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Optimizing the Empirical Parameters of the Data-Driven Algorithm for SIF Retrieval for SIFIS Onboard TECIS-1
Chu Zou1,2, Shanshan Du1, Xinjie Liu1
1Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China.
Sensors (Basel, Switzerland)
|June 2, 2021
Summary
Optimizing parameters for space-based solar-induced chlorophyll fluorescence (SIF) retrieval is crucial for accurate terrestrial photosynthesis monitoring. This study identifies key factors like fitting window and polynomial order to enhance SIF data accuracy from satellites.
Area of Science:
- Earth and Planetary Sciences
- Remote Sensing
- Ecosystem Science
Background:
- Space-based solar-induced chlorophyll fluorescence (SIF) serves as a vital proxy for terrestrial photosynthesis.
- Retrieving SIF from satellite hyperspectral data often relies on data-driven algorithms, which are sensitive to empirical parameter choices.
Purpose of the Study:
- To quantitatively investigate the influence of empirical parameters within a data-driven SIF retrieval algorithm.
- To determine optimal parameters for accurate SIF retrieval using the SIFIS instrument on the TECIS-1 satellite.
Main Methods:
- Simulations using the SIF Imaging Spectrometer (SIFIS) onboard TECIS-1.
- Quantitative analysis of parameter impacts: polynomial order (np), number of feature vectors (nSV), fluorescence emission spectrum function, and fitting window.
- Evaluation of retrieval accuracy using Root Mean Square Error (RMSE).
Main Results:
- The fitting window, polynomial order (np), and number of feature vectors (nSV) significantly impact SIF retrieval accuracy.
- Wider fitting windows generally improve accuracy, with specific optimal ranges identified for far-red and red SIF.
- Optimal parameters determined: far-red SIF (735-758 nm window, 2nd-order polynomial, 4 feature vectors), red SIF (682-697 nm window, 2nd-order polynomial, 7 feature vectors).
Conclusions:
- Optimized parameters significantly enhance the accuracy of satellite-based SIF retrieval.
- These findings provide guidance for generating precise SIF products from the TECIS-1 satellite.
- Accurate SIF data is essential for effective monitoring of terrestrial ecosystem carbon dynamics.
