Retrieval of the Fraction of Radiation Absorbed by Photosynthetic Components ( FAPAR green ) 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 (<inline-formula> <math display="inline"> <semantics> <mrow> <mi>F</mi> <mi>A</mi> <mi>P</mi> <mi>A</mi> <msub> <mi>R</mi> <mrow> <mi>g</mi> <mi>r</mi> <mi>e</mi> <mi>e</mi> <mi>n</mi> </mrow> </msub> </mrow> </semantics> </math> </inline-formula>) and the fraction of radiation absorbed by the non-photosynthetic components (<inline-formula> <math display="inline"> <semantics> <mrow> <mi>F</mi> <mi>A</mi> <mi>P</mi> <mi>A</mi> <msub> <mi>R</mi> <mrow> <mi>w</mi> <mi>o</mi> <mi>o</mi> <mi>d</mi> <mi>y</mi> </mrow> </msub> </mrow> </semantics> </math> </inline-formula>) 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 <inline-formula> <math display="inline"> <semantics> <mrow> <mi>F</mi> <mi>A</mi> <mi>P</mi> <mi>A</mi> <msub> <mi>R</mi> <mrow> <mi>c</mi> <mi>a</mi> <mi>n</mi> <mi>o</mi> <mi>p</mi> <mi>y</mi> </mrow> </msub> </mrow> </semantics> </math> </inline-formula> into <inline-formula> <math display="inline"> <semantics> <mrow> <mi>F</mi> <mi>A</mi> <mi>P</mi> <mi>A</mi> <msub> <mi>R</mi> <mrow> <mi>g</mi> <mi>r</mi> <mi>e</mi> <mi>e</mi> <mi>n</mi> </mrow> </msub> </mrow> </semantics> </math> </inline-formula> and <inline-formula> <math display="inline"> <semantics> <mrow> <mi>F</mi> <mi>A</mi> <mi>P</mi> <mi>A</mi> <msub> <mi>R</mi> <mrow> <mi>w</mi> <mi>o</mi> <mi>o</mi> <mi>d</mi> <mi>y</mi> </mrow> </msub> </mrow> </semantics> </math> </inline-formula> 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 <inline-formula> <math display="inline"> <semantics> <mrow> <mi>F</mi> <mi>A</mi> <mi>P</mi> <mi>A</mi> <msub> <mi>R</mi> <mrow> <mi>g</mi> <mi>r</mi> <mi>e</mi> <mi>e</mi> <mi>n</mi> </mrow> </msub> </mrow> </semantics> </math> </inline-formula> had higher retrieval accuracy, as well as a significantly lower bias (R² = 0.937, Root Mean Square Error (RMSE) = 0.064, and bias = &#8722;6.02% for black-sky conditions; R² = 0.997, RMSE = 0.025 and bias = &#8722;4.04% for white-sky conditions) compared to the traditional linear method (R² = 0.979, RMSE = 0.114, and bias = &#8722;18.04% for black-sky conditions; R² = 0.996, RMSE = 0.106 and bias = &#8722;16.93% for white-sky conditions). For FAPAR that did not take account of woody components (<inline-formula> <math display="inline"> <semantics> <mrow> <mi>F</mi> <mi>A</mi> <mi>P</mi> <mi>A</mi> <msub> <mi>R</mi> <mrow> <mi>n</mi> <mi>o</mi> <mi>W</mi> <mi>A</mi> <mi>I</mi> </mrow> </msub> </mrow> </semantics> </math> </inline-formula>), the corresponding results were R² = 0.920, RMSE = 0.071, and bias = &#8722;7.14% for black-sky conditions, and R² = 0.999, RMSE = 0.043, and bias = &#8722;6.41% for white-sky conditions. Finally, the dynamic <inline-formula> <math display="inline"> <semantics> <mrow> <mi>F</mi> <mi>A</mi> <mi>P</mi> <mi>A</mi> <msub> <mi>R</mi> <mrow> <mi>g</mi> <mi>r</mi> <mi>e</mi> <mi>e</mi> <mi>n</mi> </mrow> </msub> </mrow> </semantics> </math> </inline-formula>, <inline-formula> <math display="inline"> <semantics> <mrow> <mi>F</mi> <mi>A</mi> <mi>P</mi> <mi>A</mi> <msub> <mi>R</mi> <mrow> <mi>w</mi> <mi>o</mi> <mi>o</mi> <mi>d</mi> <mi>y</mi> </mrow> </msub> </mrow> </semantics> </math> </inline-formula>, <inline-formula> <math display="inline"> <semantics> <mrow> <mi>F</mi> <mi>A</mi> <mi>P</mi> <mi>A</mi> <msub> <mi>R</mi> <mrow> <mi>c</mi> <mi>a</mi> <mi>n</mi> <mi>o</mi> <mi>p</mi> <mi>y</mi> </mrow> </msub> </mrow> </semantics> </math> </inline-formula> and <inline-formula> <math display="inline"> <semantics> <mrow> <mi>F</mi> <mi>A</mi> <mi>P</mi> <mi>A</mi> <msub> <mi>R</mi> <mrow> <mi>n</mi> <mi>o</mi> <mi>W</mi> <mi>A</mi> <mi>I</mi> </mrow> </msub> </mrow> </semantics> </math> </inline-formula> 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 <inline-formula> <math display="inline"> <semantics> <mrow> <mi>F</mi> <mi>A</mi> <mi>P</mi> <mi>A</mi> <msub> <mi>R</mi> <mrow> <mi>g</mi> <mi>r</mi> <mi>e</mi> <mi>e</mi> <mi>n</mi> </mrow> </msub> </mrow> </semantics> </math> </inline-formula> against those for <inline-formula> <math display="inline"> <semantics> <mrow> <mi>F</mi> <mi>A</mi> <mi>P</mi> <mi>A</mi> <msub> <mi>R</mi> <mrow> <mi>n</mi> <mi>o</mi> <mi>W</mi> <mi>A</mi> <mi>I</mi> </mrow> </msub> </mrow> </semantics> </math> </inline-formula> and <inline-formula> <math display="inline"> <semantics> <mrow> <mi>F</mi> <mi>A</mi> <mi>P</mi> <mi>A</mi> <msub> <mi>R</mi> <mrow> <mi>c</mi> <mi>a</mi> <mi>n</mi> <mi>o</mi> <mi>p</mi> <mi>y</mi> </mrow> </msub> </mrow> </semantics> </math> </inline-formula> showed that the discrepancy between <inline-formula> <math display="inline"> <semantics> <mrow> <mi>F</mi> <mi>A</mi> <mi>P</mi> <mi>A</mi> <msub> <mi>R</mi> <mrow> <mi>g</mi> <mi>r</mi> <mi>e</mi> <mi>e</mi> <mi>n</mi> </mrow> </msub> </mrow> </semantics> </math> </inline-formula> and other FAPAR products for forest vegetation types could not be ignored. For deciduous needleleaf forest, in particular, the black-sky <inline-formula> <math display="inline"> <semantics> <mrow> <mi>F</mi> <mi>A</mi> <mi>P</mi> <mi>A</mi> <msub> <mi>R</mi> <mrow> <mi>g</mi> <mi>r</mi> <mi>e</mi> <mi>e</mi> <mi>n</mi> </mrow> </msub> </mrow> </semantics> </math> </inline-formula> was found to contribute only about 23.86% and 35.75% of <inline-formula> <math display="inline"> <semantics> <mrow> <mi>F</mi> <mi>A</mi> <mi>P</mi> <mi>A</mi> <msub> <mi>R</mi> <mrow> <mi>c</mi> <mi>a</mi> <mi>n</mi> <mi>o</mi> <mi>p</mi> <mi>y</mi> </mrow> </msub> </mrow> </semantics> </math> </inline-formula> 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 <inline-formula> <math display="inline"> <semantics> <mrow> <mi>F</mi> <mi>A</mi> <mi>P</mi> <mi>A</mi> <msub> <mi>R</mi> <mrow> <mi>n</mi> <mi>o</mi> <mi>W</mi> <mi>A</mi> <mi>I</mi> </mrow> </msub> </mrow> </semantics> </math> </inline-formula> 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 <inline-formula> <math display="inline"> <semantics> <mrow> <mi>F</mi> <mi>A</mi> <mi>P</mi> <mi>A</mi> <msub> <mi>R</mi> <mrow> <mi>g</mi> <mi>r</mi> <mi>e</mi> <mi>e</mi> <mi>n</mi> </mrow> </msub> </mrow> </semantics> </math> </inline-formula> from <inline-formula> <math display="inline"> <semantics> <mrow> <mi>F</mi> <mi>A</mi> <mi>P</mi> <mi>A</mi> <msub> <mi>R</mi> <mrow> <mi>c</mi> <mi>a</mi> <mi>n</mi> <mi>o</mi> <mi>p</mi> <mi>y</mi> </mrow> </msub> </mrow> </semantics> </math> </inline-formula>, which is of great importance for a better understanding of the energy exchange within the canopy.

Abstract licence: CC BY

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