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Open AccessArticle

Annual Gross Primary Production from Vegetation Indices: A Theoretically Sound Approach

Environmental Remote Sensing Group (UV–ERS), Departament Física de la Terra i Termodinàmica, Facultat de Física, Universitat de València, Dr. Moliner 50, 46100 Burjassot, Spain
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Academic Editors: Lenio Soares Galvao and Prasad S. Thenkabail
Remote Sens. 2017, 9(3), 193; https://doi.org/10.3390/rs9030193
Received: 29 November 2016 / Accepted: 16 February 2017 / Published: 23 February 2017
A linear relationship between the annual gross primary production (GPP) and a PAR-weighted vegetation index is theoretically derived from the Monteith equation. A semi-empirical model is then proposed to estimate the annual GPP from commonly available vegetation indices images and a representative PAR, which does not require actual meteorological data. A cross validation procedure is used to calibrate and validate the model predictions against reference data. As the calibration/validation process depends on the reference GPP product, the higher the quality of the reference GPP, the better the performance of the semi-empirical model. The annual GPP has been estimated at 1-km scale from MODIS NDVI and EVI images for eight years. Two reference data sets have been used: an optimized GPP product for the study area previously obtained and the MOD17A3 product. Different statistics show a good agreement between the estimates and the reference GPP data, with correlation coefficient around 0.9 and relative RMSE around 20%. The annual GPP is overestimated in semiarid areas and slightly underestimated in dense forest areas. With the above limitations, the model provides an excellent compromise between simplicity and accuracy for the calculation of long time series of annual GPP. View Full-Text
Keywords: annual GPP; MODIS; NDVI; EVI; PAR; Monteith model annual GPP; MODIS; NDVI; EVI; PAR; Monteith model
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MDPI and ACS Style

Gilabert, M.A.; Sánchez-Ruiz, S.; Moreno, Á. Annual Gross Primary Production from Vegetation Indices: A Theoretically Sound Approach. Remote Sens. 2017, 9, 193.

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