"Machine learning"

Spatially Autocorrelated Training and Validation Samples Inflate Performance Assessment of Convolutional Neural Networks

Deep learning and particularly Convolutional Neural Networks (CNN) in concert with remote sensing are becoming standard analytical tools in the geosciences. A series of studies has presented the seemingly outstanding performance of CNN for predictive …

Discovering Differential Equations from Earth Observation Data

Modeling and understanding the Earth system is a constant and challenging scientific endeavour. When a clear mechanistic model is unavailable, complex or uncertain, learning from data can be an alternative. While machine learning has provided …

Detection of Xylella Fastidiosa Infection Symptoms with Airborne Multispectral and Thermal Imagery: Assessing Bandset Reduction Performance from Hyperspectral Analysis

Xylella fastidiosa (Xf) is a harmful plant pathogenic bacterium, able to infect over 500 plant species worldwide. Successful eradication and containment strategies for harmful pathogens require large-scale monitoring techniques for the detection of …

Predicting Forest Cover in Distinct Ecosystems: The Potential of Multi-Source Sentinel-1 and -2 Data Fusion

The fusion of microwave and optical data sets is expected to provide great potential for the derivation of forest cover around the globe. As Sentinel-1 and Sentinel-2 are now both operating in twin mode, they can provide an unprecedented data source …

A high-performance and in-season classification system of field-level crop types using time-series Landsat data and a machine learning approach

Accurate and timely spatial classification of crop types based on remote sensing data is important for both scientific and practical purposes. Spatially explicit crop-type information can be used to estimate crop areas for a variety of monitoring and …

Reverse Engineering Model Structures for Soil and Ecosystem Respiration: The Potential of Gene Expression Programming

Accurate model representation of land- atmosphere carbon fluxes is essential for climate projections. However, the exact responses of carbon cycle processes to climatic drivers often remain uncertain. Presently, knowledge derived from experiments, …

Comparison of Radiative Transfer Model Inversions to Estimate Vegetation Physiological Status Based on Hyperspectral Data

This study compares the performance of radiative transfer model inversion techniques to estimate leaf chlorophyll content (LCC) from summer barley based on hyperspectral data. The PROSAIL model was used to simulate vegetation reflectances. Model …