25 October 2016 The role of spatial and spectral resolution on the effectiveness of satellite-based vegetation indices
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Abstract
Remote Sensing applications are designed to provide farmers with timely crop monitoring and production information. Such information can be used to identify crop needs or health problems and provide solutions for a better crop management. Vegetation indices (VIs) derived from satellite data have been widely used to assess variations in the physiological state and biophysical properties of vegetation.

In the present study, the experimental area is located near the village Eleftherion of Larissa Prefecture in the Thessaly Plain, and consisted of two adjacent agricultural fields of cotton and corn.

Imagery from WorldView-2 (WV2) satellite platform was obtained from European Space Imaging and Landsat-8 (L8) free of charge data were downloaded from the United States Geological Survey (USGS) archive. The images were selected for a four month span to evaluate continuity with respect to vegetation growth variation.

VIs for each satellite platform data such as the Normalized Difference Vegetation Index (NDVI), the Soil-Adjusted Vegetation Index (SAVI) and the Fraction Photosynthetically Radiation (FPAR) were calculated. The comparison of these VIs produced from the two satellite systems with different spatial and spectral resolution was made for each growth stage of the crops and their results were analyzed in order to examine their correlation.

Utilizing the WV2 new spectral data, several innovative chlorophyll and vegetation indices were created and evaluated so as to reveal their effectiveness in the detection of problematic plant growth areas. The Green Chlorophyll index appeared to be the most efficient index for the delineation of these areas.
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Emmanouil Psomiadis, Emmanouil Psomiadis, Nicholas Dercas, Nicholas Dercas, Nicolas R. Dalezios, Nicolas R. Dalezios, Nikolaos V. Spyropoulos, Nikolaos V. Spyropoulos, } "The role of spatial and spectral resolution on the effectiveness of satellite-based vegetation indices", Proc. SPIE 9998, Remote Sensing for Agriculture, Ecosystems, and Hydrology XVIII, 99981L (25 October 2016); doi: 10.1117/12.2241316; https://doi.org/10.1117/12.2241316
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