Retrieval of crop biophysical parameters from Sentinel-2 remote sensing imagery

Xie, Q, Dash, J, Huete, A, Jiang, A, Yin, G, Ding, Y, Peng, D, Hall, CC, Brown, LA ORCID: https://orcid.org/0000-0003-4807-9056, Shi, Y, Ye, H, Dong, Y and Huang, W 2019, 'Retrieval of crop biophysical parameters from Sentinel-2 remote sensing imagery' , International Journal of Applied Earth Observation and Geoinformation, 80 , pp. 187-195.

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Abstract

The red-edge bands place the recently available multispectral Sentinel-2 imagery at an advantage over other multispectral sensors, and hypothetically offer improved crop biophysical variable retrieval accuracy. In this study, Sentinel-2 data was tested for its ability to estimate winter wheat leaf area index (LAI), leaf chlorophyll content (LCC) and canopy chlorophyll content (CCC). Artificial neural network (ANN) and look-up table (LUT) (based on PROSAIL simulations) and vegetation index (VI) methods were applied to retrieve biophysical parameters, and compared with the biophysical processor module embedded in the Sentinel Application Platform (SNAP) software. Based on a set of in situ measurements (62 samples) and near-synchronous Sentinel-2 images, the inversion approaches were applied and validated. The results showed that: 1) Sentinel-2 red-edge bands improved the retrievals of chlorophyll / LAI compared to traditional VIs; 2) the red-edge VIs outperformed other approaches; and 3) the SNAP biophysical processor obtained comparable accuracies of LAI and CCC estimation compared to the ANN and LUT approaches, giving R2 values above 0.5 with relatively low RMSE (1.53 m2/m2 for LAI, and 148.58 μg/cm2 for CCC). We recommend VI retrieval approach for small region with ground measurements, whereas where ground data is not available, SNAP is applicable for versatile and rapid winter wheat parameter estimation (though results need to be evaluated alongside the provided quality indicators). Summarizing, the results demonstrate the suitability of Sentinel-2 data, especially its red-edge bands, for crop biophysical variables retrieval. Future studies will need to make comparisons across canopy types to better assess the capability of the SNAP biophysical processor.

Item Type: Article
Schools: Schools > School of Computing, Science and Engineering
Journal or Publication Title: International Journal of Applied Earth Observation and Geoinformation
Publisher: Elsevier
ISSN: 0303-2434
Funders: National Key R&D Program of China, Technology Development Program of Jilin Province, National Natural Science Foundation of China
Depositing User: LA Brown
Date Deposited: 28 Oct 2022 07:38
Last Modified: 28 Oct 2022 07:38
URI: https://usir.salford.ac.uk/id/eprint/65386

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