Fraction Of Absorbed Photosynthetically Active Radiation Estimation Research Articles

Identification of Robust Hybrid Inversion Models on the Crop Fraction of Absorbed Photosynthetically Active Radiation Using PROSAIL Model Simulated and Field Multispectral Data

The fraction of absorbed photosynthetically active radiation (FPAR), which represents the capability of vegetation-absorbed solar radiation to accumulate organic matter, is a crucial indicator of photosynthesis and vegetation growth status. Although a simplified semi-empirical FPAR estimation model was easily obtained using vegetation indices (VIs), the sensitivity and robustness of VIs and the optimal inversion method need to be further evaluated and developed for canola FPAR retrieval. The objective of this study was to identify the robust hybrid inversion model for estimating the winter canola FPAR. A field experiment with different sow dates and densities was conducted over two growing seasons to obtain canola FPARs. Moreover, 29 VIs, two machine learning algorithms and the PROSAIL model were incorporated to establish the FPAR inversion model. The results indicate that the OSAVI, WDRVI and mSR had better capability for revealing the variations of the FPAR. Three parameters of leaf area index (LAI), solar zenith angle (SZA) and average leaf inclination angle (ALA) accounted for over 95% of the total variance in the FPARs and OSAVI exhibited a greater resistance to changes in the leaf and canopy parameters of interest. The hybrid inversion model with an artificial neural network (ANN-VIs) performed the best for both datasets. The optimal hybrid inversion model of ANN-OSAVI achieved the highest performance for canola FPAR retrieval, with R2 and RMSE values of 0.65 and 0.051, respectively. Finally, the work highlights the usefulness of the radiation transfer model (RTM) in quantifying the crop canopy FPAR and demonstrates the potential of hybrid model methods for retrieving the canola FPAR at each growth stage.

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.

Evaluation of Global Decametric-Resolution LAI, FAPAR and FVC Estimates Derived from Sentinel-2 Imagery

Global biophysical products at decametric resolution derived from Sentinel-2 imagery have emerged as a promising dataset for fine-scale ecosystem modeling and agricultural monitoring. Evaluating uncertainties of different Sentinel-2 biophysical products over various regions and vegetation types is pivotal in the application of land surface models. In this study, we quantified the performance of Sentinel-2-derived Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), and Fractional Vegetation Cover (FVC) estimates using global ground observations with consistent measurement criteria. Our results show that the accuracy of vegetation and non-vegetated classification based on Sentinel-2 surface reflectance products is greater than 95%, which indicates the vegetation identification is favorable for the practical application of biophysical estimates, as several LAI, FAPAR, and FVC retrievals were derived for non-vegetated pixels. The rate of best retrievals is similar between LAI and FAPAR estimates, both accounting for 87% of all vegetation pixels, while it is almost 100% for FVC estimates. Additionally, the Sentinel-2 FAPAR and FVC estimates agree well with ground-measurements-derived (GMD) reference maps, whereas a large discrepancy is observed for Sentinel-2 LAI estimates by comparing with both GMD effective LAI (LAIe) and actual LAI (LAI) reference maps. Furthermore, the uncertainties of Sentinel-2 LAI, FAPAR and FVC estimates are 1.09 m2/m2, 1.14 m2/m2, 0.13 and 0.17 through comparisons to ground LAIe, LAI, FAPAR, and FVC measurements, respectively. Given the temporal difference between Sentinel-2 observations and ground measurements, Sentinel-2 LAI estimates are more consistent with LAIe than LAI values. The robustness of evaluation results can be further improved as long as more multi-temporal ground measurements across different regions are obtained. Overall, this study provides fundamental information about the performance of Sentinel-2 LAI, FAPAR, and FVC estimates, which imbues our confidence in the broad applications of these decametric products.

Generalized FPAR estimation methods from various satellite sensors and validation

Fraction of absorbed photosynthetically active radiation (FPAR) is a key parameter in ecosystem productivity and carbon balance estimation. FPAR can be estimated from various satellite images but its product might have significant differences due to the usage of various algorithms. This work proposes a generalized FPAR retrieval method for Landsat 5/ Thematic Mapper (TM), Landsat 7/Enhanced Thematic Mapper Plus (ETM+), Landsat 8/ Operational Land Imager (OLI), Moderate Resolution Imaging Spectroradiometer(MODIS), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), SPOT/VEGETATION, and HJ-1/CCD in the form of two linear models, namely, the BOA (bottom of atmosphere) model and the TOA (top of atmosphere) model, to reduce FPAR discrepancy among sensors. The BOA model estimates canopy FPAR from land surface multiband reflectance after atmospheric correction, whereas the TOA model estimates FPAR from apparent multiband reflectance at the TOA. Analysis results found that the FPAR errors from the BOA and TOA models were approximately 0.03 and 0.06, respectively, and the difference among FPAR estimated from different sensors was turned out to be less than 0.015 in theory. In addition, the FPAR difference between the two models was generally small, especially under low aerosol optical depth (AOD) and densely vegetated conditions. Ground validation using the datasets from the HiWATER and Validation of Land European Remote Sensing Instruments (VALERI) programs showed that the FPAR errors were 0.16 and 0.18 for the BOA and TOA models, respectively, which might be affected by the time interval of ground and satellite observation, spatial scale effect, and atmospheric correction errors. Moreover, this paper applied the new methods to estimate FPAR in different dates at the Heihe River basin and conduct a cross-comparison of FPAR from various sensors, and consequently obtained acceptable results.

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 fraction of absorbed photosynthetically active radiation from multiple satellite data: Model development and validation

Abstract The fraction of absorbed photosynthetically active radiation (FAPAR) is a critical input in numerous climatological and ecological models. The targeted accuracy of FAPAR products is 10%, or 0.05, for many applications. However, most of the FAPAR products in current usage have not yet fulfilled the accuracy requirement, thus requiring further improvements. In this study, a new FAPAR estimation model is developed on the basis of the radiative transfer (RT) for a horizontally homogeneous continuous canopy. The spatially explicit parameterization of leaf-scattering and soil background reflectance is derived from a 13-year Moderate Resolution Imaging Spectroradiometer (MODIS) albedo database. The new algorithm requires the input of leaf area index (LAI), which is estimated by a hybrid geometric optical-RT model suitable for both continuous and discrete vegetation canopies in this study. The model calculated radiative surface fluxes, i.e., canopy reflectance, absorption, and transmittance, are compared with the reference data from Radiation transfer Model Intercomparison (RAMI) exercise. The evaluation results show that the model estimated FAPAR has an uncertainty of 0.08 over homogeneous and heterogeneous canopies. The FAPAR estimates from the new model are intercompared with reference satellite FAPAR products and validated with ground-based measurements at the Validation of Land European Remote Sensing Instruments (VALERI) AmeriFlux experimental sites. The validation results show that the FAPAR estimates from the new model are comparable to or slightly better in performance than the MODIS and the Multi-angle Imaging SpectroRadiometer (MISR) FAPAR products when using corresponding satellite LAI product values as the input. The FAPAR estimates are further improved when using the new LAI estimates from the hybrid model as the input. The new model adequately identifies the growing seasons and produces smooth time series curves of estimated FAPAR during a specific duration. The uncertainty is reduced to 0.1 when validating with total FAPAR measurements, and 0.08 when validating with green FAPAR measurements. The improvements are apparent in grasslands and forests with an uncertainty reduction of 0.06. The regional-scale application of the presented model generates consistent FAPAR maps at spatial resolutions of 30 m, 500 m, and 1 km from the Landsat, MODIS, and MISR data, respectively.

Sensitivity analysis of retrieving fraction of absorbed photosynthetically active radiation (FPAR) using remote sensing data

The fraction of absorbed photosynthetically active radiation (FPAR) is a key biophysical variable for vegetation productivity estimation, vegetation growth condition monitoring, and climate change analysis. The object of this study is to conduct a comprehensive sensitivity analysis on the contributing factors of FPAR variation, in order to reveal the impacts of the interference factors on FPAR estimation using remote sensing and to improve the accuracy of the model. The analysis was conducted based on a coupled leaf-canopy–atmosphere radiative transfer model and a global sensitivity analysis algorithm. Due to the impact of diffuse FPAR on the variation of FPAR, we also analyzed the determinant factors of diffuse FPAR. The results showed that leaf area index (LAI) and the average leaf inclination angle (ALA) are two important canopy structural parameters determining FPAR variability, especially before canopy closure. FPAR was found to be affected differently by these two variables with canopy development. Canopy background reflectance has a great impact on FPAR estimation at low LAI values (LAI<1.0). For the effects of leaf biochemical variables, the contribution of leaf chlorophyll concentration (LCC) to FPAR variability is negligible at the early vegetative growth stage, but becomes dominant starting from the exuberant growth periods, with the contribution increasing consistently with LAI. For the external factors, solar zenith angle (SZA) and aerosol optical depth (AOD) are two important driving factors significantly influencing FPAR by changing the optical path length and the proportion of the diffuse photosynthetically active radiation (PAR). SZA influences both FPAR and diffuse FPAR throughout the growing period, while, AOD is the most important determinant factor for diffuse FPAR variation. When LAI is low, the impact of canopy background reflectance on the diffuse FPAR cannot be ignored. The analysis also revealed that the diffuse FPAR variation significantly influences on the vegetation productivity estimation. In summary, this study shows that the variation of FPAR is affected by different factors in different ways. It provides a comprehensive understanding of the contributing factors to FPAR variability including the diffuse FPAR, which could be potentially useful in developing FPAR retrieval algorithm from remote sensing data.

Evaluation of Chlorophyll-Related Vegetation Indices Using Simulated Sentinel-2 Data for Estimation of Crop Fraction of Absorbed Photosynthetically Active Radiation

In recent years, the impact of chlorophyll content on the estimation of the fraction of absorbed photosynthetically active radiation (FPAR) has attracted increased attention. In this study, chlorophyll-related vegetation indices (VIs) were selected and tested for their capability in crop FPAR estimation using simulated Sentinel-2 data. These indices can be categorized into four classes: 1) the ratio indices; 2) the normalized difference indices; 3) the triangular area-based indices; and 4) the integrated indices. Two crops, wheat and corn, with distinctive canopy and leaf structure were studied. Measured FPAR and Sentinel-2 reflectance simulated from field spectral measurements were used. The results showed that VIs using the near-infrared and red-edge reflectance, including the modified Simple Ratio-2 (mSR2), the red-edge Simple Ratio ( $\text{SR}_{705}$ ), the Red-Edge Normalized Difference Vegetation Index ( $\text{ND}_{705}$ ), MERIS Terrestrial Chlorophyll Index (MTCI), and the Revised Optimized Soil-Adjusted Vegetation Index (OSAVI[705, 750]), had a strong linear correlation with FPAR, especially in the high biomass range. When the red-edge reflectance was used, the ratio indices (e.g., mSR2 and $\text{SR}_{705}$ ) had a stronger correlation with crop FPAR than the normalized difference indices (e.g., $\text{ND}_{705}$ ). Sensitivity analysis showed that mSR2 had the strongest linear correlation with FPAR of the two crops across a growing season. Further analysis indicated that indices using the red-edge reflectance might be useful for developing FPAR retrieval algorithms that are independent of crop types. This suggests the potential for high resolution and high-quality mapping of FPAR for precision farming using the Sentinel-2 data.

Modified vegetation indices for estimating crop fraction of absorbed photosynthetically active radiation

The fraction of absorbed photosynthetically active radiation (FPAR) is an important biophysical parameter of vegetation. It is often estimated using vegetation indices (VIs) derived from remote-sensing data, such as the normalized difference VI (NDVI). Ideally a linear relationship is used for the estimation; however, most conventional VIs are affected by canopy background reflectance and their sensitivity to FPAR declines at high biomass. In this study, a multiplier, the ratio of the green to the red reflectance, was introduced to improve the linear relationship between VIs and crop FPAR. Three widely used VIs – NDVI, the green normalized difference VI (GNDVI), and the renormalized difference VI (RDVI) – were modified this way and were called modified NDVI (MNDVI), modified GNDVI (MGNDVI), and modified RDVI (MRDVI), respectively. A sensitivity study was applied to analyse the correlation between the three modified indices and the leaf area index (LAI) using the reflectance data simulated by the combined PROSPECT leaf optical properties model and SAIL canopy bidirectional reflectance model (PROSAIL model). The results revealed that these new indices reduced the saturation trend at high LAI and achieved better linearity with crop LAI at low-to-medium biomass when compared with their corresponding original versions. This has also been validated using in situ FPAR measurements over wheat and maize crops. In particular, estimation using MNDVI achieved a coefficient of determination (R2) of 0.97 for wheat and 0.86 for maize compared to 0.90 and 0.82 for NDVI, respectively, while MGNDVI achieved 0.97 for wheat and 0.88 for maize, compared to 0.90 and 0.81 for GNDVI, respectively. Algorithms based on the VIs when applied to both wheat and maize showed that MNDVI and MGNDVI achieved a better linearity relationship with FPAR (R2 = 0.92), in comparison with NDVI (R2 = 0.85) and GNDVI (R2 = 0.82). The study demonstrated that applying the green to red reflectance ratio can improve the accuracy of FPAR estimation.

Estimating FPAR of maize canopy using airborne discrete-return LiDAR data

The fraction of absorbed photosynthetically active radiation (FPAR) is a key parameter for ecosystem modeling, crop growth monitoring and yield prediction. Ground-based FPAR measurements are time consuming and labor intensive. Remote sensing provides an alternative method to obtain repeated, rapid and inexpensive estimates of FPAR over large areas. LiDAR is an active remote sensing technology and can be used to extract accurate canopy structure parameters. A method to estimating FPAR of maize from airborne discrete-return LiDAR data was developed and tested in this study. The raw LiDAR point clouds were processed to separate ground returns from vegetation returns using a filter method over a maize field in the Heihe River Basin, northwest China. The fractional cover (fCover) of maize canopy was computed using the ratio of canopy return counts or intensity sums to the total of returns or intensities. FPAR estimation models were established based on linear regression analysis between the LiDAR-derived fCover and the field-measured FPAR (R(2) = 0.90, RMSE = 0.032, p < 0.001). The reliability of the constructed regression model was assessed using the leave-one-out cross-validation procedure and results show that the regression model is not overfitting the data and has a good generalization capability. Finally, 15 independent field-measured FPARs were used to evaluate accuracy of the LiDAR-predicted FPARs and results show that the LiDAR-predicted FPAR has a high accuracy (R(2) = 0.89, RMSE = 0.034). In summary, this study suggests that the airborne discrete-return LiDAR data could be adopted to accurately estimate FPAR of maize.

Intercomparison of fraction of absorbed photosynthetically active radiation products derived from satellite data over Europe

The Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) is recognized as an essential climate variable (ECVs), playing a critical role in the estimation of the global energy and carbon balance. With multiple space-borne remote sensing FAPAR global products available from several sources the need for continual comparison and validation has become imperative. In this study, the performance of three global FAPAR algorithms (JRC-TIP, ESA/JRC MGVI and Boston University FAPAR) was evaluated over Europe for the year 2011. Results show an overall agreement among FAPAR products on sites having high and low FAPAR values, except for the north-eastern region of Europe characterized by boreal forest and the transition region with tundra biomes, where the Boston product exceeds values in other products by up to 0.5. Differences in FAPAR estimates over forest biomes suggest that assumptions on structure and optical properties of land surfaces in the different radiative transfer models play an important role in remote-sensing-derived FAPAR products. Uncertainty assessments were carried out using both quality indicators as proposed by the individual product teams as well as independent theoretical uncertainty estimates obtained with the triple collocation error model. The former revealed consistent spatial patterns but large differences in magnitudes (up to 0.1) with systematically lower uncertainties for the Boston product. The latter instead suggests similar uncertainty ranges among the three products. Finally, a comparison with ground estimates for the 2009–2011 period over four European flux tower sites showed consistent, plausible seasonal variations of remote-sensing-derived FAPAR products. Findings suggest that differences in absolute values and inconsistency in uncertainty representation among FAPAR products are still considerable. Standardization frameworks quantifying the impact of different radiative transfer formulations on the estimation of biophysical variables, independent uncertainty estimation methods and well-defined ground measurement protocols need to be put in place before FAPAR products can be reliably fed into existing biogeochemical process models.

Validation of coarse spatial resolution LAI and FAPAR time series over cropland in southwest France

This study aims at validating Leaf Area Index (LAI) and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) products derived from MODIS surface reflectance (MOD09CMG) at coarse resolution (0.05°) over crops. These Essential Climate Variables (ECVs) are estimated by using the inversion of the PROSAIL radiative transfer (BV-NNET tool) applied on MODIS BRDF (Bidirectional Reflectance Distribution Function) corrected surface reflectances and non-corrected. ECV estimates and the corresponding MCD15A3 Collection 5 and GEOLAND-2 (GEOv1) products are compared with ECV reference maps derived from BV-NNET applied on 105 high spatial resolution images (Formosat-2, 8m) which were acquired from 2006 to 2010 in Southwest France. These latter are compared with local scale in situ measurements. The validation shows an uncertainty of 0.35 and 0.07 for LAI and FAPAR, respectively.The comparison shows that the ECV estimates from the three products properly capture the crops phenology in agreement with reference maps. Results indicate that MCD15A3 uncertainties (0.23 and 0.07 for LAI and FAPAR, respectively) are similar to previous intercomparison studies. GEOv1 shows a systemic positive bias for both LAI and FAPAR. The best agreement with the reference maps is found for MODIS BV-NNET products with r2 higher than 0.9 and relative uncertainties lower than 17%. The use of BRDF-corrected surface reflectances as input of BV-NNET tool improves the uncertainty of LAI estimates (0.11, compared to 0.17 when directional surface reflectances are used as input) but not the uncertainty of FAPAR estimates. The deviation between FAPAR products which mostly affects low winter FAPAR, is related to the discrepancy of the soil directional assumption in PROSAIL model and BRDF correction method. The temporal stability of the daily MODIS BV-NNET products is better than the 4-day composite MCD15A3 products. Finally, BV-NNET tool applied at finer resolutions demonstrates that the increase of the resolution results in a decrease of the LAI and FAPAR uncertainties and a conservation of the biases.

Monitoring carbon assimilation in South America's tropical forests: Model specification and application to the Amazonian droughts of 2005 and 2010

Net primary production (NPP) is a key variable for monitoring and understanding the impacts of environmental change on ecosystems and for generating realistic global and regional carbon budgets. In this paper we present a regional algorithm (RATE) for automatically monitoring the rate of carbon fixation (as measured by NPP) of tropical forests in South America. The algorithm is based on a modification of the SITE ecosystem model and uses data obtained from the procedures for aggregation and correction of data from the MOD12Q1 and MOD15A2 products and meteorological data from the National Centers for Environmental Prediction (NCEP). A correction procedure for the MOD15A2 Leaf area index (LAI) and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) used by the RATE algorithm produced satisfactory LAI and FAPAR estimates when compared against observed values. The algorithm was successfully validated in eight field sites from two types of tropical forests in South America (Amazon rainforest and the Atlantic forest), producing an average error of only 4.72%. When applied to Amazonia, RATE indicates that NPP showed little variation in the 2005 and 2010 drought years (NPP = 1.28 kg-C m − 2 year − 1 ) in comparison to non-drought years (NPP = 1.31 kg-C m − 2 year − 1 ). RATE also provides some limited evidence for small decreasing trends in Amazonia carbon assimilation during the period 2001–2010.

On the bias of instantaneous FAPAR estimates in open-canopy forests

Global products of the fraction of absorbed photosynthetically active radiation (FAPAR) are operationally available from a variety of space agencies. A proper validation of these products is essential and hinges on the acquisition of accurate ground-based FAPAR estimates of the vegetation contained within the field of view of the space sensor at the time of satellite overpass. Often remotely sensed FAPAR products are defined with respect to theoretical rather than ambient illumination conditions which complicates in situ validation efforts. Similarly, the spatial complexity and substantial heights of certain plant environments may prevent the reliable sampling of certain radiation fluxes. As a consequence, many field campaigns are carried out on agricultural crops or within young tree plantations where canopy height is not an issue. This contribution compares different approaches for estimating instantaneous FAPAR in tall, open-canopy forest stands under a variety of architectural, spectral and illumination related conditions. The bias associated with these estimations is separated into a sampling error and a transfer bias. The former relates to the impact of both the number and location of the measurements whereas the latter addresses the quality of the theory that relates these measurements to the actual canopy FAPAR. Among the various methods tested it was the 2-flux FAPAR estimator (1 − T PAR) that performs best in open forest canopies under typical summer conditions. The quality of the 1 − T PAR canopy FAPAR estimator changes, however, with illumination conditions, foliage colour and especially with the background brightness. Similarly, the smaller the size of the area for which the FAPAR is to be estimated the larger the variability of the bias is going to be (and this irrespective of the choice of in situ estimation techniques). Evidence is provided that working under overcast sky conditions will reduce the sampling error but may well increase the transfer bias when compared to clear sky conditions. A parametric relationship is developed that allows to predict the instantaneous canopy FAPAR for arbitrary diffuse-to-total-incident-radiation ratios (at any given solar zenith angle). This approach has a similar transfer bias as the 1 − T PAR method when the forest floor is dark but dramatically outperforms the 2-flux approach under snowy background conditions (RMSE = 0.9934 versus 0.5801, respectively). The number of samples acquired was found to be crucial in reducing the variability of the bias of a given FAPAR estimator. Both random and grid-based sampling schemes result in similar FAPAR biases but do not lend themselves easily to the acquisition of hundreds of data points needed for reliable estimations under direct-only illumination conditions. Transect sampling—which is shown to deliver best results if carried out at ninety degrees to the solar azimuth angle—appears ideally suited to acquire the necessary numbers of samples enabling the generation of accurate quasi-instantaneous FAPAR estimates in open-canopy forests.

Estimation of LAI and FAPAR by constraining the leaf and soil spectral characteristics in a radiative transfer model

We evaluated the estimation of the leaf area index (LAI) and the fraction of absorbed photosynthetically active radiation (FAPAR) of green plant canopies from top-of-the-canopy (TOC) spectral indices by using the PROSAIL model under the possible constraints of leaf and soil spectra. For the LAI estimation, the ratios (B1 – B3)/(B1 + B3) and B1/B3 provided fewer estimation errors than (B4 – B3)/(B4 + B3) or B4/B3 when the variation in the soil spectral reflectance was small or LAI was large. Here, B1, B3 and B4 denote the blue, red and near-infrared bands of Landsat ETM+, respectively. For the FAPAR estimation, the ratios (B5 – B7)/(B5 + B7) and B5/B7 provided fewer estimation errors than (NIR – R)/(NIR + R) or NIR/R for a FAPAR value of 0.3–0.7 when the variation in the soil spectral reflectance was large. Here, B5 and B7 denote the bands with wavelengths 1.55–1.75 and 2.09–2.35 μm, respectively. These were maintained for various conditions of the solar incident zenith angle (θs), leaf angle distribution (LAD), canopy hotspot parameter (s 1) and clumping index (Ω).

Hyperspectral Estimation of Corn Fraction of Photosynthetically Active Radiation

Fraction of absorbed photosynthetically active radiation (FPAR) is one of the important variables in many vegetation productivity and biomass estimation models. Therefore, it is significant to retrieve FPAR accurately for the improvement of model precision. On the basis of the field experiment, this article analyzed the correlations between corn canopy FPAR and spectral reflectance, and reflectance derivative. Discussion about the mechanism of FPAR estimation with different empirical models is based upon corn canopy reflectance, reflectance derivative, NDVI (normalized difference vegetation index) and RVI (ratio vegetation index). The reflectance of visible bands showed much better correlations with FPAR than near-infrared bands. The correlation between FPAR and reflectance derivative varied more frequently and greatly than that between FPAR and reflectance, and with preferable correlation only around 520, 570, 670, 805, 950, and 1010 nm. Reflectance and reflectance derivative both had intimate correlation with FPAR at some typical single band, with the maximum R 2 of 0.791 and 0.882, respectively. In a word, reflectance derivative and vegetation index were much effective in the estimation of corn FPAR than reflectance, and the stepwise regression of multibands with reflectance derivative showed the best regression with R 2 of 0.944. Reflectance at 375 and 950 nm with absorption characteristics caused by water showed prodigious potential for FPAR precisely estimating model establishment. On the whole, vegetation index and reflectance derivative had good relationships with FPAR, and could be used for FAPR estimation. It would be effective for choosing right bands and excavating the hyperspectral data to improve FPAR estimating precision.

Assessment of forest GPP variations in central Italy

A debate is in progress concerning the possible effects of climate changes on the primary production of both natural and artificial ecosystems. The current investigation builds on the hypothesis that trends of increasing air temperature observed in several Italian regions should positively affect productivity of mountain forest ecosystems. Temperature rise in the Mugello valley (central Italy) in the period 1986–2001 was first confirmed by the analysis of data from a local station. The effects of this rise on the productivity of deciduous forest ecosystems (dominated by beech, Fagus sylvatica L.) were then analysed through estimates of the fraction of absorbed photosynthetically active radiation (FAPAR) derived from the US National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer satellite normalized difference vegetation index (NDVI) data. The use of a simplified parametric model (C-Fix) then allowed the combination of these FAPAR estimates with meteorological data (temperature and radiation) to produce annual values of forest gross primary productivity (GPP). Finally, validation of these GPP estimates was carried out by a comparison with dendrochronological measurements taken in the study forests. Because tree measurements were affected by external factors not exclusively related to forest GPP (stand aging, management practices, etc.), the comparison gave positive results only after applying a detrending operation to both series of annual GPP estimates and dendrochronological data. These results are a first indication that the rise in temperature that has occurred in Italy in the last decades has positively affected the productivity of mountain forest ecosystems.