Wheat is cultivated on more than 2.7 million hectares in Afghanistan annually, yet the country is dependent on imports to meet domestic demand. The timely estimation of domestic wheat production is highly critical to address any potential food security issues and has been identified as a priority by the Ministry of Agriculture Irrigation and Livestock (MAIL). In this study, we developed a system for in-season mapping of wheat crop area based on both optical (Sentinel-2) and synthetic aperture radar (SAR, Sentinel-1) data to support estimation of wheat cultivated area for management and food security planning. Utilizing a 2010 Food and Agriculture Organization (FAO) cropland mask, wheat sown area for 2017 was mapped integrating decision trees and machine learning algorithms in the Google Earth Engine cloud platform. Information from provincial crop calendars in addition to training and validation data from field-based surveys, and high-resolution Digitalglobe and Airbus Pleiades images were used for classification and validation. The total irrigated and rainfed wheat area were estimated as 912,525 and 562,611 ha, respectively for 2017. Province-wise accuracy assessments show the maximum accuracy of irrigated (IR) and rainfed (RF) wheat across provinces was 98.76 and 99%, respectively, whereas the minimum accuracy was found to be 48% (IR) and 73% (RF). The lower accuracy is attributed to the unavailability of reference data, cloud cover in the satellite images and overlap of spectral reflectance of wheat with other crops, especially in the opium poppy growing provinces. While the method is designed to provide estimation at different stages of the growing season, the best accuracy is achieved at the end of harvest using time-series satellite data for the whole season. The approach followed in the study can be used to generate wheat area maps for other years to aid in food security planning and policy decisions. Detail methodology and data quality information are described in the published article (Tiwari et.al 2020, Wheat Area Mapping in Afghanistan Based on Optical and SAR Time-Series Images in Google Earth Engine Cloud Environment, https://doi.org/10.3389/fenvs.2020.00077)

The annual land cover data of Afghanistan (2000-2018) have been created through the National Land Cover Monitoring System (NLCMS) for Afghanistan. The system uses freely available remote-sensing data (Landsat) and a cloud-based machine learning architecture in the Google Earth Engine (GEE) platform to generate land cover maps on an annual basis using a harmonized and consistent classification system. The NLCMS is developed by International Centre for Integrated Mountain Development (ICIMOD) together with Afghanistan’s Ministry of Agriculture, Irrigation and Livestock (MAIL) and National Statistic and Information Authority (NSIA). The NLCMS system is customized from the Regional Land Cover Monitoring System (RLCMS) which is a collaborative effort between SERVIR-HKH at ICIMOD and SERVIR-Mekong at the Asian Disaster Preparedness Center (ADPC), with co-development partners - the United States Forest Services (USFS), SilvaCarbon, and Global Land Analysis and Discovery (GLAD) group at the University of Maryland.

The annual land cover data of Nepal (2000-2022) have been created through the National Land Cover Monitoring System (NLCMS) for Nepal. The system uses freely available remote-sensing data (Landsat) and a cloud-based machine learning architecture in the Google Earth Engine (GEE) platform to generate land cover maps on an annual basis using a harmonized and consistent classification system. The NLCMS is developed by the Forest Research and Training Centre (FRTC), Ministry of Forests and Environment, Government of Nepal with support from the International Centre for Integrated Mountain Development (ICIMOD) through SERVIR Hindu Kush Himalaya (SERVIR-HKH), a joint initiative in partnership with the National Aeronautics and Space Administration (NASA) and the United States Agency for International Development (USAID). Collaborators include SERVIR–Mekong at the Asian Disaster Preparedness Center (ADPC), SilvaCarbon, Global Land Analysis and Discovery (GLAD) group at the University of Maryland, and the US Forest Service. The annual land cover data of Nepal for 2000-2019 was first published in 2022 while the data for 2020-2022 was released in 2024.

Land cover change is a significant contributor to environmental change. The degradation of forests and conversion of natural areas, forests, and farmlands to other land use impact ecosystem services and biodiversity significantly. Using multiple methodologies and input data sources, national agencies in different countries of the Hindu Kush Himalayan region have conducted land cover mapping at various times. Due to the differences in classification schema, methodologies, and input data sources used, currently available land cover data is not suitable for analysis of land cover changes over time. ICIMOD collaborated with SERVIR-Mekong at Asian Disaster Preparedness Center (ADPC), Afghanistan’s Ministry of Agriculture, Irrigation and Livestock, Bangladesh’s Forest Department, Nepal’s Forest Research and Training Centre, Myanmar’s Forest Department, SilvaCarbon, the Global Land Analysis and Discovery (GLAD) laboratory at the University of Maryland, and the United States Forest Services to develop the Regional Land Cover Monitoring System (RLCMS) for the HKH region. The system uses state-of-the-art remote sensing science and technology on the Google Earth Engine, and a standard set of input data sources to regularly generate high-quality land cover data at the regional level for the HKH, and at national levels for Afghanistan, Bangladesh, Myanmar, and Nepal. In developing the RLCMS, ICIMOD focused on collaboration and co-development with partner organizations to define different land cover typologies/classes, collect reference data samples, and validate results. Land cover maps for the HKH region spanning 2000–2022 have been produced under its SERVIR–HKH Initiative.