time series

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 …

Time-Frequency Causal Inference Uncovers Anomalous Events in Environmental Systems

Causal inference in dynamical systems is a challenge for different research areas. So far it is mostly about understanding to what extent the underlying causal mechanisms can be derived from observed time series. Here we investigate whether anomalous …

A National Assessment of Wetland Status and Trends for Canada’s Forested Ecosystems Using 33 Years of Earth Observation Satellite Data

Wetlands are important globally for supplying clean water and unique habitat, and for storing vast amounts of carbon and nutrients. The geographic extent and state of wetlands vary over time and represent a dynamic land condition rather than a …

An Image Transform Based on Temporal Decomposition

Today, very dense synthetic aperture radar (SAR) time series are available through the framework of the European Copernicus Programme. These time series require innovative processing and preprocessing approaches including novel speckle suppression …

Detecting the Spread of Invasive Species in Central Chile with a Sentinel-2 Time-Series

The presented work evaluates the potential of a Sentinel-2 time-series to detect Pinus radiata (Monterey Pine) invasions in endemic Nothofagus (Southern Beeches) forests in the Maule region, central Chile. Suitable cloud free images of the …

Lake Water Footprint Identification from Time-Series ICESat/GLAS Data

To provide high-quality data for time-series change detection of lake water level, an automatic and robust algorithm for lake water footprint (LWF) identification is developed. Based on the Ice, Cloud, and Land Elevation Satellite GLA14 data file, …

State-Dependent Errors in a Land Surface Model across Biomes Inferred from Eddy Covariance Observations on Multiple Timescales

Characterization of state-dependent model biases in land surface models can highlight model deficiencies, and provide new insights into model development. In this study, artificial neural networks (ANNs) are used to estimate the state-dependent …

Deciphering the Components of Regional Net Ecosystem Fluxes Following a Bottom-up Approach for the Iberian Peninsula

Quantification of ecosystem carbon pools is a fundamental requirement for estimating carbon fluxes and for addressing the dynamics and responses of the terrestrial carbon cycle to environmental drivers. The initial estimates of carbon pools in …

Characterizing Ecosystem-Atmosphere Interactions from Short to Interannual Time Scales

Characterizing ecosystem-atmosphere interactions in terms of carbon and water exchange on different time scales is considered a major challenge in terrestrial biogeochemical cycle research. The respective time series currently comprise an observation …