Digital mapping of soil moisture retention properties using solely satellite-based data and data mining techniques

Jeihouni, Mehrdad; Alavipanah, Seyed Kazem; Toomanian, Ara; Jafarzadeh, Ali Asghar

doi:10.1016/j.jhydrol.2020.124786

Cited by 14 publications

(7 citation statements)

References 70 publications

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“…In our study, the only indicator that could be visualized from a multispectral camera was SAVI, which showed very low values due to the fact that there was very poor vegetation cover on the study area. SAVI is an indicator that was previously used to predicting soil moisture retention properties [37]. On the other hand, [12] tested several remote sensing indicators (including SAVI) to determine moisture on bare soil, pasture and farmland and obtained values for SAVI very similar to our results.…”

supporting

confidence: 79%

Estimation of Bare Soil Moisture from Remote Sensing Indices in the 0.4–2.5 mm Spectral Range

Katarzyna

Stypułkowska

Jakub

et al. 2021

Transactions on Aerospace Research

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Soil moisture content (SMC) is an important element of the environment, influencing water availability for plants and atmospheric parameters, and its monitoring is important for predicting floods or droughts and for weather and climate modeling. Optical methods for measuring soil moisture use spectral reflection analysis in the 350–2500 nm range. Remote sensing is considered to be an effective tool for monitoring soil parameters over large areas and to be more cost effective than in situ measurements. The aim of this study was to assess the SMC of bare soil on the basis of hyperspectral data from the ASD FieldSpec 4 Hi-Res field spectrometer by determining remote sensing indices and visualization based on multispectral data obtained from UAVs. Remote sensing measurements were validated on the basis of field humidity measurements with the HH2 Moisture Meter and ML3 ThetaProbe Soil Moisture Sensor. A strong correlation between terrestrial and remote sensing data was observed for 7 out of 11 selected indexes and the determination coefficient R2 values ranged from 67%– 87%. The best results were obtained for the NINSON index, with determination coefficient values of 87%, NSMI index (83.5%) and NINSOL (81.7%). We conclude that both hyperspectral and multispectral remote sensing data of bare soil moisture are valuable, providing good temporal and spatial resolution of soil moisture distribution in local areas, which is important for monitoring and forecasting local changes in climate.

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supporting

confidence: 79%

Estimation of Bare Soil Moisture from Remote Sensing Indices in the 0.4–2.5 mm Spectral Range

Katarzyna

Stypułkowska

Jakub

et al. 2021

Transactions on Aerospace Research

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“…A soil-adjusted vegetation index such as the SAVI (soil-adjusted vegetation index) is used to reduce the soil effect, minimizing the related brightness by considering first-order soil vegetation interaction with soil-adjustment parameters [28]. Jeihouny et al [29] use this index to map soil moisture by means of data mining, finding that SAVI is an important covariate in predicting soil moisture retention properties.…”

Section: Introductionmentioning

confidence: 99%

Soil Moisture Analysis by Means of Multispectral Images According to Land Use and Spatial Resolution on Andosols in the Colombian Andes

Casamitjana

Torres-Madronero

et al. 2020

Applied Sciences

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Surface soil moisture is an important hydrological parameter in agricultural areas. Periodic measurements in tropical mountain environments are poorly representative of larger areas, while satellite resolution is too coarse to be effective in these topographically varied landscapes, making spatial resolution an important parameter to consider. The Las Palmas catchment area near Medellin in Colombia is a vital water reservoir that stores considerable amounts of water in its andosol. In this tropical Andean setting, we use an unmanned aerial vehicle (UAV) with multispectral (visible, near infrared) sensors to determine the correlation of three agricultural land uses (potatoes, bare soil, and pasture) with surface soil moisture. Four vegetation indices (the perpendicular drought index, PDI; the normalized difference vegetation index, NDVI; the normalized difference water index, NDWI, and the soil-adjusted vegetation index, SAVI) were applied to UAV imagery and a 3 m resolution to estimate surface soil moisture through calibration with in situ field measurements. The results showed that on bare soil, the indices that best fit the soil moisture results are NDVI, NDWI and PDI on a detailed scale, whereas on potatoes crops, the NDWI is the index that correlates significantly with soil moisture, irrespective of the scale. Multispectral images and vegetation indices provide good soil moisture understanding in tropical mountain environments, with 3 m remote sensing images which are shown to be a good alternative to soil moisture analysis on pastures using the NDVI and UAV images for bare soil and potatoes.

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“…Unfortunately, no ML algorithm provides a satisfactory result for all problems involving hydrological processes, and many methods remain in the development stage. Although the SVM, RF, DT, LSSVM, MARS, and GPR models have been widely employed in various research fields (e.g., Jeihouni et al., 2020; Granata et al., 2017; Kisi & Parmar, 2016; Panahi et al., 2020; Rezaali et al., 2021), their application in estimating river discharge has been limited (e.g., Tongal & Booij, 2018; Yaghoubi et al., 2019). Therefore, this study employs these techniques to explore their power/applicability in reconstructing the daily discharge in the Mekong River.…”

Section: Introductionmentioning

confidence: 99%

“…Wang et al (2020) proposed a new method to predict the evaporation of arid areas in China by applying the MARS method. In a digital application, Jeihouni et al (2020) employed the MARS model to map soil moisture retention parameters using only satellite data with less prediction uncertainty and high accuracy results. Additionally, Safari (2020) employed MARS and multi non-linear regression (MNLR) to improve the precision of predicting sediment accumulation in open channel flow areas.…”

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confidence: 99%

Reconstructing Daily Discharge in a Megadelta Using Machine Learning Techniques

Thanh

Binh

Kantoush

et al. 2022

Water Resources Research

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River discharge is a crucial indicator to understand terrestrial water cycles and supplies necessary information about water resource management (Adnan et al., 2020). Direct measurement of river discharge, such as employing the acoustic Doppler current profiler, is complicated, costly, time-consuming, and labor-intensive because it requires a number of current sensors and repeated surveys performed by boats and is thus unsafe under unfavorable flow and weather conditions (Gisen & Savenije, 2015;Matte et al., 2018). Other noncontact methods, including large-scale particle image velocimetry (LSPIV) (Akbarpour et al., 2020) and remote sensing (Kebede et al., 2020), have recently begun to be used for discharge measurements. Nevertheless, the use of these methods in contiguous monitoring of river discharge is not feasible; for example, LSPIV cannot measure discharge in large rivers because of limited camera coverage, while satellite images are not always available due to cloud cover, particularly during rainy seasons. As a result, at hydrological stations situated on rivers worldwide, flow discharge is not directly measured; rather, it is indirectly estimated either from the widely used stage-discharge rating curve (RC) method or from cubature, rating-fall, tide-correction, and coaxial graphical-correction methods (Matte et al., 2018), in which the stage (water level) is recorded at specific intervals (e.g., daily, hourly, or sub-daily) depending on the goal of the measurements. Due to technical, financial, maintenance and political instability issues, long-term flow discharge datasets may have gaps, resulting in the loss of information or the misinterpretation of historical flow regime changes and hydrological processes (Tencaliec et al., 2015). Therefore, it is important to reconstruct missing discharge values to reliably provide helpful information for water resource management at the basin scale.Several methods, including statistical methods, numerical models, and machine learning (ML) algorithms, have been employed to predict river flows. Recently, ML techniques, such as support vector regression (SVR) (Adnan et al., 2020;Luo et al., 2019), random forest (RF), Gaussian process regression (GPR) (Sun et al., 2014), M5

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Digital mapping of soil moisture retention properties using solely satellite-based data and data mining techniques

Cited by 14 publications

References 70 publications

Estimation of Bare Soil Moisture from Remote Sensing Indices in the 0.4–2.5 mm Spectral Range

Estimation of Bare Soil Moisture from Remote Sensing Indices in the 0.4–2.5 mm Spectral Range

Soil Moisture Analysis by Means of Multispectral Images According to Land Use and Spatial Resolution on Andosols in the Colombian Andes

Reconstructing Daily Discharge in a Megadelta Using Machine Learning Techniques

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