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

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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.