20 March 2018 Look-up-table approach for leaf area index retrieval from remotely sensed data based on scale information
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Abstract
Leaf area index (LAI) is a key structural characteristic of vegetation and plays a significant role in global change research. Several methods and remotely sensed data have been evaluated for LAI estimation. This study aimed to evaluate the suitability of the look-up-table (LUT) approach for crop LAI retrieval from Satellite Pour l’Observation de la Terre (SPOT)-5 data and establish an LUT approach for LAI inversion based on scale information. The LAI inversion result was validated by in situ LAI measurements, indicating that the LUT generated based on the PROSAIL (PROSPECT+SAIL: properties spectra + scattering by arbitrarily inclined leaves) model was suitable for crop LAI estimation, with a root mean square error (RMSE) of ∼0.31m2  /  m2 and determination coefficient (R2) of 0.65. The scale effect of crop LAI was analyzed based on Taylor expansion theory, indicating that when the SPOT data aggregated by 200  ×  200  pixel, the relative error is significant with 13.7%. Finally, an LUT method integrated with scale information was proposed in this article, improving the inversion accuracy with RMSE of 0.20  m2  /  m2 and R2 of 0.83.
© 2018 Society of Photo-Optical Instrumentation Engineers (SPIE)
Xiaohua Zhu, Xiaohua Zhu, Chuanrong Li, Chuanrong Li, Lingli Tang, Lingli Tang, } "Look-up-table approach for leaf area index retrieval from remotely sensed data based on scale information," Optical Engineering 57(3), 033104 (20 March 2018). https://doi.org/10.1117/1.OE.57.3.033104 . Submission: Received: 14 December 2017; Accepted: 28 February 2018
Received: 14 December 2017; Accepted: 28 February 2018; Published: 20 March 2018
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