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|Title:||Impact of atmospheric effects on crop yield modelling in Cyprus, using Landsat's satellite imagery and field spectroscopy||Authors:||Papadavid, Giorgos
Hadjimitsis, Diofantos G.
|Keywords:||Atmospheric effects;Crop yield;Field spectroscopy;Vegetation indices||Category:||Other Agricultural Sciences||Field:||Agricultural Sciences||Issue Date:||1-Jan-2015||Publisher:||SPIE||Source:||3rd International Conference on Remote Sensing and Geoinformation of the Environment, RSCy 2015; Paphos; Cyprus; 16 March 2015 through 19 March 2015||metadata.dc.doi:||10.1117/12.2195621||Abstract:||Remote sensing, as the tool for spatially continuous measurements has become a trend for estimating Crop Yield since economically efficient agricultural management is highly dependent on detailed temporal and spatial knowledge of the processes affecting physiological crop development. This paper aims at examining the use of field spectroscopy along with Landsat's satellite imagery in order to test the accuracy of raw satellite data and the impact of atmospheric effects on determining crop yield derived from models using remotely sensed data. The spectroradiometric retrieved Vegetation Indices(VI) of Durum wheat, is directly compared to the corresponding VI of Landsat 7 ETM+ and 8 OLI, sourcing from both atmospherically corrected and not corrected satellite images in order to test the effects of atmosphere upon them. Vegetation Indices are vital in the procedure for estimating Crop Yield since they are used in stochastic or empirical models for describing or predicting crop yield. Leaf Area Index, which is also inferred using VI, is also compared to the real values of LAI that are measured using the SunScan instrument, during the satellite's overpass. Crop Yield is finally determined using the Cyprus Agricultural Research Institute's Crop Yield model for Durum wheat, adapted to satellite data, and is used to examine the impact of atmospheric effects. The results have prevailed that if crop yield models using remote sensing imagery, do not apply atmospheric effects algorithms, then there is statistically significant difference in the prediction from the real yield and hence a significant error regarding the model. The study's goal is to illustrate the need of atmospheric effects removal on remotely sensed data especially for models using satellite images.||URI:||http://ktisis.cut.ac.cy/handle/10488/9508||ISBN:||978-162841700-5||Rights:||© 2015 Copyright SPIE||Type:||Conference Papers|
|Appears in Collections:||Δημοσιεύσεις σε συνέδρια/Conference papers|
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