Hyperspectral benchmark dataset on soil moisture
Riese, Felix M.; Keller, Sina
Abstract (englisch):
Hyperspectral and soil-moisture data from a field campaign based on a soil sample. Karlsruhe (Germany), 2017.
This dataset was measured in a five-day field campaign in May 2017 in Karlsruhe, Germany. An undisturbed soil sample is the centerpiece of the measurement setup. The soil sample consists of bare soil without any vegetation and was taken in the area near Waldbronn, Germany.
Introducing paper: Felix M. Riese and Sina Keller, “Introducing a Framework of Self-Organizing Maps for Regression of Soil Moisture with Hyperspectral Data,” in 2018 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Valencia, Spain, 2018, accepted.
The following sensors were deployed:
- Cubert UHD 285 hyperspectral snapshot camera recording 50 by 50 images with 125 spectral bands ranging from 450 nm to 950 nm and a spectral resolution of 4 nm.
- TRIME-PICO time-domain reflectometry (TDR) sensor in a depth of 2 cm measuring the soil moisture in percent.
This dataset was measured in a five-day field campaign in May 2017 in Karlsruhe, Germany. An undisturbed soil sample is the centerpiece of the measurement setup. The soil sample consists of bare soil without any vegetation and was taken in the area near Waldbronn, Germany.
Introducing paper: Felix M. Riese and Sina Keller, “Introducing a Framework of Self-Organizing Maps for Regression of Soil Moisture with Hyperspectral Data,” in 2018 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Valencia, Spain, 2018, accepted.
The following sensors were deployed:
- Cubert UHD 285 hyperspectral snapshot camera recording 50 by 50 images with 125 spectral bands ranging from 450 nm to 950 nm and a spectral resolution of 4 nm.
- TRIME-PICO time-domain reflectometry (TDR) sensor in a depth of 2 cm measuring the soil moisture in percent.
| Zugehörige Institution(en) am KIT | Institut für Photogrammetrie und Fernerkundung (IPF) KIT-Zentrum Klima und Umwelt (ZKU) |
| Publikationstyp | Forschungsdaten |
| Publikationsdatum | 24.04.2018 |
| Erstellungsdatum | 16.05.2017 - 26.05.2017 |
| Sprache | Englisch |
| Identifikator | KITopen-ID: 1000084164 |
| Projektinformation | FOR 1598 CAOS; TP B (DFG, DFG KOORD, HI 1289/6-2) |
| Schlagwörter | hyperspectral, dataset |
| Liesmich | Hyperspectral and soil-moisture data from a field campaign based on a soil sample. Karlsruhe (Germany), 2017. This dataset was measured in a five-day field campaign in May 2017 in Karlsruhe, Germany. An undisturbed soil sample is the centerpiece of the measurement setup. The soil sample consists of bare soil without any vegetation and was taken in the area near Waldbronn, Germany. Introducing paper: Felix M. Riese and Sina Keller, “Introducing a Framework of Self-Organizing Maps for Regression of Soil Moisture with Hyperspectral Data,” in 2018 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Valencia, Spain, 2018, accepted. The following sensors were deployed:
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