GCI present an annual dynamic global cropping intensity dataset covering the period from 2001 to 2019 at a 250-m resolution with an average overall accuracy of 89%. We used the enhanced vegetation index (EVI) of MOD13Q1 as the database via a sixth-order polynomial function to calculate the cropping intensity. The global cropping intensity dataset was packaged in the GeoTIFF file type, with the quality control band in the same format. The dataset fills the vacancy of medium-resolution, global-scale annual cropping intensity data and provides an improved map for further global yield estimations and food security analyses. GCI and GCI_QC maps at a 250-m resolution were provided for the entire world from 2001 to 2019. The datasets and their validation samples are available at the figshare repository in GeoTIFF format and provided in the GCS_WGS_1984 spatial reference system. The global cropping intensity maps contain values of 0, 1, 2 and 3, representing none, single, double, and triple cropping, respectively. The QC band maps also contain values of 0, 1, 2 and 3, representing best, good, fair, and poor pixels, respectively. The dataset extends from 70° N to 60° S latitude and from 180° W to 180° E longitude, excluding Greenland and Antarctica. The maps can be visualized and analysed in ArcGIS, QGIS, or similar software.

Cropping intensity, defined as the number of cropping cycle(s) per year, is an important indicator to measure arable land use intensity. Tracking the change in cropping intensity can help assess the past development of the food production system and inform future agro-policies. All available images of top-of-atmosphere (TOA) reflectance from Landsat-7 ETM+, Landsat-8 OLI, Sentinel-2 MSI and MODIS during 2016–2018 were used for cropping intensity mapping via the GEE platform. To overcome the multi-sensor mismatch issue, an inter-calibration approach was adopted, which converted Sentinel-2 MSI and Landsat-8 OLI TOA reflectance data to the Landsat-7 ETM+ standard. Then the calibrated images were used to composite the 16-day TOA reflectance time series based on maximum composition method. To ensure data continuity, the MODIS NDVI product was used to fill temporal gaps with the following steps. First, the 250-m MODIS NDVI product was re-sized to 30-m using the bicubic algorithm. Then, the Whittaker algorithm was applied to the gap filled NDVI time series to smooth the NDVI time series. Two phenology metrics were introduced, mid-greenup and mid-greendown, which were derived as the day of year (DOY) at the transition points in the greenup and greendown periods when the smoothed NDVI time series passes 50% of the NDVI amplitude. An interval starting from mid-greenup and ending at mid-greendown is defined as a growing phenophase, and an interval moving from mid-greendown to mid-greenup a non-growing phenophase. Based on this phenophase-based approach, the global cropping intensity at 30m resolution (GCI30) was mapped. The results were validated based on a large number of ground-based samples obtained using GVG (GPS, Video and GIS) smart phone application and other crowd-sourcing dataset. The global cropping intensity dataset at 30m includes two layers. The first layer indicates the average cropping intensity during the three years from 2016 to 2018 with noData value or masked areas assigned to -1. The valid values for the first layer are 1, 2, and 3 representing single cropping, double cropping or triple cropping. The second layer keeps the original total number of crop cycles from 2016 to 2018 with noData value or masked areas assigned to -1. Continuous cropping or number of crop cycles larger than 3 per year are indicated with value of 127. Detailed documentation on the methodology of GCI30 can be found at the following two published papers: https://www.sciencedirect.com/science/article/abs/pii/S0034425720304685 https://essd.copernicus.org/articles/13/4799/2021/

Desert Locust Monitoring, Forecasting and Assessment in Africa and Southwest Asia. Covering Ethiopia, Kenya, Somalia, Pakistan, Yemen, India, Nepal and Afghanistan. A research team RSCROP led by Prof. Huang Wenjiang and Prof. Dong Yingying of the ‘Digital Earth Science Platform’ Project in CASEarth has tracked the migration path of the Desert Locust and make a detailed analysis on the possibility of the Desert Locust invasion of China. Integrated with multi-source Earth Observation data, e.g. meteorological data, field data, and remote sensing data (such as GF series in China, MODIS and Landsat series in US, Sentinel series in EU), and self-developed models and algorithms for Desert Locust monitoring and forecasting, the research team constructed the ‘Vegetation pests and diseases monitoring and forecasting system’, which could regularly release thematical maps and reports on Desert Locust. The Desert Locust has ravaged the Horn of Africa and Southwest Asia, posing serious threats on agricultural production and food security of the inflicted regions. The Food and Agriculture Organization of the United Nations（FAO）has issued a worldwide Desert Locust warning, calling for joint efforts from multiple countries in prevention and control of the pest to ensure food security and regional stability.

Desert Locust Monitoring, Forecasting and Assessment in Africa and Southwest Asia. Covering Somalia. A research team RSCROP led by Prof. Huang Wenjiang and Prof. Dong Yingying of the ‘Digital Earth Science Platform’ Project in CASEarth has tracked the migration path of the Desert Locust and make a detailed analysis on the possibility of the Desert Locust invasion of China. Integrated with multi-source Earth Observation data, e.g. meteorological data, field data, and remote sensing data (such as GF series in China, MODIS and Landsat series in US, Sentinel series in EU), and self-developed models and algorithms for Desert Locust monitoring and forecasting, the research team constructed the ‘Vegetation pests and diseases monitoring and forecasting system’, which could regularly release thematical maps and reports on Desert Locust. The Desert Locust has ravaged the Horn of Africa and Southwest Asia, posing serious threats on agricultural production and food security of the inflicted regions. The Food and Agriculture Organization of the United Nations（FAO）has issued a worldwide Desert Locust warning, calling for joint efforts from multiple countries in prevention and control of the pest to ensure food security and regional stability.

Seasonal maximum vegetation condition index (VCIx) is a remote sensing- based indicator introduced by CropWatch in 2014 for crop growth condition monitoring. VCIx adopts the general concept of Vegetation Condition Index (VCI) but stretches the length of temporal observation window from a short time slot, fixed by satellite sensor, to a period that can reflect various crop growth stages (crop phenology). In this way, it reduces the uncertainty of remote sensing index-based crop condition monitoring caused by inter-annual shifts (delay or advance) of crop phenology over different years. In CropWatch, VCIx is presented as a raster map at global extent with 1 Km resolution, updated every three months. Pixel values usually fall between 0 and 1. Based on the VCIx values, crop growth condition can be categorized into four levels: Level 1: VCIx＜0.5, indicating poor crop growth condition which is below the average of the previous 5 years (5YA) and 0 means as bad as the worst recent year; Level 2: 0.5≤VCIx＜0.8, indicating slight above 5YA situation; Level 3: 0.8≤VCIx≤1.0, indicating that crop condition is better than the 5YA but below the optimal condition during the previous five years, 1 means as good as the best recent year. Level 4: VCIx＞1.0, indicating a new record level of crop growth condition which exceeds the optimal condition of the previous 5 years. VCIx is calculated based on NDVI time series (MODIS). Peak NDVI during the monitoring period is compared with the historic (previous five years) minimum NDVI during the same period and normalized by the historical range of NDVI values for the same period. As NDVI values may be distorted by cloud or non-vegetation pixels, an empirical minimum vegetation NDVI value (0.15) is introduced in VCIx computation. In case the minimum NDVI of the monitoring period is lower than the empirical value (0.15), the empirical value (0.15) is used in the computation. Considering the genetic development and improvement of crops seeds, crops at monitoring year are hardly comparable with the same ones cultivated ten years ago. CropWatch uses previous five years, instead of a longer period, as the reference period when deriving the historic agronomic indicators. Detailed documentation on VCIx can be found at: http://cprs.patentstar.com.cn/Search/Detail?ANE=4CAA9DHB9DFABDIA9ICC9IGFAIIA9FFDCICA5CAA9ICC9DEB

This dataset is a China terrace map at 30 m resolution in 2018. It was developed through supervised pixel-based classification using multisource and multi-temporal data based on the Google Earth Engine platform. The overall accuracy and kappa coefficient achieved 94% and 0.72, respectively. The first 30 m China terrace map will be valuable for studies on soil erosion, food security, biogeochemical cycle, biodiversity, and ecosystem service assessments. Detailed dataset description can be found at: https://essd.copernicus.org/articles/13/2437/2021/