Vegetation presents a key feature in our climate system as it affects both water and carbon cycle. Characterising large-scale vegetation dynamics can contribute to understanding complex interactions between plants and their abiotic resources. Monitoring vegetation using microwave satellite observations provides complementary information to the commonly used optical data due to the higher penetration depth of microwave radiation and their sensitivity to the vegetation water content. Therefore, microwave Vegetation Optical Depth (VOD) can be used to analyse water related processes governing vegetation growth.
VOD is available from active and passive sensors and from different frequencies in the microwave domain, for vegetation monitoring most notably L-, C-, X-, and Ku-band. The sensitivity of VOD to the vegetation cover varies with frequency and sensor type. Thus, a comparison of multiple VOD datasets with respect to the targeted vegetation properties is necessary to make full use of microwave satellite observations for describing large-scale ecosystem dynamics. Recently CLIMERS estimated the Gross Primary Production (GPP) using VOD data, and created the VOD Climate Archive (VODCA), a global VOD dataset spanning the last 30 years that is openly accessible on Zenodo.
Atmospheric CO2 is the main cause of the enhanced greenhouse effect. It is thus crucial to quantify the amount of carbon emissions taken up by the land to improve climate predictions. In this project we aim to constrain model-based predictions of carbon emissions with field observations and satellite data.
Read more on the Carbon Constellation page
CONSOLIDATION will, for the first time, combine multiple satellite-based surface soil moisture and VOD products with land surface model simulations via data assimilation. This way we will obtain long-term, consistent, gap-free and high-resolution estimates of global soil and vegetation water dynamics.
Read more on the CONSOLIDATION page
Global warming is expected to amplify the global water cycle, which will lead to an increase the frequency and intensity of storms, floods, and droughts. The negative impact of droughts on vegetation will impact future food security and reduce the efficiency of vegetation as a sink of atmospheric carbon-dioxide, thus, in turn, further exacerbating global warming. The goal of of EOWAVE is to reduce uncertainties in our knowledge on how soil moisture drives vegetation growth over space and time.
Read more on the EOWAVE page
Crop yield forecasting is a vital tool to support stakeholders and decision-makers in preparing for potential yield deficiencies. The goal of the YIPEEO project is to improve field-scale crop yield forecasts by using these datasets in combination with novel machine-learning techniques or crop growth models.
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LiDAR is one of the most important earth observation technologies for forest monitoring and management as it provides 3D information not only on the canopy but also the internal forest structure. e objective of the SBL-S1-PR project was to assess the feasibility of space borne LiDAR measurements for Alpine forest management.
Read more on the SBL-S1-PR page