Estimation of Leaf Area Index Using DEIMOS-1 Data: Application and Transferability of a Semi-Empirical Relationship between two Agricultural Areas

Remote Sensing

Francesco Vuolo¹  Nikolaus Neugebauer¹  Salvatore Falanga Bolognesi²  Clement Atzberger¹ 

[1] Institute of Surveying, Remote Sensing and Land Information (IVFL), University of Natural Resources and Life Sciences (BOKU), Peter Jordan Str. 82, A-1190 Vienna, Austria; E-Mails:;Ariespace s.r.l., Centro Direzionale Is. A3, I-80143 Napoli, Italy; E-Mail:

关键词: LAI;    DEIMOS;    satellite time-series;    calibration;    WDVI;    Random Forest;    Support Vector Machine;    regression;   

DOI  :  10.3390/rs5031274

来源: mdpi

【 摘 要 】

This work evaluates different procedures for the application of a semi-empirical model to derive time-series of Leaf Area Index (LAI) maps in operation frameworks. For demonstration, multi-temporal observations of DEIMOS-1 satellite sensor data were used. The datasets were acquired during the 2012 growing season over two agricultural regions in Southern Italy and Eastern Austria (eight and five multi-temporal acquisitions, respectively). Contemporaneous field estimates of LAI (74 and 55 measurements, respectively) were collected using an indirect method (LAI-2000) over a range of LAI values and crop types. The atmospherically corrected reflectance in red and near-infrared spectral bands was used to calculate the Weighted Difference Vegetation Index (WDVI) and to establish a relationship between LAI and WDVI based on the CLAIR model. Bootstrapping approaches were used to validate the models and to calculate the Root Mean Square Error (RMSE) and the coefficient of determination (R²) between measured and predicted LAI, as well as corresponding confidence intervals. The most suitable approach, which at the same time had the minimum requirements for fieldwork, resulted in a RMSE of 0.407 and R² of 0.88 for Italy and a RMSE of 0.86 and R² of 0.64 for the Austrian test site. Considering this procedure, we also evaluated the transferability of the local CLAIR model parameters between the two test sites observing no significant decrease in estimation accuracies. Additionally, we investigated two other statistical methods to estimate LAI based on: (a) Support Vector Machine (SVM) and (b) Random Forest (RF) regressions. Though the accuracy was comparable to the CLAIR model for each test site, we observed severe limitations in the transferability of these statistical methods between test sites with an increase in RMSE up to 24.5% for RF and 38.9% for SVM.

【 授权许可】

CC BY   
© 2013 by the authors; licensee MDPI, Basel, Switzerland.