• Energy Research

In this study, polarimetric Synthetic Aperture Radar (PolSAR) data at X-, C- and L-Bands, acquired by the satellites: TerraSAR-X (2011), Radarsat-2 (2011), ALOS (2010) and ALOS-2 (2016), were used to characterize the tundra land cover of a test site located close to the town of Tuktoyaktuk, NWT, Canada. Using available in situ ground data collected in 2010 and 2012, we investigate PolSAR scattering characteristics of common tundra land cover classes at X-, C- and L-Bands. Several decomposition features of quad-, co-, and cross-polarized data were compared, the correlation between them was investigated, and the class separability offered by their different feature spaces was analyzed. Certain PolSAR features at each wavelength were sensitive to the land cover and exhibited distinct scattering characteristics. Use of shorter wavelength imagery (X and C) was beneficial for the characterization of wetland and tundra vegetation, while L-Band data highlighted differences of the bare ground classes better. The Kennaugh Matrix decomposition applied in this study provided a unified framework to store, process, and analyze all data consistently, and the matrix offered a favorable feature space for class separation. Of all elements of the quad-polarized Kennaugh Matrix, the intensity based elements K0, K1, K2, K3 and K4 were found to be most valuable for class discrimination. These elements contributed to better class separation as indicated by an increase of the separability metrics squared Jefferys Matusita Distance and Transformed Divergence. The increase in separability was up to 57% for Radarsat-2 and up to 18% for ALOS-2 data.

Abstract Vegetation optical depth (VOD), as a microwave-based vegetation index for vegetation water and biomass content, is increasingly used to study the impact of global climate and environmental changes on vegetation. Currently, VOD is mainly retrieved from passive microwave data and few studies focused on VOD retrievals from active microwave data. The Advanced SCATterometer (ASCAT) provides long-term C-band backscatter data at Vertical-Vertical (VV) polarization. In this study, a new ASCAT INRAE Bordeaux (IB) VOD (hereafter, IB VOD), was developed based on the Water Cloud Model (WCM) coupled with the Ulaby linear model for soil backscattering. The main features of IB VOD are that (i) the ERA5-Land soil moisture (SM) dataset was used as an auxiliary SM dataset in the retrievals, (ii) pixel-based soil model parameters were mapped using Random Forest (RF), and (iii) the vegetation model parameter was calibrated for each day. The IB VOD product was retrieved over Africa during 2015–2019, and its performances were evaluated in space and time by comparing with aboveground biomass (AGB), lidar tree height (TH), normalized difference vegetation index (NDVI), enhanced vegetation index (EVI) and leaf area index (LAI). Results were inter-compared with three other VOD products at the same frequency. In terms of spatial correlation with AGB (R = 0.92) and TH (R = 0.89), IB VOD outperforms the other VOD products, suggesting IB VOD has a strong ability to capture spatial patterns of AGB and TH. By comparing all VOD products against NDVI, EVI and LAI, we found that the highest temporal correlation with NDVI (EVI, LAI) was obtained with IB VOD over 29.94% (36.65%, 30.19%) of the study region. Considering all three vegetations indices, highest temporal correlation values with IB VOD could be particularly noted for deciduous broadleaf forests, woody savannas and savannas.

Global and long-term vegetation optical depth (VOD) dataset are very useful to monitor the dynamics of the vegetation features, climate and environmental changes. In this study, the radar-based global ASCAT (Advanced SCATterometer) IB (INRAE-BORDEAUX) VOD was retrieved using a model which was recently calibrated over Africa. In order to assess the performance of IB VOD, the Saatchi biomass and three other VOD datasets (ASCAT V16, AMSR2 LPRM V5 and VODCA LPRM V6) derived from C-band observations were used in the comparison. The preliminary results show that IB VOD has a promising ability to predict biomass $(\mathrm{R}=0.74,\ \text{RMSE} =44.82\ \text{Mg}\ \text{ha}^{-1})$ , which is better than V16 VOD $(\mathrm{R}=0.64,\ \text{RMSE} =51.27\ \text{Mg} \text{ha}^{-1})$ and VODCA VOD $(\mathrm{R}=0.72,\ \text{RMSE} =47.14\ \text{Mg}\ \text{ha}^{-1})$ . Some retrieval issues for IB VOD were found in boreal regions (e.g., Eastern America, Russia). In the future, we will focus on improving our algorithm in those regions, and produce a global and long-term dataset.

The attenuation of microwave emissions through the canopy is quantified by the vegetation optical depth (VOD), which is related to the amount of water, the biomass and the structure of vegetation. To provide microwave-derived plant water estimates, one must account for biomass/structure contributions in order to extract the water component from the VOD. This study uses Aquarius scatterometer data to build an L-band global seasonality of vegetation volume fraction (d), representative of biomass/structure dynamics. The dynamic range of d is adapted for its application in a gravimetric moisture (Mg) retrieval model. Results show that d ranging from 0 to 3.35.10- 4 is needed for modelling physically reasonable Mg values. The global average of d shows consistent spatial patterns across vegetation distributions, and d seasonality is coherent with the phenology of the studied vegetation types. These findings enable the separation of information on vegetation water and biomass/structure inherent within VOD. This work was supported by “la Caixa” Foundation (ID 100010434), under agreement LCF /PR/MIT19/51840001 (MIT -Spain Seed Fund), and by the Spanish Ministry of Science, Innovation and Universities and the European Regional Development Fund (ERDF, EU) through projects ESP2017-89463-C3-3-R, RTI2018-096765-A-100, CAS19/00264 and MDM-2016-0600. Also, the authors are grateful to MIT for supporting this research with the MIT-Germany Seed Fund (D. Entekhabi, T. Jagdhuber). Peer Reviewed

Active microwave measurements have the potential to estimate vegetation optical depth (VOD), an indicator related to vegetation water content and biomass. The Advanced SCATterometer (ASCAT) provides long-term C-band backscatter data at vertical-vertical (VV) polarization from 2007. So far, very few studies have considered retrieving VOD from this active sensor. This study presents a new publicly released global long-term and continuous (2007–2020) C-band VOD dataset retrieved from the ASCAT observations, named the ASCAT INRAE-BORDEAUX or ASCAT IB VOD product. The retrieval algorithm is based on the Water Cloud Model (WCM) including the Ulaby bare soil model. The algorithm takes advantage of a multi-temporal (MT) retrieval method relying on a cost function where constraints to the retrieved parameters are implemented and a reanalysis soil moisture (SM) dataset from ERA5-Land is used as an input. The performance of ASCAT IB VOD was evaluated by inter-comparing it with ASCAT Technische Universität Wien (TUW), the Advanced Microwave Scanning Radiometer 2 (AMSR2), and VOD Climate Archive (VODCA) VOD products (the last two products are estimated from passive microwave observations). Results showed that ASCAT IB VOD presented the highest spatial correlation with aboveground biomass (R ∼ 0.83) and with the Global Ecosystem Dynamics Investigation (GEDI) canopy height (R ∼ 0.84–0.85). In terms of temporal performance, ASCAT IB VOD had the highest correlation R values with leaf area index (LAI) and Normalized Difference Water Index (NDWI) in most parts of the globe from 2013 to 2018. This contrasts with AMSR2 VODs which correlated better with Normalized Difference Vegetation Index (NDVI). The new ASCAT-based VOD product on a global scale highlighted the potential benefit of combining active (namely ASCAT) and passive (namely AMSR2) VOD products for vegetation studies.