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A three-variables cokriging method to estimate bare-surface soil moisture using multi-temporal, VV-polarization synthetic-aperture radar data

Méthode de cokrigeage à trois variables pour estimer l’humidité d’un sol à surface nue à l’aide de données radar multi-temporelles de polarisation VV à synthèse d’ouverture

Un método de cokriging de tres variables para estimar la humedad del suelo en la superficie sin vegetación utilizando datos multitemporales de radar de apertura sintética con polarización VV

使用多时段VV极化合成孔径雷达数据估算裸露土壤水分的三变量协同克里格法

Método de cokrigagem de três variáveis para estimar a umidade do solo em superfície descoberta utilizando dados multitemporais de radar de abertura sintética com polarização VV

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Abstract

A cokriging model using three variables is developed to estimate bare-surface soil moisture content based on multi-temporal synthetic-aperture radar (SAR) data. This model utilizes cross-semivariogram function to take into account spatially varied correlation among multiple variables. Here, five sentinel-1 SAR scenes were acquired on different dates using the interferometric wide-swath (IW) mode and a mean incidence angle of 39.02° to build the backscatter temporal-ratio in VV polarization. This algorithm is generally based on the assumption of contributions of soil moisture and surface roughness to the backscattering coefficient under the given radar configurations. In this study, soil moisture is the target variable, and the surface roughness and backscatter temporal-ratio in VV polarization are the auxiliary variables. A cross-semivariogram relationship is formulated among those three spatial variables; then ordinary cokriging is used, based on that cross-semivariogram formula, to estimate the spatial distribution of bare soil moisture content. The root mean square error (RMSE) of soil-moisture retrieval ranges from 2.62 to 2.66 vol%. The new empirical model described in this paper will provide new insights into the study of soil environments.

Résumé

Un modèle de cokrigeage utilisant 3 variables est développé afin d’évaluer le taux d’humidité d’un sol de surface nue sur la base des données multi-temporelles d’un radar à synthèse d’ouverture (RSO). Ce modèle utilise une fonction de semi-variogramme croisé pour prendre en considération la corrélation variée dans l’espace, entre plusieurs variables. Ici, cinq scènes RSO Sentinel 1 ont été acquises à différentes dates, utilisant le mode interférométrique à large bande et un angle d’incidence moyen de 39.02°, afin d’établir le temps relatif de rétrodiffusion en polarisation VV. Cet algorithme est généralement basé sur l’hypothèse d’une contribution de l’humidité du sol et de la rugosité des surfaces au coefficient de rétrodiffusion pour des configurations radar données. Dans cette étude, l’humidité du sol est la variable cible, et la rugosité de surface et le temps relatif de rétrodiffusion en polarisation VV sont des variables auxiliaires. Une relation de semi-variogramme croisé est formulée entre ces 3 variables spatiales; ensuite on utilise le cokrigeage classique, sur la base de cette formule, pour estimer la distribution spatiale du taux d’humidité d’un sol de surface nue. L’erreur quadratique moyenne (RMSE) de la récupération de l’humidité du sol est comprise entre 2.62 à 2.66% en volume. Le nouveau modèle empirique décrit dans le présent article fournira des nouveaux enseignements sur l’étude des environnements du sol.

Resumen

Se ha desarrollado un modelo de cokriging que utiliza tres variables para estimar el contenido de humedad del suelo en una superficie desnuda a partir de datos multitemporales del radar de apertura sintética (SAR). Este modelo utiliza una función de semivariograma cruzada para tener en cuenta la correlación espacialmente variada entre múltiples variables. Aquí se adquirieron cinco escenas de SAR sentinel-1 en diferentes fechas utilizando el modo interferométrico de barrido amplio (IW) y un ángulo de incidencia medio de 39.02° para construir la relación temporal de retrodispersión en la polarización VV. Este algoritmo se basa generalmente en el supuesto de que la humedad del suelo y la rugosidad de la superficie contribuyen al coeficiente de retrodispersión en las configuraciones dadas del radar. En este estudio, la humedad del suelo es la variable objetivo, y la rugosidad de la superficie y el coeficiente de retrodispersión temporal en la polarización VV son las variables auxiliares. Se formula una relación de semivariograma cruzado entre esas tres variables espaciales; luego se utiliza el cokriging ordinario, basado en esa fórmula de semivariograma cruzado, para estimar la distribución espacial del contenido de humedad del suelo sin vegetación. El error cuadrático medio de la raíz (RMSE) de la recuperación de la humedad del suelo oscila entre el 2.62% y el 2.66% del volumen. El nuevo modelo empírico que se describe en este documento proporcionará nuevas perspectivas para el estudio de los ambientes del suelo.

摘要

建立了使用三个变量的协同克里格模型,以基于多时段合成孔径雷达(SAR)数据估算裸露表面的土壤水分含量。该模型利用交叉半变异函数来考虑多个变量之间的空间变化相关性。在这里,使用干涉宽频(IW)模式和平均入射角为39.02°在不同的日期获取了5个哨兵1 号SAR场景,以建立VV极化的反向散射时间比。该算法通常基于在给定雷达设置下土壤水分和表面粗糙度对反向散射系数存在贡献的假设。在这项研究中,土壤水分是目标变量,VV极化的表面粗糙度和反向散射时间比是辅助变量。在这三个空间变量之间建立了交叉半变异函数关系。然后根据交叉半变异函数公式,使用普通协同克里格法估算裸露土壤水分的空间分布。土壤水分反演的均方根误差(RMSE)为2.62至2.66 vol%。本文描述的新的经验模型将为土壤环境研究提供新的参考。

Resumo

Um modelo de cokrigagem utilizando três variáveis é desenvolvido para estimar o teor de umidade do solo em superfície descoberta, com base em dados multitemporais de radar de abertura sintética (synthetic-aperture radar, SAR). Esse modelo utiliza uma função de semivariograma cruzado para levar em consideração a correlação espacialmente variada entre diversas variáveis. Aqui, cinco cenas de SAR sentinel-1 foram adquiridas em datas diferentes, utilizando o modo interferometria de ampla faixa (interferometric wide-swath, IW) e um ângulo de incidência médio de 39.02° para construir a razão temporal de retroespalhamento na polarização VV. Esse algoritmo é geralmente baseado na suposição de contribuições da umidade do solo e da rugosidade da superfície para o coeficiente de retroespalhamento sob as configurações de radar fornecidas. Neste estudo, a umidade do solo é a variável alvo e a rugosidade da superfície e a razão temporal de retroespalhamento na polarização VV são as variáveis auxiliares. Uma relação entre semivariograma é formulada entre essas três variáveis espaciais; então, a cokrigagem ordinária é aplicada, com base nessa fórmula do semivariograma cruzado, para estimar a distribuição espacial do teor de umidade do solo. A raiz do erro quadrático médio (REMQ) da recuperação da umidade do solo varia entre 2.62 e 2.66% em volume. O novo modelo empírico descrito neste artigo fornecerá novas ideias para estudos no ambiente do solo.

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Acknowledgements

Thanks go to Dr. Gareth Fabbro and Dr. Xuanlong Ma for reviewing the English text.

Funding

This research was jointly supported by the Research Initiation Fund for Teacher Development from Chengdu University of Technology (10912-2019KYQD07430), the research projects from the “National Natural Science Foundation of China” (41672325 and 1212011085468), the research project from “National Key R&D Program of China” (2017YFC0601505), and a research project from the “The State Key Research Project in 13th Five-Year” (51569018).

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Authors and Affiliations

  1. Geomathematics Key Laboratory of Sichuan Province, Chengdu University of Technology, Chengdu, 610059, China

    Ling Zeng & Ke Guo

  2. State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu, 610059, China

    Qingyun Shi

  3. State Key Laboratory of Geological Processes and Mineral Resources (GPMR), Faculty of Earth Sciences, China University of Geosciences, Wuhan, 430074, China

    Shuyun Xie

  4. Facility for Analysis Characterization Testing Simulation, Nanyang Technological University, Singapore, 639798, Singapore

    Jason Scott Herrin

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  1. Ling Zeng
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Correspondence to Ling Zeng.

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Zeng, L., Shi, Q., Guo, K. et al. A three-variables cokriging method to estimate bare-surface soil moisture using multi-temporal, VV-polarization synthetic-aperture radar data. Hydrogeol J 28, 2129–2139 (2020). https://doi.org/10.1007/s10040-020-02177-z

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