Open AccessThis article is
- freely available
Decomposing Dual Scale Soil Surface Roughness for Microwave Remote Sensing Applications
Department of Geography, Ludwig-Maximilians University, Luisenstrasse 37, D-80333 Munich, Germany
* Author to whom correspondence should be addressed.
Received: 15 May 2012; in revised form: 28 June 2012 / Accepted: 30 June 2012 / Published: 6 July 2012
Abstract: Soil surface roughness, as investigated in this study, is decomposed in a dual scale process. Therefore, we investigated photogrammetrically acquired roughness information over different agricultural fields in the size of 6–22 m2 and decomposed them into a dual scale process by using geostatistical techniques. For the characterization of soil surface roughness, we calculated two different roughness indices (the RMS height s and the autocorrelation length l) differing significantly for each scale. While we could relate the small scale roughness pattern clearly to the seedbed rows, the larger second scale pattern could be related to the appearance of wheel tracks of the tillage machine used. As a result, major progress was made in the understanding of the different scales in soil surface roughness characterization and its quantification possibilities.
Keywords: soil surface roughness; photogrammetry; SAR; synthetic aperture radar; detrending; RMS height; autocorrelation
Article StatisticsClick here to load and display the download statistics.
Notes: Multiple requests from the same IP address are counted as one view.
Cite This Article
MDPI and ACS Style
Marzahn, P.; Seidel, M.; Ludwig, R. Decomposing Dual Scale Soil Surface Roughness for Microwave Remote Sensing Applications. Remote Sens. 2012, 4, 2016-2032.
Marzahn P, Seidel M, Ludwig R. Decomposing Dual Scale Soil Surface Roughness for Microwave Remote Sensing Applications. Remote Sensing. 2012; 4(7):2016-2032.
Marzahn, Philip; Seidel, Moritz; Ludwig, Ralf. 2012. "Decomposing Dual Scale Soil Surface Roughness for Microwave Remote Sensing Applications." Remote Sens. 4, no. 7: 2016-2032.