Synthesis of remote sensing and novel ground truth sensors to develop high resolution soil moisture forecasts in China and the UK

水的供应是农业生产力的关键驱动力。它直接影响植物生长，在许多国家和地区，它供应不足或过剩。水供应对全球粮食生产的影响被视为一个关键的全球风险和挑战。水的供应是一个极具争议的国际问题，而全球气候变化可能导致干旱和洪水的增加，被认为是一个复合因素。该项目旨在开发农业技术解决方案，以帮助缓解农业用水问题，并为生产者最终提高用水效率。土壤湿度直接影响作物生长，它驱动灌溉系统，一旦土壤达到其容纳能力，多余的水迅速流入排水系统，可能影响洪水风险和系统排水能力。水辩论中最重要的挑战之一是，几乎没有简单可靠的系统来测量土壤湿度。很难准确测量。开发的技术包括现场THD电容传感器，中子探头的使用，最近使用遥感技术。目前，还没有一个系统可以准确地测量农田中的土壤水分分布，遥感的分辨率也不足以用于农业应用或当地的水管理以减少洪水风险。在这个项目中，我们将汇集一套新技术，提高土壤水分测量的分辨率，使其适用于农田和景观尺度的农业应用。（一个静态的，一个移动的），测量土壤水分含量作为宇宙产生的快中子的衰变量的函数（Cosmos传感器，由美国Hydroinova设计）。静态传感器测量距离测量点200米半径范围内的土壤湿度。一个静态传感器网络将部署在河南省和河北省（生产中国40%的小麦），中国。移动的传感器将部署在定制的自动驾驶车辆或漫游车上，以测量田地内的土壤水分变化。该车辆将在该项目内开发，并将成为该传感器技术的首次自主部署。来自土壤湿度传感器的数据将用于校准Sentinel-1卫星上的InSARS传感器，以监测中国境内的土壤湿度，分辨率在500米x500米以内。这是目前SARS解决方案的5倍改进。最终，该技术将使近真实的时间预测的土壤湿度在外地规模。这些信息对于农业生产者和洪水风险预测将是非常宝贵的，包括提高用水效率、灌溉做法、土地排水和实施精准农业技术的关键见解。这是一个雄心勃勃的多学科项目。该项目协调了四个关键团体的专业知识，即林肯大学（机器人技术，自动驾驶车辆的测绘和部署），中国气象科学研究院生态与农业气象研究所（IEAM），信息科学与技术大学，生态与水文中心（Wallingford）和阿伯里斯特思大学地理与地球科学学院。相当重视知识交流，不仅与农业和水文界，而且在项目内的国际合作伙伴之间。我们预计，英国将受益于了解在中国发展传感器网络的挑战，在规模和环境方面存在显着差异。中国团队将花相当长的时间与英国学者一起学习遥感，传感器部署和自动驾驶汽车方面的新技能。

The availability of water is a key driver of agricultural productivity. It directly impacts plant growth, and in many countries and locations it is in short or over supply. The impact of water availability on global food production is seen as a key global risk and challenge. Water availability is a hugely contentious international issue, and global climate change, potentially driving increased droughts and flooding, is considered a compounding factor. This projects seeks to develop agri-tech solutions to help alleviate the issue of water in agriculture, and for producers to ultimately drive water use efficiency. Soil moisture directly impacts crop growth, it drives irrigation systems and once a soil has reached its holding capacity excess water rapidly runs into the drainage system, potentially impacting flood risk and system drainage capacity. One of the most significant challenges within the water debate is that there are few simple and reliable systems to measure soil moisture. It is difficult to accurately measure. Techniques developed include spot THD capacitance sensors, the use of neutron probes and more recently the use of remote sensing techniques. Currently, there is no system to measure soil moisture distribution accurately across a field, and the resolution of remote sensing has not been sufficient for agricultural application, or local water management to reduce flood risk. In this project we will bring together a suite of new technologies which increase the resolution of soil moisture measurement to render it applicable for agricultural application on a field, as well as, landscape scale.The project will deploy two new sensors (one static, one mobile) within China that measures soil moisture content as a function of the albedo of cosmically generated fast neutrons (Cosmos sensor, designed by Hydroinova, US). The static sensor measures soil moisture within a field up to a 200m radius from the measurement point. A mesh of static sensors will be deployed within Henan and Hebei province (which produces 40% wheat of China), China. The mobile sensor will be deployed on a bespoke autonomous vehicle or rover to measure soil moisture variation within a field. The vehicle will be developed within the project and will be the first autonomous deployment of this sensor technology. Data from the soil moisture sensors will be used to calibrate the InSARS sensor on the Sentinel-1 satellite to monitor soil moisture within China to within a 500m x 500m resolution. This is a 5-fold improvement on current resolution from SARS. Ultimately, the technology will enable near real time forecasts of soil moisture at a field scale. This information will be invaluable to agricultural producers and for flood risk forecasting, including key insights to improve water use efficiency, irrigation practices, land drainage and the implementation of precision agricultural techniques. This is an ambitious multi disciplinary project. The project coordinates the expertise of four key groups, the University of Lincoln (robotics, mapping and deployment of autonomous vehicles), the Institute of Ecology and Agrometeorology (IEAM) of Chinese Academy of Meteorological Sciences, University of Information Science &Technology, the Centre for Ecology and Hydrology (Wallingford) and the School of Geography and Earth Sciences, The University of Aberystwth. Considerable focus is placed on knowledge exchange, not just with the agricultural and hydrological communities, but also between international partners within the project. We anticipate that the UK will benefit from understanding the challenges of developing sensor networks in China, with significant differences in scale and environment. The Chinese team will spend considerable periods embedded with the UK academics to learn new skills in remote sensing, sensor deployment and autonomous vehicles.