Estimation of Soil Moisture in Vegetation-Covered Floodplains with Sentinel-1 SAR Data Using Support Vector Regression

  • Ann-Kathrin Holtgrave
  • Michael Förster
  • Felix Greifeneder
  • Claudia Notarnicola
  • Birgit Kleinschmit
Original Article
  • 32 Downloads

Abstract

Soil moisture (SM) is a significant parameter influencing various environmental processes in hydrology, ecology, and climatology. SAR-derived remote sensing products can be valuable input features for estimating SM. In the past, the results often lacked a sufficient spatial resolution for a local application. With the new Sentinel-1 sensor it seems possible to derive more detailed SM-maps. Therefore, we utilized this sensor to test the applicability of a support vector regression (SVR) based method for SM retrieval for two different grassland-covered floodplains in north-east Germany. The model was then tested for its transferability. Moreover, it was operating exclusively with free and publicly available data. In situ data of volumetric SM were collected in 2015 for both study areas at the Elbe and Peene rivers. Remote sensing input data were VV and VH backscatter, and local incidence angle derived from Sentinel-1 images, as well as height, slope, and aspect derived from SRTM images. Additionally, the Landsat 8 NDVI product was included to compensate vegetation influences on SAR backscatter. Overall, the SVR is capable of estimating SM reasonably accurate (RMSE 9.7 and 13.8 Vol% for the individual study sites). Nevertheless, the performance highly depends on the in situ data—particularly, on the amount of samples (here 98 Peene and 71 Elbe) and the value range (10–100 Vol% Peene and 12–87 Vol% Elbe). Furthermore, the optimal input feature combination and the best performance differed between study sites. In summary, backscatter, elevation, and NDVI were the most important features for SM prediction. Models using only Radar-derived features where only 1.05 or 2.01 Vol% worse than models including optical data and are, therefore, able to estimate SM. Combining samples from both study sites slightly impaired the model. Due to varying site-conditions in terms of humidity and vegetation cover, the transferability of the SVR models is not possible for the studied sites.

Keywords

Soil moisture SVR SAR Sentinel-1 Floodplains Vegetation 

Zusammenfassung

Bodenfeuchtebestimmung in vegetationsbedeckten Flussauen mit Sentinel-1 SAR-Daten mittels Support-Vektor-Regression. Bodenfeuchte (BF) ist ein wichtiger Parameter, der verschiedene Umweltprozesse in der Hydrologie, Ökologie und Klimatologie beeinflusst. SAR-basierte Fernerkundungsprodukte können wertvolle Daten für die Abschätzung von BF sein. In der Vergangenheit fehlte oft eine ausreichende räumliche Auflösung für eine lokale Anwendung. Mit dem neuen Sentinel-1 Sensor ist es möglich, detailliertere BF-Karten abzuleiten. Daher haben wir Sentinel-1 verwendet, um die Anwendbarkeit der Support Vector Regression (SVR) Methode für die BF Abschätzung für zwei verschiedene Grasland-Überschwemmungsgebiete in Nordostdeutschland zu testen. Anschließend wurde das Modell auf seine Übertragbarkeit getestet. Darüber hinaus wurde ausschließlich mit freien und öffentlich zugänglichen Daten gearbeitet. In-situ-Daten des volumetrischen BF wurden im Jahr 2015 für die Untersuchungsgebiete an den Flüssen Elbe und Peene erhoben. Die Fernerkundungs-Eingabedaten waren VV- und VH-Rückstreuung und der lokale Einfallswinkel aus Sentinel-1-Bildern sowie Höhe, Neigung und Exposition ausSRTM-Bildern. Zusätzlich wurden Landsat 8 NDVI Produkte verwendet, um Vegetationseinflüsse auf die SAR-Rückstreuungen zu kompensieren. Insgesamt ist die SVR in der Lage, BF relativ genau zu schätzen (RMSE 9.7 und 13.8 Vol% für die einzelnen Untersuchungsgebiete). Dennoch hängt die Performance stark von den in-situ Daten ab – insbesondere von der Anzahl der Proben (hier 98 Peene bzw. 71 Elbe) und dem Wertebereich (10 - 100 Vol% Peene bzw. 12 - 87 Vol% Elbe). Darüber hinaus unterschieden sich die optimale Kombination von Eingabemerkmalen und die beste Leistung zwischen den einzelnen Untersuchungsgebieten. Zusammengefasst waren Rückstreuung, Höhe und NDVI die wichtigsten Merkmale für die BF-Vorhersage. Modelle, die ausschließlich von Radar abgeleitete Merkmale verwenden, sind nur um 1,05 bzw. 2,01 Vol% schlechter als Modelle mit optischen Daten und können daher BF bestimmen. Die Kombination von Daten aus beiden Untersuchungsgebieten führte zu einer leichten Beeinträchtigung des Modells. Die Übertragbarkeit der SVR-Modelle ist für die untersuchten Standorte aufgrund unterschiedlicher Standortbedingungen in Bezug auf Bodenfeuchtigkeit und Vegetationsdeckung nicht möglich.

Notes

Acknowledgements

This work has been prepared in the framework of the Indicator-based soil moisture monitoring of river flood plains synergistically utilizing Sentinel and other remote sensing data (InBoMo) project. This is a joint research project of the planning office Luftbild Umwelt Planung (LUP), Technical University Berlin, German Research Centre for Geosciences (GFZ) and others.

Supplementary material

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Supplementary material 1 (pdf 70 KB)

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Copyright information

© Deutsche Gesellschaft für Photogrammetrie, Fernerkundung und Geoinformation (DGPF) e.V. 2018

Authors and Affiliations

  1. 1.Geoinformation in Environmental Planning LaboratoryTechnical University of BerlinBerlinGermany
  2. 2.Institute for Applied Remote SensingEurac ResearchBolzanoItaly

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