Desert Locust Monitoring, Forecasting and Assessment in Africa and Southwest Asia. Covering Pakistan. 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 Ethiopia. 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 Yemen. 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.

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.

Cropwatch is China's leading agricultural monitoring system, using remote sensing and ground observation data to assess crop growth, yield and related information on national and global scales. The Cropwatch scientific team is affiliated to the Aerospace Information Research Institute (AIR) under the Chinese Academy of Sciences (CAS) since 1998. The CropWatch Cloud (http://cloud.cropwatch.com.cn) is a crop-monitoring platform developed to give users access to recent Earth observation data and innovative crop monitoring technology . The open and shared cloud-based agricultural production information services (APIS) reduces the food market volatility, in line with the community of shared future for mankind. CropWatch Cloud also upgrades current satellite data downloading to real-time processing and analyses. CropWatch Cloud provides an open and customizable APIS that stakeholders over the world can calibrate, localize, customize and automatically generate agro-climatic and agronomic indicators according to their own specific requirements (area, phenology, and crop) in areas of their interest. Taking advantages of cloud storage and cloud computing capacity, developing countries and stakeholders over the world can independently carry out crop monitoring and make their own analyses, at various scales from subnational to global, in areas of their interest on the CropWatch Cloud, without additional investments on hardware and software, which are the main constrains that prevent developing countries from building up their crop monitoring capacity. CropWatch Cloud provides real-time or near real-time APIS and food security early warning through releasing quarterly CropWatch Bulletins including information on agro-climatic situations, natural disasters, crop conditions, crop yield and production at global, regional, national and sub-national levels. CropWatch Cloud allows stakeholders to carry out collaborative information analyses to improve the credibility and transparency of agricultural production information, which is essential for ironing the speculations on the world food market. CropWatch Bulletins have been downloaded by stakeholders from more than 160 countries and regions around the world and the CropWatch Cloud platform recognized as a valuable tool for supporting developing countries in the implementation of the sustainable development goals (SDGs), in particular SDGs 2 - Zero Hunger.

Open and synergistic global land cover present a global land cover dataset which is available every 5 year from 1990 to 2020. The overall accuracies of land cover maps were around 75% and the accuracy for change detection was over 70%. This product also showed good similarity with the FAO and existing land cover maps. Multiple datasets were used in this study, including the FROM-GLC global land cover map in 2017, which was the most up to date and accurate land cover map among the three FROM-GLC maps in 2010, 2015 and 2017. Landsat surface reflectance dataset, The Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM), ESA-CCI and three recent single-type land cover datasets. Open and synergistic land cover maps were provided for the entire world from 1990 to 2020 every 5 years. The global land cover map contains values of 1 to 10, representing cropland, forest, grassland, shrubland, wetland, water, tundra, impervious surface, bareland and ice&snow, respectively. The dataset extends from 90° N to 60° S latitude and from 180° W to 180° E longitude. The dataset can be visualized and analysed directly through Google Earth Engine (GEE) cloud computing platform and it could also be exported to local equipment through GEE.