scholarly journals The Combined Use of Remote Sensing and Wireless Sensor Network to Estimate Soil Moisture in Golf Course

2021 ◽  
Vol 11 (24) ◽  
pp. 11769
Author(s):  
Pedro V. Mauri ◽  
Lorena Parra ◽  
David Mostaza-Colado ◽  
Laura Garcia ◽  
Jaime Lloret ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Regression Models ◽  
Absolute Error ◽  
Wireless Sensor ◽  
Golf Course ◽  
Natural Soil ◽  
Moisture Management ◽  
Using Data ◽  
The One

In gardening, particularly in golf courses, soil moisture management is critical for maximizing water efficiency. Remote sensing has been used to estimate soil moisture in recent years with relatively low accuracies. In this paper, we aim to use remote sensing and wireless sensor networks to generate soil moisture indexes for a golf course. In the golf course, we identified three types of soil, and data was gathered for three months. Mathematical models were obtained using data from Sentinel-2, bands with a resolution of 10 and 20 m, and sensed soil moisture. Models with acceptable accuracy were obtained only for one out of three soil types, the natural soil in which natural vegetation is grown. Two multiple regression models are presented with an R2 of 0.46 for bands at 10 m and 0.70 for bands at 20 m. Their mean absolute error was lower than 3% in both cases. For the modified soils, the greens, and the golf course fairway, it was not feasible to obtain regression models due to the temporal uniformity of the grass and the range of variation of soil moisture. The developed moisture indexes were compared with existing options. The attained accuracies improve the current models. The verification indicates that the model generated with band 4 and band 12 is the one with better accuracy.

scholarly journals Validation of Remote Sensing Retrieval Products using Data from a Wireless Sensor-Based Online Monitoring in Antarctica

Sensors ◽  
2016 ◽  
Vol 16 (11) ◽  
pp. 1938 ◽  
Author(s):  
Xiuhong Li ◽  
Xiao Cheng ◽  
Rongjin Yang ◽  
Qiang Liu ◽  
Yubao Qiu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Online Monitoring ◽  
Wireless Sensor ◽  
Using Data

scholarly journals Effects of Winter Snow Cover on Spring Soil Moisture Based on Remote Sensing Data Product over Farmland in Northeast China

2020 ◽  
Vol 12 (17) ◽  
pp. 2716
Author(s):  
Shuang Liang ◽  
Xiaofeng Li ◽  
Xingming Zheng ◽  
Tao Jiang ◽  
Xiaojie Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Linear Regression ◽  
Snow Cover ◽  
Snow Depth ◽  
Northeast China ◽  
Regression Models ◽  
Linear Regression Models ◽  
Winter Snow ◽  
The Relationship

Spring soil moisture (SM) is of great importance for monitoring agricultural drought and waterlogging in farmland areas. While winter snow cover has an important impact on spring SM, relatively little research has examined the correlation between winter snow cover and spring SM in great detail. To understand the effects of snow cover on SM over farmland, the relationship between winter snow cover parameters (maximum snow depth (MSD) and average snow depth (ASD)) and spring SM in Northeast China was examined based on 30 year passive microwave snow depth (SD) and SM remote-sensing products. Linear regression models based on winter snow cover were established to predict spring SM. Moreover, 4 year SD and SM data were applied to validate the performance of the linear regression models. Additionally, the effects of meteorological factors on spring SM also were analyzed using multiparameter linear regression models. Finally, as a specific application, the best-performing model was used to predict the probability of spring drought and waterlogging in farmland in Northeast China. Our results illustrated the positive effects of winter snow cover on spring SM. The average correlation coefficient (R) of winter snow cover and spring SM was above 0.5 (significant at a 95% confidence level) over farmland. The performance of the relationship between snow cover and SM in April was better than that in May. Compared to the multiparameter linear regression models in terms of fitting coefficient, MSD can be used as an important snow parameter to predict spring drought and waterlogging probability in April. Specifically, if the relative SM threshold is 50% when spring drought occurs in April, the prediction probability of the linear regression model concerning snow cover and spring SM can reach 74%. This study improved our understanding of the effects of winter snow cover on spring SM and will be beneficial for further studies on the prediction of spring drought.

scholarly journals Machine learning methods for soil moisture prediction in vineyards using digital images

2020 ◽  
Vol 167 ◽  
pp. 02004
Author(s):  
Chantal Saad Hajjar ◽  
Celine Hajjar ◽  
Michel Esta ◽  
Yolla Ghorra Chamoun
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Regression Models ◽  
Digital Images ◽  
Pearson Correlation ◽  
Support Vector ◽  
Learning Methods ◽  
Nonlinear Regression Models ◽  
Machine Learning Methods ◽  
The One

In this paper, we propose to estimate the moisture of vineyard soils from digital photography using machine learning methods. Two nonlinear regression models are implemented: a multilayer perceptron (MLP) and a support vector regression (SVR). Pixels coded with RGB colour model extracted from soil digital images along with the associated known soil moisture levels are used to train both models in order to predict moisture content from newly acquired images. The study is conducted on samples of six soil types collected from Chateau Kefraya terroirs in Lebanon. Both methods succeeded in forecasting moisture giving high correlation values between the measured moisture and the predicted moisture when tested on unknown data. However, the method based on SVR outperformed the one based on MLP yielding Pearson correlation coefficient values ranging from 0.89 to 0.99. Moreover, it is a simple and noninvasive method that can be adopted easily to detect vineyards soil moisture.

scholarly journals Monitoring of Expansive Clays over Drought-Rewetting Cycles Using Satellite Remote Sensing

Atmosphere ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1262
Author(s):  
André Burnol ◽  
Michael Foumelis ◽  
Sébastien Gourdier ◽  
Jacques Deparis ◽  
Daniel Raucoules
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Clayey Soil ◽  
Vertical Displacement ◽  
Similar Treatment ◽  
Soil Moisture Sensors ◽  
In Situ Data ◽  
Multi Temporal ◽  
One Year ◽  
The One

New capabilities for measuring and monitoring are needed to prevent the shrink-swell risk caused by drought-rewetting cycles. A clayey soil in the Loire Valley at Chaingy (France) has been instrumented with two extensometers and several soil moisture sensors. Here we show by direct comparison between remote and in situ data that the vertical ground displacements due to clay expansion are well-captured by the Multi-Temporal Synthetic Aperture Radar Interferometry (MT-InSAR) technique. In addition to the one-year period, two sub-annual periods that reflect both average ground shrinking and swelling timeframes are unraveled by a wavelet-based analysis. Moreover, the relative phase difference between the vertical displacement and surface soil moisture show local variations that are interpreted in terms of depth and thickness of the clay layer, as visualized by an electrical resistivity tomography. With regard to future works, a similar treatment relying fully on remote sensing observations may be scaled up to map larger areas in order to better assess the shrink-swell risk.

scholarly journals Evaluating Evapotranspiration By Using Data Mining Instead of The Physical Based Model In Remote Sensing

2021 ◽  
Author(s):  
Lamya Neissi ◽  
Mona Golabi ◽  
Mohammad Albaji ◽  
Abd Ali Naseri
Keyword(s):  
Remote Sensing ◽  
Data Mining ◽  
Decision Tree ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Plant Production ◽  
Tree Model ◽  
Water Index ◽  
Input Variables ◽  
Using Data

Abstract Precise calculations for plant water requirements and evapotranspiration is very crucial in determining the volume of water consumption for plant production. In order to estimate evapotranspiration in the extended area, different remote sensing algorithms required many climatological variables. Climatological variable measurements will cover small limited areas which can cause an error in extended areas. By using data mining and remote sensing, the evapotranspiration process can be modeled. In this research, the physical-based SEBAL evapotranspiration algorithm was modeled by M5 decision tree equations in GIS. Input variables of the M5 decision tree consisted of albedo, emissivity, and Normalized Difference Water Index (NDWI) which are represented as absorbed light, transformed light, and plant moisture, respectively. After extracting the best equations in the M5 decision tree model for 8 April 2019, these equations were modeled in GIS by using python scripts for 8 April 2019 and 3 April 2020. The calculated correlation coefficient (R2), Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) for 8 April 2019 were 0.92, 0.54, and 0.42 and for 3 April 2020 were 0.95, 0.31, and 0.23, respectively. Also, sensitivity and uncertainty analysis were considered for more model evaluation. Those analysis revealed that evapotranspiration is sensitive to albedo more than the two other model inputs and the estimated evapotranspiration obtained by data mining is in acceptable range of certainty.

scholarly journals Integration of soil moisture remote sensing and hydrologic modeling using data assimilation

1998 ◽  
Vol 34 (12) ◽  
pp. 3405-3420 ◽  
Author(s):  
Paul R. Houser ◽  
W. James Shuttleworth ◽  
James S. Famiglietti ◽  
Hoshin V. Gupta ◽  
Kamran H. Syed ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Data Assimilation ◽  
Hydrologic Modeling ◽  
Using Data

System Information Model of Obtaining and Using Data of Remote Sensing the Earth

2008 ◽  
Vol 40 (11) ◽  
pp. 46-56
Author(s):  
Ludmila I. Samoilenko ◽  
Sergey A. Baulin ◽  
Tatyana V. Ilyenko ◽  
Margarita A. Kirnosova ◽  
Ludmila N. Kolos ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Information Model ◽  
The Earth ◽  
System Information ◽  
Using Data

Soil Moisture Retrieval Using Data Cube Representation of Radar Scattering

PIERS Online ◽  
2010 ◽  
Vol 6 (6) ◽  
pp. 504-508 ◽  
Author(s):  
Seung-Bum Kim ◽  
Eni Gerald Njoku
Keyword(s):  
Soil Moisture ◽  
Data Cube ◽  
Radar Scattering ◽  
Using Data
Sign in / Sign up

Export Citation Format

Share Document