A data record, spanning 24 years, is presented of global atmospheric total aerosol optical depth and also the aerosol optical depth due to fine-mode constituents, typically of anthropogenic origin. Original measurements of reflectance were provided at approximately 1-km resolution by a series of dual-view satellite instruments: the Along-Track Scanning Radiometer 2 (ATSR-2), Advanced Along-Track Scanning Radiometer (AATSR), and Sea and Land Surface Temperature Radiometers (SLSTRs). These were processed to retrieve aerosol properties at 10-km resolution and then collated over daily and monthly timescales on a 1° × 1° latitude-longitude grid. Retrievals are evaluated against ground-based sun-photometer measurements from the Aerosol Robotic Network and Maritime Aerosol Network and compared to other satellite-derived datasets. The data record has implications for directly constraining the Earth’s radiation budget, allowing benchmarking and improvement of models to represent aerosol in the climate system, air quality monitoring and adding to the long-term record of emission trends related to sources such as fire, dust and sulphate pollution. After release, the SLSTR datasets will be regularly extended in time.

NASA's (National Aeronautics and Space Administration) ICESat-2 with a Photon Counting LiDAR (Light Detection And Ranging) Sensor sensitively detects signal photons at high speed with an advanced detection system called the Advanced Topographic Laser Altimeter System (ATLAS). However, the sensor also extracts a large amount of background photon noise coming from the atmosphere, ground, sun, or other radiation. This condition is particularly evident in forest areas. This study proposes an automatic machine learning approach to utilize data for forestry applications to improve data availability compared to NASA's official product. Our method uses only a very limited number (10%) of sample points for training, ensuring operational efficiency and training accuracy. We conclude that the integrated learning performance generally outperforms single models, and the mean F1 score of all tests is approximately 0.9. The mean F1 score of the Stacked Ensembles model is 0.957 ahead of the other models. The top three variables used in training models are kNNdist5, kNNdist10, and h. These three variables could explain 51.6% of the components of the models. Over the regions tested, the proposed method could improve the proportion of signals correctly identified by 6.4%, 12.2%, 2.7%, 9.3%, and 1.4% in five datasets. The model performs better in low signal-to-noise (SNR) datasets less than 7.5. The method would be largely unaffected by differences in topography, noise distribution, and SNR. The classifiers could correct misclassified labels in ATL08 products and show good stability in different conditions.

We present the first extended validation of a new SYNERGY global aerosol product (SY_2_AOD), which is based on synergistic use of data from the Ocean and Land Color Instrument (OLCI) and the Sea and Land Surface Temperature Radiometer (SLSTR) sensors aboard the Copernicus Sentinel-3A (S3A) and Sentinel-3B (S3B) satellites. Validation covers period from 14 January 2020 to 30 September 2021. Several approaches, including statistical analysis, time series analysis, and comparison with similar aerosol products from the other spaceborne sensor, the Moderate Resolution Imaging Spectroradiometer (MODIS), were applied for validation and evaluation of S3A and S3B SY_2 aerosol products, including aerosol optical depth (AOD) provided at different wavelengths, AOD pixel-level uncertainties, fine-mode AOD, and Angström exponent. Over ocean, the performance of SY_2 AOD (syAOD) retrieved at 550 nm is good: for S3A and S3B, Pearson correlation coefficients with the Maritime Aerosol Network (MAN) component of the AErosol RObotic NETwork (AERONET) are 0.88 and 0.85, respectively; 88.6 % and 89.5 % of pixels fit into the MODIS error envelope (EE) of ±0.05 ± 0.2 × AOD. Over land, correlation coefficients with AERONET AOD (aAOD) are 0.60 and 0.63 for S3A and S3B, respectively; 51.4 % and 57.9 % of pixels fit into MODIS EE. Reduced performance over land is expected since the surface reflectance and angular distribution of scattering are higher and more difficult to predict over land than over ocean. The results are affected by a large number of outliers. Evaluation of the per-retrieval uncertainty with the χ2 test indicates that syAOD prognostic uncertainties (PU) are slightly underestimated (χ2 = 3.1); if outliers are removed, PU describes the syAOD error well (χ2 = 1.6). The regional analysis of the Angström exponent, which relates to the aerosol size distribution, shows spatial correlation with expected sources. For 40 % of the matchups with AERONET in the Northern Hemisphere (NH) and for 60 % of the matchups in the Southern Hemisphere (SH), which fit into the AE size range of [1, 1.8], an offset between SY_2 AE (syAE) and AERONET AE (aAE) is within ±0.25. General overestimation of low (< 0.5) syAE and underestimation of high (> 1.8) syAE results in high (0.94, globally) overall bias. Good agreement (bias < 0.03) was observed between Sy_2 fine-mode AOD (syFMAOD) and AERONET fine-mode AOD (aFMAOD) for aFMAOD < 1. At aFMAOD > 1, syFMAOD is considerably underestimated (by 0.3–0.5 in different aFMAOD ranges) in the NH. In the SH, only a few aFMAOD values above 1 are measured. The fine-mode fraction (FMF) in the SY_2 AOD product (syFMF) in the range of [0, 0.7] is overestimated; the positive offset of 0.3–0.5 for low (< 0.25) FMF gradually decreases. Differences between the annual and seasonal AOD values from SY_2 and MODIS (mod) Dark Target and Deep Blue products are within 0.02 for the study area (30∘ S–60∘ N, 80∘ W–45∘ E). The agreement is better over ocean; however, a difference up to 0.6 exists between syFMF and modFMF. Over bright land surface (Saharan desert) the difference in AOD between the two products is highest (up to 0.11); the sign of the difference varies over time and space. For both S3A and S3B AOD products, validation statistics are often slightly better in the Southern Hemisphere. In general, the performance of S3B is slightly better.

Despite the implication of aerosols for the radiation budget, there are persistent differences in data for the aerosol optical depth (τ) for 1998–2019. This study presents a comprehensive evaluation of the large‐scale spatio‐temporal patterns of mid‐visible τ from modern data sets. In total, we assessed 94 different global data sets from eight satellite retrievals, four aerosol‐climate model ensembles, one operational ensemble product, two reanalyses, one climatology and one merged satellite product. We include the new satellite data SLSTR and aerosol‐climate simulations from the Coupled Model Intercomparison Project Phase 6 (CMIP6) and the Aerosol Comparisons between Observations and Models Phase 3 (AeroCom‐III). Our intercomparison highlights model differences and observational uncertainty. Spatial mean τ for 60°N – 60°S ranges from 0.124 to 0.164 for individual satellites, with a mean of 0.14. Averaged τ from aerosol‐climate model ensembles fall within this satellite range, but individual models do not. Our assessment suggests no systematic improvement compared to CMIP5 and AeroCom‐I. Although some regional biases have been reduced, τ from both CMIP6 and AeroCom‐III are for instance substantially larger along extra‐tropical storm tracks compared to the satellite products. The considerable uncertainty in observed τ implies that a model evaluation based on a single satellite product might draw biased conclusions. This underlines the need for continued efforts to improve both model and satellite estimates of τ, for example, through measurement campaigns in areas of particularly uncertain satellite estimates identified in this study, to facilitate a better understanding of aerosol effects in the Earth system.

Despite the implication of aerosols for radiative forcing, there are di erences in aerosol estimates from both, observations and models. This study quanti es di erences between current estimates of aerosol optical depth (AOD) to address two questions: (1) How well do we know the large-scale spatio-temporal pattern of present-day AOD across state-of-the-art data? (2) Has the representation of AOD improved across phases of aerosol-climate model intercomparison projects? To answer these questions, we analyze spatio-temporal patterns of the present-day monthly mean AOD from 94 global datasets. The data is taken from eight satellite retrievals, four aerosol-climate model intercomparison projects, two global reanalyses, one operational ensemble product, one climatology and one merged satellite product covering periods between 1998 and 2019. The evaluation includes new satellite data from SLSTR and aerosol-climate models of CMIP6 and AeroCom-III. The comprehensive data assessment allows us to evaluate the performance of individual products and models concerning di erent spatial and temporal aspects. Our assessment is based on metrics for a detailed investigation with respect to di erent spatio-temporal characteristics of AOD. Our results highlight spatio-temporal di erences in AOD across datasets, were the performance of individual data sets varies with respect to the di erent spatio-temporal metrics assessed. Global mean AOD of individual satellites ranges between -11% to +17% around a satellite mean of 0.14. The ensemble means from the aerosol-climate model intercomparison projects fall within the satellite range, but individual models can di er considerably. Reanalyses and climalologies are typically closer to the satellite mean than aerosol-climate models. No systematic improvement from earlier to later phases of CMIP and AeroCom is found, although some regional biases have been reduced. Compared to the satellite and reanalysis data, all aerosol-climate ensemble means tend to overestimate AOD along extra-tropical storm tracks and underestimate AOD in regions of high aerosol load in South America, South Africa, India, and Southeast Asia. The identi ed di erences may be used to guide further e orts to improve satellite retrievals and model simulations for aerosols. In addition, the uncertainty in observed AOD implies that a model evaluation based on a single satellite product might draw biased conclusions. This underlines the need for continued e orts to improve both model and satellite estimates of AOD to facilitate a better understanding of aerosol e ects in the Earth system. At the same time, our analysis suggests that an assimilation of multiple satellite products for AOD would be bene cial to account for observational uncertainty. Reference: Vogel, A., Alessa, G., Scheele, R., Weber, L., Dubovik, O., North, P., Fiedler, S. (2022). Uncertainty in aerosol optical depth from modern aerosol-climate models, reanalyses, and satellite products. Journal of Geophysical Research: Atmospheres, 127, e2021JD035483. https://doi.org/10.1029/2021JD035483

Within the framework of the AeroCom (Aerosol Comparisons between Observations and Models) initiative, the state-of-the-art modelling of aerosol optical properties is assessed from 14 global models participating in the phase III control experiment (AP3). The models are similar to CMIP6/AerChemMIP Earth System Models (ESMs) and provide a robust multi-model ensemble. Inter-model spread of aerosol species lifetimes and emissions appears to be similar to that of mass extinction coefficients (MECs), suggesting that aerosol optical depth (AOD) uncertainties are associated with a broad spectrum of parameterised aerosol processes. Total AOD is approximately the same as in AeroCom phase I (AP1) simulations. However, we find a 50 % decrease in the optical depth (OD) of black carbon (BC), attributable to a combination of decreased emissions and lifetimes. Relative contributions from sea salt (SS) and dust (DU) have shifted from being approximately equal in AP1 to SS contributing about 2∕3 of the natural AOD in AP3. This shift is linked with a decrease in DU mass burden, a lower DU MEC, and a slight decrease in DU lifetime, suggesting coarser DU particle sizes in AP3 compared to AP1. Relative to observations, the AP3 ensemble median and most of the participating models underestimate all aerosol optical properties investigated, that is, total AOD as well as fine and coarse AOD (AODf, AODc), Ångström exponent (AE), dry surface scattering (SCdry), and absorption (ACdry) coefficients. Compared to AERONET, the models underestimate total AOD by ca. 21 % ± 20 % (as inferred from the ensemble median and interquartile range). Against satellite data, the ensemble AOD biases range from −37 % (MODIS-Terra) to −16 % (MERGED-FMI, a multi-satellite AOD product), which we explain by differences between individual satellites and AERONET measurements themselves. Correlation coefficients (R) between model and observation AOD records are generally high (R>0.75), suggesting that the models are capable of capturing spatio-temporal variations in AOD. We find a much larger underestimate in coarse AODc (∼ −45 % ± 25 %) than in fine AODf (∼ −15 % ± 25 %) with slightly increased inter-model spread compared to total AOD. These results indicate problems in the modelling of DU and SS. The AODc bias is likely due to missing DU over continental land masses (particularly over the United States, SE Asia, and S. America), while marine AERONET sites and the AATSR SU satellite data suggest more moderate oceanic biases in AODc. Column AEs are underestimated by about 10 % ± 16 %. For situations in which measurements show AE > 2, models underestimate AERONET AE by ca. 35 %. In contrast, all models (but one) exhibit large overestimates in AE when coarse aerosol dominates (bias ca. +140 % if observed AE < 0.5). Simulated AE does not span the observed AE variability. These results indicate that models overestimate particle size (or underestimate the fine-mode fraction) for fine-dominated aerosol and underestimate size (or overestimate the fine-mode fraction) for coarse-dominated aerosol. This must have implications for lifetime, water uptake, scattering enhancement, and the aerosol radiative effect, which we can not quantify at this moment. Comparison against Global Atmosphere Watch (GAW) in situ data results in mean bias and inter-model variations of −35 % ± 25 % and −20 % ± 18 % for SCdry and ACdry, respectively. The larger underestimate of SCdry than ACdry suggests the models will simulate an aerosol single scattering albedo that is too low. The larger underestimate of SCdry than ambient air AOD is consistent with recent findings that models overestimate scattering enhancement due to hygroscopic growth. The broadly consistent negative bias in AOD and surface scattering suggests an underestimate of aerosol radiative effects in current global aerosol models. Considerable inter-model diversity in the simulated optical properties is often found in regions that are, unfortunately, not or only sparsely covered by ground-based observations. This includes, for instance, the Sahara, Amazonia, central Australia, and the South Pacific. This highlights the need for a better site coverage in the observations, which would enable us to better assess the models, but also the performance of satellite products in these regions. Using fine-mode AOD as a proxy for present-day aerosol forcing estimates, our results suggest that models underestimate aerosol forcing by ca. −15 %, however, with a considerably large interquartile range, suggesting a spread between −35 % and +10 %.

Holm oak decline is a complex phenomenon mainly influenced by the presence of Phytophthora cinnamomi and water stress. Plant functional traits (PTs) are altered during the decline process — initially affecting the physiological condition of the plants with non-visual symptoms and subsequently the leaf pigment content and canopy structure — being its quantification critical for the development of scalable detection methods for effective management. This study examines the relationship between spectral-based PTs and oak decline incidence and severity. We evaluate the use of high-resolution hyperspectral and thermal imagery (< 1 m) together with a 3-D radiative transfer model (RTM) to assess a supervised classification model of holm oak decline. Field surveys comprising more than 1100 trees with varying disease incidence and severity were used to train and validate the model and predictions. Declining trees showed decreases of model-based PTs such as water, chlorophyll, carotenoid, and anthocyanin contents, as well as fluorescence and leaf area index, and increases in crown temperature and dry matter content, compared to healthy trees. Our classification model built using different PT indicators showed up to 82% accuracy for decline detection and successfully identified 34% of declining trees that were not detected by visual inspection and confirmed in a re-evaluation 2 years later. Among all variables analysed, canopy temperature was identified as the most important variable in the model, followed by chlorophyll fluorescence. This methodological approach identified spectral plant traits suitable for the detection of pre-symptomatic trees and mapping of oak forest disease outbreaks up to 2 years in advance of identification via field surveys. Early detection can guide management activities such as tree culling and clearance to prevent the spread of dieback processes. Our study demonstrates the utility of 3-D RTM models to untangle the PT alterations produced by oak decline due to its heterogeneity. In particular, we show the combined use of RTM and machine learning classifiers to be an effective method for early detection of oak decline potentially applicable to many other forest diseases worldwide.

Abstract. Seasonal maps of dual view retrieved mid-visible AOD and AODf for four selected years (1998, 2008, 2019, 2020) are introduced and assessed in comparisons to MODIS retrievals and general data of an aerosol climatology. Due to different sensor capabilities (ATSR-2, AATSR and SLSTR) there are still unresolved inconsistencies so that decadal regional trends are not as detectable as with MODIS retrievals. SLSTR retrieval, however, agree with MODIS retrievals that 2020 Covid impacts on AOD values (via comparisons to the pre-COVID 2019 reference) are at best minor and secondary to natural anomalies by wildfires and dust. In radiative transfer applications the dual view AOD data for the four years are processed in the MAC climatology environment to determine aerosol associated radiative effects for total aerosol and for anthropogenic aerosol. Even though the calculated radiative effects are affected by retrieval AOD retrieval tendencies, climate relevant TOA net-flux changes are consistent to result with AOD data from other satellite sensors and a general climatology: −0.9 W/m2 for total aerosol with a significant greenhouse effect and −0.8 and −0.2 W/m2 for anthropogenic aerosol with and without indirect effects, respectively. Aside from global averages, seasonal maps highlight the diversity of regional and seasonal radiative effects.

A major international effort has been made to monitor sun-induced chlorophyll fluorescence (SIF) from space as a proxy for the photosynthetic activity of terrestrial vegetation. However, the effect of spatial heterogeneity on the SIF retrievals from canopy radiance derived from images with medium and low spatial resolution remains uncharacterised. In images from forest and agricultural landscapes, the background comprises a mixture of soil and understory and can generate confounding effects that limit the interpretation of the SIF at the canopy level. This paper aims to improve the understanding of SIF from coarse spatial resolutions in heterogeneous canopies by considering the separated contribution of tree crowns, understory and background components, using a modified version of the FluorFLIGHT radiative transfer model (RTM). The new model is compared with others through the RAMI model intercomparison framework and is validated with airborne data. The airborne campaign includes high-resolution data collected over a tree-grass ecosystem with the HyPlant imaging spectrometer within the FLuorescence EXplorer (FLEX) preparatory missions. Field data measurements were collected from plots with a varying fraction of tree and understory vegetation cover. The relationship between airborne SIF calculated from pure tree crowns and aggregated pixels shows the effect of the understory at different resolutions. For a pixel size smaller than the mean crown size, the impact of the background was low (R2 > 0.99; NRMSE < 0.01). By contrast, for a pixel size larger than the crown size, the goodness of fit decreased (R2 < 0.6; NRMSE > 0.2). This study demonstrates that using a 3D RTM model improves the calculation of SIF significantly (R2 = 0.83, RMSE = 0.03 mW m−2 sr−1 nm−1) when the specific contribution of the soil and understory layers are accounted for, in comparison with the SIF calculated from mixed pixels that considers only one layer as background (R2 = 0.4, RMSE = 0.28 mW m−2 sr−1 nm−1). These results demonstrate the need to account for the contribution of SIF emitted by the understory in the quantification of SIF within tree crowns and within the canopy from aggregated pixels in heterogeneous forest canopies.

Providing accurate information on fire effects is critical to understanding post-fire ecological processes and to design appropriate land management strategies. Multispectral imagery from optical passive sensors is commonly used to estimate fire damage, yet this type of data is only sensitive to the effects in the upper canopy. This paper evaluates the sensitivity of full waveform LiDAR data to estimate the severity of wildfires using a 3D radiative transfer model approach. The approach represents the first attempt to evaluate the effect of different fire impacts, i.e. changes in vegetation structure as well as soil and leaf color, on the LiDAR signal. The FLIGHT 3D radiative transfer model was employed to simulate full waveform data for 10 plots representative of Mediterranean ecosystems along with a wide range of post-fire scenarios characterized by different severity levels, as defined by the composite burn index (CBI). A new metric is proposed, the waveform area relative change (WARC), which provides a comprehensive severity assessment considering all strata and accounting for changes in structure and leaf and soil color. It showed a strong correlation with CBI values (Spearman's Rho = 0.9 +/- 0.02), outperforming the relative change of LiDAR metrics commonly applied for vegetation modeling, such as the relative height of energy quantiles (Spearman's Rho = 0.56 +/- 0.07, for the relative change of RH60, the second strongest correlation). Logarithmic models fitted for each plot based on the WARC yielded very good performance with R-2 (+/- standard deviation) and RMSE (+/- standard deviation) of 0.8 (+/- 0.05) and 0.22 (+/- 0.03), respectively. LiDAR metrics were evaluated over the King Fire, California, U.S., for which pre- and post-fire discrete return airborne LiDAR data were available. Pseudo-waveforms were computed after radiometric normalization of the intensity data. The WARC showed again the strongest correlation with field measures of GeoCBI values (Spearman's Rho = 0.91), closely followed by the relative change of RH40 (Spearman's Rho = 0.89). The logarithmic model fitted using WARC offered an R-2 of 0.78 and a RMSE of 0.37. The accurate results obtained for the King Fire, with very different vegetation characteristics compared to our simulated data, demonstrate the robustness of the new metric proposed and its generalization capabilities to estimate the severity of fires.

ICESat (The Ice, Cloud, and Land Elevation Satellite)-2, as the new generation of NASA (National Aeronautics and Space Administration)?s ICESat mission, had been successfully launched in September 2018. The sensor onboard the satellite is a newly designed photon counting LiDAR (Light Detection And Ranging) system for the first time used in space. From the currently?released airborne simulation data, it can be seen that there exist numerous noise photons scattering from the atmosphere to even below the ground, especially for the vegetation areas. Therefore, relevant research on methods to distinguish the signal photons effectively?is crucial for further forestry applications. In this paper, a machine learning based approach was proposed to detect the potential signal photons from 14 MATLAS datasets across 3 sites in the USA. We found that k-NN (k-Nearest Neighbour) distance and the reachability of the photon towards the nearby signal centre showed good stability and contributed to a robust model establishment. The relevant quantitative assessment demonstrated that the machine learning approach could achieve high detection accuracy over 85% based on a very limited number of samples even in rough terrain conditions. Further analysis proved the potential of model transferability across different sites.

Satellite instruments provide a vantage point for studying aerosol loading consistently over different regions of the world. However, the typical lifetime of a single satellite platform is on the order of 5–15 years; thus, for climate studies, the use of multiple satellite sensors should be considered. Discrepancies exist between aerosol optical depth (AOD) products due to differences in their information content, spatial and temporal sampling, calibration, cloud masking, and algorithmic assumptions. Users of satellite-based AOD time-series are confronted with the challenge of choosing an appropriate dataset for the intended application. In this study, 16 monthly AOD products obtained from different satellite sensors and with different algorithms were inter-compared and evaluated against Aerosol Robotic Network (AERONET) monthly AOD. Global and regional analyses indicate that products tend to agree qualitatively on the annual, seasonal and monthly timescales but may be offset in magnitude. Several approaches were then investigated to merge the AOD records from different satellites and create an optimised AOD dataset. With few exceptions, all merging approaches lead to similar results, indicating the robustness and stability of the merged AOD products. We introduce a gridded monthly AOD merged product for the period 1995–2017. We show that the quality of the merged product is as least as good as that of individual products. Optimal agreement of the AOD merged product with AERONET further demonstrates the advantage of merging multiple products. This merged dataset provides a long-term perspective on AOD changes over different regions of the world, and users are encouraged to use this dataset.

To better understand and characterize current uncertainties in the important observational constraint of climate models of aerosol optical depth (AOD), we evaluate and intercompare 14 satellite products, representing nine different retrieval algorithm families using observations from five different sensors on six different platforms. The satellite products (super-observations consisting of 1∘×1∘ daily aggregated retrievals drawn from the years 2006, 2008 and 2010) are evaluated with AErosol RObotic NETwork (AERONET) and Maritime Aerosol Network (MAN) data. Results show that different products exhibit different regionally varying biases (both under- and overestimates) that may reach ±50 %, although a typical bias would be 15 %–25 % (depending on the product). In addition to these biases, the products exhibit random errors that can be 1.6 to 3 times as large. Most products show similar performance, although there are a few exceptions with either larger biases or larger random errors. The intercomparison of satellite products extends this analysis and provides spatial context to it. In particular, we show that aggregated satellite AOD agrees much better than the spatial coverage (often driven by cloud masks) within the 1∘×1∘ grid cells. Up to ∼50 % of the difference between satellite AOD is attributed to cloud contamination. The diversity in AOD products shows clear spatial patterns and varies from 10 % (parts of the ocean) to 100 % (central Asia and Australia). More importantly, we show that the diversity may be used as an indication of AOD uncertainty, at least for the better performing products. This provides modellers with a global map of expected AOD uncertainty in satellite products, allows assessment of products away from AERONET sites, can provide guidance for future AERONET locations and offers suggestions for product improvements. We account for statistical and sampling noise in our analyses. Sampling noise, variations due to the evaluation of different subsets of the data, causes important changes in error metrics. The consequences of this noise term for product evaluation are discussed.

ICESat-2 is the new generation of NASA's ICESat (Ice, Cloud and land Elevation Satellite) mission launched in September 2018. We investigate the potential of forest parameter estimation using metrics from photon counting LiDAR data, using an integrated dataset including photon counting LiDAR data from SIMPL (the Slope Imaging Multi-polarization Photon-counting LiDAR), airborne small footprint LiDAR data from G-LiHT and a stem map in Howland Research Forest, USA. First, we propose a noise filtering method based on a local outlier factor (LOF) with elliptical search area to separate the ground and canopy surfaces from noise photons. Next, a co-registration technique based on moving profiling is applied between SIMPL and G-LiHT data to correct geolocation error. Then, we calculate height metrics from both SIMPL and G-LiHT. Finally, we investigate the relationship between the two sets of metrics, using a stem map from field measurement to validate the results. Results of the ground and canopy surface extraction show that our methods can detect the potential signal photons effectively from a quite high noise rate environment in relatively rough terrain. In addition, results from co-registration between SIMPL and G-LiHT data indicate that the moving profiling technique to correct the geolocation error between these two datasets achieves favorable results from both visual and statistical indicators validated by the stem map. Tree height retrieval using SIMPL showed error of less than 3 m. We find good consistency between the metrics derived from the photon counting LiDAR from SIMPL and airborne small footprint LiDAR from G-LiHT, especially for those metrics related to the mean tree height and forest fraction cover, with mean R2 value of 0.54 and 0.6 respectively. The quantitative analyses and validation with field measurements prove that these metrics can describe the relevant forest parameters and contribute to possible operational products from ICESat-2.

The Ice, Cloud, and land Elevation Satellite (ICESat)-2 is the next generation of National Aeronautics and Space Administration (NASA)'s ICESat mission launched in September 2018. The new photon-counting LiDAR onboard ICESat-2 introduces new challenges to the estimation of forest parameters and their dynamics, the greatest being the abundant photon noise appearing in returns from the atmosphere and below the ground. To identify the potential forest signal photons, we propose an approach by using a local outlier factor (LOF) modified with ellipse searching area. Six test data sets from two types of photon-counting LiDAR data in the USA are used to test and evaluate the performance of our algorithm. The classification results for noise and signal photons showed that our approach has a good performance not only in lower noise rate with relatively flat terrain surface but also works even for a quite high noise rate environment in relatively rough terrain. The quantitative assessment indicates that the horizontal ellipse searching area gives the best results compared with the circle or vertical ellipse searching area. These results demonstrate our methods would be useful for ICESat-2 vegetation study.

Outbreaks of Xylella fastidiosa (Xf) in Europe generate considerable economic and environmental damage, and this plant pest continues to spread. Detecting and monitoring the spatio-temporal dynamics of the disease symptoms caused by Xf at a large scale is key to curtailing its expansion and mitigating its impacts. Here, we combined 3-D radiative transfer modelling (3D-RTM), which accounts for the seasonal background variations, with passive optical satellite data to assess the spatio-temporal dynamics of Xf infections in olive orchards. We developed a 3D-RTM approach to predict Xf infection incidence in olive orchards, integrating airborne hyperspectral imagery and freely available Sentinel-2 satellite data with radiative transfer modelling and field observations. Sentinel-2A time series data collected over a two-year period were used to assess the temporal trends in Xf-infected olive orchards in the Apulia region of southern Italy. Hyperspectral images spanning the same two-year period were used for validation, along with field surveys; their high resolution also enabled the extraction of soil spectrum variations required by the 3D-RTM to account for canopy background effect. Temporal changes were validated with more than 3000 trees from 16 orchards covering a range of disease severity (DS) and disease incidence (DI) levels. Among the wide range of structural and physiological vegetation indices evaluated from Sentinel-2 imagery, the temporal variation of the Atmospherically Resistant Vegetation Index (ARVI) and Optimized Soil-Adjusted Vegetation Index (OSAVI) showed superior performance for DS and DI estimation (r(VALUES)(2) > 0.7, p < 0.001). When seasonal understory changes were accounted for using modelling methods, the error of DI prediction was reduced 3-fold. Thus, we conclude that the retrieval of DI through model inversion and Sentinel-2 imagery can form the basis for operational vegetation damage monitoring worldwide. Our study highlight the value of interpreting temporal variations in model retrievals to detect anomalies in vegetation health.

We present new coarse resolution (0 .5 × 0.5) vegetation height and vegetation-cover fraction data sets between 60 S and 60 N for use in climate models and ecological models. The data sets are derived from 2003–2009 measurements collected by the Geoscience Laser Altimeter System (GLAS) on the Ice, Cloud and land Elevation Satellite (ICESat), the only LiDAR instrument that provides close to global coverage. Initial vegetation height is calculated from GLAS data using a development of the model of Rosette et al. (2008) with further calibration on desert sites. Filters are developed to identify and eliminate spurious observations in the GLAS data, e.g. data that are affected by clouds, atmosphere and terrain and as such result in erroneous estimates of vegetation height or vegetation cover. Filtered GLAS vegetation height estimates are aggregated in histograms from 0 to 70 m in 0.5 m intervals for each 0 .5 ×0.5. The GLAS vegetation height product is evaluated in four ways. Firstly, the Vegetation height data and data filters are evaluated using aircraft LiDAR measurements of the same for ten sites in the Americas, Europe, and Australia. Application of filters to the GLAS vegetation height estimates increases the correlation with aircraft data fromr = 0.33 to r = 0.78, decreases the root-mean-square error by a factor 3 to about 6 m (RMSE) or 4.5 m (68 % error distribution) and decreases the bias from 5.7 m to−1.3 m. Secondly, the global aggregated GLAS vegetation height product is tested for sensitivity towards the choice of data quality filters; areas with frequent cloud cover and areas with steep terrain are the most sensitive to the choice of thresholds for the filters. The changes in height estimates by applying different filters are, for the main part, smaller than the overall uncertainty of 4.5–6 m established from the site measurements. Thirdly, the GLAS global vegetation height product is compared with a global vegetation height product typically used in a climate model, a recent global tree height product, and a vegetation greenness product and is shown to produce realistic estimates of vegetation height. Finally, the GLAS bare soil cover fraction is compared globally with the MODIS bare soil fraction (r = 0.65) and with bare soil cover fraction estimates derived from AVHRR NDVI data ( r = 0.67); the GLAS treecover fraction is compared with the MODIS tree-cover fraction (r = 0.79). The evaluation indicates that filters applied to the GLAS data are conservative and eliminate a large proportion of spurious data, while only in a minority of cases at the cost of removing reliable data as well. The new GLAS vegetation height product appears more realistic than previous data sets used in climate models and ecological models and hence should significantly improve simulations that involve the land surface.

The authors wish to make the following corrections to this paper [...]

Plant pathogens cause significant losses to agricultural yields and increasingly threaten food security1, ecosystem integrity and societies in general2-5. Xylella fastidiosa is one of the most dangerous plant bacteria worldwide, causing several diseases with profound impacts on agriculture and the environment6. Primarily occurring in the Americas, its recent discovery in Asia and Europe demonstrates that X. fastidiosa's geographic range has broadened considerably, positioning it as a reemerging global threat that has caused socioeconomic and cultural damage7,8. X. fastidiosa can infect more than 350 plant species worldwide9, and early detection is critical for its eradication8. In this article, we show that changes in plant functional traits retrieved from airborne imaging spectroscopy and thermography can reveal X. fastidiosa infection in olive trees before symptoms are visible. We obtained accuracies of disease detection, confirmed by quantitative polymerase chain reaction, exceeding 80% when high-resolution fluorescence quantified by three-dimensional simulations and thermal stress indicators were coupled with photosynthetic traits sensitive to rapid pigment dynamics and degradation. Moreover, we found that the visually asymptomatic trees originally scored as affected by spectral plant-trait alterations, developed X. fastidiosa symptoms at almost double the rate of the asymptomatic trees classified as not affected by remote sensing. We demonstrate that spectral plant-trait alterations caused by X. fastidiosa infection are detectable previsually at the landscape scale, a critical requirement to help eradicate some of the most devastating plant diseases worldwide.