Evaluation of Sentinel-3A and Sentinel-3B ocean land colour instrument green instantaneous fraction of absorbed photosynthetically active radiation

This article presents the evaluation of the Copernicus Sentinel-3 Ocean Land Colour Instrument (OLCI) operational terrestrial products corresponding to the green instantaneous Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) and its associated rectified channels. These products are estimated using OLCI spectral measurements acquired at the top of the atmosphere by a physically-based approach and are available operationally at full (300 m) and reduced (1.2 km) spatial resolution daily. The evaluation of the quality of the FAPAR OLCI values was based on the availability of data acquired over several years by Sentinel-3A (S3A) and Sentinel-3B (S3B). The evaluation exercise consisted of several stages: first, an overall comparison of the two S3 platform products was carried out during the tandem phase; second, comparison with an FAPAR climatology derived from the Medium Resolution Imaging Spectrometer (MERIS) provided information on the seasonality of various types of land cover. Then, direct comparisons were made with the same type of FAPAR products retrieved from two sensors, the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Sentinel-2 (S2) Multispectral Instrument (MSI), and with several ground-based estimates. In addition, an analysis of the efficiency of the retrieval algorithm with 3D radiative transfer simulations was performed. The results indicated that the consistency between daily and monthly S3A and S3B on a global scale was very good during the tandem phase (RMSD = 0.01 and a correlation R2 of 0.99 with a bias of 0.003); we found an agreement with a correlation of 0.95 and 0.93 (RMSD = 0.07 and 0.09) with JRC FAPAR S2 and JRC FAPAR MODIS, respectively. Compatibility with the ground-based data was between 0.056 and 0.24 in term of RMSD depending on the type of vegetation with an overall R2 of 0.89. Immler diagrams demonstrate that their variances were lower than the total uncertainties. The quality assurance using 3D radiative transfer model has shown that the apparent performance of the algorithm depends strongly on the type of in-situ measurement and canopy type

Improving the MODIS LAI compositing using prior time-series information

80NSSC21K1960 - NASA; 80NSSC22K0052 - NASA/Goddard Space Flight Center; 80NSSC22K0052 - NASAFirst author draf

Stage 1 Validation of Plant Area Index from the Global Ecosystem Dynamics Investigation

The Global Ecosystem Dynamics Investigation (GEDI) aims to provide improved characterization of forest structure, and plant area index (PAI) is one of many variables provided in the official GEDI Level 2B (L2B) product suite. However, since release, few quantitative validation studies have been conducted. To reach Stage 1 of the validation hierarchy proposed by the Land Product Validation (LPV) sub-group of the Committee on Earth Observation Satellites (CEOS) Working Group on Calibration and Validation (WGCV), we provide an initial assessment of PAI estimates from GEDI’s L2B product. This is achieved using 18 in situ reference measurements available through the Copernicus Ground Based Observations for Validation (GBOV) service. We show that GEDI L2B PAI retrievals provide a nearly unbiased estimate of effective (PAI e ) (RMSD = 0.95, bias = 0.02, slope = 1.07), but systematically underestimate PAI (RMSD = 1.42, bias = -0.91, slope = 0.77). This is attributed to an assumed random distribution of plant material in the algorithm. To reach Stage 2 of the CEOS WGCV LPV hierarchy, continued work is needed to validate the product against additional in situ reference measurements covering further locations and time periods

Sensor-independent LAI/FPAR CDR: reconstructing a global sensor-independent climate data record of MODIS and VIIRS LAI/FPAR from 2000 to 2022

Leaf area index (LAI) and fraction of photosynthetically active radiation (FPAR) are critical biophysical parameters for the characterization of terrestrial ecosystems. Long-term global LAI/FPAR products, such as the moderate resolution imaging spectroradiometer (MODIS) and the Visible Infrared Imaging Radiometer Suite (VIIRS), provide the fundamental dataset for accessing vegetation dynamics and studying climate change. However, existing global LAI/FPAR products suffer from several limitations, including spatial–temporal inconsistencies and accuracy issues. Considering these limitations, this study develops a sensor-independent (SI) LAI/FPAR climate data record (CDR) based on Terra-MODIS/Aqua-MODIS/VIIRS LAI/FPAR standard products. The SI LAI/FPAR CDR covers the period from 2000 to 2022, at spatial resolutions of 500 m/5 km/0.05∘, 8 d/bimonthly temporal frequencies and available in sinusoidal and WGS1984 projections. The methodology includes (i) comprehensive analyses of sensor-specific quality assessment variables to select high-quality retrievals, (ii) application of the spatial–temporal tensor (ST-tensor) completion model to extrapolate LAI and FPAR beyond areas with high-quality retrievals, (iii) generation of SI LAI/FPAR CDR in various projections and various spatial and temporal resolutions, and (iv) evaluation of the CDR by direct comparisons with ground data and indirectly through reproducing results of LAI/FPAR trends documented in the literature. This paper provides a comprehensive analysis of each step involved in the generation of the SI LAI/FPAR CDR, as well as evaluation of the ST-tensor completion model. Comparisons of SI LAI (FPAR) CDR with ground truth data suggest an RMSE of 0.84 LAI (0.15 FPAR) units with R2 of 0.72 (0.79), which outperform the standard Terra/Aqua/VIIRS LAI (FPAR) products. The SI LAI/FPAR CDR is characterized by a low time series stability (TSS) value, suggesting a more stable and less noisy dataset than sensor-dependent counterparts. Furthermore, the mean absolute error (MAE) of the CDR is also lower, suggesting that SI LAI/FPAR CDR is comparable in accuracy to high-quality retrievals. LAI/FPAR trend analyses based on the SI LAI/FPAR CDR agree with previous studies, which indirectly provides enhanced capabilities to utilize this CDR for studying vegetation dynamics and climate change. Overall, the integration of multiple satellite data sources and the use of advanced gap filling modeling techniques improve the accuracy of the SI LAI/FPAR CDR, ensuring the reliability of long-term vegetation studies, global carbon cycle modeling, and land policy development for informed decision-making and sustainable environmental management. The SI LAI/FPAR CDR is open access and available under a Creative Commons Attribution 4.0 License at https://doi.org/10.5281/zenodo.8076540 (Pu et al., 2023a).</p

HemiPy: A Python module for automated estimation of forest biophysical variables and uncertainties from digital hemispherical photographs

Digital hemispherical photography (DHP) is widely used to derive forest biophysical variables including leaf, plant, and green area index (LAI, PAI and GAI), the fraction of intercepted photosynthetically active radiation (FIPAR), and the fraction of vegetation cover (FCOVER). However, the majority of software packages for processing DHP data are based on a graphical user interface, making programmatic analysis difficult. Meanwhile, few natively support analysis of RAW image formats, while none incorporate the propagation or provision of uncertainties. To address these limitations, we present HemiPy, an open‐source Python module for deriving forest biophysical variables and uncertainties from DHP images in an automated manner. We assess HemiPy using simulated hemispherical images, in addition to multiannual time‐series and litterfall data from several forested National Ecological Observatory Network (NEON) sites, as well as comparison against the CAN‐EYE software package. Multiannual time‐series of PAI, FIPAR and FCOVER demonstrate HemiPy's outputs realistically represent expected temporal patterns. Comparison against litterfall data reveals reasonable accuracies are achievable, with RMSE values close to the error of ~1 unit typically attributed to optical LAI measurement approaches. HemiPy's PAI, FIPAR and FCOVER outputs demonstrate good agreement with CAN‐EYE. Consistent with previous studies, when compared to simulated hemispherical images, better agreement is observed for PAI derived using gap fraction near the hinge angle of 57.5° only, as opposed to values derived using gap fraction over a wider range of zenith angles. HemiPy should prove a useful tool for processing DHP images, and its open‐source nature means that it can be adopted, extended and further refined by the user community

Land potential assessment and trend-analysis using 2000–2021 FAPAR monthly time-series at 250 m spatial resolution

The article presents results of using remote sensing images and machine learning to map and assess land potential based on time-series of potential Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) composites. Land potential here refers to the potential vegetation productivity in the hypothetical absence of short–term anthropogenic influence, such as intensive agriculture and urbanization. Knowledge on this ecological land potential could support the assessment of levels of land degradation as well as restoration potentials. Monthly aggregated FAPAR time-series of three percentiles (0.05, 0.50 and 0.95 probability) at 250 m spatial resolution were derived from the 8-day GLASS FAPAR V6 product for 2000–2021 and used to determine long-term trends in FAPAR, as well as to model potential FAPAR in the absence of human pressure. CCa 3 million training points sampled from 12,500 locations across the globe were overlaid with 68 bio-physical variables representing climate, terrain, landform, and vegetation cover, as well as several variables representing human pressure including: population count, cropland intensity, nightlights and a human footprint index. The training points were used in an ensemble machine learning model that stacks three base learners (extremely randomized trees, gradient descended trees and artificial neural network) using a linear regressor as meta-learner. The potential FAPAR was then projected by removing the impact of urbanization and intensive agriculture in the covariate layers. The results of strict cross-validation show that the global distribution of FAPAR can be explained with an R2 of 0.89, with the most important covariates being growing season length, forest cover indicator and annual precipitation. From this model, a global map of potential monthly FAPAR for the recent year (2021) was produced, and used to predict gaps in actual vs. potential FAPAR. The produced global maps of actual vs. potential FAPAR and long-term trends were each spatially matched with stable and transitional land cover classes. The assessment showed large negative FAPAR gaps (actual lower than potential) for classes: urban, needle-leave deciduous trees, and flooded shrub or herbaceous cover, while strong negative FAPAR trends were found for classes: urban, sparse vegetation and rainfed cropland. On the other hand, classes: irrigated or post-flooded cropland, tree cover mixed leaf type, and broad-leave deciduous showed largely positive trends. The framework allows land managers to assess potential land degradation from two aspects: as an actual declining trend in observed FAPAR and as a difference between actual and potential vegetation FAPAR

Copernicus Cal/Val Solution - D3.3 - Copernicus operational FRM network and supersites

- Identify measurement gaps, considering the existing ground-based Cal/Val measurement campaigns and networks (as outcome from Tasks 2.4 and 2.5) - Identify rationalization and optimization pathways: e.g., use of common instrumentation, protocols, and standards across sites; cross-Sentinel use of generic measurements; “supersite” approaches to minimize maintenance costs, as well as possible synergies with other European or international programs - Define a minimum set of requirements for a “Copernicus” label for measurement sites, addressing measurement protocols, documentation, availability, data policy; define a certification process - Principles and need to evaluate degree of equivalence between individual networks and sites (inter-comparisons) and for other comparison measurement

Not just a pretty picture: Mapping Leaf Area Index at 10 m resolution using Sentinel-2

Achieving the Global Climate Observing System goal of 10 m resolution leaf area index (LAI) maps is critical for applications related to climate adaptation, sustainable agriculture, and ecosystem monitoring. Five strategies for producing 10 m LAI maps from Sentinel-2 (S2) imagery are evaluated: i. bi-cubic interpolation of 20 m resolution S2 LAI maps from the Simplified Level 2 Prototype Processor Version 1 (SL2PV1) as currently performed by the Sentinel Applications Platform (SNAP), ii. applying SL2PV1 to S2 reflectance bands spatially downscaled to 10 m using bi-cubic interpolation (BICUBIC), iii. Applying SL2PV1 to S2 reflectance bands spatially downscaled to 10 m using Area to Point Regression Kriging (ATPRK), iv. using a recalibrated version of SL2PV1 (SL2PV2) requiring only three S2 10m bands, and iv) a novel use of the previously developed Active Learning Regularization (ALR) approach to locally approximate the SL2PV1 algorithm using only 10 m bands. Algorithms were assessed in terms of per-pixel accuracy and spatial metrics when comparing 10 m LAI maps produced using either actual S2 imagery or S2 imagery synthesized from airborne hyperspectral imagery to reference 10 m LAI maps traceable to in-situ fiducial reference measurements at 10 sites across the continental US. ATPRK and ALR algorithms had the lowest precision error of ~0.15 LAI, compared to 0.19 LAI for SNAP and BICUBIC and 0.35 LAI for SL2PV2, and ranked highest in terms of local correlation and Structural Similarity Index measure as well as qualitative agreement with reference maps. SL2PV2 LAI showed evidence of saturation over forests related to decreased sensitivity of input visible reflectance. All algorithms had a similar uncertainty of ~0.55 LAI compared to traceable reference maps, due to the trade-off between bias and precision. However, ATPRK and ALR uncertainty reduced to 0.11 LAI and 0.16 LAI, respectively, when compared to reference maps that ignored canopy clumping. These results suggest that both ATPRK and ALR are suitable for producing 10 m S2 LAI maps assuming bias due to local clumping can be corrected in the underlying SL2PV1 algorithm

A Method of Retrieving 10-m Spectral Surface Albedo Products from Sentinel-2 and MODIS data

This study proposed a new method of retrieving 10-m spectral surface albedo products. Three crucial components are incorporated into this high-resolution surface albedo generation system. Firstly, a deep learning system, CloudFCN based on the U-net paradigm has been developed. This produces the best available cloud detection of any algorithm published to date. Secondly, an advanced atmospheric correction model, the Sensor Invariant Atmospheric Correction (SIAC) is employed. The SIAC method considers the surface BRDF effects as these are usually ignored, because the atmosphere correction is a large signal and the largest uncertainty in converting top-of-atmosphere reflectance to top-of-canopy surface reflectance. Thirdly, an endmember-based new technology will be used to retrieve high-resolution albedo from high-resolution reflectance by combining downscaled MODIS BRDF. These methods are further described alongside results shown of the different stages and the final high resolution albedo

Intercomparison of Surface Albedo Retrievals from MISR, MODIS, CGLS Using Tower and Upscaled Tower Measurements

Surface albedo is of crucial interest in land–climate interaction studies, since it is a key parameter that affects the Earth’s radiation budget. The temporal and spatial variation of surface albedo can be retrieved from conventional satellite observations after a series of processes, including atmospheric correction to surface spectral bi-directional reflectance factor (BRF), bi-directional reflectance distribution function (BRDF) modelling using these BRFs, and, where required, narrow-to-broadband albedo conversions. This processing chain introduces errors that can be accumulated and then affect the accuracy of the retrieved albedo products. In this study, the albedo products derived from the multi-angle imaging spectroradiometer (MISR), moderate resolution imaging spectroradiometer (MODIS) and the Copernicus Global Land Service (CGLS), based on the VEGETATION and now the PROBA-V sensors, are compared with albedometer and upscaled in situ measurements from 19 tower sites from the FLUXNET network, surface radiation budget network (SURFRAD) and Baseline Surface Radiation Network (BSRN) networks. The MISR sensor onboard the Terra satellite has 9 cameras at different view angles, which allows a near-simultaneous retrieval of surface albedo. Using a 16-day retrieval algorithm, the MODIS generates the daily albedo products (MCD43A) at a 500-m resolution. The CGLS albedo products are derived from the VEGETATION and PROBA-V, and updated every 10 days using a weighted 30-day window. We describe a newly developed method to derive the two types of albedo, which are directional hemispherical reflectance (DHR) and bi-hemispherical reflectance (BHR), directly from three tower-measured variables of shortwave radiation: downwelling, upwelling and diffuse shortwave radiation. In the validation process, the MISR, MODIS and CGLS-derived albedos (DHR and BHR) are first compared with tower measured albedos, using pixel-to-point analysis, between 2012 to 2016. The tower measured point albedos are then upscaled to coarse-resolution albedos, based on atmospherically corrected BRFs from high-resolution Earth observation (HR-EO) data, alongside MODIS BRDF climatology from a larger area. Then a pixel-to-pixel comparison is performed between DHR and BHR retrieved from coarse-resolution satellite observations and DHR and BHR upscaled from accurate tower measurements. The experimental results are presented on exploring the parameter space associated with land cover type, heterogeneous vs. homogeneous and instantaneous vs. time composite retrievals of surface albedo