Enabling wide area persistent remote sensing for agriculture applications by developing and coordinating multiple heterogeneous platforms

该项目是通过开发和协调卫星、无人机、飞艇甚至地面无人机等一系列传感平台，发展广域、持久的农业应用遥感能力。它的目的是提供可持续农业所需的前所未有的高密度空间和时间信息。农业目前在确保世界人口粮食供应方面面临严峻挑战。随着老龄化人口结构的加剧，全球人口在不久的将来将继续增长。因此，随着全球人口的增长和中国等快速发展国家日益壮大的中产阶级对肉类和乳制品的需求，对食品的需求预计将继续上升。同样，能源和淡水的总需求将因中国的经济增长而增加。极端天气事件发生的频率增加将严重阻碍粮食生产。人们普遍认为，可持续集约农业是应对这一挑战的答案，其目的是在不对自然资源和环境造成不利影响的情况下增加粮食产量。通过培育资源效率和产量潜力更高的品种，更好地部署这些品种，以及更好地管理作物以减少因不利因素(如虫害、疾病、洪水、干旱)造成的作物损失，实现了粮食产量的增加。遥感在中国等国发展可持续农业中发挥着关键作用。遥感提供有关作物及其生长环境和其他关键相关因素的及时、天气、成本效益和重复的信息，如迁徙昆虫和疾病。来自遥感的观测数据可以得到广泛的应用；例如，不仅为农场管理或疾病的早期发现提供及时的信息，而且还有助于了解农业中涉及的生物科学，并为未来的预测建立统计作物模型。尽管遥感平台特别是无人驾驶飞行器取得了所有进展，但它们仍然不能提供所需的广域持久遥感能力；例如，需要宏观和微观数据来了解一些疾病和虫害的爆发和传播。该项目将通过两个途径提高现有的遥感能力：1)进一步改进现有的遥感平台，特别是飞艇和小型无人机，包括指向系统和车辆；2)更重要的是，根据现有的不同类型的传感平台(即卫星、无人机或飞艇)的性能和特点来协调它们。为了在满足特定应用的遥感要求的同时，降低业务成本和对操作员经验/技能的依赖，将开发部署平台的战略和战术决策、规划和协调工具，以利用自主系统技术使大多数农业遥感任务自动化。

This project is to develop wide area, persistent remote sensing capability for agriculture applications by developing and coordinating a number of sensing platforms such as satellites, unmanned aerial vehicles, airships, and even ground unmanned vehicles. It is aimed to provide an unprecedented high density of spatial and temporal information required in sustainable agriculture. Agriculture is currently facing serious challenges in securing food supply to the world population. Global population will continue to grow in the near future with an increasing aging-population structure. Thus the demand for food is expected to continue to rise as global population grows and a rising middle class desires more meat and dairy products in rapidly developing countries like China. Similarly, the total demand for energy and fresh water will increase as a result of economic growth in China. The increased frequency of extreme weather events occurring will seriously hamper food production. Sustainable intensive agriculture is widely perceived to be the answer to the challenge, which aims to increase food production without adversely damaging natural resources and environment. This increased food production is achieved through breeding cultivars with increased resource efficiency and yield potential, better deployment of these cultivars, and better crop husbandry to reduce crop losses due to adverse factors (e.g. pests, diseases, flooding, drought). Remote sensing plays a key role in developing sustainable agriculture for China and other countries. Remote sensing provides timely, synoptic, cost-effective and repetitive information about crops, their growth environments and other key relevant elements such as migratory insects and diseases. The observed data from the remote sensing can find a wide range of applications; for example, not only providing timely information for farm management or early detection of diseases but also for understanding the biological science involved in agriculture and developing statistical crop modelling for future predictions. Despite all the advances in remote sensing platforms particularly unmanned aerial vehicles, they are still not able to provide required wide area persistent remote sensing capability; for example, both macroscopic and microscopic data are required in understanding outbreak and the propagation of some diseases and pests. This project is to advance the current remote sensing capability by two approaches: 1) further improving the current sensing platforms particularly airships and small scale unmanned aircraft including both pointing systems and vehicles; 2) more importantly coordinating different types of existing sensing platforms (i.e. satellites, unmanned aircraft, or airship) based on their performance and characteristics. With the aim of reducing operation cost and the reliance on the operator's experience/skills while fulfilling the remote sensing requirements for a specific application, both strategical and tactical decision making, planning and coordination tools for the deployment of the platforms will be developed to automate most of the remote sensing tasks for agriculture using autonomous system technologies.