Joint Use of PROSAIL and DART for Fast LUT Building: Application to Gap Fraction and Leaf Biochemistry Estimations over Sparse Oak Stands

• Main Authors: Thomas Miraglio, Karine Adeline, Margarita Huesca, Susan Ustin, Xavier Briottet
• Format: Article
• Language: English
• Published: MDPI AG 2020-09-01
• Series: Remote Sensing
• Subjects: leaf mass per area; equivalent water thickness; chlorophylls; carotenoids; open canopy
• Online Access: https://www.mdpi.com/2072-4292/12/18/2925

Gap Fraction, leaf pigment contents (content of chlorophylls <i>a</i> and <i>b</i> (C<inline-formula><math display="inline"><semantics><msub><mrow></mrow><mrow><mi>a</mi><mi>b</mi></mrow></msub></semantics></math></inline-formula>) and carotenoids content (Car)), Leaf Mass per Area (LMA), and Equivalent Water Thickness (EWT) are considered relevant indicators of forests’ health status, influencing many biological and physical processes. Various methods exist to estimate these variables, often relying on the extensive use of Radiation Transfer Models (RTMs). While 3D RTMs are more realistic to model open canopies, their complexity leads to important computation times that limit the number of simulations that can be considered; 1D RTMs, although less realistic, are also less computationally expensive. We investigated the possibility to approximate the outputs of a 3D RTM (DART) from a 1D RTM (PROSAIL) to generate in very short time numerous extensive Look-Up Tables (LUTs). The intrinsic error of the approximation model was evaluated through comparison with DART reference values. The model was then used to generate LUTs used to estimate Gap Fraction, C<inline-formula><math display="inline"><semantics><msub><mrow></mrow><mrow><mi>a</mi><mi>b</mi></mrow></msub></semantics></math></inline-formula>, Car, EWT, and LMA of Blue Oak-dominant stands in a woodland savanna from AVIRIS-C data. Performances of the approximation model for estimation purposes compared to DART were evaluated using Wilmott’s index of agreement (<inline-formula><math display="inline"><semantics><msub><mi>d</mi><mi>r</mi></msub></semantics></math></inline-formula>), and estimation accuracy was measured with coefficients of determination (<inline-formula><math display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula>) and Root Mean Squared Error (RMSE). The low approximation error of the proposed model demonstrated that the model could be considered for canopy covers as low as 30%. Gap Fraction estimations presented similar performances with either DART or the approximation (<inline-formula><math display="inline"><semantics><msub><mi>d</mi><mi>r</mi></msub></semantics></math></inline-formula> 0.78 and 0.77, respectively), while C<inline-formula><math display="inline"><semantics><msub><mrow></mrow><mrow><mi>a</mi><mi>b</mi></mrow></msub></semantics></math></inline-formula> and Car showed improved performances (<inline-formula><math display="inline"><semantics><msub><mi>d</mi><mi>r</mi></msub></semantics></math></inline-formula> increasing from 0.65 to 0.77 and 0.34 to 0.65, respectively). No satisfying estimation methods were found for LMA and EWT using either models, probably due to the high sensitivity of the scene’s reflectance to Gap Fraction and soil modeling at such low LAI. Overall, estimations using the approximated reflectances presented either similar or improved accuracy. Our findings show that it is possible to approximate DART reflectances from PROSAIL using a minimal number of DART outputs for calibration purposes, drastically reducing computation times to generate reflectance databases: 300,000 entries could be generated in 1.5 h, compared to the 12,666 total CPU hours necessary to generate the 21,840 calibration entries with DART.