Soil moisture is an important component of precision agriculture as it directly impacts the growth and quality of vegetation. Forecasting soil moisture is essential to schedule the irrigation and optimize the use of water. Physics based soil moisture models need rich features and heavy computation which is not scalable. In recent literature, conventional machine learning models have been applied for this problem. These models are fast and simple, but they often fail to capture the spatio-temporal correlation that soil moisture exhibits over a region. In this work, we propose a novel graph neural network based solution that learns temporal graph structures and forecast soil moisture in an end-to-end framework. Our solution is able to handle the problem of missing ground truth soil moisture which is common in practice. We show the merit of our algorithm on real-world soil moisture data.
Soil moisture is an important variable that determines floods, vegetation health, agriculture productivity, and land surface feedbacks to the atmosphere, etc. Accurately modeling soil moisture has important implications in both weather and climate models. The recently available satellite-based observations give us a unique opportunity to build data-driven models to predict soil moisture instead of using land surface models, but previously there was no uncertainty estimate. We tested Monte Carlo dropout (MCD) with an aleatoric term for our long short-term memory models for this problem, and ask…
DeepQC: A Deep Learning System for Automatic Quality Control of In-situ Soil Moisture Sensor Time Series Data
Poolsville High School（普尔维尔高中）；A. James Clark School of Engineering, University of Maryland（马里兰大学A.詹姆斯·克拉克工程学院）；Precission Sustainable Agriculture（精准可持续农业机构）
IBM Research（IBM研究院）；Sandia National Lab（桑迪亚国家实验室）；Texas A&M（德克萨斯A&M大学）；IBM Corporate Social Responsibility（IBM企业社会责任部）
Climate change, population growth, and water scarcity present unprecedented challenges for agriculture. This project aims to forecast soil moisture using domain knowledge and machine learning for crop management decisions that enable sustainable farming. Traditional methods for predicting hydrological response features require significant computational time and expertise. Recent work has implemented machine learning models as a tool for forecasting hydrological response features, but these models neglect a crucial component of traditional hydrological modeling that spatially close units can ha…
SPADE: A Large Language Model Framework for Soil Moisture Pattern Recognition and Anomaly Detection in Precision Agriculture
organization= H. Milton Stewart School of Industrial；Systems Engineering, Georgia Institute of Technology , city= Atlanta , state= GA , country= USA；organization= Panhandle Research；Extension Center, University of Nebraska-Lincoln , city= Scottsbluff , state= NE , country= USA；organization= Department of Computer Science；Engineering, University of Nebraska-Lincoln , city= Lincoln , state= NE , country= USA；organization= Department of Biological Systems Engineering, University of Nebraska-Lincoln , city= Lincoln , state= NE , country= USA；organization= Institute for Robotics；Intelligent Machines, Georgia Institute of Technology , city= Atlanta , state= GA , country= USA；organization= School of Civil \& Environmental Engineering, Georgia Institute of Technology, Georgia Institute of Technology , city= Atlanta , state= GA , country= USA
Max Planck Institute for Biogeochemistry（马克斯·普朗克生物地球化学研究所）
While soil moisture information is essential for a wide range of hydrologic and climate applications, spatially-continuous soil moisture data is only available from satellite observations or model simulations. Here we present a global, long-term dataset of soil moisture generated from in-situ measurements using machine learning, SoMo.ml. We train a Long Short-Term Memory (LSTM) model to extrapolate daily soil moisture dynamics in space and in time, based on in-situ data collected from more than 1,000 stations across the globe. SoMo.ml provides multi-layer soil moisture data (0-10 cm, 10-30 cm,…
University of Tennessee, Knoxville（田纳西大学诺克斯维尔分校）；University of Delaware, Newark（特拉华大学纽瓦克分校）
The current availability of soil moisture data over large areas comes from satellite remote sensing technologies (i.e., radar-based systems), but these data have coarse resolution and often exhibit large spatial information gaps. Where data are too coarse or sparse for a given need (e.g., precision agriculture), one can leverage machine-learning techniques coupled with other sources of environmental information (e.g., topography) to generate gap-free information and at a finer spatial resolution (i.e., increased granularity). To this end, we develop a spatial inference engine consisting of mod…
Institute of Photogrammetry and Remote Sensing, Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院摄影测量与遥感研究所）
In this paper, we investigate the potential of estimating the soil-moisture content based on VNIR hyperspectral data combined with LWIR data. Measurements from a multi-sensor field campaign represent the benchmark dataset which contains measured hyperspectral, LWIR, and soil-moisture data conducted on grassland site. We introduce a regression framework with three steps consisting of feature selection, preprocessing, and well-chosen regression models. The latter are mainly supervised machine learning models. An exception are the self-organizing maps which combine unsupervised and supervised lea…
The main objective of this study is to combine remote sensing and machine learning to detect soil moisture content. Growing population and food consumption has led to the need to improve agricultural yield and to reduce wastage of natural resources. In this paper, we propose a neural network architecture, based on recent work by the research community, that can make a strong social impact and aid United Nations Sustainable Development Goal of Zero Hunger. The main aims here are to: improve efficiency of water usage; reduce dependence on irrigation; increase overall crop yield; minimise risk of…
Comparative Assessment of Multimodal Earth Observation Data for Soil Moisture Estimation
BEYOND EO Centre, IAASARS, National Observatory of Athens（BEYOND EO中心、IAASARS、雅典国家天文台）；Artificial Intelligence, Wageningen University & Research（人工智能，瓦赫宁根大学与研究所）
Internet of Things-Based Smart Precision Farming in Soilless Agriculture: Opportunities and Challenges for Global Food Security
Chitkara University Institute of Engineering and Technology（吉特卡拉大学工程技术学院）；Free University of Bozen-Bolzano (Unibz)（博尔扎诺自由大学）；DBS Global University（DBS全球大学）；IILM University（IILM大学）；SR University（SR大学）；King Saud University（沙特国王大学）
Agriculture is the essential ingredients to mankind which is a major source of livelihood. Agriculture work in Bangladesh is mostly done in old ways which directly affects our economy. In addition, institutions of agriculture are working with manual data which cannot provide a proper solution for crop selection and yield prediction. This paper shows the best way of crop selection and yield prediction in minimum cost and effort. Artificial Neural Network is considered robust tools for modeling and prediction. This algorithm aims to get better output and prediction, as well as, support vector ma…
An iterative energy-based multimodal transformer for joint retrieval of wheat soil moisture, leaf area index, and plant height from Sentinel-1 and Sentinel-2 time series
Department of Urban and Environmental Policy and Program, Tufts University（Tufts大学城市与环境政策与项目系）；Electrical and Computer Engineering Department, Mississippi State University（密苏里州立大学电气与计算机工程系）；Department of Physics, Indian Institute of Technology (BHU)（印度理工学院（BHU）物理系）；Institute of Environment and Sustainable Development, Banaras Hindu University（巴纳尔斯赫尔大学环境与可持续发展研究所）
Causal discovery with endogenous context variables
German Aerospace Center (DLR)（德国航空航天中心 (DLR)）；Technische Universität Berlin（柏林工业大学）；Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI) Dresden / Leipzig（德累斯顿/莱比锡可扩展数据分析与人工智能中心 (ScaDS.AI)）；Technische Universität Dresden（德累斯顿工业大学）
Progressive Scale Convolutional Network for Spatio-Temporal Downscaling of Soil Moisture: A Case Study Over the Tibetan Plateau
organization= College of Geography；Remote Sensing, Hohai University , city= Nanjing , postcode= 211100 , country= China；organization= School of Geosciences；Info-Physics, Central South University , city= Changsha , postcode= 410100 , state= Hunan , country= China；organization= School of Naval Architecture \& Intelligent Manufacturing, Jiangsu Maritime Institute , city= Nanjing , postcode= 211100 , country= China；organization= Key Laboratory of Soil；Water Processes in Watershed, Hohai University , city= Nanjing , postcode= 211100 , country= China；organization= School of Earth Sciences；Engineering, Hohai University , city= Nanjing , postcode= 211100 , country= China
Estimating properties of a homogeneous bounded soil using machine learning models
Mathematics Research Center, Academy of Athens, Greece（希腊雅典学院数学研究中心）；Department of Natural Resources Development and Agricultural Engineering, Agricultural University of Athens, Greece（希腊雅典农业大学自然资源开发与农业工程系）；Department of Mathematics, National Technical University of Athens, Greece（希腊雅典国家技术大学数学系）
