Estimating salinity stress in sugarcane fields with spaceborne hyperspectral vegetation indices |
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| Affiliation: | 1. College of Resources and Environment Science, Xinjiang University, Urumqi 830046, China;2. Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830046, China;3. Key Laboratory of Xinjiang Wisdom City and Environment Modeling, Urumqi 830046, China;4. Department of Earth Sciences, the University of Memphis, Memphis, TN 38152, USA;5. Institute of Geographic Sciences and Natural Resources Research, University of Chinese Academy of Sciences, Beijing 100101, China;6. Department of Physical and Environmental Sciences, Colorado Mesa University Grand Junction, CO 81501, USA;1. School of Geography, South China Normal University, Guangzhou, Guangdong 510631, China;2. College of the Environment & Ecology, Xiamen University, South Xiangan Road, Xiangan District, Xiamen, Fujian 361102, China;3. Center for Urban and Environmental Change, Department of Earth and Environmental Systems, Indiana State University, Terre Haute, IN 47809, USA;4. Department of Remote Sensing and GIS, University of Tehran, Tehran, Iran |
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| 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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| Keywords: | Salinity stress Vegetation indices Hyperion Hyperspectral Sugarcane |
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