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Developing a machine learning framework for estimating soil moisture with VNIR hyperspectral data

Keller, Sina; Riese, Felix M.; Stötzer, Johanna; Maier, Philipp M.; Hinz, Stefan

In this paper, we investigate the potential of estimating the soil-moisture content based on VNIR hyperspectral data combined with IR data. Measurements from a multi-sensor field campaign represent the benchmark dataset which contains measured hyperspectral, IR, and soil-moisture data. We introduce a regression framework with three steps consisting of feature selection, preprocessing, and well-chosen regression models. The latter are mainly supervised machine learning models. An exception are the self-organizing maps which are a combination of unsupervised and supervised learning. We analyze the impact of the distinct preprocessing methods on the regression results. Of all regression models, the extremely randomized trees model without preprocessing provides the best estimation performance. Our results reveal the potential of the respective regression framework combined with the VNIR hyperspectral data to estimate soil moisture. In conclusion, the results of this paper provide a basis for further improvements in different research directions.

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Verlagsausgabe §
DOI: 10.5445/IR/1000082405
Veröffentlicht am 02.10.2018
DOI: 10.5194/isprs-annals-IV-1-101-2018
Zitationen: 4
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Photogrammetrie und Fernerkundung (IPF)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2018
Sprache Englisch
Identifikator urn:nbn:de:swb:90-824051
KITopen-ID: 1000082405
Erschienen in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences - Symposium “Innovative Sensing – From Sensors to Methods and Applications”, Karlsruhe, Germany, 10–12 October 2018. Volume: IV-1
Verlag ISPRS
Seiten 101-108
Nachgewiesen in Scopus
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