• Energy Research

In 2017, the new SMOS-IC retrieval product of soil moisture (SM) and L-band Vegetation Optical depth (L-VOD) was developed. This product relies on a two-parameter inversion of the L-MEB model (L-band Microwave Emission of the Biosphere) which requires little ancillary information and was found to be accurate, making it very well-suited for application in agriculture, hydrology, climate and vegetation monitoring. In this communication we present recent improvements in the SMOS-IC retrieval algorithm and recent applications using the soil moisture or VOD retrievals from the SMOS-IC data set. SMOS-IC SM is available at the French CATDS center.

Abstract Passive microwave remote sensing observations at L-band provide key and global information on surface soil moisture and vegetation water content, which are related to the Earth water and carbon cycles. Only two space-borne L-band sensors are currently operating: SMOS, launched end of 2009 and thus providing now a 10-year global data set and SMAP, launched beginning of 2015. This study provides a state-of-the-art scientific overview of the SMOS-IC retrieval data set based on the SMOS L-band observations. This SMOS product aims at improved performance and independence of auxiliary data, key features for robust applications. The SMOS-IC product includes both a soil moisture (SM) and a L-band vegetation optical depth (L-VOD) data set which are currently at the basis of several studies evaluating the impact of climate and anthropogenic activities on aboveground carbon stocks. Since the release of the first version, the algorithm has been significantly changed in support to key applications, but no document is available to report these changes. This paper fills this gap by analyzing key science questions related to the product development, reviewing application results and presenting an extensive description of the last version of the product (version 2) considering changes in comparison to the previous version (V105). For the future it is planned to merge the SMOS and SMAP L-VOD data sets to ensure L-VOD data continuity in the event of failure of one of the space-borne SMOS or SMAP sensors.

This article outlines the major scientific objectives of the SHui project that seeks to optimize soil and water use in agricultural systems in the EU and China, by considering major current scientific challenges in this area. SHui (for Soil Hydrology research platform underpinning innovation to manage water scarcity in European and Chinese cropping systems) is large cooperative project that aims to provide significant advances through transdisciplinary research at multiple scales (plot, field, catchment and region). This paper explains our research platform of long-term experiments established at plot scale, approaches taken to integrate crop and hydrological models at field scale; coupled crop models and satellite-based observations at regional scales; decision support systems for specific farming situations; and the integration of these technologies to provide policy recommendations through socio-economic analysis of the impact of soil and water saving technologies. It also outlines the training of stakeholders to develop a basic common curriculum despite the subject being distributed across different disciplines and professions. As such, this article provides a review of major challenges for improving soil and water use in EU and China as well as information about the potential to access information made available by SHui, and to allow others to engage with the project. This work has been supported by Project SHui which is co-funded by the European Union Project GA 773903 and the Chinese MOST. This work has been supported by P12-AGR-0931 (Andalusian Government), RTA2014-00063- C04-03 (Spanish government), SHui (European Commission Grant Agreement number: 773903) and EU‒FEDER funds Peer reviewed

In preparation for active microwave-based data assimilation into a crop modeling system, the mapping of daily 1-km AquaCrop model (v6.1) biomass and surface soil moisture to backscatter was optimised, using two forward operators, i.e. the Water Cloud Model (WCM) and the Support Vector Regression (SVR). Both forward operators were calibrated (2014–2018) with 1-km Sentinel-1 backscatter ( ) observations in VV and VH polarisation, for three different study domains in Europe. For the validation period (2019–2021), the simulations showed reasonable performances around Czech Republic and the Iberian Peninsula, to good performances over Belgium, but with strong variations within each domain. The domain-averaged root mean square difference between the model and Sentinel-1 remained below 2 dB for both forward operators and all three study domains, and the mean bias for VV remained close to 0 dB, and close 0.5 dB for the VH polarisation. The WCM and SVR performed better in VV than VH and overall the SVR performed slightly better in mapping the AquaCrop soil moisture and vegetation to backscatter than the WCM. Additionally, the assumed linear relationship in the WCM between soil moisture and soil holds better for VV than for VH. The remaining differences between WCM or SVR simulations and Sentinel-1 observations are mainly caused by AquaCrop model errors.

This repository contains the setup and data related to the peer-reviewed article "Net irrigation requirement under different climate scenarios using AquaCrop over Europe" accepted for HESS (https://hess.copernicus.org/preprints/hess-2021-631/). The README.txt file contains all information about the repository. Please contact Louise Busschaert (louise.busschaert@kuleuven.be) or Gabrielle De Lannoy (gabrielle.delannoy@kuleuven.be) for any further questions.