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Crop biophysical parameter retrieval from Sentinel-1 SAR data with a multi-target inversion of Water Cloud Model
International Journal of Remote Sensing ( IF 3.4 ) Pub Date : 2020-04-28 , DOI: 10.1080/01431161.2020.1734261
Dipankar Mandal 1 , Vineet Kumar 1, 2 , Juan M. Lopez-Sanchez 3 , Avik Bhattacharya 1 , Heather McNairn 4 , Y. S. Rao 1
Affiliation  

ABSTRACT Estimation of bio-and geophysical parameters from Earth observation (EO) data is essential for developing applications on crop growth monitoring. High spatio-temporal resolution and wide spatial coverage provided by EO satellite data are key inputs for operational crop monitoring. In Synthetic Aperture Radar (SAR) applications, a semi-empirical model (viz., Water Cloud Model (WCM)) is often used to estimate vegetation descriptors individually. However, a simultaneous estimation of these vegetation descriptors would be logical given their inherent correlation, which is seldom preserved in the estimation of individual descriptors by separate inversion models. This functional relationship between biophysical parameters is essential for crop yield models, given that their variations often follow different distribution throughout crop development stages. However, estimating individual parameters with independent inversion models presume a simple relationship (potentially linear) between the biophysical parameters. Alternatively, a multi-target inversion approach would be more effective for this aspect of model inversion compared to an individual estimation approach. In the present research, the multi-output support vector regression (MSVR) technique is used for inversion of the WCM from C-band dual-pol Sentinel-1 SAR data. Plant Area Index (PAI, m2 m−2) and wet biomass (W, kg m−2) are used as the vegetation descriptors in the WCM. The performance of the inversion approach is evaluated with in-situ measurements collected over the test site in Manitoba (Canada), which is a super-site in the Joint Experiment for Crop Assessment and Monitoring (JECAM) SAR inter-comparison experiment network. The validation results indicate a good correlation with acceptable error estimates (normalized root mean square error–nRMSE and mean absolute error–MAE) for both PAI and wet biomass for the MSVR approach and a better estimation with MSVR than single-target models (support vector regression–SVR). Furthermore, the correlation between PAI and wet biomass is assessed using the MSVR and SVR model. Contrary to the single output SVR, the correlation between biophysical parameters is adequately taken into account in MSVR based simultaneous inversion technique. Finally, the spatio-temporal maps for PAI and W at different growth stages indicate their variability with crop development over the test site.

中文翻译:

使用水云模型的多目标反演从 Sentinel-1 SAR 数据中检索作物生物物理参数

摘要 从地球观测 (EO) 数据中估算生物和地球物理参数对于开发作物生长监测应用至关重要。EO 卫星数据提供的高时空分辨率和广泛的空间覆盖是业务作物监测的关键输入。在合成孔径雷达 (SAR) 应用中,通常使用半经验模型(即水云模型 (WCM))来单独估计植被描述符。然而,考虑到它们的内在相关性,同时估计这些植被描述符将是合乎逻辑的,这在通过单独的反演模型估计单个描述符时很少保留。生物物理参数之间的这种函数关系对于作物产量模型至关重要,鉴于它们的变化通常在整个作物发育阶段遵循不同的分布。然而,用独立的反演模型估计单个参数假定生物物理参数之间存在简单的关系(可能是线性的)。或者,与单个估计方法相比,多目标反演方法对于模型反演的这方面更有效。在目前的研究中,多输出支持向量回归 (MSVR) 技术用于从 C 波段双极化 Sentinel-1 SAR 数据反演 WCM。植物面积指数(PAI,m2 m-2)和湿生物量(W,kg m-2)用作 WCM 中的植被描述符。反演方法的性能通过在马尼托巴省(加拿大)的试验场收集的原位测量值进行评估,这是作物评估和监测联合实验(JECAM)SAR比对实验网络中的超级站点。验证结果表明 MSVR 方法的 PAI 和湿生物量的可接受误差估计(归一化均方根误差 -nRMSE 和平均绝对误差 -MAE)具有良好的相关性,并且 MSVR 比单目标模型(支持向量回归-SVR)。此外,使用 MSVR 和 SVR 模型评估 PAI 和湿生物量之间的相关性。与单输出 SVR 不同,基于 MSVR 的同时反演技术充分考虑了生物物理参数之间的相关性。最后,不同生长阶段的 PAI 和 W 的时空图表明它们在试验地点随作物发育的变化。
更新日期:2020-04-28
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