Optimizing the Empirical Parameters of the Data-Driven Algorithm for SIF Retrieval for SIFIS Onboard TECIS-1 Satellite
Space-based solar-induced chlorophyll fluorescence (SIF) has been widely demonstrated as a great proxy for monitoring terrestrial photosynthesis and has been successfully retrieved from satellite-based hyperspectral observations using a data-driven algorithm. As a semi-empirical algorithm, the data-...
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MDPI AG
2021-05-01
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| author | Chu Zou Shanshan Du Xinjie Liu Liangyun Liu Yuyang Wang Zhen Li |
| author_facet | Chu Zou Shanshan Du Xinjie Liu Liangyun Liu Yuyang Wang Zhen Li |
| author_sort | Chu Zou |
| collection | DOAJ |
| description | Space-based solar-induced chlorophyll fluorescence (SIF) has been widely demonstrated as a great proxy for monitoring terrestrial photosynthesis and has been successfully retrieved from satellite-based hyperspectral observations using a data-driven algorithm. As a semi-empirical algorithm, the data-driven algorithm is strongly affected by the empirical parameters in the model. Here, the influence of the data-driven algorithm’s empirical parameters, including the polynomial order (n<sub>p</sub>), the number of feature vectors (n<sub>SV</sub>), the fluorescence emission spectrum function, and the fitting window used in the retrieval model, were quantitatively investigated based on the simulations of the SIF Imaging Spectrometer (SIFIS) onboard the First Terrestrial Ecosystem Carbon Inventory Satellite (TECIS-1). The results showed that the fitting window, n<sub>p</sub>, and n<sub>SV</sub> were the three main factors that influenced the accuracy of retrieval. The retrieval accuracy was relatively higher for a wider fitting window; the root mean square error (RMSE) was lower than 0.7 mW m<sup>−2</sup> sr<sup>−1</sup> nm<sup>−1</sup> with fitting windows wider than 735–758 nm and 682–691 nm for the far-red band and the red band, respectively. The RMSE decreased first and then increased with increases in n<sub>p</sub> range from 1 to 5 and increased in n<sub>SV</sub> range from 2 to 20. According to the specifications of SIFIS onboard TECIS-1, a fitting window of 735–758 nm, a second-order polynomial, and four feature vectors are the optimal parameters for far-red SIF retrieval, resulting in an RMSE of 0.63 mW m<sup>−2</sup> sr<sup>−1</sup> nm<sup>−1</sup>. As for red SIF retrieval, using second-order polynomial and seven feature vectors in the fitting window of 682–697 nm was the optimal choice and resulted in an RMSE of 0.53 mW m<sup>−2</sup> sr<sup>−1</sup> nm<sup>−1</sup>. The optimized parameters of the data-driven algorithm can guide the retrieval of satellite-based SIF and are valuable for generating an accurate SIF product of the TECIS-1 satellite after its launch. |
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| institution | Directory Open Access Journal |
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| language | English |
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| spelling | doaj.art-2d03f753746444b1bbe73785a23b1e922023-11-21T20:03:04ZengMDPI AGSensors1424-82202021-05-012110348210.3390/s21103482Optimizing the Empirical Parameters of the Data-Driven Algorithm for SIF Retrieval for SIFIS Onboard TECIS-1 SatelliteChu Zou0Shanshan Du1Xinjie Liu2Liangyun Liu3Yuyang Wang4Zhen Li5Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaInstitute of Remote Sensing Satellite, China Academy of Space Technology, Beijing 100095, ChinaInstitute of Remote Sensing Satellite, China Academy of Space Technology, Beijing 100095, ChinaSpace-based solar-induced chlorophyll fluorescence (SIF) has been widely demonstrated as a great proxy for monitoring terrestrial photosynthesis and has been successfully retrieved from satellite-based hyperspectral observations using a data-driven algorithm. As a semi-empirical algorithm, the data-driven algorithm is strongly affected by the empirical parameters in the model. Here, the influence of the data-driven algorithm’s empirical parameters, including the polynomial order (n<sub>p</sub>), the number of feature vectors (n<sub>SV</sub>), the fluorescence emission spectrum function, and the fitting window used in the retrieval model, were quantitatively investigated based on the simulations of the SIF Imaging Spectrometer (SIFIS) onboard the First Terrestrial Ecosystem Carbon Inventory Satellite (TECIS-1). The results showed that the fitting window, n<sub>p</sub>, and n<sub>SV</sub> were the three main factors that influenced the accuracy of retrieval. The retrieval accuracy was relatively higher for a wider fitting window; the root mean square error (RMSE) was lower than 0.7 mW m<sup>−2</sup> sr<sup>−1</sup> nm<sup>−1</sup> with fitting windows wider than 735–758 nm and 682–691 nm for the far-red band and the red band, respectively. The RMSE decreased first and then increased with increases in n<sub>p</sub> range from 1 to 5 and increased in n<sub>SV</sub> range from 2 to 20. According to the specifications of SIFIS onboard TECIS-1, a fitting window of 735–758 nm, a second-order polynomial, and four feature vectors are the optimal parameters for far-red SIF retrieval, resulting in an RMSE of 0.63 mW m<sup>−2</sup> sr<sup>−1</sup> nm<sup>−1</sup>. As for red SIF retrieval, using second-order polynomial and seven feature vectors in the fitting window of 682–697 nm was the optimal choice and resulted in an RMSE of 0.53 mW m<sup>−2</sup> sr<sup>−1</sup> nm<sup>−1</sup>. The optimized parameters of the data-driven algorithm can guide the retrieval of satellite-based SIF and are valuable for generating an accurate SIF product of the TECIS-1 satellite after its launch.https://www.mdpi.com/1424-8220/21/10/3482solar-induced chlorophyll fluorescence (SIF)data-driven algorithmTerrestrial Ecosystem Carbon Inventory Satellite (TECIS-1)parameter optimization |
| spellingShingle | Chu Zou Shanshan Du Xinjie Liu Liangyun Liu Yuyang Wang Zhen Li Optimizing the Empirical Parameters of the Data-Driven Algorithm for SIF Retrieval for SIFIS Onboard TECIS-1 Satellite Sensors solar-induced chlorophyll fluorescence (SIF) data-driven algorithm Terrestrial Ecosystem Carbon Inventory Satellite (TECIS-1) parameter optimization |
| title | Optimizing the Empirical Parameters of the Data-Driven Algorithm for SIF Retrieval for SIFIS Onboard TECIS-1 Satellite |
| title_full | Optimizing the Empirical Parameters of the Data-Driven Algorithm for SIF Retrieval for SIFIS Onboard TECIS-1 Satellite |
| title_fullStr | Optimizing the Empirical Parameters of the Data-Driven Algorithm for SIF Retrieval for SIFIS Onboard TECIS-1 Satellite |
| title_full_unstemmed | Optimizing the Empirical Parameters of the Data-Driven Algorithm for SIF Retrieval for SIFIS Onboard TECIS-1 Satellite |
| title_short | Optimizing the Empirical Parameters of the Data-Driven Algorithm for SIF Retrieval for SIFIS Onboard TECIS-1 Satellite |
| title_sort | optimizing the empirical parameters of the data driven algorithm for sif retrieval for sifis onboard tecis 1 satellite |
| topic | solar-induced chlorophyll fluorescence (SIF) data-driven algorithm Terrestrial Ecosystem Carbon Inventory Satellite (TECIS-1) parameter optimization |
| url | https://www.mdpi.com/1424-8220/21/10/3482 |
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