover and cropland extent.

The Kaziranga Eco-Sensitive Zone is located on the edge of the Eastern Himalayan biodiversity hotspot region. In 1985, the Kaziranga National Park (KNP) was declared a World Heritage Site by UNESCO. Nowadays, anthropogenic interference has created a significant negative impact on this national park. As a result, the area under natural habitat is gradually decreasing. The current study attempted to analyze the land use land cover (LULC) change in the Kaziranga Eco-Sensitive Zone using remote sensing data with CA-Markov models. Satellite remote sensing and the geographic information system (GIS) are widely used for monitoring, mapping, and change detection of LULC change dynamics. The changing rate was assessed using thirty years (1990–2020) of Landsat data. The study analyses the significant change in LULC, with the decrease in the waterbody, grassland and agricultural land, and the increase of sand or dry river beds, forest, and built-up areas. Between 1990 and 2020, waterbody, grassland, and agricultural land decreased by 18.4, 9.96, and 64.88%, respectively, while sand or dry river beds, forest, and built-up areas increased by 103.72, 6.96, and 89.03%, respectively. The result shows that the area covered with waterbodies, grassland, and agricultural land is mostly converted into built-up areas and sand or dry river bed areas. According to this study, by 2050, waterbodies, sand or dry river beds, and forests will decrease by 3.67, 3.91, and 7.11%, respectively; while grassland and agriculture will increase by up to 16.67% and 0.37%, respectively. The built-up areas are expected to slightly decrease during this period (up to 2.4%). The outcome of this study is expected to be useful for the long-term management of the Kaziranga Eco-Sensitive Zone.

Floods represent a significant threat to human life, property, and agriculture, especially in low-lying floodplains. This study assesses flood susceptibility in the Brahmaputra River basin, which spans China, India, Bhutan, and Bangladesh—an area notorious for frequent flooding due to the saturation of river water intake capacity. We developed and evaluated several innovative models for predicting flood susceptibility by employing Multi-Criteria Decision Making (MCDM) and Machine Learning (ML) techniques. The models showed robust performance, evidenced by Area Under the Receiver Operating Characteristic Curve (AUC-ROC) scores exceeding 70% and Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) scores below 30%. Our findings indicate that approximately one-third of the studied region is categorized as moderately to highly flood-prone, while over 40% is classified as low to very low flood-risk areas. Specific regions with high to very high flood susceptibility include Dhemaji, Dibrugarh, Lakhimpur, Majuli, Darrang, Nalbari, Barpeta, Bongaigaon, and Dhubri districts in Assam; Coochbihar and Jalpaiguri districts in West Bengal; and Kurigram, Gaibandha, Bogra, Sirajganj, Pabna, Jamalpur, and Manikganj districts in Bangladesh. Owing to their strong performance and the suitability of the training datasets, we recommend the application of the developed MCDM techniques and ML algorithms in geographically similar areas. This study holds significant implications for policymakers, regional administrators, environmentalists, and engineers by informing flood management and prevention strategies, serving as a climate change adaptive response within the Brahmaputra River basin.

Climate change and anthropogenic factors have exacerbated flood risks in many regions across the globe, including the Himalayan foothill region in India. The Jia Bharali River basin, situated in this vulnerable area, frequently experiences high-magnitude floods, causing significant damage to the environment and local communities. Developing accurate and reliable flood susceptibility models is crucial for effective flood prevention, management, and adaptation strategies. In this study, we aimed to generate a comprehensive flood susceptibility zone model for the Jia Bharali catchment by integrating statistical methods with expert knowledge-based mathematical models. We applied four distinct models, including the Frequency Ratio model, Fuzzy Logic (FL) model, Multi-criteria Decision Making based Analytical Hierarchy Process model, and Fuzzy Analytical Hierarchy Process model, to evaluate the flood susceptibility of the basin. The results revealed that approximately one-third of the Jia Bharali basin area fell within moderate to very high flood-prone zones. In contrast, over 50% of the area was classified as low to very low flood-prone zones. The applied models demonstrated strong performance, with ROC-AUC scores exceeding 70% and MAE, MSE, and RMSE scores below 30%. FL and AHP were recommended for application among the models in areas with similar physiographic characteristics due to their exceptional performance and the training datasets. This study offers crucial insights for policymakers, regional administrative authorities, environmentalists, and engineers working in the Himalayan foothill region. By providing a robust flood susceptibility model, the research enhances flood prevention efforts and management, thereby serving as a vital climate change adaptation strategy for the Jia Bharali River basin and similar regions. The findings also have significant implications for disaster risk reduction and sustainable development in vulnerable areas, contributing to the global efforts towards achieving the United Nations' Sustainable Development Goals.