Retrieval of crop biophysical parameters from Sentinel-2 remote sensing imagery
Retrieval of crop biophysical parameters from Sentinel-2 remote sensing imagery
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.
187-195
Xie, Qiaoyun
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Dash, Jadunandan
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Huete, Alfredo
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Jiang, Aihui
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Yin, Gaofei
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Ding, Yanling
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Peng, Dailiang
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Hall, Christopher
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Brown, Luke
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Shi, Yue
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Ye, Huichun
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Dong, Yingying
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Huang, Wenjiang
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August 2019
Xie, Qiaoyun
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Dash, Jadunandan
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Huete, Alfredo
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Jiang, Aihui
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Yin, Gaofei
057e7d58-d3f9-44c6-9dc9-649e0dab02c9
Ding, Yanling
8c210587-5c29-4ace-960c-adc8557dc3ea
Peng, Dailiang
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Hall, Christopher
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Brown, Luke
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Shi, Yue
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Ye, Huichun
f6c792be-f48f-4a65-9f89-ea466aaf761e
Dong, Yingying
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Huang, Wenjiang
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Xie, Qiaoyun, Dash, Jadunandan, Huete, Alfredo, Jiang, Aihui, Yin, Gaofei, Ding, Yanling, Peng, Dailiang, Hall, Christopher, Brown, Luke, Shi, Yue, Ye, Huichun, Dong, Yingying and Huang, Wenjiang
(2019)
Retrieval of crop biophysical parameters from Sentinel-2 remote sensing imagery.
International Journal of Applied Earth Observation and Geoinformation, 80, .
(doi:10.1016/j.jag.2019.04.019).
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.
Text
Retrieval of crop biophysical parameters from Sentinel-2 imagery_V1
- Accepted Manuscript
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Accepted/In Press date: 25 April 2019
e-pub ahead of print date: 1 May 2019
Published date: August 2019
Identifiers
Local EPrints ID: 431851
URI: http://eprints.soton.ac.uk/id/eprint/431851
ISSN: 0303-2434
PURE UUID: b14004f0-c57b-4cff-b400-c30df28073f9
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Date deposited: 19 Jun 2019 16:30
Last modified: 16 Apr 2024 04:04
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Contributors
Author:
Qiaoyun Xie
Author:
Alfredo Huete
Author:
Aihui Jiang
Author:
Gaofei Yin
Author:
Yanling Ding
Author:
Dailiang Peng
Author:
Christopher Hall
Author:
Luke Brown
Author:
Yue Shi
Author:
Huichun Ye
Author:
Yingying Dong
Author:
Wenjiang Huang
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