| Autor: |
Felix Rembold, Michele Meroni, Viola Otieno, Oliver Kipkogei, Kenneth Mwangi, João Maria de Sousa Afonso, Isidro Metódio Tuleni Johannes Ihadua, Amílcar Ernesto A. José, Louis Evence Zoungrana, Amjed Hadj Taieb, Ferdinando Urbano, Maria Dimou, Hervé Kerdiles, Petar Vojnovic, Matteo Zampieri, Andrea Toreti |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Remote Sensing, Vol 15, Iss 17, p 4284 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2072-4292 |
| DOI: |
10.3390/rs15174284 |
| Popis: |
The Anomaly hotSpots of Agricultural Production (ASAP) Decision Support System was launched operationally in 2017 for providing timely early warning information on agricultural production based on Earth Observation and agro-climatic data in an open and easy to use online platform. Over the last three years, the system has seen several methodological improvements related to the input indicators and to system functionalities. These include: an improved dataset of rainfall estimates for Africa; a new satellite indicator of biomass optimised for near-real-time monitoring; an indicator of crop and rangeland water stress derived from a water balance accounting scheme; the inclusion of seasonal precipitation forecasts; national and sub-national crop calendars adapted to ASAP phenology; and a new interface for the visualisation and analysis of high spatial resolution Sentinel and Landsat data. In parallel to these technical improvements, stakeholders and users uptake was consolidated through the set up of regionally adapted versions of the ASAP system for Eastern Africa in partnership with the Intergovernmental Authority on Development (IGAD) Climate Prediction and Applications Centre (ICPAC), for North Africa with the Observatoire du Sahara et du Sahel (OSS), and through the collaboration with the Angolan National Institute of Meteorology and Geophysics (INAMET), that used the ASAP system to inform about agricultural drought. Finally, ASAP indicators have been used as inputs for quantitative crop yield forecasting with machine learning at the province level for Algeria’s 2021 and 2022 winter crop seasons that were affected by drought. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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