A Machine Learning Based Reconstruction Method for Satellite Remote Sensing of Soil Moisture Images with In Situ Observations

Xing, Chenjie; Chen, Nengcheng; Zhang, Xiang; Gong, Jianhua

doi:10.3390/rs9050484

Cited by 35 publications

(13 citation statements)

References 54 publications

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“…; Xing et al . ). Many authors used ML algorithms to link different soil and environmental parameters with soil moisture (Islam et al .…”

Section: Introductionmentioning

confidence: 97%

“…Along with statistical modelling, machine learning (ML) techniques have been increasingly flourished for data inference in particular, for earth science studies (Najafi, Moradkhani, and Wherry 2011;Cracknell and Reading 2013;Cracknell, Reading, and McNeill 2014;Hoshyaripour et al 2016;Xing et al 2017). ML techniques are categorized as supervised and unsupervised learning.…”

mentioning

confidence: 99%

“…Supervised learning employs an algorithm to learn the patterns from the input to the output data, while unsupervised learning is to model the underlying structure in the data such as clustering (Taghizadeh Mehrjardi et al 2015;Karandish and Simunek 2016;Lary et al 2016). Among different supervised ML algorithms, artificial neural network (ANN) proved to be efficient and robust in exploring the patterns and predictions (Poulton and Birken 1998;Mahrooghy et al 2016;Xing et al 2017). Many authors used ML algorithms to link different soil and environmental parameters with soil moisture (Islam et al 2013;Ihamouten et al 2016;Karandish and Simunek 2016;Song et al 2016).…”

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confidence: 99%

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Machine learning to estimate soil moisture from geophysical measurements of electrical conductivity

Moghadas

Badorreck

2019

Near Surface Geophysics

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Soil water content (θ) is a key variable in different earth science disciplines since it mediates the water and energy exchange between the surface and atmosphere. Electrical and electromagnetic geophysical techniques have been widely used to estimate soil electrical conductivity (σ) and soil moisture. However, obtaining the σ−θ relationship is not straightforward due to the non‐linearity and also dependency on many different soil and environmental properties. The purpose of this paper is to determine if artificial neural network is an appropriate machine learning technique for relating electrical conductivity to soil water content. In this respect, time‐lapse electrical resistivity tomography measurements were carried out along a transect in the Chicken Creek catchment (Brandenburg, Germany). To ensure proper retrieval of the σ and θ, reference values were measured near the beginning of the transect via an excavated pit using 5TE capacitance sensors installed at different depths. We explored robustness and pertinence of the artificial neural network approach in comparison with Rhoades model (as a commonly used petrophysical relationship) to convert the inversely estimated σ from electrical resistivity tomography to the θ. The proposed approach was successfully validated and benchmarked by comparing the estimated values with the reference data. This study showed the superiority of the artificial neural network approach to the Rhoades model to obtain σ−θ relationship. In particular, artificial neural network allowed for more accurate estimation of the temporal wetting front than the petrophysical model. The proposed methodology thus offers a great promise for deriving spatiotemporal soil moisture patterns from geophysical data and obtaining the in situ σ−θ relationship, taking into account the non‐linear variations of the soil moisture.

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“…; Xing et al . ). Many authors used ML algorithms to link different soil and environmental parameters with soil moisture (Islam et al .…”

Section: Introductionmentioning

confidence: 97%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Machine learning to estimate soil moisture from geophysical measurements of electrical conductivity

Moghadas

Badorreck

2019

Near Surface Geophysics

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“…On the ground, the International Soil Moisture Network (ISMN) provides a global network of soil moisture in situ observations [6]. is network measures soil moisture at specific locations; thus, the data are in the form of discrete values as opposed to a soil moisture spatial distribution, although they provide temporally continuous observations [7]. Microwave synthetic-aperture radar (SAR) collects data over a large area with high spatial resolution and provides an effective technological means of monitoring and assessing soil moisture.…”

Section: Introductionmentioning

confidence: 99%

Combined Use of GF-3 and Landsat-8 Satellite Data for Soil Moisture Retrieval over Agricultural Areas Using Artificial Neural Network

Meng

Zhang

Xie

et al. 2018

Advances in Meteorology

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Soil moisture is the basic condition required for crop growth and development. Gaofen-3 (GF-3) is the first C-band synthetic-aperture radar (SAR) satellite of China, offering broad land and ocean imaging applications, including soil moisture monitoring. This study developed an approach to estimate soil moisture in agricultural areas from GF-3 data. An inversion technique based on an artificial neural network (ANN) is introduced. The neural network was trained and tested on a training sample dataset generated from the Advanced Integral Equation Model. Incidence angle and HH or VV polarization data were used as input variables of the ANN, with soil moisture content (SMC) and surface roughness as the output variables. The backscattering contribution from the vegetation was eliminated using the water cloud model (WCM). The acquired soil backscattering coefficients of GF-3 and in situ measurement data were used to validate the SMC estimation algorithm, which achieved satisfactory results (R2 = 0.736; RMSE = 0.042). These results highlight the contribution of the combined use of the GF-3 synthetic-aperture radar and Landsat-8 images based on an ANN method for improving SMC estimates and supporting hydrological studies.

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“…This fact draws on a requirement for the abundance of historical satellite data and contemporary in situ SM observations. If the satellite data and in situ SM archive is not abundant enough, then the relation values cannot be fully represented by historical observation pairs [49]. Notably, this study is the first to spatially estimate SM using MODIS LST corrected using the CM technique in South Korea.…”

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confidence: 99%

A Study of Spatial Soil Moisture Estimation Using a Multiple Linear Regression Model and MODIS Land Surface Temperature Data Corrected by Conditional Merging

et al. 2017

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Abstract:This study attempts to estimate spatial soil moisture in South Korea (99,000 km 2 ) from January 2013 to December 2015 using a multiple linear regression (MLR) model and the Terra moderate-resolution imaging spectroradiometer (MODIS) land surface temperature (LST) and normalized distribution vegetation index (NDVI) data. The MODIS NDVI was used to reflect vegetation variations. Observed precipitation was measured using the automatic weather stations (AWSs) of the Korea Meteorological Administration (KMA), and soil moisture data were recorded at 58 stations operated by various institutions. Prior to MLR analysis, satellite LST data were corrected by applying the conditional merging (CM) technique and observed LST data from 71 KMA stations. The coefficient of determination (R 2 ) of the original LST and observed LST was 0.71, and the R 2 of corrected LST and observed LST was 0.95 for 3 selected LST stations. The R 2 values of all corrected LSTs were greater than 0.83 for total 71 LST stations. The regression coefficients of the MLR model were estimated seasonally considering the five-day antecedent precipitation. The p-values of all the regression coefficients were less than 0.05, and the R 2 values were between 0.28 and 0.67. The reason for R 2 values less than 0.5 is that the soil classification at each observation site was not completely accurate. Additionally, the observations at most of the soil moisture monitoring stations used in this study started in December 2014, and the soil moisture measurements did not stabilize. Notably, R 2 and root mean square error (RMSE) in winter were poor, as reflected by the many missing values, and uncertainty existed in observations due to freezing and mechanical errors in the soil. Thus, the prediction accuracy is low in winter due to the difficulty of establishing an appropriate regression model. Specifically, the estimated map of the soil moisture index (SMI) can be used to better understand the severity of droughts with the variability of soil moisture.

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A Machine Learning Based Reconstruction Method for Satellite Remote Sensing of Soil Moisture Images with In Situ Observations

Cited by 35 publications

References 54 publications

Machine learning to estimate soil moisture from geophysical measurements of electrical conductivity

Machine learning to estimate soil moisture from geophysical measurements of electrical conductivity

Combined Use of GF-3 and Landsat-8 Satellite Data for Soil Moisture Retrieval over Agricultural Areas Using Artificial Neural Network

A Study of Spatial Soil Moisture Estimation Using a Multiple Linear Regression Model and MODIS Land Surface Temperature Data Corrected by Conditional Merging

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