Surface soil moisture estimate from Sentinel-1 and Sentinel-2 data in agricultural fields in areas of high vulnerability to climate variations: the Marche region (Italy) case study

Surface soil moisture is a key hydrologic state variable that greatly influences the global environment and human society. Its significant decrease in the Mediterranean region, registered since the 1950s, and expected to continue in the next century, threatens soil health and crops. Microwave remote sensing techniques are becoming a key tool for the implementation of climate-smart agriculture, as a means for surface soil moisture retrieval that exploits the correlation between liquid water and the dielectric properties of soil. In this study, a workflow in Google Earth Engine was developed to estimate surface soil moisture in the agricultural fields of the Marche region (Italy) through Synthetic Aperture Radar data. Firstly, agricultural areas were extracted with both Sentinel-2 optical and Sentinel-1 radar satellites, investigating the use of Dual-Polarimetric Entropy-Alpha decomposition's bands to improve the accuracy of radar data classification. The results show that Entropy and Alpha bands improve the kappa index obtained from the radar data only by 4% (K = 0.818), exceeding optical accuracy in urban and water areas. However, they still did not allow to reach the overall optical accuracy (K = 0.927). The best classification results are reached with the total dataset (K = 0.949). Subsequently, Water Cloud and Tu Wien models were implemented on the crop areas using calibration parameters derived from literature, to test if an acceptable accuracy is reached without in situ observation. While the first model’s accuracy was inadequate (RMSD = 12.3), the extraction of surface soil moisture using Tu Wien change detection method was found to have acceptable accuracy (RMSD = 9.4)

Fusion of Heterogeneous Earth Observation Data for the Classification of Local Climate Zones

This paper proposes a novel framework for fusing multi-temporal, multispectral satellite images and OpenStreetMap (OSM) data for the classification of local climate zones (LCZs). Feature stacking is the most commonly-used method of data fusion but does not consider the heterogeneity of multimodal optical images and OSM data, which becomes its main drawback. The proposed framework processes two data sources separately and then combines them at the model level through two fusion models (the landuse fusion model and building fusion model), which aim to fuse optical images with landuse and buildings layers of OSM data, respectively. In addition, a new approach to detecting building incompleteness of OSM data is proposed. The proposed framework was trained and tested using data from the 2017 IEEE GRSS Data Fusion Contest, and further validated on one additional test set containing test samples which are manually labeled in Munich and New York. Experimental results have indicated that compared to the feature stacking-based baseline framework the proposed framework is effective in fusing optical images with OSM data for the classification of LCZs with high generalization capability on a large scale. The classification accuracy of the proposed framework outperforms the baseline framework by more than 6% and 2%, while testing on the test set of 2017 IEEE GRSS Data Fusion Contest and the additional test set, respectively. In addition, the proposed framework is less sensitive to spectral diversities of optical satellite images and thus achieves more stable classification performance than state-of-the art frameworks.Comment: accepted by TGR

Towards a 20m global building map from Sentinel-1 SAR Data

This study introduces a technique for automatically mapping built-up areas using synthetic aperture radar (SAR) backscattering intensity and interferometric multi-temporal coherence generated from Sentinel-1 data in the framework of the Copernicus program. The underlying hypothesis is that, in SAR images, built-up areas exhibit very high backscattering values that are coherent in time. Several particular characteristics of the Sentinel-1 satellite mission are put to good use, such as its high revisit time, the availability of dual-polarized data, and its small orbital tube. The newly developed algorithm is based on an adaptive parametric thresholding that first identifies pixels with high backscattering values in both VV and VH polarimetric channels. The interferometric SAR coherence is then used to reduce false alarms. These are caused by land cover classes (other than buildings) that are characterized by high backscattering values that are not coherent in time (e.g., certain types of vegetated areas). The algorithm was tested on Sentinel-1 Interferometric Wide Swath data from five different test sites located in semiarid and arid regions in the Mediterranean region and Northern Africa. The resulting building maps were compared with the Global Urban Footprint (GUF) derived from the TerraSAR-X mission data and, on average, a 92% agreement was obtained.Peer ReviewedPostprint (published version

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 to build dense spatial and temporal high-resolution time series across a variety of wavelengths. This study investigates (i) the ability of the individual sensors and (ii) their joint potential to delineate forest cover for study sites in two highly varied landscapes located in Germany (temperate dense mixed forests) and South Africa (open savanna woody vegetation and forest plantations). We used multi-temporal Sentinel-1 and single time steps of Sentinel-2 data in combination to derive accurate forest/non-forest (FNF) information via machine-learning classifiers. The forest classification accuracies were 90.9% and 93.2% for South Africa and Thuringia, respectively, estimated while using autocorrelation corrected spatial cross-validation (CV) for the fused data set. Sentinel-1 only classifications provided the lowest overall accuracy of 87.5%, while Sentinel-2 based classifications led to higher accuracies of 91.9%. Sentinel-2 short-wave infrared (SWIR) channels, biophysical parameters (Leaf Area Index (LAI), and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)) and the lower spectrum of the Sentinel-1 synthetic aperture radar (SAR) time series were found to be most distinctive in the detection of forest cover. In contrast to homogenous forests sites, Sentinel-1 time series information improved forest cover predictions in open savanna-like environments with heterogeneous regional features. The presented approach proved to be robust and it displayed the benefit of fusing optical and SAR data at high spatial resolution

Remote sensing for the Spanish forests in the 21st century: a review of advances, needs, and opportunities

[EN] Forest ecosystems provide a host of services and societal benefits, including carbon storage, habitat for fauna, recreation, and provision of wood or non-wood products. In a context of complex demands on forest resources, identifying priorities for biodiversity and carbon budgets require accurate tools with sufficient temporal frequency. Moreover, understanding long term forest dynamics is necessary for sustainable planning and management. Remote sensing (RS) is a powerful means for analysis, synthesis, and report, providing insights and contributing to inform decisions upon forest ecosystems. In this communication we review current applications of RS techniques in Spanish forests, examining possible trends, needs, and opportunities offered by RS in a forestry context. Currently, wall-to-wall optical and LiDAR data are extensively used for a wide range of applications-many times in combination-whilst radar or hyperspectral data are rarely used in the analysis of Spanish forests. Unmanned Aerial Vehicles (UAVs) carrying visible and infrared sensors are gaining ground in acquisition of data locally and at small scale, particularly for health assessments. Forest fire identification and characterization are prevalent applications at the landscape scale, whereas structural assessments are the most widespread analyses carried out at limited extents. Unparalleled opportunities are offered by the availability of diverse RS data like those provided by the European Copernicus programme and recent satellite LiDAR launches, processing capacity, and synergies with other ancillary sources to produce information of our forests. Overall, we live in times of unprecedented opportunities for monitoring forest ecosystems with a growing support from RS technologies.Part of this work was funded by the Spanish Ministry of Science, innovation and University through the project AGL2016-76769-C2-1-R "Influence of natural disturbance regimes and management on forests dynamics. structure and carbon balance (FORESTCHANGE)".Gómez, C.; Alejandro, P.; Hermosilla, T.; Montes, F.; Pascual, C.; Ruiz Fernández, LÁ.; Álvarez-Taboada, F.... (2019). Remote sensing for the Spanish forests in the 21st century: a review of advances, needs, and opportunities. Forest Systems. 28(1):1-33. https://doi.org/10.5424/fs/2019281-14221S133281Ungar S, Pearlman J, Mendenhall J, Reuter D, 2003. Overview of the Earth Observing-1 (EO-1) mission. IEEE T Geosci Remote 41: 1149−1159.Valbuena R, Mauro F, Arjonilla FJ, Manzanera JA, 2011. Comparing Airborne Laser Scanning-Imagery Fusion Methods Based on Geometric Accuracy in Forested Areas. 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SENTINEL-1 DATA TO MAP FLOODED AREAS: THE ROLE OF INSAR COHERENCE AND POLARIMETRIC INFORMATION

Τα SAR δεδομένα παρατήρησης της Γης μπορούν να προσφέρουν χάρτες πλημμυρικής έκτασης και πληροφοριών υψηλής ποιότητας για την καλύτερη εκτίμηση του κινδύνου πλημμύρας κατά συνέπεια το σχεδιασμό, καθώς και για την υποστήριξη των πολιτικών αρχών υπέρ της προστασίας κατά τη φάση έκτακτης ανάγκης. Το πεδίο εφαρμογής του παρόντος εγγράφου είναι να δημιουργήσει χάρτες πλημμυρικής έκτασης από μια σειρά εικόνων SAR της λεκάνης του Έβρου, που αντιπροσωπεύει μια διασυνοριακή κοίτη πλημμυρών. Η μελέτη χρησιμοποιεί χρονολογικές σειρές εικόνων SAR του Copernicus δορυφορικού συστήματος Sentinel-1 που καλύπτει την περίοδο Οκτώβριος 2014-Μάιος 2015. Η μεθοδολογία προσπαθεί να προσδιορίσει την πλημμύρα που συμβαίνει σε τρεις κύριες κατηγορίες κάλυψης γης, όπως είναι οι αστικές περιοχές, γυμνά ή κακώς βλάστηση εδάφους και περιοχές με βλάστηση, εκμεταλλευόμενοι τα εναλλασσόμενη πόλωση SAR κανάλια backscattering, και τη συνάφεια συμβολομετρίας για τον καλύτερο χαρακτηρισμό του τοπίου. Χρησιμοποιώντας εναλλασσόμενη πόλωση SAR δεδομένα παρέχει την ευκαιρία να υπάρχει μια καλύτερη κατανόηση και ερμηνεία της ανίχνευσης πλημμύρας λόγω του διαφορετικού τρόπου που αντιδρά η κάλυψη γης σε διαφορετικές 1731 πολώσεις. Έτσι, με την εφαρμογή της εκτίμησης της συμβολομετρικής συνάφειας μπορούμε να επιτύχουμε ένα καλύτερη καταγραφή και γνώση των πλημμυρισμένων περιοχών, στη πάροδο του χρόνου, στη συγκεκριμένη περιοχή.SAR earth observation data can provide high quality flood maps and information to better assess the flood risk accordingly planning as well as to support civil protection authorities during emergency phase. The scope of this paper is to create flood extent maps from a series of SAR scenes of the Evros basin which represents a transboundary floodplain. The study uses time series SAR images of Sentnel-1 ESA’s Copernicus satellite system covering the period October 2014 to May 2015. The methodology tries to identify the flood that occurs in three main land cover classes, such as urban areas, bare or poorly vegetated soil and vegetated areas, taking advantage of co- and cross-polarized SAR backscattering channels, and the InSAR coherence to better characterize the landscape. Dual-pol SAR data provides the opportunity to have a better understanding and interpretation of flood detection due to way different land cover react to different polarizations. Thus, with the implementation of InSAR coherence estimation we may achieve a better record and knowledge of the flooded areas, over time, in the specific region.