Representativeness of FLUXNET Sites Across Latin America

Villarreal, Samuel; Vargas, Rodrigo

doi:10.1029/2020jg006090

Cited by 38 publications

(28 citation statements)

References 113 publications

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“…Grand challenges in the field of biogeosciences are global and require international collaborations to address them. Integrated and coordinated efforts are needed for success, but organizational and cultural challenges for global collaborations present barriers to interoperability (Villarreal & Vargas, 2021 ). Organizational barriers relate to challenges regarding institutional responsibility and authority, as well as the inequality of resources (Mirtl et al., 2018 ; Vargas et al., 2017 ).…”

Section: Global Collaboration Technology Transfer and Applicationmentioning

confidence: 99%

“…For example, challenges exist in balancing geographic representativeness and the need for environmental–ecological stratification (Guerin et al., 2020 ). Geographic gaps are common in data networks, especially for emerging nations, which directly impact data integration and openness (Villarreal & Vargas, 2021 ). While satellite imagery and open‐access platforms for data acquisition and processing can partially mitigate these geographic biases, inequity in resources, training, and access due to political restrictions and low funding in emerging nations greatly limit seamless integration.…”

Section: Field Experimental Remote Sensing and Real‐time Data For Bio...mentioning

confidence: 99%

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Biogeosciences Perspectives on Integrated, Coordinated, Open, Networked (ICON) Science

Dwivedi

Santos

Barnard

et al. 2022

Earth and Space Science

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This article is composed of three independent commentaries about the state of Integrated, Coordinated, Open, Networked (ICON) principles in the American Geophysical Union Biogeosciences section, and discussion on the opportunities and challenges of adopting them. Each commentary focuses on a different topic: (a) Global collaboration, technology transfer, and application (Section 2), (b) Community engagement, community science, education, and stakeholder involvement (Section 3), and (c) Field, experimental, remote sensing, and real‐time data research and application (Section 4). We discuss needs and strategies for implementing ICON and outline short‐ and long‐term goals. The inclusion of global data and international community engagement are key to tackling grand challenges in biogeosciences. Although recent technological advances and growing open‐access information across the world have enabled global collaborations to some extent, several barriers, ranging from technical to organizational to cultural, have remained in advancing interoperability and tangible scientific progress in biogeosciences. Overcoming these hurdles is necessary to address pressing large‐scale research questions and applications in the biogeosciences, where ICON principles are essential. Here, we list several opportunities for ICON, including coordinated experimentation and field observations across global sites, that are ripe for implementation in biogeosciences as a means to scientific advancements and social progress.

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Section: Global Collaboration Technology Transfer and Applicationmentioning

confidence: 99%

Section: Field Experimental Remote Sensing and Real‐time Data For Bio...mentioning

confidence: 99%

Biogeosciences Perspectives on Integrated, Coordinated, Open, Networked (ICON) Science

Dwivedi

Santos

Barnard

et al. 2022

Earth and Space Science

Self Cite

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“…To overcome the issue of regional biases when performing a local scale model calibration/validation, we built a network of flux towers from regional initiatives to represent the wide-ranging climate and ecosystem diversity of South America [34]. In addition to the most used flux tower network data compilation [35] from the large-scale biosphereatmosphere experiment in the Amazon (LBA-ECO) [36], we also included data from the SULFLUX (South Brazilian network of surface fluxes and climate change), ONDACBC (National Observatory of Water and Carbon Dynamics in the Caatinga Biome) [37], and the PELD Pantanal (long-term ecological research in Pantanal) [38], in conjunction with flux measurements supported by Brazilian universities, including the UFMT (Federal University of Mato Grosso) [39] and the USP (University of Sao Paulo) [40], funded by national and regional research agencies.…”

Section: Introductionmentioning

confidence: 99%

“…In fact, previous applications of ANNs for G prediction at the local scale [46,47] have yielded promising results. Considering the availability of the long time series of measured G, representative of the South American landscape [34,41], and the readily available remote sensing data, the usage of the ANN approach for large-scale applications is encouraged as a next step to assess its efficiency in predicting G.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network Model of Soil Heat Flux over Multiple Land Covers in South America

et al. 2021

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Soil heat flux (G) is an important component for the closure of the surface energy balance (SEB) and the estimation of evapotranspiration (ET) by remote sensing algorithms. Over the last decades, efforts have been focused on parameterizing empirical models for G prediction, based on biophysical parameters estimated by remote sensing. However, due to the existing models’ empirical nature and the restricted conditions in which they were developed, using these models in large-scale applications may lead to significant errors. Thus, the objective of this study was to assess the ability of the artificial neural network (ANN) to predict mid-morning G using extensive remote sensing and meteorological reanalysis data over a broad range of climates and land covers in South America. Surface temperature (Ts), albedo (α), and enhanced vegetation index (EVI), obtained from a moderate resolution imaging spectroradiometer (MODIS), and net radiation (Rn) from the global land data assimilation system 2.1 (GLDAS 2.1) product, were used as inputs. The ANN’s predictions were validated against measurements obtained by 23 flux towers over multiple land cover types in South America, and their performance was compared to that of existing and commonly used models. The Jackson et al. (1987) and Bastiaanssen (1995) G prediction models were calibrated using the flux tower data for quadratic errors minimization. The ANN outperformed existing models, with mean absolute error (MAE) reductions of 43% and 36%, respectively. Additionally, the inclusion of land cover information as an input in the ANN reduced MAE by 22%. This study indicates that the ANN’s structure is more suited for large-scale G prediction than existing models, which can potentially refine SEB fluxes and ET estimates in South America.

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“…For example, Villarreal et al [6] analyzed the representativeness of the eddy covariance sites of AmeriFlux based on the ecosystem functional types categories represented by each network. Villarreal and Vargas [7] assessed the representativeness of registered FLUXNET sites across Latin America using GPP and evapotranspiration. Pallandt et al [8] compared the environmental conditions observed at the tower locations and those within the larger Arctic domain, and mapped the representativeness of these eddy covariance network.…”

Section: Introductionmentioning

confidence: 99%

Spatial Representativeness of Gross Primary Productivity from Carbon Flux Sites in the Heihe River Basin, China

Zhang

Sun

2021

Remote Sensing

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Studying the spatial representativeness of carbon flux measurement data for typical land cover types can provide important information for benchmarking Earth system models and validating multiple-scale remote sensing products. In our study, daily gross primary productivity (GPP) was firstly derived from eddy covariance observation systems and seasonal variations in field GPP were analyzed at nine flux tower sites for typical land cover types in the Heihe River Basin, China. Then, the real-time footprint distance and climate footprint distance of the field GPP were obtained by using a footprint source area model. Lastly, multiple-scale GPP products were validated at footprint scale, and the impacts (measurement height, surface roughness and turbulent state of the atmosphere) on the footprint distance of field GPP were analyzed. The results of this paper demonstrated that climate footprint distances ranged from about 500 m to 1500 m for different land cover types in the Heihe River Basin. The accuracy was higher when validating MODIS GPP products at footprint scale (R2 = 0.56, RMSE = 3.07 g C m−2 d−1) than at field scale (R2 = 0.51, RMSE = 3.34 g C m−2 d−1), and the same situation occurred in the validation of high-resolution downscaled GPP (R2 = 0.85, RMSE = 1.34 g C m−2 d−1 when validated at footprint scale; R2 = 0.82, RMSE = 1.47 g C m−2 d−1 when validated at field scale). The results of this study provide information about the footprints of field GPP for typical land cover types in arid and semi-arid areas in Northwestern China, and reveal that precision may be higher when validating multiple-scale remote sensing GPP products at the footprint scale than at the field scale.

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Representativeness of FLUXNET Sites Across Latin America

Cited by 38 publications

References 113 publications

Biogeosciences Perspectives on Integrated, Coordinated, Open, Networked (ICON) Science

Biogeosciences Perspectives on Integrated, Coordinated, Open, Networked (ICON) Science

Artificial Neural Network Model of Soil Heat Flux over Multiple Land Covers in South America

Spatial Representativeness of Gross Primary Productivity from Carbon Flux Sites in the Heihe River Basin, China

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