Radiative Transfer Model Simulations Research Articles

Investigation of spectral bands and sensor parameters for methane emission detection imaging spectrometer

Recently, interest has increased in developing remote sensing (RS) sensor systems with a high spatial and spectral resolution for quantifying methane (CH4) emissions from point sources. Evaluating sensor parameters can lead to a better understanding of technological advancements in CH4 emission estimation. This study addresses several important topics, including the robust algorithm for point source CH4 emission detection, selecting optimum CH4 absorption bands and their sensitivity analysis, and evaluating sensor parameters such as spatial and spectral resolution and signal-to-noise ratio (SNR). Since the matched filter algorithm efficiently detects point source emissions with palpable accuracy, it has been used here for the Airborne Visible InfraRed Imaging Spectrometer – Next Generation (AVIRIS-NG) sensor data acquired over the USA. The optimum band selection for CH4 detection was determined using a normalized sensitivity method to investigate the effects of aerosol, water vapor (WV), and surface albedo variations. The impact of these parameters on the CH4 absorption bands in the short-wave infrared (SWIR) range has been analyzed using MODerate resolution atmospheric TRANsmission (MODTRAN) radiative transfer (RT) model simulations. Using AVIRIS-NG data as a testbed, various methods have been used to reconstruct original data and generate data sets with different sensor specifications. The effects of the sensor parameters, such as spatial and spectral resolution and SNR, on the detection accuracy were analyzed. The results show the significance of SNR over the spatial and spectral resolution for better emission detection. It indicates the reasonable selection of sensor parameters that can help to create an optimal sensor system that will ultimately support the development of an imaging spectrometer suitable for CH4 emission estimation.

Twenty-Year Climatology of Solar UV and PAR in Cyprus: Integrating Satellite Earth Observations with Radiative Transfer Modeling

In this study, we present comprehensive climatologies of effective ultraviolet (UV) quantities and photosynthetically active radiation (PAR) over Cyprus for the period 2004 to 2023, leveraging the synergy of earth observation (EO) data and radiative transfer model simulations. The EO dataset, encompassing satellite and reanalysis data for aerosols, total ozone column, and water vapor, alongside cloud modification factors, captures the nuanced dynamics of Cyprus’s atmospheric conditions. With a temporal resolution of 15 min and a spatial of 0.05° × 0.05°, these climatologies undergo rigorous validation against established satellite datasets and are further evaluated through comparisons with ground-based global horizontal irradiance measurements provided by the Meteorological Office of Cyprus. This dual-method validation approach not only underscores the models’ accuracy but also highlights its proficiency in capturing intra-daily cloud coverage variations. Our analysis extends to investigating the long-term trends of these solar radiation quantities, examining their interplay with changes in cloud attenuation, aerosol optical depth (AOD), and total ozone column (TOC). Significant decreasing trends in the noon ultraviolet index (UVI), ranging from −2 to −4% per decade, have been found in autumn, especially marked in the island’s northeastern part, mainly originating from the (significant) positive trends in TOC. The significant decreasing trends in TOC, of −2 to −3% per decade, which were found in spring, do not result in correspondingly significant positive trends in the noon UVI since variations in cloudiness and aerosols also have a strong impact on the UVI in this season. The seasonal trends in the day light integral (DLI) were generally not significant. These insights provide a valuable foundation for further studies aimed at developing public health strategies and enhancing agricultural productivity, highlighting the critical importance of accurate and high-resolution climatological data.

Assessing Lidar Ratio Impact on CALIPSO Retrievals Utilized for the Estimation of Aerosol SW Radiative Effects across North Africa, the Middle East, and Europe

North Africa, the Middle East, and Europe (NAMEE domain) host a variety of suspended particles characterized by different optical and microphysical properties. In the current study, we investigate the importance of the lidar ratio (LR) on Cloud-Aerosol Lidar with Orthogonal Polarization–Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIOP-CALIPSO) aerosol retrievals towards assessing aerosols’ impact on the Earth-atmosphere radiation budget. A holistic approach has been adopted involving collocated Aerosol Robotic Network (AERONET) observations, Radiative Transfer Model (RTM) simulations, as well as reference radiation measurements acquired using spaceborne (Clouds and the Earth’s Radiant Energy System-CERES) and ground-based (Baseline Surface Radiation Network-BSRN) instruments. We are assessing the clear-sky shortwave (SW) direct radiative effects (DREs) on 550 atmospheric scenes, identified within the 2007–2020 period, in which the primary tropospheric aerosol species (dust, marine, polluted continental/smoke, elevated smoke, and clean continental) are probed using CALIPSO. RTM runs have been performed relying on CALIOP retrievals in which the default and the DeLiAn (Depolarization ratio, Lidar ratio, and Ångström exponent)-based aerosol-speciated LRs are considered. The simulated fields from both configurations are compared against those produced when AERONET AODs are applied. Overall, the DeLiAn LRs leads to better results mainly when mineral particles are either solely recorded or coexist with other aerosol species (e.g., sea-salt). In quantitative terms, the errors in DREs are reduced by ~26–27% at the surface (from 5.3 to 3.9 W/m2) and within the atmosphere (from −3.3 to −2.4 W/m2). The improvements become more significant (reaching up to ~35%) for moderate-to-high aerosol loads (AOD ≥ 0.2).

Integrating UAV hyperspectral data and radiative transfer model simulation to quantitatively estimate maize leaf and canopy nitrogen content

Crop nitrogen (N) content reflects crop nutrient status and plays an important role in precision nutrient management. Accurate crop N content estimation from remote sensing has been well documented. However, the robustness (i.e., the ability of a model to perform consistently across various conditions) of these methods under varied soil conditions or different growth stages has rarely been considered. We proposed a hybrid method that integrates in-situ measurements and the data simulated by a mechanistic model to improve the estimation of maize N content. In-situ data included hyperspectral images collected by Unmanned Aerial Vehicle (UAV), and leaf and canopy N content (LNC and CNC). A mechanistic radiative transfer model (PROSAIL-PRO) was used to generate simulated data, i.e., canopy reflectance paired with target crop traits (i.e., LNC, CNC). We compared the performance from the hybrid method with a machine learning method (Gaussian Process Regression) and six different vegetation indices (VIs) on four in-situ datasets collected at three study sites from 2021 to 2022. Results show that the hybrid method consistently performed the best for LNC estimation across four testing datasets (RRMSE ranging from 10.08% to 10.84%). For CNC estimation, the hybrid method had the best estimation results on two out of the four testing datasets and performed comparably to the best method on the other two datasets (RRMSE ranging from 13.89% to 25.21%). Next, we assessed the estimation robustness of the hybrid method, the machine learning, and the best-VI by comparing the mean (µ) and standard deviation (σ) of RRMSE across diverse water and N treatments (condition #1) and different growth stages (condition #2). Among 16 total cases (two crop traits by four study sites by two conditions), the hybrid method had 11 cases of smallest µ and seven cases of smallest σ, outperforming the machine learning (0/16 for µ, 4/16 for σ) and the best-VI (5/16 for µ, 5/16 for σ). These results underscore the greater robustness of the hybrid method. This study highlights the potential of integrating in-situ measurements and simulated data to improve estimation accuracy and robustness for maize LNC and CNC. The promising performance of the hybrid method suggests its applicability to a broader range of crops and various crop traits.

Comparing and combining data-driven and model-driven approaches to monitor wheat green area index with high spatio-temporal resolution satellites

Monitoring crops with high spatio-temporal resolution satellites provides valuable observations to ensure food security in the global change context. This study focuses on estimating the Green Area Index (GAI) to monitor wheat crops with a spatial resolution of 3 m and daily satellite observations from the SuperDove constellation. With an easier access to large training datasets of ground GAI measurements, and the improvement of the realism of radiative transfer model simulations, the choice of the optimal approach (data-driven or model-driven) constitutes a key question when retrieving GAI from satellite observations.This study compares a data-driven and a model-driven approach to estimate GAI from the SuperDove satellites. Both approaches are based on Gaussian Process Regression (GPR) machine learning techniques. The data-driven approach uses over 300 ground GAI measurements collected from 12 sites in China and France, each with 20 to 51 contrasting plots. The model-driven approach uses 10,000 simulations of top of canopy reflectance and the corresponding GAI values generated by the LESS radiative transfer model applied to 3D scenes built with the ADEL-Wheat (Architectural model of Development based on L-systems) model.Results confirm that the SuperDove reflectance are reliable and consistent with Sentinel-2 values. When estimating GAI using GPR with SuperDove top of canopy reflectance, the model-driven approach (R2 = 0.83, RMSE = 0.80, Accuracy = 0.01 and Precision = 0.80) generally outperforms the data-driven approach (R2 = 0.80, RMSE = 0.88, Accuracy = −0.13 and Precision = 0.87), except for small GAI values. In-silico experiments show that the uncertainties in the ground-measured GAI and the size and diversity of the training datasets limit the data-driven approach. In contrast, the model-driven approach is mostly constrained by the realism of the reflectance simulations, particularly for low GAI values.Two ensemble solutions based on the weighted average of the two previous approaches are then proposed: the global ensemble solution (R2 = 0.86, RMSE = 0.75, A = −0.06 and P = 0.74) where the weight is assumed independent from the GAI values, and the adaptive ensemble solution (R2 = 0.85, RMSE = 0.76, A = −0.08 and P = 0.76) where the weight depends on the GAI values. Both solutions perform similarly, improving both data-driven and model-driven approaches. Finally, applying both solutions to monitor wheat plots along the growth cycle allows clear differentiation of nitrogen modalities and cultivar effects. However, a minimum plot size of 12 m × 12 m (4 × 4 pixels) is recommended to minimize the co-registration errors and increase estimate precision.

CO2 concentration retrieval and emission rate estimation over Indian thermal power plants using radiative transfer approach and AVIRIS-NG data

Carbon dioxide (CO2) emissions from thermal power plants (TPPs) are regarded as the primary source of CO2 at the boundary-level atmosphere. The first step toward mitigating these emissions is to improve emission estimation methods at the facility level and establish a reliable emission reporting system. This study aims to establish a transparent and reliable methodology for detecting point source CO2 emissions from TPP and estimating the emission rate released from the stakes of TPP. A remote sensing method continuum interpolated band ratio (CIBR) has been utilized to detect and estimate CO2 emissions over Indian TPPs using Airborne Visible/InfraRed Imaging Spectrometer - Next Generation (AVIRIS-NG) data acquired by the ISRO-NASA joint campaign over India in 2015 and 2018. MODerate resolution atmospheric TRANsmission (MODTRAN) 5.3 radiative transfer (RT) model simulations were carried out for TPP sites to develop a CIBR algorithm for CO2 retrieval. Two AVIRIS-NG short-wave infrared (SWIR) bands were utilized for CO2 concentration retrieval, and the results from each band were compared based on their emission detection capabilities. Comparatively, SWIR-2 (1960–2120 nm) bands show higher sensitivity of CO2 than SWIR-1 (1560–1640 nm). Therefore, we utilize SWIR-2 results for the CO2 retrieval and emission rate estimation. Maximum CO2 emissions of 53.10, 46.36, 96.84, and 53.49 ppmv were detected at Maithon, Dhariwal, Udupi, and Parangipettai TPPs. The study includes one USA-based TPP to compare emission results for validation and test the method in diverse atmospheric conditions. In addition, we compared CO2 estimation results with AVIRIS-NG benchmark data. The simple band ratio method uses only three SWIR bands and operates with minimal computational facility, making it suitable for CO2 retrieval in hyperspectral data. Further, the integrated mass enhancement (IME) method has been used for emission rate estimation using CO2 concentration values from SWIR-2 retrieval results and wind speed at the sites. Yearly 5.96, 6.82, 2.29, and 3.52 Mt emissions were estimated for Maithon, Dhariwal, Udupi, and Parangipettai TPPs, respectively. The emission results have been validated by establishing a relationship between TPP's coal consumption and CO2 emissions. Further, estimated emission rates have been validated with former investigations and the CO2 baseline emission database of India's Central Electricity Authority (CEA) reports. Not having in-situ measurements and uncertainty in wind data may limit the study.

Exploring the Potential of GEDI in Characterizing Tree Height Composition Based on Advanced Radiative Transfer Model Simulations

Tree height composition describes the relative abundance of trees in different height levels and performs as a critical characteristic for community ecology. The recent launched full-waveform spaceborne LiDAR (Light Detection and Ranging), i.e., Global Ecosystem Dynamics Investigation (GEDI), can map canopy height, but whether this observation reflects tree height composition remains untested. In this study, we firstly conduct numerical simulations to explore to what extent tree height composition can be obtained from GEDI waveform signals. We simulate waveforms for diverse forest scenarios using GEDI simulator coupled with LESS (LargE-Scale remote sensing data and image Simulation), a state-of-the-art radiative transfer model. We devise a minimalistic model, Tree generation based on Asymmetric generalized Gaussian (TAG), for customizing tree objects to accelerate forest scene creation. The results demonstrate that tree objects generated by TAG perform similarly in LiDAR simulation with objects from commercial 3-dimensional software. Results of simulated GEDI waveforms reasonably respond to the variation of crown architectures in even-aged forests. GEDI waveforms have an acceptable ability to identify different height layers within multi-layer forests, except for fir forests with a cone-shaped crown. The shape metric of waveforms reflects the height of each layer, while retrieval accuracy decreases with the increases in height variations within each layer. A 5-m interval between layers is the minimum requirement so that the different height layers can be separated. A mixture of different tree species reduces the retrieval accuracy of tree height layers. We also utilize real GEDI observations to retrieve tree heights in multi-height-layer forests. The findings indicate that GEDI waveforms are also efficient in identifying tree height composition in practical forest scenarios. Overall, results from this study demonstrate that GEDI waveforms can reflect the height composition within typical forest stands.

Hybrid retrieval of grass biophysical variables based-on radiative transfer, active learning and regression methods using Sentinel-2 data in Marakele National Park

Biophysical variables such as leaf area index (LAI) and leaf chlorophyll content (LCC) are cited as essential biodiversity variables. A comprehensive comparison and integration of retrieval methods is needed for the estimation of biophysical variables such as LAI and LCC over a multispecies grass canopy. This study tested an assortment of five potentially robust, nonparametric regression methods (NPRMs) for inversion of radiative transfer model (RTM) to retrieve grass LAI and LCC in the Marakele National Park (MNP) of South Africa. The NPRMs used were, namely (i) Partial least squares regression (PLSR), (ii) Principle components regression (PCR), (iii) Kernel ridge regression (KRR), (iv) Random forest regression (RFR), and (v) K-nearest neighbours regression (KNNR). Furthermore, the study attempted to constrain the inversion process by using active learning (AL) techniques which ensured the selection of informative samples from a large pool of RTM simulations. Results show the most accurate grass LAI and LCC retrievals had lower relative root mean squared errors (RRMSEs) of 39.87% and 16.58% respectively. These findings have significant implications for the development of transferable rangeland monitoring systems in protected mountainous regions.

Towards practical semi-empirical models for the estimation of leaf and canopy water contents from hyperspectral reflectance

The knowledge of crop water status provides valuable information for agricultural water management. Most remote sensing methods for estimating water contents are based on statistical models and spectral features in the shortwave infrared region where water absorption dominates. However, the effects of leaf structure (Ns) and dry matter (expressed as leaf mass per area, LMA) on the spectral features in this region are not fully considered when estimating leaf water content (LWC) and canopy water content (CWC). This issue would lead to the data-specific empirical model, which lacks generality and is not transferable among different conditions. To fill this gap, this study for the first time evaluated the Ns and LMA effects on the spectral features, including the widely used normalized difference (ND)-type vegetation index (VI) and wavelet feature (WF) from wavelet transform. The leaf and canopy radiative transfer model simulations showed that the ND-type VI was sensitive to Ns at both leaf and canopy levels, whereas WF was sensitive to LMA at the canopy level. For example, the relationships between WF and CWC varied clearly with the level of LMA, especially for high values of CWC. Next, the optimal water index (OWI = ND1200,1500) and optimal WF (OWF = WF1600,8) were determined by minimizing their correlations with the Ns and LMA based on the leaf and canopy simulations. Unlike the existing spectral features applicable to only leaf or canopy level alone, OWI and OWF showed more consistent performance on estimating LWC and CWC. OWI- and OWF-based semi-empirical models were calibrated using model simulations and then evaluated using the independent measured datasets. The semi-empirical model based on OWI exhibited moderate estimation accuracy of LWC (R2 = 0.62, RMSE = 24.33 g/m2) and CWC (R2 = 0.69, RMSE = 0.15 kg/m2), outperforming previous ND-type VIs. In comparison, the best estimation accuracies for LWC (R2 = 0.80, RMSE = 17.47 g/m2) and CWC (R2 = 0.85, RMSE = 0.11 kg/m2) were obtained by the OWF-based semi-empirical model when the prior information of LMA was incorporated at the canopy level. These findings have great potential for mapping crop water status over large regions with universal semi-empirical models and hyperspectral satellite imagery.

Evaluation of EPIC oxygen bands stability with radiative transfer simulations over the South Pole

The Earth Polychromatic Imaging Camera (EPIC) onboard the Deep Space Climate Observatory (DSCOVR) satellite orbiting the Sun at the Lagrange-1 point was launched without onboard calibration systems. Vicarious calibration is conducted for 8 of the 10 UV/VIS/NIR channels using other low earth orbiting satellite instruments, while its two O2 bands (688 nm and 764 nm) rely on indirect moon-view calibrations because the same narrow-band O2 bands are not readily available from other in-flight instruments. This study compares EPIC measurements from the four O2 bands aiming at examining sensor stability over a uniquely suited location, i.e., the permanently snow-covered South Pole. The study utilizes radiative transfer model simulations with in-situ atmospheric soundings taken at South Pole during months of December and January from 2015 to 2022. The absolute discrepancy between the model simulations and observations is less than 1.0% for the two reference bands, but 5.75% and 15.63% for the 688 nm, and 764 nm absorption bands, respectively. The simulated A-band and B-band ratios are 16.09% and 4.74% higher than that from the observations. Various sensitivities are conducted to estimate possible contributions to the discrepancies from input atmospheric profiles, spectral surface albedos and surface BRDF. While none of the input uncertainties is likely to account for the large discrepancies in the oxygen absorption bands, a small shift in the instrument response function could be the main reason for these biases. On the other hand, the model simulations are able to capture systematic variations with observed angular measurements and explain the multi-year trends found in observed O2 band ratios due to satellite orbit shifting. When model simulated contributions from the angle variations are deducted from the observed O2 band ratios, the residual O2 band ratios are found to be stable since 2015.

Spire RO Thermal Profiles for Climate Studies: Initial Comparisons of the Measurements from Spire, NOAA-20 ATMS, Radiosonde, and COSMIC-2

Global Navigation Satellite System (GNSS) Radio Occultation (RO) data play an essential role in improving numerical weather prediction (NWP) and monitoring climate change. The NOAA Commercial RO Purchase Program (CDP) purchased RO data provided by Spire Global Inc. To ensure the data quality from Spire Global Inc. is consistent with other RO missions, we need to quantify their accuracy and retrieval uncertainty carefully. In this work, Spire Wet Profile (wet temperature profile) data from 7 September 2021 to 31 October 2022, processed by the University Corporation for Atmospheric Research (UCAR), and COSMIC-2 (Constellation Observing System for Meteorology, Ionosphere, and Climate-2/Formosa Satellite Mission 7) data are evaluated through comparison with NOAA-20 Advanced Technology Microwave Sounder (ATMS) microwave sounder measurements and collocated RS41 radiosonde measurements. Through the Community Radiative Transfer Model (CRTM) simulation, we convert the Spire and COSMIC-2 RO retrievals to ATMS brightness temperature (BT) at sounding channels CH07 to CH14 (temperature channels), with weighting function peak heights from 8 km to 35 km, and CH19 to CH22 (water vapor channels), with weighting function peak heights ranging from 3.2 km to 6.7 km, and compare the simulations with the collocated NOAA-20 ATMS measurements over ocean. Using ATMS observations as references, Spire and COSMIC-2 BTs agree well with ATMS within 0.07 K for CH07-14 and 0.20 K for CH19-22. The trends between Spire and COSMIC-2 are consistent within 0.07 K/year over the oceans for ATMS CH07-CH13 and CH19-22, indicating that Spire/COSMIC-2 wet profiles are, in general, compatible with each other over oceans. The RO retrievals and RS41 radiosonde observation (RAOB) comparison shows that above 0.2 km altitude, RS41 RAOB matches Spire/COSMIC-2 temperature profiles well with a temperature difference of <0.13 K, and the trends between Spire and COSMIC-2 are consistent within 0.08 K/year over land, indicating that Spire/COSMIC-2 wet profiles are overall compatible with each other through RS41 RAOB measurements over land. In addition, the consistency of Spire and COSMIC-2 based on different latitude intervals, local times, and signal-to-noise ratios (SNRs) through ATMS was evaluated. The results show that the performance of Spire is comparable to COSMIC-2, even though COSMIC-2 has a higher SNR. The high quality of RO profiles from Spire is expected to improve short- and medium-range global numerical weather predictions and help construct consistent climate temperature records.

A novel global snow depth retrieval method considering snow metamorphism and forest influence

Accurate snow depth (SD) information at global scales is highly important for validation and calibration of hydrological model, snowmelt runoff prediction, and global water supplies estimation. Passive microwave remote sensing has been proven to be an efficient method to detect SD at regional or global scales. However, numerous heterogeneous snow property parameters and forest parameters affect the microwave radiation characteristics, which is a great challenge for SD retrieval. In this study, a novel SD retrieval algorithm that comprehensively considered the multiple sensitive factors was developed at global scales by coupling the Microwave Emission Model of Layered Snowpacks (MEMLS) model and machine learning (ML) technique. The exponential correlation length (Pec), snow density (ρ), and forest cover fraction (fforest) are three sensitive parameters for retrieving SD from passive microwave observations based on the extended Fourier amplitude sensitivity test (EFAST) algorithm. The key parameter Pec, which characterizes the snow microstructure information inside snowpacks, was optimized by minimizing the brightness temperature between satellite observations and microwave radiative transfer model simulations by integrating the MEMLS, forest impact, and atmospheric radiation attenuation. The random forest (RF) ML technique was used, and the optimal combination of independent variables was selected by correlation heatmap and importance ranking of variables. Six selected essential parameters, including Pec, vertical polarized brightness temperature differences between 18.7 and 36.5 GHz, ρ from ERA5, fforest, elevation, and longitude together with target SD output were used to train the RF model. Seventy percent of the entire data at meteorological stations were randomly chosen to train the RF model, and the remaining 30% were adopted as the validation data from 2013 to 2020 to ensure the mutual independence of training data and validation data. Compared with independent in situ measurements, the overall RMSE of SD is 13.6 cm, which was noticeably lower than those of the international SD remote sensing products and reanalysis datasets, including AMSR2 (36.2 cm), GlobSnow 3.0 (25.3 cm), ERA5 (38.2 cm), and ERA5-land (29.9 cm). The results demonstrated that the new proposed algorithm, by coupling the MEMLS model with the ML technique, could significantly enhance the overall performance in SD retrieval at global scales.

Validation of Simplified Level 2 Prototype Processor Sentinel-2 fraction of canopy cover, fraction of absorbed photosynthetically active radiation and leaf area index products over North American forests

Canopy biophysical variables such as the fraction of canopy cover (fCOVER), fraction of absorbed photosynthetically active radiation (fAPAR), and leaf area index (LAI) are widely used for ecosystem modelling and monitoring. The Sentinel-2 mission was designed for systematic global mapping of these variables at 20 m resolution using imagery from the MultiSpectral Instrument. The Simplified Level 2 Prototype Processor (SL2P) is available as a baseline mapping solution. Previous validation over limited sites indicates that SL2P generally satisfies user requirements for all three variables over crops, but underestimates LAI over forests. In this study, Sentinel-2 fAPAR, fCOVER, and LAI products, from SL2P, were validated over 281 sites representative of most North American forest ecozones and also compared to Moderate Resolution Imaging Spectrometer (MODIS) and Copernicus Global Land Service (CGLS) products. In addition to meeting the Committee on Earth Observation Satellites Stage 3 validation requirements for these areas, our study also explores the relationship between bias in SL2P products and canopy clumping and provides empirical bias correction functions for each variable.SL2P was implemented within the Landscape Evolution and Forecasting Toolbox in Google Earth Engine both for efficiency and due to bugs in the Sentinel Application Platform implementation. SL2P was found to underestimate LAI by 20% to 50% over forests with LAI > 2; in agreement with other studies and with comparisons to MODIS and CGLS products. SL2P bias for fCOVER and fAPAR transitions from ∼0.1 at low values to ∼ − 0.1 at high values. Precision error, at one standard deviation, was ∼0.5 for LAI and slightly less than ∼0.1 for fCOVER and fAPAR. Total uncertainty was dominated by bias for LAI and was slightly greater than precision error for fCOVER and fAPAR. Target user requirements were satisfied for 51% of LAI, 37% of fCOVER and 31% of fAPAR comparisons to in-situ measurements. For all variables, accuracy exhibited weak to moderate linear relationships to clumping (r2 ≤0.52), but scatter plots indicated larger negative LAI biases over northern latitude sites where canopies exhibited greater clumping. With the exception of evergreen broadleaf forests, empirical bias correction using in-situ data reduced accuracy error by 40% for fCOVER, 57% for fAPAR and, 92% for LAI and increased the agreement rate with uncertainty requirements by up to 8%. Users of SL2P LAI over forests are recommended to apply bias correction or consider recalibrating SL2P with spatially heterogenous radiative transfer model simulations.

Volcanic Clouds Characterization of the 2020–2022 Sequence of Mt. Etna Lava Fountains Using MSG-SEVIRI and Products’ Cross-Comparison

From December 2020 to February 2022, 66 lava fountains (LF) occurred at Etna volcano (Italy). Despite their short duration (an average of about two hours), they produced a strong impact on human life, environment, and air traffic. In this work, the measurements collected from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) instrument, on board Meteosat Second Generation (MSG) geostationary satellite, are processed every 15 min to characterize the volcanic clouds produced during the activities. In particular, a quantitative estimation of volcanic cloud top height (VCTH) and ash/ice/SO2 masses’ time series are obtained. VCTHs are computed by integrating three different retrieval approaches based on coldest pixel detection, plume tracking, and HYSPLIT models, while particles and gas retrievals are realized simultaneously by exploiting the Volcanic Plume Retrieval (VPR) real-time procedure. The discrimination between ashy and icy pixels is carried out by applying the Brightness Temperature Difference (BTD) method with thresholds obtained by making specific Radiative Transfer Model simulations. Results indicate a VCTH variation during the entire period between 4 and 13 km, while the SO2, ash, and ice total masses reach maximum values of about 50, 100, and 300 Gg, respectively. The cumulative ash, ice, and SO2 emitted from all the 2020–2022 LFs in the atmosphere are about 750, 2300, and 670 Gg, respectively. All the retrievals indicate that the overall activity can be grouped into 3 main periods in which it passes from high (December 2020 to March 2021), low (March to June 2021), and medium/high (June 2021 to February 2022). The different products have been validated by using TROPOspheric Monitoring Instrument (TROPOMI) polar satellite sensor, Volcano Observatory Notices for Aviation (VONA) bulletins, and by processing the SEVIRI data considering a different and more accurate retrieval approach. The products’ cross-comparison shows a generally good agreement, except for the SO2 total mass in case of high ash/ice content in the volcanic cloud.

Fractional vegetation cover ratio estimated from radiative transfer modeling outperforms spectral indices to assess fire severity in several Mediterranean plant communities

The obtention of wall-to-wall fire severity estimates through reliable remote sensing-based techniques that align with management needs is a critical factor in post-fire decision-making processes. In this paper, we novelty proposed a multi-date change detection framework based on the variation in fractional vegetation cover (FCOVER), with enough ecological sense and physical basis to be generalizable across different plant communities and burned landscapes with varying environmental conditions. This framework meets the definition of fire severity operationally used in the field as a biophysical indicator when fire effects on the understory and overstory layers are linked. The FCOVER was retrieved from Sentinel-2 surface reflectance scenes by inverting PROSAIL-D radiative transfer model (RTM) simulations using the random forest regression algorithm. FCOVER retrievals were validated in the field using burned and unburned control plots. We computed the FCOVERr metric as the ratio of post-fire to pre-fire FCOVER. We tested the relationship of the FCOVERr and the most common bi-temporal spectral indices in the literature, i.e. the differenced Normalized Burn Ratio (dNBR), the Relative dNBR (RdNBR) and the Relativized Burn Ratio (RBR), with the Composite Burn Index (CBI) measured in field plots for validation purposes in two case-study wildfires in the western Mediterranean Basin. We also calculated the transferability of FCOVERr and the spectral indices between different plant communities within each site, as well as between sites. The predictive errors of pre and post-fire FCOVER retrievals were found to be low (RMSE ≈ 10%) for the two study sites. Overall, the FCOVERr metric provided more accurate CBI estimations (R2 = 0.87 ± 0.04) than spectral indices (R2 = 0.71 ± 0.13). The CBI was linearly related with the FCOVERr metric for both sites, whereas the type of relationship with spectral indices was not consistent, which translated into better transferability performance of the FCOVERr metric (nRMSE = 14.27% ± 3.75%) than that of the spectral indices (nRMSE = 21.97% ± 8.09%), not only between different Mediterranean plant communities within sites, but also between the two sites. Spectral indices underestimated moderate to high fire severity to a greater extent than FCOVERr in the CBI field plots, and misclassified fire severity in several areas with patchiness fire effects identified in the field. The FCOVERr product proposed in this study may be a sound choice for the operational identification of priority areas for post-fire management.

Observations of microphysical properties and radiative effects of a contrail cirrus outbreak over the North Atlantic

Abstract. Contrail cirrus constitute the largest radiative forcing (RF) component to the total aviation effect on climate. However, the microphysical properties and radiative effects of contrail cirrus and natural cirrus clouds in the same meteorological conditions are still not completely resolved. Motivated by these uncertainties, we investigate an extended cirrus region perturbed by aviation in the North Atlantic region (NAR) on 26 March 2014 during the Midlatitude Cirrus (ML-CIRRUS) experiment. On that day, high air traffic density in the NAR combined with large scale cold and humid ambient conditions favored the formation of a contrail cirrus outbreak situation. In addition, low coverage by low-level water clouds and the homogeneous oceanic albedo increased the sensitivity for retrieving cirrus properties and their radiative effect from satellite remote sensing. This allowed us to extend the current knowledge on contrail cirrus by combining airborne in situ, lidar and satellite observations. In the synoptic context of a ridge cirrus, an extended thin ice cloud with many persistent contrails and contrail cirrus has been observed for many hours with the geostationary Meteosat Second Generation (MSG)/Spinning Enhanced Visible and InfraRed Imager (SEVIRI) from the early morning hours until dissipation after noon. Airborne lidar observations aboard the German High Altitude and LOng Range Research Aircraft (HALO) suggest that this cirrus has a significant anthropogenic contribution from aviation. A new method based on in situ measurements was used to distinguish between contrails, contrail cirrus and natural cirrus based on ice number and gas phase NO concentrations. Results show that contrail effective radii (Reff) reach at most 11 µm, while contrail cirrus Reff can be as large as 51 µm. Contrail and contrail cirrus mean Reff is 18 % smaller than that of natural cirrus. We find that a difference in Reff between contrail cirrus and natural cirrus survives in this contrail cirrus outbreak event. As for radiative effects, a new method to estimate top-of-atmosphere instantaneous RF in the solar and thermal range is developed based on radiative transfer model simulations exploiting in situ and lidar measurements, satellite observations and ERA5 reanalysis data for both cirrus and cirrus-free regions. Broadband irradiances estimated from our simulations compare well with satellite observations from MSG, indicating that our method provides a good representation of the real atmosphere and can thus be used to determine the RF of ice clouds. For a larger spatial area around the flight path, we find that the contrail cirrus outbreak is warming in the early morning and cooling during the day. The methods presented here and the results will be valuable for future research to constrain uncertainties in the assessment of radiative impacts of contrail cirrus and natural cirrus and for the formulation and evaluation of contrail mitigation options.

Fraction of absorbed photosynthetically active radiation inversion algorithm of GF-1 data combining radiative transfer model simulation and deep learning

Fraction of absorbed photosynthetically active radiation inversion algorithm of GF-1 data combining radiative transfer model simulation and deep learning

A Calibrated Lunar Microwave Radiative Transfer Model Based on Satellite Observations

As a potential external calibration reference for spaceborne microwave sounding instruments, accurate and reliable information of lunar disk-averaged radiance at millimeter band are important and fundamental. Based on study for 2-D lunar scans of the Advanced Technology Microwave Sounder (ATMS) on board the NOAA-20 satellite, the lunar radiance spectrum from 23 to 183 GHz at full moon phase has been reported in our previous work. In this study, the performance of a lunar microwave radiative transfer model (RTM) developed by Keihm was investigated (cited as Keihm model in this paper) . By taking the ATMS observations as the reference truth, the surface emissivity in the lunar RTM can be calibrated. The calibrated RTM model was then evaluated by independent satellite observation data sets from AMSU (Advanced Microwave Sounding Unit) and MHS (Microwave Humidity Sounder) instruments on several NOAA satellites. Results show that with the calibrated model, significant improvement can be made to reduce the uncertainties in the lunar microwave RTM simulations at millimeter wavelengths.

Validation of Remote-Sensing Algorithms for Diffuse Attenuation of Downward Irradiance Using BGC-Argo Floats

Estimates of the diffuse attenuation coefficient (Kd) at two different wavelengths and band-integrated (PAR) were obtained using different published algorithms developed for open ocean waters spanning in type from explicit-empirical, semi-analytical and implicit-empirical and applied to data from spectral radiometers on board six different satellites (MODIS-Aqua, MODIS-Terra, VIIRS–SNPP, VIIRS-JPSS, OLCI-Sentinel 3A and OLCI-Sentinel 3B). The resultant Kds were compared to those inferred from measurements of radiometry from sensors on board autonomous profiling floats (BGC-Argo). Advantages of BGC-Argo measurements compared to ship-based ones include: 1. uniform sampling in time throughout the year, 2. large spatial coverage, and 3. lack of shading by platform. Over 5000 quality-controlled matchups between Kds derived from float and from satellite sensors were found with values ranging from 0.01 to 0.67 m−1. Our results show that although all three algorithm types provided similarly ranging values of Kd to those of the floats, for most sensors, a given algorithm produced statistically different Kd distributions from the two others. Algorithm results diverged the most for low Kd (clearest waters). Algorithm biases were traced to the limitations of the datasets the algorithms were developed and trained with, as well as the neglect of sun angle in some algorithms. This study highlights: 1. the importance of using comprehensive field-based datasets (such as BGC-Argo) for algorithm development, 2. the limitation of using radiative-transfer model simulations only for algorithm development, and 3. the potential for improvement if sun angle is taken into account explicitly to improve empirical Kd algorithms. Recent augmentation of profiling floats with hyper-spectral radiometers should be encouraged as they will provide additional constraints to develop algorithms for upcoming missions such as NASA’s PACE and SBG and ESA’s CHIME, all of which will include a hyper-spectral radiometer.

Retrieving global leaf chlorophyll content from MERIS data using a neural network method

Leaf chlorophyll content (LCC) is an indicator of plant physiological function and is an important parameter in estimating the carbon and water fluxes of terrestrial ecosystems. Spatiotemporally continuous LCC products are therefore needed at scales from the site level to the globe. In this study, we developed a neural network model for LCC retrieval from ENVISAT MERIS data based on radiative transfer model simulations. By considering the influence of canopy non-photosynthetic materials and the co-variations between LCC and biophysical parameters, a synthetic database was generated using the PROSAIL model with a good approximation to the canopy reflectance collection of MERIS data. Using a neural network trained from the synthetic database, we derived more realistic seasonal patterns of LCC than those using neural network models trained from synthetic databases generated without considering the influence of canopy non-photosynthetic materials or the parameter co-variations. A new global LCC product (GLOBMAP MERIS LCC) at 300-m resolution in 2003–2012 was generated using the neural network. It shows an improvement over the previous MERIS LCC product in capturing LCC seasonal variations in different plant functional types, and is potentially useful in improving the integration of physiological information within terrestrial ecosystem modeling and ecological monitoring across a range of spatial and temporal scales.