The fraction of Photosynthetically Active Radiation (fPAR) plays a pivotal role in determining the carbon flux in ecosystems. Although the MODIS fPAR product has demonstrated effectiveness in the Northern Hemisphere, its validity still needs to be verified in the context of Tropical Dry Forests (TDFs), which constitute 40% of all tropical forests. This study utilized a Wireless Sensor Network (WSN) to generate an in-situ Green fPAR dataset at the Santa Rosa National Park Environmental Monitoring Supersite, aiming to validate MODIS fPAR products from 2013 to 2017. This study employs a 2-flux fPAR estimation approach for the in-situ dataset, followed by Savitzky–Golay derivative-based smoothing, univariate-wavelet transforms, and cross-wavelet analysis to compare phenological variables between the in-situ and MODIS fPAR datasets. Our findings reveal a significant temporal disparity between the MODIS fPAR products and ground-based data, with MODIS consistently lagging in detecting the onset of green-up or senescence in TDFs by 18–55 days. However, the annual and inter-seasonal patterns were statistically significant (p<0.05) and replicated in the MODIS and in-situ datasets. Notably, these patterns deviate during extreme water conditions (droughts and hurricanes), with MODIS underestimating the effects of drought and failing to represent hurricane impact. Furthermore, MODIS fPAR products do not effectively capture small-scale fPAR variations and intra-seasonal differences. Therefore, this study underscores the limited accuracy of MODIS fPAR observations in the context of TDFs. Consequently, caution is warranted when relying on MODIS fPAR products to monitor rapid phenological changes in Tropical Dry Forests.

The fraction of photosynthetic active radiation (fPAR) attempts to quantify the amount of enery that is absorbed by vegetation for use in photosynthesis. Despite the importance of fPAR, there has been little research into how fPAR may change with biome and latitude, or the extent and number of ground networks required to validate satellite products. This study provides the first attempt to quantify the variability and uncertainties related to in-situ 2-flux fPAR estimation within a tropical dry forest (TDF) via co-located sensors. Using the wireless sensor network (WSN) at the Santa Rosa National Park Environmental Monitoring Super Site (Guanacaste, Costa Rica), this study analyzes the 2-flux fPAR response to seasonal, environmental, and meteorological influences over a period of five years (2013–2017). Using statistical tests on the distribution of fPAR measurements throughout the days and seasons based on the sky condition, solar zenith angle, and wind-speed, we determine which conditions reduce variability, and their relative impact on in-situ fPAR estimation. Additionally, using a generalized linear mixed effects model, we determine the relative impact of the factors above, as well as soil moisture on the prediction of fPAR. Our findings suggest that broadleaf deciduous forests, diffuse light conditions, and low wind patterns reduce variability in fPAR, whereas higher winds and direct sunlight increase variability between co-located sensors. The co-located sensors used in this study were found to agree within uncertanties; however, this uncertainty is dominated by the sensor drift term, requiring routine recalibration of the sensor to remain within a defined criteria. We found that for the Apogee SQ-110 sensor using the manufacturer calibration, recalibration around every 4 years is needed to ensure that it remains within the 10% global climate observation system (GCOS) requirement. We finally also find that soil moisture is a significant predictor of the distribution and magnitude of fPAR, and particularly impacts the onset of senescence for TDFs.

Fractals have been widely used to determine bifurcation patterns in trees or to analyse the homeostasis of the development of plants to different environments. In a few instances, fractals have been used to predict tree or stand metrics. Here, we explore the use of fractal geometry based on the voxel‐counting method (VC) to predict tree and stands metrics on point clouds derived from terrestrial laser scanning. This was explored using 189 leaf‐on and leaf‐off point clouds from seven databases around the world. Four metrics were estimated at the tree level: height, diameter at breast height, crown area and tree volume. At the stand level, artificial stands were created by adding trees to a given plot, and then the basal area, stand volume and area coverage by crowns were estimated. The VC was applied to trees or stands creating voxels of different volumes (S) while counting the number of voxels (N) required to fill it. Log–log relationships between N and 1/S were used to estimate the fractal dimension (dMB) and the interceptMB. At the tree level, the interceptMB shows a stronger relationship with metrics for leaf‐on (r2 = 0.26‒0.90) and leaf‐off point clouds (r2 = 0.18‒0.87) than dMB (r2 < 0.34); however, dMB seems to describe better the complexity embedded within leaf‐on/leaf‐off point clouds. The predictions by the interceptMB are affected by the presence/absence of leaves, but less affected by the random effects of the databases. At the stand level, both fractal geometry parameters (interceptMB and dMB) tend to predict the variability of stand metrics (r2 = 0.61‒0.98). The estimation of tree and stand metrics based on fractal geometry equations can be considered a fast approach for predicting irregular structures. Using fractals on point clouds also allows us to understand the structural complexity of how trees or stands occupy their 3D space. This complexity can be further used as a structural trait of trees or forest ecosystems. Fractal geometry equations can also help towards the development of large‐scale biomass maps at different ecosystems.

The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) was launched to the International Space Station on 29 June 2018 by the National Aeronautics and Space Administration (NASA). The primary science focus of ECOSTRESS is centered on evapotranspiration (ET), which is produced as Level-3 (L3) latent heat flux (LE) data products. These data are generated from the Level-2 land surface temperature and emissivity product (L2_LSTE), in conjunction with ancillary surface and atmospheric data. Here, we provide the first validation (Stage 1, preliminary) of the global ECOSTRESS clear-sky ET product (L3_ET_PT-JPL, Version 6.0) against LE measurements at 82 eddy covariance sites around the world. Overall, the ECOSTRESS ET product performs well against the site measurements (clear-sky instantaneous/time of overpass: r(2) = 0.88; overall bias = 8%; normalized root-mean-square error, RMSE = 6%). ET uncertainty was generally consistent across climate zones, biome types, and times of day (ECOSTRESS samples the diurnal cycle), though temperate sites are overrepresented. The 70-m-high spatial resolution of ECOSTRESS improved correlations by 85%, and RMSE by 62%, relative to 1-km pixels. This paper serves as a reference for the ECOSTRESS L3 ET accuracy and Stage 1 validation status for subsequent science that follows using these data. Key Points ECOSTRESS is a state-of-the-art combination of thermal bands, spatial and temporal resolutions, and measurement accuracy and precision Data from 82 eddy covariance sites were coalesced concurrently with the first year of ECOSTRESS for Stage 1 validation Clear-sky ET from ECOSTRESS compared well against a wide range of eddy covariance sites, vegetation classes, climate zones, and times of day

Accurate estimates of Essential Climate Variables (ECV) such as the fraction of Absorbed Photosynthetically Active Radiation (fAPAR) are essential for assessing global carbon balances. ESA's Sentinel-2 (S2) mission with its decametric resolution enables to derive fAPAR information at single forest stand. Validation studies on previously existing satellite-derived fAPAR products have found considerable discrepancies, especially in forest ecosystems that exceed relative discrepancies of 10% (i.e. 0.05 for absolute values) set as target accuracy by the Global Climate Observing System (GCOS). This study presents the validation of S2 fAPAR products using direct radiation measurements of 2017 at three different forests, located in Central Europe (mixed-coniferous forest in temperate mid-latitude), North America (boreal-deciduous forest) and Central America (tropical dry forest, TDF). We measured incoming and transmitted PAR every 10 min synchronously using Wireless Sensor Networks (WSN) and calculated a two-flux fAPAR estimate. We validated the S2 fAPAR product with different instantaneous ground fAPAR estimates (e.g. instantaneous fAPAR at 10:00 and 14:00 local solar time) and daily average fAPAR. We considered uncertainties of ground data, i.e. bias related to the presence of colored autumn leaves and influences of solar zenith angle. Overall, we found high discrepancies between the S2 fAPAR product and ground measurements, indicating that the S2 fAPAR product systematically underestimated (negative values for bias in percent) the ground observations. The highest agreement was observed at the boreal-deciduous forest stand with a bias of -13% (R-2 = 0.67). The Central American and European sites reported deviations of -20% (R-2 = 0.68) and -25% (R-2 = 0.26), respectively. At all sites, we found evidence that particularly the influence of colored leaves during the senescence periods lead to bias of the ground data. Further, the choice of temporal fAPAR estimate, i.e. daily average fAPAR or a certain instantaneous fAPAR estimate, lead to partly different results in the correlation analysis with the S2 fAPAR product. However, considering sources of uncertainties of ground data, we emphasize that only the boreal-deciduous site in Canada fulfilled the accuracy requirements set by the GCOS. In contrast to absolute values, we found strong agreement on phenological changes at all three sites. Specifically, the influence of species composition on seasonal variability of fAPAR across the European mixed coniferous site was well-represented in the S2 fAPAR product. As for the representation of spatial variability, we found highest agreement at the boreal-deciduous forest stand (BIAS = -22%, R-2 = 0.93), whereas spatial variability was least represented at the TDF site (BIAS = 125%, R-2 = 0.97). We conclude that the S2 fAPAR product has strong capabilities for assessing temporal variability of fAPAR, but due to low accuracy of absolute values currently limited options to feed global production efficiency models and assess global carbon balances.

Tropical dry forests (TDFs) present strong seasonal greenness signals ideal for tracking phenology and primary productivity using remote sensing techniques. The tightly synchronized relationship these ecosystems have with water availability offer a valuable natural experiment for observing the complex interactions between the atmosphere and the biosphere in the tropics. To investigate how well the MODIS vegetation indices (normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI)) represented the phenology of different successional stages of naturally regenerating TDFs, within a widely conserved forest fragment in the semi-arid southeast of Brazil, we installed several canopy towers with radiometric sensors to produce high temporal resolution near-surface vegetation greenness indices. Direct comparison of several years of ground measurements with a combined Aqua/Terra 8 day satellite product showed similar broad temporal trends, but MODIS often suffered from cloud contamination during the onset of the growing season and occasionally during the peak growing season. The strength of the in-situ and MODIS linear relationship was greater for NDVI than for EVI across sites but varied with forest stand age. Furthermore, we describe the onset dates and duration of canopy development phases for three years of in-situ monitoring. A seasonality analysis revealed significant discrepancies between tower and MODIS phenology transitions dates, with up to five weeks differences in growing season length estimation. Our results indicate that 8 and 16 day MODIS satellite vegetation monitoring products are suitable for tracking general patterns of tropical dry forest phenology in this region but are not temporally sufficient to characterize inter-annual differences in phenology phase onset dates or changes in productivity due to mid-season droughts. Such rapid transitions in canopy greenness are important indicators of climate change sensitivity of these already endangered forest ecosystems and should be further monitored using both ground and satellite approaches.

Tropical dry forests (TDFs) are ecosystems with long drought periods, a mean temperature of 25 °C, a mean annual precipitation that ranges from 900 to 2000 mm, and that possess a high abundance of deciduous species (trees and lianas). What remains of the original extent of TDFs in the Americas remains highly fragmented and at different levels of ecological succession. It is estimated that one of the main fingerprints left by global environmental and climate change in tropical environments is an increase in liana coverage. Lianas are non-structural elements of the forest canopy that eventually kill their host trees. In this paper we evaluate the use of a terrestrial laser scanner (TLS) in combination with hemispherical photographs (HPs) to characterize changes in forest structure as a function of ecological succession and liana abundance. We deployed a TLS and HP system in 28 plots throughout secondary forests of different ages and with different levels of liana abundance. Using a canonical correlation analysis (CCA), we addressed how the VEGNET, a terrestrial laser scanner, and HPs could predict TDF structure. Likewise, using univariate analyses of correlations, we show how the liana abundance could affect the prediction of the forest structure. Our results suggest that TLSs and HPs can predict the differences in the forest structure at different successional stages but that these differences disappear as liana abundance increases. Therefore, in well known ecosystems such as the tropical dry forest of Costa Rica, these biases of prediction could be considered as structural effects of liana presence. This research contributes to the understanding of the potential effects of lianas in secondary dry forests and highlights the role of TLSs combined with HPs in monitoring structural changes in secondary TDFs.