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Biomass Assessment of Agricultural Crops Using Multi-temporal Dual-Polarimetric TerraSAR-X Data

Biomass Assessment of Agricultural Crops Using Multi-temporal Dual-Polarimetric TerraSAR-X Data The biomass of three agricultural crops, winter wheat (Triticum aestivum L.), barley (Hordeum vulgare L.), and canola (Brassica napus L.), was studied using multi-temporal dual-polarimetric TerraSAR-X data. The radar backscattering coefficient sigma nought of the two polarization channels HH and VV was extracted from the satellite images. Subsequently, combinations of HH and VV polarizations were calculated (e.g. HH/VV, HH + VV, HH × VV) to establish relationships between SAR data and the fresh and dry biomass of each crop type using multiple stepwise regression. Additionally, the semi-empirical water cloud model (WCM) was used to account for the effect of crop biomass on radar backscatter data. The potential of the Random Forest (RF) machine learning approach was also explored. The split sampling approach (i.e. 70% training and 30% testing) was carried out to validate the stepwise models, WCM and RF. The multiple stepwise regression method using dual-polarimetric data was capable to retrieve the biomass of the three crops, particularly for dry biomass, with R 2 > 0.7, without any external input variable, such as information on the (actual) soil moisture. A comparison of the random forest technique with the WCM reveals that the RF technique remarkably outperformed the WCM in biomass estimation, especially for the fresh biomass. For example, the R 2 > 0.68 for the fresh biomass estimation of different crop types using RF whereas WCM show R 2 < 0.35 only. However, for the dry biomass, the results of both approaches resembled each other. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science Springer Journals

Biomass Assessment of Agricultural Crops Using Multi-temporal Dual-Polarimetric TerraSAR-X Data

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Publisher
Springer Journals
Copyright
Copyright © 2019 by Deutsche Gesellschaft für Photogrammetrie, Fernerkundung und Geoinformation (DGPF) e.V.
Subject
Geography; Remote Sensing/Photogrammetry; Geographical Information Systems/Cartography; Signal,Image and Speech Processing; Computer Imaging, Vision, Pattern Recognition and Graphics; Astronomy, Observations and Techniques; Aerospace Technology and Astronautics
ISSN
2512-2789
eISSN
2512-2819
DOI
10.1007/s41064-019-00076-x
Publisher site
See Article on Publisher Site

Abstract

The biomass of three agricultural crops, winter wheat (Triticum aestivum L.), barley (Hordeum vulgare L.), and canola (Brassica napus L.), was studied using multi-temporal dual-polarimetric TerraSAR-X data. The radar backscattering coefficient sigma nought of the two polarization channels HH and VV was extracted from the satellite images. Subsequently, combinations of HH and VV polarizations were calculated (e.g. HH/VV, HH + VV, HH × VV) to establish relationships between SAR data and the fresh and dry biomass of each crop type using multiple stepwise regression. Additionally, the semi-empirical water cloud model (WCM) was used to account for the effect of crop biomass on radar backscatter data. The potential of the Random Forest (RF) machine learning approach was also explored. The split sampling approach (i.e. 70% training and 30% testing) was carried out to validate the stepwise models, WCM and RF. The multiple stepwise regression method using dual-polarimetric data was capable to retrieve the biomass of the three crops, particularly for dry biomass, with R 2 > 0.7, without any external input variable, such as information on the (actual) soil moisture. A comparison of the random forest technique with the WCM reveals that the RF technique remarkably outperformed the WCM in biomass estimation, especially for the fresh biomass. For example, the R 2 > 0.68 for the fresh biomass estimation of different crop types using RF whereas WCM show R 2 < 0.35 only. However, for the dry biomass, the results of both approaches resembled each other.

Journal

PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation ScienceSpringer Journals

Published: Oct 1, 2019

References