Sentinel-1 soil moisture content and its uncertainty over sparsely vegetated fields

IF 3.1 Q2 GEOSCIENCES, MULTIDISCIPLINARY Journal of Hydrology X Pub Date : 2020-12-01 DOI:10.1016/j.hydroa.2020.100066
Harm-Jan F. Benninga , Rogier van der Velde , Zhongbo Su
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引用次数: 17

Abstract

Soil moisture content (SMC) retrievals from synthetic aperture radar (SAR) observations do not exactly match with in situ references due to imperfect retrieval algorithms, and uncertainties in the model parameters, SAR observations and in situ references. Information on the uncertainty of SMC retrievals would contribute to their applicability. This paper presents a methodology for deriving the SMC retrieval uncertainty and decomposing this in its constituents. A Bayesian calibration framework was used for deriving the total uncertainty and the model parameter uncertainty. The methodology was demonstrated with the integral equation method (IEM) surface scattering model, which was employed for reproducing Sentinel-1 backscatter (σ0) observations and the retrieval of SMC over four sparsely vegetated fields in the Netherlands. For two meadows the calibrated surface roughness parameter distributions are remarkably similar between the ascending and the descending Sentinel-1 orbits as well as between the two meadows, and yield consistent SMC retrievals for the calibration and validation periods (RMSDs of 0.076 m3 m−3 to 0.11 m3 m−3). These results are promising for operational retrieval of SMC over meadows. In contrast, the surface roughness parameter distributions of two fallow maize fields differ significantly and the surface roughness conditions changing over time result in less consistent SMC retrievals (calibration RMSDs of 0.096 m3 m−3 and 0.13 m3 m−3 versus validation RMSDs of 0.26 m3 m−3). The SMC retrieval uncertainty derived with the Bayesian calibration successfully reproduces the uncertainty estimated empirically using in situ references. The main uncertainty originates from the in situ references and the Sentinel-1 observations, whereas the contribution from the surface roughness parameters is relatively small. The presented research yields further insights into the surface roughness of agricultural fields and SMC retrieval uncertainties, and these insights can be used to guide SAR-based SMC product developments.

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稀疏植被地区Sentinel-1土壤水分含量及其不确定性
由于反演算法不完善,以及模型参数、SAR观测值和现场参考值的不确定性,从合成孔径雷达(SAR)观测值中反演的土壤含水量(SMC)与现场参考值不完全匹配。关于SMC检索不确定性的信息将有助于其适用性。本文提出了一种推导SMC检索不确定性的方法,并将其分解为其组成部分。贝叶斯校准框架用于推导总不确定性和模型参数不确定性。该方法用积分方程法(IEM)表面散射模型进行了验证,该模型用于再现荷兰四个植被稀疏的田地上的Sentinel-1反向散射(σ0)观测和SMC的反演。对于两个草地,Sentinel-1上升轨道和下降轨道之间以及两个草地之间的校准表面粗糙度参数分布非常相似,并且在校准和验证期间产生一致的SMC回收(RMSD为0.076 m3 m−3至0.11 m3 m−)。这些结果对草地上SMC的操作回收很有希望。相反两个休耕玉米田的表面粗糙度参数分布存在显著差异,表面粗糙度条件随时间变化导致SMC反演不太一致(校准RMSD为0.096 m3 m−3和0.13 m3 m−2,而验证RMSD为0.26 m3 m–3)。用贝叶斯校准推导出的SMC反演不确定性成功地再现了使用现场参考文献经验估计的不确定性。主要的不确定性来自现场参考和Sentinel-1观测,而表面粗糙度参数的贡献相对较小。所提出的研究进一步深入了解了农田的表面粗糙度和SMC检索的不确定性,这些见解可用于指导基于SAR的SMC产品开发。
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来源期刊
Journal of Hydrology X
Journal of Hydrology X Environmental Science-Water Science and Technology
CiteScore
7.00
自引率
2.50%
发文量
20
审稿时长
25 weeks
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