Fraction Of Absorbed Photosynthetically Active Radiation Products Research Articles

Rapid Vegetation Growth due to Shifts in Climate from Slow to Sustained Warming over Terrestrial Ecosystems in China from 1980 to 2018

The fraction of absorbed photosynthetically active radiation (FPAR) is a key biophysiological parameter of terrestrial ecosystems. However, due to a lack of data with adequate spatial resolution and in long enough time series, there have been limitations in exploring the spatiotemporal changes of vegetation and response to climate change. In this study, a 1 km spatial resolution and 8-day period length dataset (FPARANN) was developed covering the years 1980 to 2018 and evaluated on spatiotemporal change consistency by validating with Gross Primary Production (GPP) observations from the Chinese Flux Observation and Research Network (ChinaFLUX), and comparison with other FPAR products. FPARANN provided a comparable performance in capturing seasonal change observed through GPP, according to the coefficient of determination (R2): 0.50, 0.51, 0.70 and 0.74 averaged for all sites, forest sites, grassland sites and cropland flux sites, respectively. The new data had more spatial similarity to the MODIS FPAR product (FPARMCD15A2) with a greater R2 (0.77) and a lower RMSE (0.12) than other products. With a newly developed dataset, combined with FPARANN (1980–2003) and FPARMCD15A2 (2004–2018), an overall increasing trend in FPAR was found for over 81% of the vegetated area of China from 1980 to 2018. FPAR increased more rapidly for over 83.7% of the area in the 2010s, and at a slower pace for over 62.1% of the area in the early 2000s, which was attributed to a decadal shifting of climate change. This study implies the new dataset is useful in quantifying vegetation changes and would be an important data source for future study of the carbon cycle, soil erosion, or evapotranspiration, with great application potential.

The development of a global LAI and FAPAR product using GCOM-C/SGLI data

The Japan Aerospace Exploration Agency (JAXA) launched the Global Change Observation Mission - Climate (GCOM-C) satellite on December 23rd, 2017. As a part of the standard products of JAXA’s GCOM-C satellite, we developed the Leaf Area Index (LAI) and the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) product. In comparison to other global LAI and FAPAR products by the National Aeronautics and Space Administration (NASA) and the Copernicus Global Land Service (CGLS), our products have the following advantageous features: high spatial and temporal resolution (250 m and daily); the retrieval algorithm (look-up-table) is based on “the Forest Light Environmental Simulator” (FLiES) which is a full 3-D canopy radiative transfer model as well as a vegetation landscape model and a global forest landscape map created for this study. The algorithm and ancillary data sets are completely independent of the existing products that are more or less influenced by the MODIS LAI/FAPAR, providing an alternate and independent source of LAI and FAPAR. The dataset has been created since April 2018 and is freely available on JAXA’s G-Portal site (https://gportal.jaxa.jp/gpr). As a result of accuracy assessment with in-situ data, the Root Mean Square Errors (RMSEs) for total LAI and FAPAR were 0.776 and 0.112, respectively. We also compared our LAI and FAPAR estimates with three existing global datasets (PROBA-V/OLCI, MODIS and VIIRS product). The spatial distribution of LAI and FAPAR showed different characteristics among the datasets although their temporal trends were similar. The differences in estimation results among datasets due to differences in the retrieval algorithms, base map (or land cover data) and input data need to be evaluated in the future.

Estimation of daily FAPAR from MODIS instantaneous observations at forest sites

The daily Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) is crucial for vegetation dynamics monitoring and terrestrial carbon modeling. However, current FAPAR products only provide instantaneous values at the satellite transit times and a large data gap exists between instantaneous and daily FAPAR. To fill this gap, this study aimed to estimate daily FAPAR from instantaneous values retrieved from Moderate-resolution Imaging Spectroradiometer (MODIS) data. A framework combining method to estimate instantaneous FAPAR (instantaneous FAPAR method) and method to calculate daily FAPAR (instantaneous-to-daily FAPAR method) was proposed. Instantaneous FAPAR method for estimating intra-daily FAPAR from reduced observations was evaluated. Three instantaneous-to-daily FAPAR methods (ratio, simple average and solar zenith angle weighted average methods) were compared to estimate the daily FAPAR from continuous instantaneous values. The daily FAPAR was finally estimated from MODIS instantaneous values. The estimation and validation were performed at seven forest sites distributed in North America and Western Europe.The results show that the instantaneous FAPAR method can accurately simulate the intra-daily instantaneous FAPAR (RMSE ∼ 0.01, bias ∼ 0). The three instantaneous-to-daily FAPAR estimation methods showed similar performances (R2 ∼1, RMSE ∼ 0.01, and bias ∼ 0). The in situ instantaneous FAPAR values at the MODIS acquisition times underestimated the daily value during the non-peak growing season (FAPAR < 0.9) and the underestimation is also affected by the diffuse PAR radiation. MODIS instantaneous FAPAR underestimates in situ daily FAPAR over the year. The daily FAPAR estimates from in situ instantaneous values at MODIS acquisition times or the MODIS instantaneous FAPAR data reduce the gap between instantaneous and daily values. Validated by in situ data, the MODIS instantaneous FAPAR products should be further optimized to improve the accuracy of daily FAPAR estimation. This study lays the foundation for estimating the daily FAPAR from instantaneous values to improve GPP estimation.

Rapid Estimation of Decameter FPAR from Sentinel-2 Imagery on the Google Earth Engine

As a direct indicator of vegetation photosynthesis, the fraction of absorbed photosynthetically active radiation (FPAR) serves as a critical input in a series of land surface models. While existing satellite FPAR products are generally at coarse resolutions ranging from 250 m to 1 km, operational FPAR products at fine resolution are urgently needed in studying land surface processes at the plot scale. However, existing methods for estimating fine-resolution FPAR were mainly designed for Landsat data, and few studies have attempted to develop algorithms for Sentinel-2 data. In particular, the operational estimation of decameter FPAR has a higher requirement for the algorithms in terms of generalizability, efficiency, accuracy, and adaptability to Sentinel-2 data. In this paper, we developed a retrieval chain on the Google Earth Engine (GEE) platform to estimate FPAR by learning the relationship between MODIS FPAR and Sentinel-2 surface reflectance. Scale-consistent multilinear models were used to model the relationship between MODIS FPAR and Sentinel-2 surface reflectance, and the model coefficients were regressed from the selected training samples. To account for the spectral and spatial characteristics of the Sentinel-2 data, we designed criteria for selecting training samples and compared different band combinations. Three strategies for band combination were used: (1) green, red, and near infrared (NIR) bands at 10 m resolution (i.e., three bands); (2) green, red, NIR, and red edge (RE) 1, RE2, and RE3 bands at 20 m resolution (i.e., five bands); and (3) green, red, NIR, RE1, RE2, RE3, shortwave infrared1 (SWIR1) and SWIR2 bands at 20 m resolution (i.e., eight bands). Meanwhile, the official Sentinel Application Platform (SNAP) method has also been implemented to estimate the Sentinel FPAR at 10 m and 20 m resolutions for comparison. Both methods were applied to the western Guanzhong area, Shaanxi Province, China, for FPAR estimation of all cloud-free Sentinel-2 images in 2021. The results show that the scaling-based method using five bands at 20 m resolution was the most accurate compared to the in situ measurements (RMSE = 0.076 and R² = 0.626), which outperformed the SNAP method at 10 m and 20 m resolutions and the scaling-based method using other strategies. The results of the scaling-based method using all three strategies were highly consistent with the MODIS FPAR product, while the SNAP method systematically underestimated FPAR values compared to the MODIS FPAR products. The proposed method is more ready-to-use and more efficient than SNAP software. Considering that the service of the MODIS sensor is overdue, the proposed method can be extended to alternatives to MODIS products, such as VIIRS and Sentinel-3 data.

Global land surface 250 m 8 d fraction of absorbed photosynthetically active radiation (FAPAR) product from 2000 to 2021

Abstract. The fraction of absorbed photosynthetically active radiation (FAPAR) is a critical land surface variable for carbon cycle modeling and ecological monitoring. Several global FAPAR products have been released and have become widely used; however, spatiotemporal inconsistency remains a large issue for the current products, and their spatial resolutions and accuracies can hardly meet the user requirements. An effective solution to improve the spatiotemporal continuity and accuracy of FAPAR products is to take better advantage of the temporal information in the satellite data using deep learning approaches. In this study, the latest version (V6) of the FAPAR product with a 250 m resolution was generated from Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data and other information, as part of the Global LAnd Surface Satellite (GLASS) product suite. In addition, it was aggregated to multiple coarser resolutions (up to 0.25∘ and monthly). Three existing global FAPAR products (MODIS Collection 6; GLASS V5; and PRoject for On-Board Autonomy–Vegetation, PROBA-V, V1) were used to generate the time-series training samples, which were used to develop a bidirectional long short-term memory (Bi-LSTM) model. Direct validation using high-resolution FAPAR maps from the Validation of Land European Remote sensing Instrument (VALERI) and ImagineS networks revealed that the GLASS V6 FAPAR product has a higher accuracy than PROBA-V, MODIS, and GLASS V5, with an R2 value of 0.80 and root-mean-square errors (RMSEs) of 0.10–0.11 at the 250 m, 500 m, and 3 km scales, and a higher percentage (72 %) of retrievals for meeting the accuracy requirement of 0.1. Global spatial evaluation and temporal comparison at the AmeriFlux and National Ecological Observatory Network (NEON) sites revealed that the GLASS V6 FAPAR has a greater spatiotemporal continuity and reflects the variations in the vegetation better than the GLASS V5 FAPAR. The higher quality of the GLASS V6 FAPAR is attributed to the ability of the Bi-LSTM model, which involves high-quality training samples and combines the strengths of the existing FAPAR products, as well as the temporal and spectral information from the MODIS surface reflectance data and other information. The 250 m 8 d GLASS V6 FAPAR product for 2020 is freely available at https://doi.org/10.5281/zenodo.6405564 and https://doi.org/10.5281/zenodo.6430925 (Ma, 2022a, b) as well as at the University of Maryland for 2000–2021 (http://glass.umd.edu/FAPAR/MODIS/250m, last access 1 November 2022).

Evaluation of Global Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) Products at 500 m Spatial Resolution

The fraction of absorbed photosynthetically active radiation (FAPAR) is a key biophysical variable directly associated with the photosynthetic activity of plants. Several global FAPAR products with different spatial resolutions have been generated from remote sensing data, and much work has focused on validating them. However, those studies have primarily evaluated global FAPAR products at a spatial resolution of 1 km or more, whereas few studies have evaluated the global 500 m resolution FAPAR product distributed in recent years. Furthermore, there are a few FAPAR products, including black-sky, white-sky and blue-sky FAPAR datasets, and almost no studies have evaluated these products. In this article, three global FAPAR products at 500 m resolution, namely MODIS (only black-sky FAPAR), MUSES and EBR (black-sky, white-sky and blue-sky FAPAR) were compared to evaluate their temporal and spatial discrepancies and direct validation was conducted to compare these FAPAR products with the FAPAR values derived from the high-resolution reference maps from the Validation of Land European Remote Sensing Instrument (VALERI) and Implementing Multi-Scale Agricultural Indicators Exploiting Sentinels (IMAGINES) projects. The results showed that the MUSES FAPAR product exhibited the best spatial integrity, whereas the MODIS and EBR FAPAR products had many missing pixels in the equatorial rainforest regions and at high latitudes in the Northern Hemisphere. The MODIS, MUSES and EBR FAPAR products were generally consistent in their spatial patterns. However, a relatively large discrepancy among these FAPAR products was present in the equatorial rainforest regions and the middle and high latitude regions where the main vegetation type was forest. The differences between the black-sky and white-sky FAPAR datasets at the global scale were significant. In January, the MUSES and EBR black-sky FAPAR values were larger than their white-sky FAPAR values in the region north of 30° north latitude but they were smaller than their white-sky FAPAR values in the region south of 30° north latitude. In July, the MUSES and EBR black-sky FAPAR values were lower than their white-sky FAPAR values in the region north of 30° south latitude and they were larger than their white-sky FAPAR values in the region south of 30° south latitude. The temporal profiles of the MUSES FAPAR product were continuous and smooth, whereas those of the MODIS and EBR FAPAR products showed many fluctuations, particularly during the growing seasons. Direct validation indicated that the MUSES FAPAR product had the best accuracy (R2 = 0.6932, RMSE = 0.1495) compared to the MODIS FAPAR product (R2 = 0.6202, RMSE = 0.1710) and the EBR FAPAR product (R2 = 0.5746, RMSE = 0.1912).

Generating high spatial resolution GLASS FAPAR product from Landsat images

The fraction of absorbed photosynthetically active radiation (FAPAR) is an essential biophysical variable for monitoring vegetation growth and quantitatively describing the efficiency of light absorption by vegetation. Several long-term global satellite FAPAR products, such as MODIS, GLASS, GEOV2, and GIMMS3g-FAPAR, have been produced at coarse spatial resolutions, however, they are inclined to criticism over spatial heterogeneous landscapes due to mixed pixel effects. As the successor of GLASS FAPAR products, the high spatial resolution GLASS (Hi-GLASS) FAPAR product suite has been generated in this study. We developed a hybrid algorithm through integration of physically-based radiative transfer models and machine learning to estimate the black-sky, white-sky and blue-sky Hi-GLASS FAPAR products at 30 m resolution from Landsat imagery globally. The coupled soil-leaf-canopy (SLC) radiative transfer model was applied to depict the physical relationship between FAPAR and Landsat surface reflectance data, and its simulations were executed with uniform distributions of input parameters. Separate random forest models were trained with these simulation samples and applied to Landsat data to generate the Hi-GLASS FAPAR products. Extensive validation of the FAPAR products was performed for different sensors and biomes using in situ measurements from the VAlidation of Land European Remote sensing Instruments (VALERI), ImagineS, and Copernicus Ground Based Observations for Validation (GBOV) projects.Results demonstrated that our FAPAR products were highly accurate for blue-sky (R2 = 0.91, RMSE = 0.09, bias = −0.03), black-sky (R2 = 0.94, RMSE = 0.11, bias = −0.02), and white-sky (R2 = 0.88, RMSE = 0.10, bias = −0.02). R2 and RMSE values for different Landsat sensors were slightly different in the order of TM (0.84, 0.13), ETM+ (0.92, 0.12), and OLI (0.95, 0.11), while the corresponding statistics for biomes were ranked from the best to the worst: shrubs (0.78, 0.08), crops (0.93, 0.09), grasslands (0.88, 0.11), and forests (0.85, 0.13). Additionally, Hi-GLASS products displayed reasonable spatial distribution and seasonal variation of vegetation dynamics. The developed method performed better than those for producing the decametric-resolution and coarse resolution FAPAR products, enabling us to generate long-term 30 m FAPAR records from the 1980s.

A machine learning method trained by radiative transfer model inversion for generating seven global land and atmospheric estimates from VIIRS top-of-atmosphere observations

The Visible Infrared Imaging Radiometer Suite (VIIRS) has observed the Earth since 2011 and will continue for several decades. Unfortunately, few high-level land surface products have been produced and publicly released. The numerical inversion of radiative transfer model (RTM) has long been used for the retrieval of land surface and atmospheric variables from satellite data; however, it is computationally intensive. Some methods (e.g., using look-up tables) are efficient but are most suitable for estimating individual variable. Various machine learning (ML) models have been developed, mostly trained by either ground measurements that may not be adequate to represent different conditions, or RTM simulations that may not be realistic due to model uncertainty and unnecessary combinations of illumination-viewing geometries and variable values. In this study, we replaced the numerical inversion scheme by the ML algorithm that is trained using RTM inversion for estimating multiple variables simultaneously. Our training dataset for the ML models was generated by inverting the land and atmospheric variables from VIIRS top-of-atmosphere (TOA) reflectance data using a coupled land-surface–atmosphere RT model. Two multi-output ML algorithms were explored: backpropagation neural networks and random forest (RF) regression. The best-performing RF model was then applied to estimate seven land and atmospheric variables globally from VIIRS TOA data: the leaf area index (LAI), incident photosynthetically active radiation (PAR), fraction of absorbed photosynthetically active radiation (FAPAR), incident shortwave radiation (ISR), land surface albedo, land surface reflectance, and TOA albedo. The inversion results were validated using ground measurements at 54 sites and showed comparable accuracy to the numerical inversion scheme that is suitable for only small regions. An experiment was also conducted to test this method on a much larger region for detailed evaluation. Finally, seven global estimates in 2013 were experimentally produced. Inter-comparison with the existing satellite products showed that the VIIRS estimates had high consistency with the LAI and FAPAR products of the Global LAnd Surface Satellite (GLASS) products suite. Moreover, the VIIRS officially released the VNP43MA3 albedo and VNP09A1 surface reflectance products, the retrieved estimates also matched them well. It was also shown that simultaneous estimation of all seven variables was more accurate and efficient than estimating individual variable sequentially.

Quantifying Fundamental Vegetation Traits over Europe Using the Sentinel-3 OLCI Catalogue in Google Earth Engine.

Thanks to the emergence of cloud-computing platforms and the ability of machine learning methods to solve prediction problems efficiently, this work presents a workflow to automate spatiotemporal mapping of essential vegetation traits from Sentinel-3 (S3) imagery. The traits included leaf chlorophyll content (LCC), leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), and fractional vegetation cover (FVC), being fundamental for assessing photosynthetic activity on Earth. The workflow involved Gaussian process regression (GPR) algorithms trained on top-of-atmosphere (TOA) radiance simulations generated by the coupled canopy radiative transfer model (RTM) SCOPE and the atmospheric RTM 6SV. The retrieval models, named to S3-TOA-GPR-1.0, were directly implemented in Google Earth Engine (GEE) to enable the quantification of the traits from TOA data as acquired from the S3 Ocean and Land Colour Instrument (OLCI) sensor.Following good to high theoretical validation results with normalized root mean square error (NRMSE) ranging from 5% (FAPAR) to 19% (LAI), a three fold evaluation approach over diverse sites and land cover types was pursued: (1) temporal comparison against LAI and FAPAR products obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) for the time window 2016-2020, (2) spatial difference mapping with Copernicus Global Land Service (CGLS) estimates, and (3) direct validation using interpolated in situ data from the VALERI network. For all three approaches, promising results were achieved. Selected sites demonstrated coherent seasonal patterns compared to LAI and FAPAR MODIS products, with differences between spatially averaged temporal patterns of only 6.59%. In respect of the spatial mapping comparison, estimates provided by the S3-TOA-GPR-1.0 models indicated highest consistency with FVC and FAPAR CGLS products. Moreover, the direct validation of our S3-TOA-GPR-1.0 models against VALERI estimates indicated with regard to jurisdictional claims in good retrieval performance for LAI, FAPAR and FVC. We conclude that our retrieval workflow of spatiotemporal S3 TOA data processing into GEE opens the path towards global monitoring of fundamental vegetation traits, accessible to the whole research community.

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.

Variation of intra-daily instantaneous FAPAR estimated from the geostationary Himawari-8 AHI data

The fraction of absorbed photosynthetically active radiation (FAPAR) quantifies the efficiency of light absorption by vegetation. The intra-daily variation in FAPAR is important for monitoring vegetation growth but current studies to estimate the intra-daily FAPAR from remote sensing are limited. This study proposes to estimate the intra-daily variations of the direct, diffuse, and total FAPARs on clear days from the geostationary Himawari-8 AHI data. A look-up table inversion method was developed based on simulations of a radiative transfer model that couples a soil reflectance (GSV, General Spectral Vectors), canopy (PROSAIL), and atmospheric models (6SV, Second Simulation of a Satellite Signal in the Solar Spectrum). Field measurements were conducted over paddy rice fields in Northeast China from June 22 to August 25, 2019, to obtain continuous FAPAR measurements using an AccuPAR ceptometer and digital hemispherical photography. The instantaneous FAPAR values were analyzed and evaluated with field measurements and MODIS and GEOV2 FAPAR products. The results show that the retrieved AHI FAPAR values are consistent with the field measurements (R2 > 0.75). The AHI, MODIS, and GEOV2 FAPAR values display similar spatial and temporal patterns over the growing season (R2 > 0.66). The intra-daily variations in the total and direct FAPAR values agree with the field measurements, and both show a “bowl-shaped” pattern with the smallest value found at noon. The diffuse FAPAR remains stable before 16:00 local solar time (LST). The instantaneous total and direct FAPAR values at 10:00 – 10:30 LST usually assumed to be daily values underestimate the daily values, while the values at 9:30 and 14:30 LST can better represent the daily FAPAR. As a future study, global intra-daily instantaneous and daily FAPAR products could be generated by combining all geostationary satellite data.

Cross-ECV consistency at global scale: LAI and FAPAR changes

A framework is proposed for assessing the physical consistency between two terrestrial Essential Climate Variables (ECVs) products retrieved from Earth Observation at global scale. The methodology assessed the level of agreement between the temporal variations of Leaf Area Index (LAI) and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR). The simultaneous changes were classified according to their sign, magnitude and level of confidence, whereby the respective products uncertainties were taken into consideration. A set of proposed agreement metrics were used to identify temporal and spatial biases of non-coherency, non-significance, sensitivity and the overall level of agreement of the temporal changes between two ECVs. We applied the methodology using the Joint Research Center (JRC) Two-stream Inversion Package (TIP) products at 1 km, those provided by the Copernicus Global Land Service (CGLS) based on the SPOT/VGT and Proba-V at 1 km, and the MODIS MCD15A3 at 500 m. In addition, the same analysis was applied with aggregated products at a larger scale over Southern Africa. We found that the CGLS LAI and FAPAR products lacked consistency in their spatial and temporal changes and were severely affected by trends. The MCD15A3 products were characterized by the highest number of non-coherent changes between the two ECVs but temporal inconsistencies were mainly located over the eastern hemisphere. The JRC-TIP products were highly consistent. The results showed the advantages of physically-based retrieval algorithms, in both JRC-TIP and MODIS products, and indicated also that, except for MODIS over forests, aggregated products using an uncertainty-based weighted average led to higher agreement between the ECVs changes.

Evaluating the Performance of Sentinel-3A OLCI Land Products for Gross Primary Productivity Estimation Using AmeriFlux Data

Accurate and reliable estimation of gross primary productivity (GPP) is of great significance in monitoring global carbon cycles. The fraction of absorbed photosynthetically active radiation (FAPAR) and vegetation index products of the Moderate Resolution Imaging Spectroradiometer (MODIS) are currently the most widely used data in evaluating GPP. The launch of the Ocean and Land Colour Instrument (OLCI) onboard the Sentinel-3 satellite provides the FAPAR and the OLCI Terrestrial Chlorophyll Index (OTCI) products with higher temporal resolution and smoother spatial distribution than MODIS, having the potential to monitor terrain GPP. OTCI is one of the red-edge indices and is particularly sensitive to canopy chlorophyll content related to GPP. The purpose of the study is to evaluate the performance of OLCI FAPAR and OTCI for the estimation of GPP across seven biomes in 2017–2018. To this end, OLCI FAPAR and OTCI products in combination with insitu meteorological data were first integrated into the MODIS GPP algorithm and in three OTCI-driven models to simulate GPP. The modeled GPP (GPPOLCI-FAPAR and GPPOTCI) were then compared with flux tower GPP (GPPEC) for each site. Furthermore, the GPPOLCI-FAPAR and GPP derived from the MODIS FAPAR (GPPMODIS-FAPAR) were compared. Results showed that the performance of GPPOLCI-FAPAR was varied in different sites, with the highest R2 of 0.76 and lowest R2 of 0.45. The OTCI-driven models that include APAR data exhibited a significant relationship with GPPEC for all sites, and models using only OTCI provided the most varied performance, with the relationship between GPPOTCI and GPPEC from strong to nonsignificant. Moreover, GPPOLCI-FAPAR (R2 = 0.55) performed better than GPPMODIS-FAPAR (R2 = 0.44) across all biomes. These results demonstrate the potential of OLCI FAPAR and OTCI products in GPP estimation, and they also provide the basis for their combination with the soon-to-launch Fluorescence Explorer satellite and their integration with the Sentinel-3 land surface temperature product into light use models for GPP monitoring at regional and global scales.

ANALYSIS OF TEMPORAL AND SPATIAL CHARACTERISTICS OF MANGROVE PHYSIOLOGICAL STRUCTURE PARAMETERS QUANTITATIVE INVERSION BASED ON TIME SERIES SENTINEL-2 – TAKING SHANKOU MANGROVE NATURE RESERVE AS AN EXAMPLE

Abstract. The physiological structure parameter of vegetation is an important index to measure the ecological health status of mangroves, and it is of great value to measure the ecological health status. Taking Shankou Mangrove Nature Reserve of Guangxi as the study area, The Chlorophyll-A/B(CAB),Leaf Area Index (LAI),Fractional Vegetation Cover (FVC) and Fraction of absorbed photo synthetically active radiation (FAPAR) were calculated by using Sentinel-2 image data to calculate the chlorophyll content of mangrove vegetation in the study area. The BP neural network algorithm is used to verify the accuracy difference between the inversion results and the corresponding products of MODIS, and the dynamic changes of physiological structure parameters of vegetation in mangroves are further studied. Results showed that: (1)The correlation coefficients between LAI, CAB, FAPAR, FVC and MODIS products were higher than 0.71 in 95% confidence interval in mangrove years, it is proved that Sentinel-2A/B multispectral image inversion of mangrove physiological structure parameters has high accuracy and quality. (2)The physiological structure parameters of mangroves fluctuated during the year. In February, the lowest values of LAI, CAB, FAPAR and FVC were 0.30, 0.08, 0.08 and 0.13, respectively. The highest values were 0.69 in October, 0.29 in December, 0.27 in August, 0.40 in April, 0.24 and 0.24 in September, and 0.27 and 0.33 in LAI in November, respectively, with the highest LAI in October, 0.29 in December, 0.27 in August, 0.40 in April, 0.24 and 0.24 in September, and 0.27 and 0.33 in November. The results provide the basis for the monitoring of mangrove vegetation change and provide a reference for ecological assessment and protection.

Inversion of the Fraction of Absorbed Photosynthetically Active Radiation (FPAR) from FY-3C MERSI Data

An accurate inversion of the fraction of absorbed photosynthetically active radiation (FPAR) based on remote sensing data is particularly important for understanding global climate change. At present, there are relatively few studies focusing on the inversion of FPAR using Chinese autonomous satellites. This work intends to investigate the inversion of the FPAR obtained from the FengYun-3C (FY-3C) data of domestic satellites by using the PROSAIL model and the look-up table (LUT) algorithm for different vegetation types from various places in China. After analyzing the applicability of existing models using FY-3C data and MOD09GA data, an inversion strategy for FY-3C data is implemented. This strategy is applied to areas with various types of vegetation, such as grasslands, croplands, shrubs, broadleaf forests, and needleleaf forests, and produces FPAR products, which are cross-validated against the FPAR products from the Moderate Resolution Imaging Spectro Radiometer (MODIS), Geoland Version 1 (GEOV1), and Global Land Surface Satellite (GLASS). Accordingly, the results show that the FPAR retrieved from the FY-3C data has good spatial and temporal consistency and correlation with the three FPAR products. However, this technique does not favor all types of vegetation equally; the FY-FPAR is relatively more suitable for the inversion of grasslands and croplands during the lush period than for others. Therefore, the inversion strategy provides the potential to generate large-area and long-term sequence FPAR products from FY-3C data.

Retrieval of the Fraction of Radiation Absorbed by Photosynthetic Components (FAPARgreen) for Forest using a Triple-Source Leaf-Wood-Soil Layer Approach

The fraction of absorbed photosynthetically active radiation (FAPAR) is generally divided into the fraction of radiation absorbed by the photosynthetic components (FAPARgreen) and the fraction of radiation absorbed by the non-photosynthetic components (FAPARwoody) of the vegetation. However, most global FAPAR datasets do not take account of the woody components when considering the canopy radiation transfer. The objective of this study was to develop a generic algorithm for partitioning FAPARcanopy into FAPARgreen and FAPARwoody based on a triple-source leaf-wood-soil layer (TriLay) approach. The LargE-Scale remote sensing data and image simulation framework (LESS) model was used to validate the TriLay approach. The results showed that the TriLay FAPARgreen had higher retrieval accuracy, as well as a significantly lower bias (R2 = 0.937, Root Mean Square Error (RMSE) = 0.064, and bias = −6.02% for black-sky conditions; R2 = 0.997, RMSE = 0.025 and bias = −4.04% for white-sky conditions) compared to the traditional linear method (R2 = 0.979, RMSE = 0.114, and bias = −18.04% for black-sky conditions; R2 = 0.996, RMSE = 0.106 and bias = −16.93% for white-sky conditions). For FAPAR that did not take account of woody components (FAPARnoWAI), the corresponding results were R2 = 0.920, RMSE = 0.071, and bias = −7.14% for black-sky conditions, and R2 = 0.999, RMSE = 0.043, and bias = −6.41% for white-sky conditions. Finally, the dynamic FAPARgreen, FAPARwoody, FAPARcanopy and FAPARnoWAI products for a North America region were generated at a resolution of 500 m for every eight days in 2017. A comparison of the results for FAPARgreen against those for FAPARnoWAI and FAPARcanopy showed that the discrepancy between FAPARgreen and other FAPAR products for forest vegetation types could not be ignored. For deciduous needleleaf forest, in particular, the black-sky FAPARgreen was found to contribute only about 23.86% and 35.75% of FAPARcanopy at the beginning and end of the year (from January to March and October to December, JFM and OND), and 75.02% at the peak growth stage (from July to September, JAS); the black-sky FAPARnoWAI was found to be overestimated by 38.30% and 28.46% during the early (JFM) and late (OND) part of the year, respectively. Therefore, the TriLay approach performed well in separating FAPARgreen from FAPARcanopy, which is of great importance for a better understanding of the energy exchange within the canopy.

Can We Use Satellite-Based FAPAR to Detect Drought?

Drought in Australia has widespread impacts on agriculture and ecosystems. Satellite-based Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) has great potential to monitor and assess drought impacts on vegetation greenness and health. Various FAPAR products based on satellite observations have been generated and made available to the public. However, differences remain among these datasets due to different retrieval methodologies and assumptions. The Quality Assurance for Essential Climate Variables (QA4ECV) project recently developed a quality assurance framework to provide understandable and traceable quality information for Essential Climate Variables (ECVs). The QA4ECV FAPAR is one of these ECVs. The aim of this study is to investigate the capability of QA4ECV FAPAR for drought monitoring in Australia. Through spatial and temporal comparison and correlation analysis with widely used Moderate Resolution Imaging Spectroradiometer (MODIS), Satellite Pour l’Observation de la Terre (SPOT)/PROBA-V FAPAR generated by Copernicus Global Land Service (CGLS), and the Standardized Precipitation Evapotranspiration Index (SPEI) drought index, as well as the European Space Agency’s Climate Change Initiative (ESA CCI) soil moisture, the study shows that the QA4ECV FAPAR can support agricultural drought monitoring and assessment in Australia. The traceable and reliable uncertainties associated with the QA4ECV FAPAR provide valuable information for applications that use the QA4ECV FAPAR dataset in the future.

Evaluation of Three Long Time Series for Global Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) Products

The fraction of absorbed photosynthetically active radiation (FAPAR) is a critical input parameter in many climate and ecological models. Long time series of global FAPAR products are required for many applications, such as vegetation productivity, carbon budget calculations, and global change studies. Three long time series of global FAPAR products have been existing since the 1980s: Global LAnd Surface Satellite (GLASS) Advanced Very High Resolution Radiometer (AVHRR), National Centers for Environmental Information (NCEI) AVHRR, and third-generation Global Inventory Monitoring and Modeling System (GIMMS3g). Currently, no intercomparison studies exist that have evaluated these FAPAR products to understand their differences for effective applications. In this paper, these three long time series of global FAPAR products are first intercompared to evaluate their spatial and temporal consistencies, and then compared with FAPAR values derived from high-resolution reference maps of VAlidation of Land European Remote sensing Instruments sites. Our results demonstrate that the GLASS AVHRR FAPAR product is spatially complete, whereas the NCEI AVHRR and GIMMS3g FAPAR products contain many missing pixels, especially in rainforest regions and in middle- and high-latitude zones of the Northern Hemisphere. The GLASS AVHRR, NCEI AVHRR, and GIMMS3g FAPAR products are generally consistent in their spatial patterns. However, a relatively large discrepancy among these FAPAR products is observed in tropical forest regions and around 55°N–65°N. In latitudes between 15°N and 25°N, the mean GIMMS3g FAPAR values are clearly larger than the mean GLASS AVHRR and NCEI AVHRR FAPAR values during July–October each year. The GLASS AVHRR FAPAR product provides smooth FAPAR temporal profiles, whereas the NCEI AVHRR and GIMMS3g FAPAR products showed fluctuating trajectories, especially during the growing seasons. All three FAPAR products show high agreement coefficients (ACs) in vegetation regions with obvious seasonal variations and low ACs in tropical forest regions and sparsely vegetated areas. A comparison of these FAPAR products with the FAPAR values derived from high-resolution reference maps demonstrates that the GLASS AVHRR FAPAR product has the best performance [root mean square deviation (RMSD) = 0.0819 and bias = 0.0043], followed by the NCEI AVHRR FAPAR product (RMSD = 0.1061 and bias = 0.0371), and then finally, the GIMMS3g FAPAR product (RMSD = 0.1152 and bias = 0.0248).

Improving Satellite Estimates of the Fraction of Absorbed Photosynthetically Active Radiation Through Data Integration: Methodology and Validation

The fraction of absorbed photosynthetically active radiation (FAPAR) is a critical input in many climate and ecological models. The accuracy of satellite FAPAR products directly influences estimates of ecosystem productivity and carbon stocks. The targeted accuracy of FAPAR products is 10% or 0.05 for many applications. However, most current FAPAR products do not meet such requirements, and further improvements are still needed. In this paper, a data fusion scheme based on the multiple resolution tree (MRT) approach is developed to integrate multiple satellite FAPAR estimates at site and regional scales. MRT was chosen because of the superior computational efficiency compared with other fusion methods. The fusion scheme removed the bias in FAPAR estimates and resulted in a 15% increase in the $R^{2}$ and 3% reduction in the root-mean-square error compared with the average of individual FAPAR estimates. The regional-scale fusion filled in the missing values, and provided spatially consistent FAPAR distributions at different resolutions. Overall, MRT can be used to efficiently and accurately generate spatially and temporally continuous FAPAR data across both site and regional scales.

Estimation of FAPAR over Croplands Using MISR Data and the Earth Observation Land Data Assimilation System (EO-LDAS)

The Fraction of Absorbed Photosynthetically-Active Radiation (FAPAR) is an important parameter in climate and carbon cycle studies. In this paper, we use the Earth Observation Land Data Assimilation System (EO-LDAS) framework to retrieve FAPAR from observations of directional surface reflectance measurements from the Multi-angle Imaging SpectroRadiometer(MISR) instrument. The procedure works by interpreting the reflectance data via the semi-discrete Radiative Transfer (RT) model, supported by a prior parameter distribution and a dynamic regularisation model and resulting in an inference of land surface parameters, such as effective Leaf Area Index (LAI), leaf chlorophyll concentration and fraction of senescent leaves, with full uncertainty quantification. The method is demonstrated over three agricultural FLUXNET sites, and the EO-LDAS results are compared with eight years of in situ measurements of FAPAR and LAI, resulting in a total of 24 site years. We additionally compare three other widely-used EO FAPAR products, namely the MEdium Resolution Imaging Spectrometer (MERIS) Full Resolution, the MISR High Resolution (HR) Joint Research Centre Two-stream Inversion Package (JRC-TIP) and MODIS MCD15 FAPAR products. The EO-LDAS MISR FAPAR retrievals show a high correlation with the ground measurements ( r 2 &gt; 0.8), as well as the lowest average R M S E (0.14), in line with the MODIS product. As the EO-LDAS solution is effectively interpolated, if only measurements that are coincident with MISR observations are considered, the correlation increases ( r 2 &gt; 0.85); the R M S E is lower by 4–5%; and the bias is 2% and 7%. The EO-LDAS MISR LAI estimates show a strong correlation with ground-based LAI (average r 2 = 0.76), but an underestimate of LAI for optically-thick canopies due to saturation (average R M S E = 2.23). These results suggest that the EO-LDAS approach is successful in retrieving both FAPAR and other land surface parameters. A large part of this success is based on the use of a dynamic regularisation model that counteracts the poor temporal sampling from the MISR instrument.