@misc{BendixLimburgerBreueretal., author = {Bendix, J{\"o}rg and Limburger, Oliver and Breuer, Lutz and Dantas de Paula, Mateus and Fries, Andreas and Gonzalez-Jaramillo, Victor Hugo and Grigusova, Paulina and Hickler, Thomas and Murkute, Charuta and Pucha-Cofrep, Franz and Trachte, Katja and Windhorst, David}, title = {Simulation of latent heat flux over a high altitude pasture in the tropical Andes with a coupled land surface framework}, series = {The science of the total environment}, volume = {981}, journal = {The science of the total environment}, publisher = {Elsevier}, address = {Amsterdam}, issn = {1879-1026}, doi = {10.1016/j.scitotenv.2025.179510}, pages = {1 -- 21}, abstract = {Latent heat flux is a central element of land-atmosphere interactions under climate change. Knowledge is particularly poor in the biodiversity hotspot of the Andes, where heat flux measurements using eddy covariance stations are scarce and land surface models (LSMs) often oversimplify the complexity of the ecosystems. The main objective of this study is to perform latent heat flux simulations for the tropical South Eastern (SE) Ecuadorian Andes using a coupled LSM framework, and to test the performance with heat flux and soil moisture data collected from a tropical high-altitude pasture. Prior to testing, we applied multi-criteria model calibration of sensitive model parameters, focusing on improving simulated soil water conditions and radiation fluxes as a prerequisite for proper heat flux simulations. The most sensitive parameters to improve soil moisture and radiation flux simulations were soil porosity, saturated hydraulic conductivity, leaf area index, soil colour and NIR (Near Infrared) leaf optical properties. The best calibrated model run showed a very good performance for half-hourly latent heat flux simulations with an R² of 0.8 and an RMSE of 34.0 W m⁻², outperforming simulations with uncalibrated and uncoupled LSM simulations in comparable areas. The slight overall overestimation in the simulated latent heat flux can be related to (i) simulation uncertainties in the canopy heat budget, (ii) an imbalance in the observed flux data and (iii) slight overestimations in the simulated soil moisture. Although our study focuses on latent heat fluxes and their relation to simulated radiation fluxes and soil moisture, model outputs of sensible heat fluxes were also discussed. The systematic overestimation of sensible heat flux in the model seems to be mainly a result of overestimated canopy temperatures. The improved simulation for latent heat flux has a high translational potential to support land use strategies in the tropical Andes under climate change.}, language = {en} } @misc{LimbergerHomeierGonzalezJaramilloetal., author = {Limberger, Oliver and Homeier, J{\"u}rgen and Gonzalez-Jaramillo, Victor and Fries, Andreas and Murkute, Charuta and Trachte, Katja and Bendix, J{\"o}rg}, title = {Foliar trait retrieval models based on hyperspectral satellite imagery perform well in a biodiversity hotspot of the SE Ecuadorian Andes}, series = {International journal of remote sensing}, journal = {International journal of remote sensing}, publisher = {Taylor \& Francis}, address = {London}, issn = {1366-5901}, doi = {10.1080/01431161.2025.2511211}, pages = {1 -- 19}, abstract = {The determination of the spatial distribution of canopy traits is crucial for understanding the spatio-temporal dynamics of ecosystem functions, such as carbon sequestration, water and energy fluxes in tropical montane forests. Especially in remote areas such as the Andes of south-east Ecuador, remote sensing using satellites has been proven to provide valuable information on canopy traits. However, the performance of the multispectral models to date is limited. Here, we analyse the potential of DESIS (DLR Earth Sensing Imaging Spectrometer) hyperspectral surface reflectance data for the prediction of specific leaf area (SLA), foliar toughness, nitrogen (N) and phosphorus (P) content by calibrating a PLSR model for each foliar trait. Model validation showed a high explanation of variance (R2 : 0.78-0.92) and a low model error (mean-normalized RMSE: 8-13\%) for all traits, showing an improved performance compared to models based on multispectral imagery and ancillary data within the same forest type. Hyperspectral models predicting foliar traits from individual tree crowns showed a lower performance for most traits, probably related to the difference in spatial resolution compared to this study. Given the high species richness of the Andean biodiversity hotspot and the complex topography, the high quality of the DESIS models is remarkable, considering that the multispectral models from the region require topographic predictors in addition to reflectances to achieve a good performance. The spatial trait maps generated by our models based on the reflectances only show clear relationships with terrain elevation. Thus, the hyperspectral trait maps from satellite orbit are capable of supporting large-scale ecological analyses between features, diversity and ecosystem functions.}, language = {en} }