Retrieval of crop biophysical parameters from Sentinel-2 remote sensing imagery
Retrieval of crop biophysical parameters from Sentinel-2 remote sensing imagery
复制标题
从Sentinel-2遥感影像中检索作物生物物理参数
DOI：
10.1016/j.jag.2019.04.019
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发表时间：
2019
影响因子：
7.5
通讯作者：
Wenjiang Huang
中科院分区：
文献类型：
--
作者：
Qiaoyun Xie;Jadun;an Dash;Alfredo Huete;Aihui Jiang;Gaofei Yin;Yanling Ding;Dailiang Peng;Christopher Hall;Luke A. Brown;Yue Shi;Huichun Ye;Yingying Dong;Wenjiang Huang
摘要红边带使最近可用的多光谱Sentinel-2图像比其他多光谱传感器具有优势，并假设提供改进的作物生物物理变量检索精度。利用Sentinel-2数据对冬小麦叶面积指数（LAI）、叶片叶绿素含量（LCC）和冠层叶绿素含量（CCC）进行了估算。人工神经网络（ANN）和查找表（LUT）（基于PROSAIL模拟）和植被指数（VI）的方法来检索生物物理参数，并与生物物理处理器模块嵌入在哨兵应用平台（SNAP）软件进行了比较。基于一组原位测量（62个样品）和近同步Sentinel-2图像，应用并验证了反演方法。结果表明：1）Sentinel-2红边波段比传统维斯波段更好地反演了叶绿素/叶面积指数，2）红边维斯波段的反演效果优于其他方法;与ANN和LUT方法相比，SNAP生物物理处理器获得了可比的LAI和CCC估计精度，给出了R2值大于0.5且RMSE相对较低叶面积指数（LAI）为1.53 m2/m2，玉米蛋白质含量（CCC）为148.58 μg/cm 2。我们建议VI检索方法与地面测量的小区域，而地面数据是不可用的，SNAP是适用于通用的和快速的冬小麦参数估计（虽然结果需要与提供的质量指标一起评估）。总之，结果表明，适合的哨兵-2数据，特别是其红边带，作物生物物理变量检索。未来的研究将需要进行跨冠层类型的比较，以更好地评估SNAP生物物理处理器的能力。
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.