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Please use this identifier to cite or link to this item: https://dspace.ucuenca.edu.ec/handle/123456789/45906
Title: Gross primary productivity estimation through remote sensing and machine learning techniques in the high Andean Region of Ecuador
Authors: Urgiles Avila, Cindy Carolina
Orellana Alvear, Johanna Marlene
Crespo Sanchez, Patricio Xavier
Carrillo Rojas, Galo Jose
metadata.dc.ucuenca.correspondencia: Urgiles Avila, Cindy Carolina, cindyurgiles1996@gmail.com
Keywords: Gross primary productivity (GPP)
Páramo
Random Forest
Support vector regression
Tropical Andes
metadata.dc.ucuenca.areaconocimientofrascatiamplio: 1. Ciencias Naturales y Exactas
metadata.dc.ucuenca.areaconocimientofrascatidetallado: 1.5.8 Ciencias del Medioambiente
metadata.dc.ucuenca.areaconocimientofrascatiespecifico: 1.5 Ciencias de la Tierra y el Ambiente
metadata.dc.ucuenca.areaconocimientounescoamplio: 05 - Ciencias Físicas, Ciencias Naturales, Matemáticas y Estadísticas
metadata.dc.ucuenca.areaconocimientounescodetallado: 0522 - Medio Ambiente y Vida Silvestre
metadata.dc.ucuenca.areaconocimientounescoespecifico: 052 - Medio Ambiente
Issue Date: 2024
metadata.dc.ucuenca.embargoend: 31-Dec-2090
metadata.dc.ucuenca.volumen: Volumen 69, número 3
metadata.dc.source: International Journal of Biometeorology
metadata.dc.identifier.doi: 10.1007/s00484-024-02832-0
metadata.dc.type: ARTÍCULO
Abstract: 
Accurately estimating gross primary productivity (GPP) is crucial for simulating the carbon cycle and addressing the challenges of climate change. However, estimating GPP is challenging due to the absence of direct measurements at scales larger than the leaf level. To overcome this challenge, researchers have developed indirect methods such as remote sensing and modeling approaches. This study estimated GPP in a humid páramo ecosystem in the Andean Mountains using machine learning models (ML), specifically Random Forest (RF) and Support Vector Regression (SVR), and compared them with traditional models. The study's objective was to analyze the strength and complex nonlinear relationships that govern GPP and to perform an uncertainty analysis for future climate projections. The methodology used to estimate GPP showed that ML-based models outperformed traditional models. The performance of ML models varied significantly among seasons, with the correlation coefficient (R) ranging from 0.24 to 0.86. The RF model performed better in capturing the temporal changes and magnitude of GPP in the less humid season, displaying the highest R (0.86), lowest root mean squared error (0.37 g C*m−2), and percentage bias (-3%). Additionally, the analysis indicates that solar radiation is the primary predictor of GPP in the páramo biome, rather than water. The study presents a method for deriving daily GPP fluxes and evaluates the impact of various variables on GPP estimates. This information can be employed in the development of vegetation prediction models
URI: https://www.scopus.com/record/display.uri?eid=2-s2.0-85210488876&origin=resultslist&sort=plf-f&src=s&sot=b&sdt=b&s=TITLE-ABS-KEY%28Gross+primary+productivity+estimation+through+remote+sensing+and+machine+learning+techniques+in+the+high+Andean+Region+of+Ecuador%29&sessionSearchId=c665a656d375177602470d21d9a1c0ec
metadata.dc.ucuenca.urifuente: https://link.springer.com/journal/484
ISSN: 0020-7128
Appears in Collections:Artículos

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