期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2008
卷号：XXXVII-B8
页码：971-978
出版社：Copernicus Publications
摘要：In the agricultural industry, precision farming is the most important issue that attracts lots of intentions. One of the major sources of uncertainties in remote sensing of vegetation parameters is the presence of soil dust on the leaves especially broad leaf plants such as sugarcane. To quantify the errors due to this effect little field work has so far been conducted. Digital photos have been used to quantify these effects. The objective of this research was to supply a standard equation for assessment of the quality of the vegetation indices produced by satellite imageries. A simple way for implementing this task (supplying a standard equation) is to prepare an algorithm quantifying the interaction of light with the plant in the visible and near infrared region of the spectrum using a digital camera. To do this 63 digital photographs were supplied in three campaigns on Sep29, 30 and Oct1, 2006 from Amirkabir and Dea`bal-Khazaie sites south-west of Iran. These photographs conclude 9 different stages of the sugarcane growth. To find an algorithm for determination of plant phonological date in Degree-Date (DD), four methods were deployed. These were Rectangular Method (RM), Maximum Likelihood Method (MLM), Thresholding Method (TM) and Hybrid Method (HM). Thus all photographs have been classified into two classes of soil and vegetation and their relevant percentages were calculated. R 2 value of the best fit to the plot of DD values with respect to the percentage coverage was about 0.987. To test the ability of different methods in prediction of DDs, three photographs with known DDs and coverage percentage were used. Entering these predicted DDs and true values in the Wilcoxon signed-rank test which is a nonparametric test, the degree of significance of each method's predicted values were evaluated. As a result MLM with R 2 of 0.987 and TM with R 2 of 0.989 both with significance of 0.827 were the best methods for estimation of phenological date using broadband digital cameras
关键词：Vegetation; Dust Effects; Digital Camera; Remote Sensing
