International journal of applied earth observation and geoinformation2019，Vol.809.DOI:10.1016/j.jag.2019.04.019

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

Xie, Qiaoyun Dash, Jadu Huete, Alfredo Jiang, Aihui Yin, Gaofei Ding, Yanling Peng, Dailiang Hall, Christopher C. Brown, Luke Shi, Yue Ye, Huichun Dong, Yingying Huang, Wenjiang
International journal of applied earth observation and geoinformation2019，Vol.809.DOI:10.1016/j.jag.2019.04.019
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  • NSTL
  • Elsevier

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

Xie, Qiaoyun 1Dash, Jadu 2Huete, Alfredo 1Jiang, Aihui 3Yin, Gaofei 4Ding, Yanling 5Peng, Dailiang 6Hall, Christopher C. 1Brown, Luke 2Shi, Yue 6Ye, Huichun 6Dong, Yingying 6Huang, Wenjiang6
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作者信息

  • 1. Univ Technol Sydney, Fac Sci, Sydney, NSW 2007, Australia
  • 2. Univ Southampton, Sch Geog & Environm Sci, Southampton SO17 1BJ, Hants, England
  • 3. Shandong Normal Univ, Coll Geog & Environm, Jinan 250358, Shandong, Peoples R China
  • 4. Southwest Jiaotong Univ, Fac Geosci & Environm Engn, Chengdu 610031, Sichuan, Peoples R China
  • 5. Northeast Normal Univ, Sch Geog Sci, Changchun 130024, Jilin, Peoples R China
  • 6. Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China
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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 R-2 values above 0.5 with relatively low RMSE (1.53 m(2)/m(2) for LM, and 148.58 mu g/cm(2) 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.

Key words

Leaf area index/Chlorophyll content/Artificial neural network/Look-up table/Vegetation index

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出版年

2019
International journal of applied earth observation and geoinformation

International journal of applied earth observation and geoinformation

SCI
ISSN：0303-2434
被引量43
参考文献量48
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