Abstract
In this paper, we address the retrieval of spatially distributed latent heat flux ( λ E) over a tropical dry forest using multi-spectral and thermal unmanned aerial vehicle (UAV) imagery. The study was carried out in the Santa Rosa National Park Environmental Monitoring Super-Site, Costa Rica, in June 2016. The triangle method was used to derive λ E from the UAV imagery and the results were compared to λ E measurements of an eddy covariance system within the coincident eddy flux tower footprint. The tower footprint was derived using a two-dimensional parameterization model for flux footprint prediction. The comparisons with the flux tower measurements showed a mean relative difference of 10.98% with a slight overestimation of the UAV-based flux retrievals by nearly 7.7 Wm − 2 . The results are in good agreement with satellite-based retrievals, as provided by the literature, for which the triangle method was initially developed and mostly used so far. This study proved to be a promising approach for transferring the triangle method to UAV imagery in ecosystems such as tropical dry forests. With the presented approach, new details in spatially distributed latent heat flux estimates at ultra-high resolution are now possible, thereby potentially closing the gap in spatial resolution between satellites and flux towers. Even more, it allows tracing the latent heat flux from single trees at leaf level. Besides, this approach also opens new perspectives for the monitoring of latent heat fluxes in tropical dry forests.
Highlights
In recent years remote sensing has become an important element to the provisioning of spatially distributed input data for Earth system models
Limitations of satellite systems due to technical capabilities set the boundaries for the temporal and spatial resolution of many data sets [1]. This is a limitation for the validation of emerging remote sensing products with wide disparities among ground truth data [2], especially where there are significant inconsistencies between sensor and ground-based information. This is evident for EssentialClimate Variables (ECV) such as evapotranspiration; i.e., the latent heat flux. λE is a key element for improving our understanding of climate-mediated changes of energy and water cycling at global to Forests 2020, 11, 604; doi:10.3390/f11060604
With a positional accuracy of below one pixel, the visible and near-infrared (VIS-NIR) orthomosaic is of high qualtiy [40,41]
Summary
In recent years remote sensing has become an important element to the provisioning of spatially distributed input data for Earth system models. Space Agency’s Climate Change Initiative (ESA-CCI), global monitoring programs through satellite data are envisaged. Limitations of satellite systems due to technical capabilities set the boundaries for the temporal and spatial resolution of many data sets [1] This is a limitation for the validation of emerging remote sensing products with wide disparities among ground truth data [2], especially where there are significant inconsistencies between sensor and ground-based information. This is evident for ECVs such as evapotranspiration; i.e., the latent heat flux (λE). This is evident for ECVs such as evapotranspiration; i.e., the latent heat flux (λE). λE is a key element for improving our understanding of climate-mediated changes of energy and water cycling at global to Forests 2020, 11, 604; doi:10.3390/f11060604 www.mdpi.com/journal/forests
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