Retrieval of Leaf Area Index (LAI) and Soil Water Content (WC) Using Hyperspectral Remote Sensing under Controlled Glass House Conditions for Spring Barley and Sugar Beet

Borzuchowski, Jaromir und Schulz, Karsten (Juli 2010): Retrieval of Leaf Area Index (LAI) and Soil Water Content (WC) Using Hyperspectral Remote Sensing under Controlled Glass House Conditions for Spring Barley and Sugar Beet. In: Remote Sensing, Bd. 2, Nr. 7: S. 1702-1721

Abstract

Leaf area index (LAI) and water content (WC) in the root zone are two major hydro-meteorological parameters that exhibit a dominant control on water, energy and carbon fluxes, and are therefore important for any regional eco-hydrological or climatological study. To investigate the potential for retrieving these parameter from hyperspectral remote sensing, we have investigated plant spectral reflectance (400-2,500 nm, ASD FieldSpec3) for two major agricultural crops (sugar beet and spring barley) in the mid-latitudes, treated under different water and nitrogen (N) conditions in a greenhouse experiment over the growing period of 2008. Along with the spectral response, we have measured soil water content and LAI for 15 intensive measurement campaigns spread over the growing season and could demonstrate a significant response of plant reflectance characteristics to variations in water content and nutrient conditions. Linear and non-linear dimensionality analysis suggests that the full band reflectance information is well represented by the set of 28 vegetation spectral indices (SI) and most of the variance is explained by three to a maximum of eight variables. Investigation of linear dependencies between LAI and soil WC and pre-selected SI's indicate that: (1) linear regression using single SI is not sufficient to describe plant/soil variables over the range of experimental conditions, however, some improvement can be seen knowing crop species beforehand; (2) the improvement is superior when applying multiple linear regression using three explanatory SI's approach. In addition to linear investigations, we applied the non-linear CART (Classification and Regression Trees) technique, which finally did not show the potential for any improvement in the retrieval process.

• Dokumententyp: Zeitschriftenartikel
• Publikationsform: Publisher's Version
• Keywords: hyperspectral remote sensing; spectral index; water stress; soil moisture; LAI; CART
• Fakultät: Geowissenschaften > Department für Geographie
• Themengebiete: 500 Naturwissenschaften und Mathematik > 550 Geowissenschaften, Geologie
• ISSN: 2072-4292
• Ort: POSTFACH, CH-4005 BASEL, SWITZERLAND
• Sprache: Englisch