Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 433493
Title Estimating salinity stress in sugarcane fields with spaceborne hyperspectral vegetation indices
Author(s) Hamzeh, S.; Naseri, A.A.; Alavi Panah, S.K.; Mojaradi, B.; Bartholomeus, H.; Clevers, J.G.P.W.; Behzad, M.
Source International Journal of applied Earth Observation and Geoinformation 21 (2013). - ISSN 0303-2434 - p. 282 - 290.
DOI https://doi.org/10.1016/j.jag.2012.07.002
Department(s) Laboratory of Geo-information Science and Remote Sensing
PE&RC
Publication type Refereed Article in a scientific journal
Publication year 2013
Keyword(s) salt-affected soils - difference water index - spectral reflectance - precision agriculture - chlorophyll content - canopy reflectance - plant-leaves - fresh-water - hyperion - leaf
Abstract The presence of salt in the soil profile negatively affects the growth and development of vegetation. As a result, the spectral reflectance of vegetation canopies varies for different salinity levels. This research was conducted to (1) investigate the capability of satellite-based hyperspectral vegetation indices (VIs) for estimating soil salinity in agricultural fields, (2) evaluate the performance of 21 existing VIs and (3) develop new VIs based on a combination of wavelengths sensitive for multiple stresses and find the best one for estimating soil salinity. For this purpose a Hyperion image of September 2, 2010, and data on soil salinity at 108 locations in sugarcane (Saccharum officina L.) fields were used. Results show that soil salinity could well be estimated by some of these VIs. Indices related to chlorophyll absorption bands or based on a combination of chlorophyll and water absorption bands had the highest correlation with soil salinity. In contrast, indices that are only based on water absorption bands had low to medium correlations, while indices that use only visible bands did not perform well. From the investigated indices the optimized soil-adjusted vegetation index (OSAVI) had the strongest relationship (R2 = 0.69) with soil salinity for the training data, but it did not perform well in the validation phase. The validation procedure showed that the new salinity and water stress indices (SWSI) implemented in this study (SWSI-1, SWSI-2, SWSI-3) and the Vogelmann red edge index yielded the best results for estimating soil salinity for independent fields with root mean square errors of 1.14, 1.15, 1.17 and 1.15 dS/m, respectively. Our results show that soil salinity could be estimated by satellite-based hyperspectral VIs, but validation of obtained models for independent data is essential for selecting the best model.
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