NCEO NC ODA Extension 2020-2021

国家环境委员会国家信息通报-官方发展援助方案侧重于与发展挑战特别相关的一系列一般科学问题：包括植被变化和土壤湿度在内的地表状况的定性和预测;森林碳的演变和毁林和退化引起的碳通量的定性;火灾的动态性质、火灾向大气的排放以及大规模空气污染的发展;培养一支受过最先进地球观测培训的研究人员和应用专家骨干队伍。我们将处理发援会国家面临的具体问题：非洲半干旱地区作物产量易受干旱影响，保护和加强肯尼亚在整个撒哈拉以南非洲的森林资源以减缓气候变化的挑战，掌握露天生物质燃烧造成的东南亚有害空气质量所需的预报技能，以及目前许多非洲国家缺乏有效利用卫星地球观测数据的能力。该计划分为四个WP。相关的联合国可持续发展目标有：2（零饥饿），3（良好的健康和福祉），13（气候行动），15（陆地生命）和17（目标的伙伴关系）。WP 1将通过对多个EO数据流（例如有效叶面积指数和土壤湿度）进行数据同化，改进加纳以及整个撒哈拉以南非洲的作物产量建模。该研究将产生关于数据模型系统中精确EO数据参数的价值的新知识，以更好地监测作物变化并提高预测技能，检查从景观到国家尺度的升级，并提高土壤湿度预测技能[SGD 2，15]。WP 2将扩大撒哈拉以南非洲毁林所致碳排放量基线，通过合成孔径雷达、光学和激光测距（LiDAR）数据确定不同类型的毁林和退化，并确定适合造林的地区。它将在肯尼亚测试新的数据集，以确保有效性，并继续支持肯尼亚政府的2030年愿景，该愿景旨在到2030年将森林覆盖率从6%提高到10%。这项工作将确定区域范围内的森林参考排放水平和地上碳储存。这项研究是了解REDD+政策背景下碳循环估计的关键。[SGDs 13、15]。WP 3将开发和展示新的数据源，以提高火灾期间大规模空气污染的预测准确性。目前，预测模型使用的火灾估计数无法捕捉动态大型森林和泥炭地火灾以及农业残留物燃烧引起的火灾的规模和变化。该研究将改进火灾的污染物排放估计，烟雾羽流气溶胶的基于EO的检索和生物质燃烧羽流的自动识别。我们将与东盟国家的利益相关者合作，共同开发和展示新系统，改进可持续发展，并培训工作人员解释复杂的EO数据。我们将在撒哈拉以南非洲实施WP 3的火灾部分，改进碳估计，并将干旱与火灾知识结合起来。[SDGs 3，13]。WP 4将通过与地球观测相关的国际倡议进行能力建设，包括地球观测组织（GEO）非洲全球测地系统倡议和地球观测卫星委员会（CEOS）能力建设和数据民主工作组，以改善非洲国家和其他发展援助委员会国家对当代地球观测数据集的访问和使用。工作将包括确定与联合王国有关的地球观测项目和与非洲地球观测确定的需求有关的专家的范围，将WP 1 -3的培训扩展到更广泛的发援会国家和战略能力建设，与GFOI和GEOGLAM的相关地球观测倡议协调工作，并支持发援会国家获得地球观测数据。

The NCEO NC-ODA programme is focussed on a series of generic science issues that are particularly relevant to development challenges: characterisation and forecasting of land surface state including vegetation change and soil moisture; the evolution of forest carbon and characterisation of carbon fluxes arising from deforestation and degradation; the dynamic nature of fires, their emissions into the atmosphere and the development of large-scale air pollution; the development of a cadre of researchers and applications specialists trained in state-of-the-art Earth Observation (EO). We will address specific problems faced by DAC countries: the vulnerability of crop yields in semi-arid regions in Africa to drought, the challenge of protecting and enhancing forest resources from Kenya across sub-Saharan Africa, to mitigate climate change, the forecast skill necessary to capture hazardous air quality in South-East Asia stemming from open biomass burning, and the current lack of capacity of many African nations to make effective use of satellite EO data. The programme is structured into four WPs. Relevant UN Sustainable Development Goals are: 2 (Zero hunger), 3 (Good health and well-being), 13 (Climate action), 15 (Life on Land), and 17 (Partnership for the goals). WP 1 will improve crop yield modelling in Ghana and then across sub-Saharan Africa through data assimilation of multiple EO data streams, for example effective leaf area index and soil moisture. The research will yield new knowledge on the value of accurate EO data parameters in a data-model system, to better characterise crop change and increase predictive skill, to examine upscaling from landscape to country scale, and improve soil moisture forecast skill [SGDs 2, 15]. It will support an improved TAMSAT-ALERT system.WP 2 will extend a baseline of carbon emissions from deforestation across sub-Saharan Africa, identify different types of deforestation and degradation from synthetic aperture radar, optical and laser ranging (LiDAR) data, and establish areas that are suitable for afforestation. It will test new datasets in Kenya to ensure validity and to continue to support the Vision 2030 of the Kenyan Government that aims to increase forest cover from 6 to 10 per cent by 2030. The work will establish forest reference emission levels and above-ground carbon stocks in a regional context. This research is key to understanding carbon cycling estimates in a REDD+ policy context. [SGDs 13, 15]. WP 3 will develop and demonstrate new data sources that can improve forecast accuracy for large-scale air pollution during fire events. Currently, forecast models use estimates of fires that fail to capture the magnitude and variability of dynamic large forest and peatland fires and fires due to agricultural residue burning. The research will improve pollutant emissions estimates from fires, EO-based retrievals of smoke plume aerosols and auto-identification of biomass-burning plumes. We will work with stakeholders in the ASEAN countries to co-develop and demonstrate new systems, characterise improvements and train staff in the interpretation of complex EO data. We will implement the fire component of WP3 in sub-Saharan Africa improving carbon estimates and coupling drought with fire knowledge. [SDGs 3, 13]. WP 4 will build capacity through international EO-related initiatives, including the Group on Earth Observations (GEO) AfriGEOSS initiative and the Committee on Earth Observation Satellites (CEOS) Working Group on Capacity Building & Data Democracy, to improve access to and use of contemporary EO datasets in African nations and other DAC nations. Work will include scoping of UK-related EO projects and experts related to AfriGEO identified needs, extension of the training of WP1-3 to wider DAC countries and to strategic capacity building, co-ordinated work with the relevant GEO initiatives of GFOI and GEOGLAM, and support of access to EO data for DAC countries.