Remote Sensing for Agriculture using UAV and Satellite data with Machine Learning

农业在发达国家和发展中国家的经济中都发挥着主导作用，是加拿大的一个重要部门。精准农业旨在优化田间规模的投入，如化肥、水和农药，以提高作物的质量和产量，降低农民的成本，并最大限度地减少对环境的负面影响。现代遥感传感器技术，如无人驾驶航空器上的传感器，用于极高空间分辨率的田间变量检索和测绘，以及卫星用于相对高空间分辨率的大面积测绘，使精准农业成为可能。然而，在将遥感用于农业时，仍存在一些尚未解决的问题和挑战。例如，光学卫星图像受到云层覆盖的影响。在世界某些地区，作物生长期间有持续的云层覆盖，因此很难或不可能获得多时相光学卫星图像。在作物类型分类中，目前还不能利用卫星遥感对间作类型进行分类。利用合成孔径雷达（SAR）卫星数据反演生物物理参数，仍不能满足时间序列和真实的时间序列的单模SAR作物监测要求。大多数可用的无人机传感器都是多光谱传感器，只有几个宽光谱波段，并且只覆盖一部分光谱范围，它们提取作物生化变量的能力有限。为了解决这些问题和挑战，在本提案中，（1）我们将开发新方法，通过使用深度学习算法融合多时相和多源（光学和雷达）卫星数据，进行准确和详细的作物/间作类型分类。(2)我们将开发新的方法，利用多时相和多模式SAR卫星数据，通过机器/深度学习，准确反演作物高度和土壤湿度的时间序列。(3)我们将使用基于无人机的高光谱和光探测与测距（LiDAR）数据以及深度学习来检索田间植物生化和生物物理变量，并进行生物量和产量估计。在此赠款资助期间，将培训6名博士，5名硕士，5名本科生和其他博士后。改进后的作物分类算法和生物物理、生化参数的反演算法不仅将为遥感图像分析、作物和土壤信息遥感反演等研究领域做出贡献，而且将为作物监测和精准农业等领域做出贡献。上述方法将用于作物模型、作物监测和产量预测，并将为可持续和智能农业提供急需的信息。该研究将使加拿大在粮食安全、可持续农业、改善作物管理、适应全球气候变化和提高加拿大人的生活质量方面受益。

Agriculture plays a dominant role in the economies of both developed and developing countries and is an important sector in Canada. Precision agriculture aims to optimize inputs at field-scale such as fertilizer, water, and pesticides to improve quality and yield of crops, reduce costs to farmers, and minimize negative environmental impacts. Precision agriculture is enabled by the modern remote sensing sensor technology, such as sensors on Unmanned Aerial Vehicles (UAVs) for very high-spatial-resolution intra-field variable retrieval and mapping, and satellites for relatively high-spatial-resolution large area mapping. However, when using remote sensing for agriculture, there are unresolved issues and challenges. For example, optical satellite images are affected by cloud cover. In some parts of the world, there is constant cloud cover during the crop growing period, making it difficult or impossible to obtain multi-temporal optical satellite images. In crop type classification, to our knowledge, the intercrop (planting more than one crop together) types could not be classified yet using satellite remote sensing. The retrieval of biophysical parameters using Synthetic Aperture Radar (SAR) satellite data still could not meet the requirements of time series and real time crop monitoring using single mode SAR. Most available UAV sensors are multispectral sensors with a few broad spectral bands, and only cover a portion of spectral range, and their ability to extract crop biochemical variables is limited. To solve these issues and challenges, in this proposal, (1) we will develop new methods for accurate and detailed crop/intercrop type classification from the fusion of multi-temporal and multi-source (optical and radar) satellite data using deep learning algorithms. (2) We will develop new methods for accurate retrieval of time series crop height and soil moisture using multi-temporal and multi-mode SAR satellite data with machine/deep learning. (3) We will use the UAV-based hyperspectral and Light Detection and Ranging (LiDAR) data with deep learning to retrieve intra-field plant biochemical and biophysical variables, and for biomass and yield estimation. During this grant funding period, six PhDs, five Master's, five undergraduate students and additional post-doctors will be trained. The new and improved algorithms for crop classification and retrieval of biophysical and biochemical variables will contribute not only to the research fields of remote sensing image analysis, plant and soil information retrieval from remote sensing, but also to crop monitoring and precision agriculture. The methods proposed above will be used for crop models, crop monitoring and yield prediction and will provide much needed information for sustainable and smart agriculture. The research will benefit Canada in terms of food security, sustainable agriculture, improving crop management, adapting to global climate change and improving the quality of life of Canadians.