The fraction of absorbed photosynthetically active radiation (FAPAR) is a significant biochemical and physiological variable used in tracing the exchanges of energy, mass, and momentum, and is also widely used in many climate, ecological, biogeochemical, agricultural, and hydrology models [1
]. FAPAR is, therefore, an important input parameter and widely used in satellite-based Production Efficiency Models (PEMs) [3
] to estimate gross primary productivity (GPP) or net primary production (NPP).
In general, the FAPAR inversion algorithms could be divided into two types: empirical statistical models based on vegetation indexes and physical methods based on the canopy radiation transfer model. Although the empirical statistical model based on vegetation indexes is relatively simple, involves only a few parameters and has high computational efficiency, it is subject to many uncertainties due to factors such as the atmospheric environment, vegetation type, and quality of remote sensing data. The physically based methods could be further divided into two categories. The first type is the direct inversion method, which uses the canopy radiation transfer model to link FAPAR with the canopy spectra [7
]. For example, the Moderate Resolution Imaging Spectroradiometer (MODIS) algorithm uses the three-dimensional radiation transmission model to invert FAPAR from the bi-directional reflectance [10
]. The Joint Research Centre (JRC) FAPAR algorithm is also based on a physical model that uses a continuous vegetation canopy model [13
] to link land surface reflectance with FAPAR. However, these methods are mostly based on the radiative transfer model; thus, the inversion process is complicated for retrieval of FAPAR. The main problem with such methods is that it is difficult to overcome the uncertainty caused by model coupling and spatial heterogeneity. The second type of physically based method is the forward modeling method [14
]. Most models of this type are based on the gap fraction model, which determines FAPAR according to canopy structure parameters such as LAI and the clumping index. The disadvantage of this approach is that it relies too much on the accuracy of the canopy structure parameters. Furthermore, it is difficult to accurately determine the soil albedo and extinction coefficient, which are also important parameters needed to determine the contribution of multiple scattering between the soil and canopy to FAPAR [17
Recently, several global FAPAR products have become available, including the Moderate Resolution Imaging Spectroradiometer (MODIS) [20
], Energy Balance Residual (EBR) [15
], Multi-angle Imaging SpectroRadiometer (MISR) [11
], CYCLOPES [22
], GLOBCARBON [23
], Global Land Surface Satellite (GLASS) [14
], the Medium Resolution Imaging Spectrometer (MERIS) [24
], Joint Research Center Two Stream Inversion Package (JRC-TIP) [25
], and European Space Agency (ESA) products [26
]. These global FAPAR products have been widely validated, with reported errors varying from 0.08 to 0.23 [14
]. However, most of the global FAPAR products do not consider the effect of non-photosynthetic components in the radiative transfer process, which introduces errors, especially for forest types. Moreover, many researchers have used the fraction of radiation absorbed by photosynthetic components (
) instead of the fraction of radiation absorbed by the canopy (
) to monitor and estimate the light use efficiency (LUE), radiation use efficiency (RUE), and productivity at different temporal scales [33
The forest vegetation ecosystem plays an important role in the global ecosystem. However, quantifying the temporal variation in
for a forest ecosystem represents an important challenge for remote sensing and ecology researchers as it is extremely difficult to measure
at large scales over plant growing seasons directly. Also, previous studies have shown that the contribution of woody components is relatively large: for instance, Asner et al. [37
] found that stems increased
by 10–40%. Therefore, the partitioning of absorbed radiation into photosynthetic and non-photosynthetic parts is very important for better modeling of vegetation photosynthesis and energy exchange within the canopy.
Already, some studies have looked at the estimation of
from remote sensing data. Hall et al. [38
using a simple linear relationship between
denotes the total leaf area index including green and senescent leaves, while
represents the green leaf area index). However, this simple partitioning is problematic because the green and woody components within the canopy do not constitute a simple linear mix in terms of radiation transfer. Zhang et al. [39
] first retrieved the biophysical and biochemical variables using the modified PROSPECT model coupled with the SAIL-2 model (hereafter called PROSAIL-2 model), and then calculated
using the forward simulation approach. However,
retrieval using the PROSAIL-2 model is relatively complex and needs several physiological and biochemical parameters as model inputs. Gitelson et al. [40
] also separated
into photosynthetically active green components (
) and non-photosynthetic active components using the ratio
for maize and soybeans. The relationship between vegetation indices and
was also used to retrieve
]. Nevertheless, to date, the current
products do not take into account the effect of non-photosynthetic components on canopy radiative transfer.
In this study, we aim to develop an operational algorithm for partitioning into and for forest types. A simple triple-source leaf–wood–soil layer model (TriLay) that describes the radiation transfer within the canopy-soil system is presented. is first separated into the fraction of PAR absorbed by the canopy for downwelling radiation () and the fraction of PAR absorbed by the canopy for the upwelling radiation reflected by the soil background (). Then, and are further split into the fraction of radiation absorbed by photosynthetic components () and that absorbed by non-photosynthetic components () using the TriLay model. Finally, the , , and products are generated using the MODIS albedo (MCD43A3), LAI (MCD15A2H), land cover (MCD12Q1), clumping index (CI), and soil albedo products based on the TriLay approach, and the discrepancies between different FAPAR products are used to investigate the contributions of woody components to the canopy-absorbed radiation. The partitioning of absorbed radiation into green and woody parts using the TriLay model is done not just to provide and —which is of great importance for better understanding the energy exchange within the canopy. The consideration of woody components should also improve the accuracy of estimates, which is important for better modeling of vegetation photosynthesis.
In this paper, a triple-source leaf–wood–soil layer (TriLay) method for separating
using the MODIS LAI, land cover, and non-linear spectral mixture model (NSM)-retrieved soil albedo [15
] together with global CI products [49
] was proposed.
According to the validation carried out using LESS-simulated FAPAR values, the TriLay was more accurate (R2 = 0.937, 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) than 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), and also more accurate than FAPAR obtained without the consideration of woody components () (R2 = 0.920, RMSE = 0.071 and bias = −7.14% for black-sky conditions; R2 = 0.999, RMSE = 0.043 and bias = −6.41% for white-sky conditions). A comparison of the results for black-sky against and showed that the discrepancies between the black-sky and other FAPAR products could not be ignored for forest types. In particular, for deciduous needleleaf forest, the black-sky contributed only about 23.86% and 35.75% of during the early and late stages (JFM and OND) of the year, respectively, and 75.02% during the peak growth stage (JAS). There were also smaller discrepancies between the black-sky and . For deciduous needleleaf forests, in particular, the black-sky was overestimated by 38.30% and 28.46%, respectively, during the early and late stages of the year (JFM and OND).
Overall, this study provides a new method for partitioning into and for forest types and will improve the understanding of energy exchange within the canopy. In addition, the exclusion of the contribution of woody components may certainly improve the accuracy of the estimates for forest types, which is significant in terms of the better modeling of vegetation photosynthesis.