Related papers: MIS-ME: A Multi-modal Framework for Soil Moisture …
Accurate soil moisture prediction during extreme events remains a critical challenge for earth system modeling, with profound implications for drought monitoring, flood forecasting, and climate adaptation strategies. While land surface…
In this study, a novel machine learning algorithm is presented for disaggregation of satellite soil moisture (SM) based on self-regularized regressive models (SRRM) using high-resolution correlated information from auxiliary sources. It…
Radiometric accuracy of data is crucial in quantitative precision agriculture, to produce reliable and repeatable data for modeling and decision making. The effect of exposure time and gain settings on the radiometric accuracy of…
Climate change has increased the severity and frequency of weather disasters all around the world. Flood inundation mapping based on earth observation data can help in this context, by providing cheap and accurate maps depicting the area…
Accurate assessment of dietary intake requires improved tools to overcome limitations of current methods including user burden and measurement error. Emerging technologies such as image-based approaches using advanced machine learning…
A computational framework integrating optimization algorithms, parallel computing and plant physiology was developed to explore crop ideotype design. The backbone of the framework is a plant physiology model that accurately tracks water use…
Agriculture is increasingly challenged by climate change, soil degradation, and resource depletion, and hence requires advanced data-driven crop classification and recommendation solutions. This work presents an explainable ensemble…
In forest industry, mechanical site preparation by mounding is widely used prior to planting operations. One of the main problems when planning planting operations is the difficulty in estimating the number of mounds present on a planting…
Precipitation forecasting remains a persistent challenge in tropical regions like Vietnam, where complex topography and convective instability often limit the accuracy of Numerical Weather Prediction (NWP) models. While data-driven…
Recent advances in Earth Observation have focused on large-scale foundation models. However, these models are computationally expensive, limiting their accessibility and reuse for downstream tasks. In this work, we investigate compact…
Multi-modal data in Earth Observation (EO) presents a huge opportunity for improving transfer learning capabilities when pre-training deep learning models. Unlike prior work that often overlooks multi-modal EO data, recent methods have…
Analog forecasting has been applied in a variety of fields for predicting future states of complex nonlinear systems that require flexible forecasting methods. Past analog methods have almost exclu- sively been used in an empirical…
Depth estimation, essential for autonomous driving, seeks to interpret the 3D environment surrounding vehicles. The development of radar sensors, known for their cost-efficiency and robustness, has spurred interest in radar-camera…
Developing accurate models of crop stress, phenology and productivity is of paramount importance, given the increasing need of food. Earth observation remote sensing data provides a unique source of information to monitor crops in a…
Rapid progress in the field of machine-learning for weather prediction has led to the emergence of algorithms whose forecasting skill can exceed that of traditional physically based models. This development represents an opportunity to…
Climate change is global, yet its concrete impacts can strongly vary between different locations in the same region. Seasonal weather forecasts currently operate at the mesoscale (> 1 km). For more targeted mitigation and adaptation,…
Habitats integrate the abiotic conditions, vegetation composition and structure that support biodiversity and sustain nature's contributions to people. Most habitats face mounting pressures from human activities, which requires accurate,…
Ground-based whole sky cameras have opened up new opportunities for monitoring the earth's atmosphere. These cameras are an important complement to satellite images by providing geoscientists with cheaper, faster, and more localized data.…
Urban heatwaves, droughts, and land degradation are pressing and growing challenges in the context of climate change. A valuable approach to studying them requires accurate spatio-temporal information on land surface conditions. One of the…
Monitoring the fuel moisture content (FMC) of vegetation is crucial for managing and mitigating the impact of wildland fires. The combination of in situ FMC observations with numerical weather prediction (NWP) models and satellite…