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

Qiaoyun Xie, Jadu Dash, Alfredo Huete, Aihui Jiang, Gaofei Yin, Yanling Ding, Dailiang Peng, Christopher C. Hall, Luke Brown, Yue Shi, Huichun Ye, Yingying Dong, Wenjiang Huang

Research output: Contribution to journalArticlepeer-review

135 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)187-195
Number of pages9
JournalInternational Journal of Applied Earth Observation and Geoinformation
Volume80
DOIs
Publication statusPublished - Aug 2019
Externally publishedYes

Fingerprint

Dive into the research topics of 'Retrieval of crop biophysical parameters from Sentinel-2 remote sensing imagery'. Together they form a unique fingerprint.

Cite this