scholarly journals Estimation of Soil Moisture in an Alpine Catchment with RADARSAT2 Images

2011 ◽  
Vol 2011 ◽  
pp. 1-12 ◽  
Author(s):  
L. Pasolli ◽  
C. Notarnicola ◽  
L. Bruzzone ◽  
G. Bertoldi ◽  
S. Della Chiesa ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Support Vector ◽  
Distributed Data ◽  
Remotely Sensed Data ◽  
Ancillary Data ◽  
Sar Images ◽  
Biophysical Parameter ◽  
Spatially Distributed ◽  
Microwave Signals ◽  
The Alps

Soil moisture retrieval is one of the most challenging problems in the context of biophysical parameter estimation from remotely sensed data. Typically, microwave signals are used thanks to their sensitivity to variations in the water content of soil. However, especially in the Alps, the presence of vegetation and the heterogeneity of topography may significantly affect the microwave signal, thus increasing the complexity of the retrieval. In this paper, the effectiveness of RADARSAT2 SAR images for the estimation of soil moisture in an alpine catchment is investigated. We first carry out a sensitivity analysis of the SAR signal to the moisture content of soil and other target properties (e.g., topography and vegetation). Then we propose a technique for estimating soil moisture based on the Support Vector Regression algorithm and the integration of ancillary data. Preliminary results are discussed both in terms of accuracy over point measurements and effectiveness in handling spatially distributed data.

scholarly journals The Integration of Multi-source Remotely-Sensed Data in Support of the Classification of Wetlands

2019 ◽  
Vol 11 (13) ◽  
pp. 1537 ◽  
Author(s):  
Aaron Judah ◽  
Baoxin Hu
Keyword(s):  
Surface Temperature ◽  
Remotely Sensed ◽  
Image Features ◽  
Wildlife Habitat ◽  
Support Vector ◽  
Svm Classifier ◽  
Classification Algorithms ◽  
K Nearest Neighbor ◽  
Remotely Sensed Data ◽  
Ancillary Data

Wetlands play a key role in regional and global environments, and are critically linked to major issues such as climate change, wildlife habitat, biodiversity, water quality protection, and global carbon and methane cycles. Remotely-sensed imagery provides a means to detect and monitor wetlands on large scales and with regular frequency. In this project, methodologies were developed to classify wetlands (Open Bog, Treed Bog, Open Fen, Treed Fen, and Swamps) from multi-source remotely sensed data using advanced classification algorithms. The data utilized included multispectral optical and thermal data (Landsat-5) and Radar imagery from RADARSAT-2 and Sentinel-1. The goals were to determine the best way to combine the aforementioned imagery to classify wetlands, and determine the most significant image features. Classification algorithms investigated in this study were Naive Bayes, K-Nearest Neighbor (K-NN), Support Vector Machine (SVM), and Random Forest (RF). Based on the test results in the study area in Northern Ontario, Canada (49°31′.34N, 80°43′37.04W), a RF based classification methodology produced the most accurate classification result (87.51%). SVM, in some cases, produced results of comparable or better accuracy than RF. Our work also showed that the use of surface temperature (an untraditional feature choice) could aid in the classification process if the image is from an abnormally warm spring. This study found that wetlands were best classified using the NDVI (Normalized Difference Vegetative Index) calculated from optical imagery obtained in the spring months, radar backscatter coefficients, surface temperature, and ancillary data such as surface slope, computed through either an RF or SVM classifier. It was also found that preselection of features using Log-normal or RF variable importance analysis was an effective way of identifying low quality features and to a lesser extent features which were of higher quality.

DownscaleConcept 2.3 User Manual. Downscaled, Spatially Distributed Soil Moisture Calculator

2011 ◽  
Author(s):  
M. L. Coleman ◽  
A. Q. Dozier ◽  
C. M. Fields ◽  
J. D. Niemann
Keyword(s):  
Soil Moisture ◽  
Spatially Distributed

scholarly journals Multilayer Soil Moisture Mapping at a Regional Scale from Multisource Data via a Machine Learning Method

2019 ◽  
Vol 11 (3) ◽  
pp. 284 ◽  
Author(s):  
Linglin Zeng ◽  
Shun Hu ◽  
Daxiang Xiang ◽  
Xiang Zhang ◽  
Deren Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Regional Scale ◽  
Remotely Sensed ◽  
Temporal Variations ◽  
Training Data ◽  
Estimation Accuracy ◽  
Learning Approaches ◽  
Remotely Sensed Data ◽  
Deep Soil

Soil moisture mapping at a regional scale is commonplace since these data are required in many applications, such as hydrological and agricultural analyses. The use of remotely sensed data for the estimation of deep soil moisture at a regional scale has received far less emphasis. The objective of this study was to map the 500-m, 8-day average and daily soil moisture at different soil depths in Oklahoma from remotely sensed and ground-measured data using the random forest (RF) method, which is one of the machine-learning approaches. In order to investigate the estimation accuracy of the RF method at both a spatial and a temporal scale, two independent soil moisture estimation experiments were conducted using data from 2010 to 2014: a year-to-year experiment (with a root mean square error (RMSE) ranging from 0.038 to 0.050 m3/m3) and a station-to-station experiment (with an RMSE ranging from 0.044 to 0.057 m3/m3). Then, the data requirements, importance factors, and spatial and temporal variations in estimation accuracy were discussed based on the results using the training data selected by iterated random sampling. The highly accurate estimations of both the surface and the deep soil moisture for the study area reveal the potential of RF methods when mapping soil moisture at a regional scale, especially when considering the high heterogeneity of land-cover types and topography in the study area.

Methods for detecting and correcting contextual data quality problems

2021 ◽  
Vol 25 (4) ◽  
pp. 763-787
Author(s):  
Alladoumbaye Ngueilbaye ◽  
Hongzhi Wang ◽  
Daouda Ahmat Mahamat ◽  
Ibrahim A. Elgendy ◽  
Sahalu B. Junaidu
Keyword(s):  
Data Quality ◽  
Quality Evaluation ◽  
Web Applications ◽  
Support Vector ◽  
Similarity Metrics ◽  
Distributed Data ◽  
Contextual Data ◽  
Formal Taxonomy ◽  
E Learning ◽  
High Scalability

Knowledge extraction, data mining, e-learning or web applications platforms use heterogeneous and distributed data. The proliferation of these multifaceted platforms faces many challenges such as high scalability, the coexistence of complex similarity metrics, and the requirement of data quality evaluation. In this study, an extended complete formal taxonomy and some algorithms that utilize in achieving the detection and correction of contextual data quality anomalies were developed and implemented on structured data. Our methods were effective in detecting and correcting more data anomalies than existing taxonomy techniques, and also highlighted the demerit of Support Vector Machine (SVM). These proposed techniques, therefore, will be of relevance in detection and correction of errors in large contextual data (Big data).

scholarly journals Evaluating Downscaling Factors of Microwave Satellite Soil Moisture Based on Machine Learning Method

2021 ◽  
Vol 13 (1) ◽  
pp. 133
Author(s):  
Hao Sun ◽  
Yajing Cui
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Land Surface ◽  
Regional Scale ◽  
Support Vector ◽  
Machine Learning Method ◽  
Learning Method ◽  
Feed Forward Neural Network ◽  
Comparative Performance ◽  
Geographical Factors

Downscaling microwave remotely sensed soil moisture (SM) is an effective way to obtain spatial continuous SM with fine resolution for hydrological and agricultural applications on a regional scale. Downscaling factors and functions are two basic components of SM downscaling where the former is particularly important in the era of big data. Based on machine learning method, this study evaluated Land Surface Temperature (LST), Land surface Evaporative Efficiency (LEE), and geographical factors from Moderate Resolution Imaging Spectroradiometer (MODIS) products for downscaling SMAP (Soil Moisture Active and Passive) SM products. This study spans from 2015 to the end of 2018 and locates in the central United States. Original SMAP SM and in-situ SM at sparse networks and core validation sites were used as reference. Experiment results indicated that (1) LEE presented comparative performance with LST as downscaling factors; (2) adding geographical factors can significantly improve the performance of SM downscaling; (3) integrating LST, LEE, and geographical factors got the best performance; (4) using Z-score normalization or hyperbolic-tangent normalization methods did not change the above conclusions, neither did using support vector regression nor feed forward neural network methods. This study demonstrates the possibility of LEE as an alternative of LST for downscaling SM when there is no available LST due to cloud contamination. It also provides experimental evidence for adding geographical factors in the downscaling process.

scholarly journals A GNSS-IR Method for Retrieving Soil Moisture Content from Integrated Multi-Satellite Data That Accounts for the Impact of Vegetation Moisture Content

2021 ◽  
Vol 13 (13) ◽  
pp. 2442
Author(s):  
Jichao Lv ◽  
Rui Zhang ◽  
Jinsheng Tu ◽  
Mingjie Liao ◽  
Jiatai Pang ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Satellite Data ◽  
Vegetation Index ◽  
Soil Moisture Content ◽  
Support Vector ◽  
Observation Data ◽  
Multipath Effect ◽  
Mars Model ◽  
The Impact

There are two problems with using global navigation satellite system-interferometric reflectometry (GNSS-IR) to retrieve the soil moisture content (SMC) from single-satellite data: the difference between the reflection regions, and the difficulty in circumventing the impact of seasonal vegetation growth on reflected microwave signals. This study presents a multivariate adaptive regression spline (MARS) SMC retrieval model based on integrated multi-satellite data on the impact of the vegetation moisture content (VMC). The normalized microwave reflection index (NMRI) calculated with the multipath effect is mapped to the normalized difference vegetation index (NDVI) to estimate and eliminate the impact of VMC. A MARS model for retrieving the SMC from multi-satellite data is established based on the phase shift. To examine its reliability, the MARS model was compared with a multiple linear regression (MLR) model, a backpropagation neural network (BPNN) model, and a support vector regression (SVR) model in terms of the retrieval accuracy with time-series observation data collected at a typical station. The MARS model proposed in this study effectively retrieved the SMC, with a correlation coefficient (R2) of 0.916 and a root-mean-square error (RMSE) of 0.021 cm3/cm3. The elimination of the vegetation impact led to 3.7%, 13.9%, 11.7%, and 16.6% increases in R2 and 31.3%, 79.7%, 49.0%, and 90.5% decreases in the RMSE for the SMC retrieved by the MLR, BPNN, SVR, and MARS model, respectively. The results demonstrated the feasibility of correcting the vegetation changes based on the multipath effect and the reliability of the MARS model in retrieving the SMC.

scholarly journals Synergetic Use of Sentinel-1 and Sentinel-2 Data for Soil Moisture Mapping at Plot Scale

2018 ◽  
Vol 10 (8) ◽  
pp. 1285 ◽  
Author(s):  
Reza Attarzadeh ◽  
Jalal Amini ◽  
Claudia Notarnicola ◽  
Felix Greifeneder
Keyword(s):  
Soil Moisture ◽  
Optical Data ◽  
Estimation Accuracy ◽  
Support Vector ◽  
Practical Applications ◽  
Initial Stage ◽  
Object Based ◽  
Single Polarization ◽  
Selection Of

This paper presents an approach for retrieval of soil moisture content (SMC) by coupling single polarization C-band synthetic aperture radar (SAR) and optical data at the plot scale in vegetated areas. The study was carried out at five different sites with dominant vegetation cover located in Kenya. In the initial stage of the process, different features are extracted from single polarization mode (VV polarization) SAR and optical data. Subsequently, proper selection of the relevant features is conducted on the extracted features. An advanced state-of-the-art machine learning regression approach, the support vector regression (SVR) technique, is used to retrieve soil moisture. This paper takes a new look at soil moisture retrieval in vegetated areas considering the needs of practical applications. In this context, we tried to work at the object level instead of the pixel level. Accordingly, a group of pixels (an image object) represents the reality of the land cover at the plot scale. Three approaches, a pixel-based approach, an object-based approach, and a combination of pixel- and object-based approaches, were used to estimate soil moisture. The results show that the combined approach outperforms the other approaches in terms of estimation accuracy (4.94% and 0.89 compared to 6.41% and 0.62 in terms of root mean square error (RMSE) and R2), flexibility on retrieving the level of soil moisture, and better quality of visual representation of the SMC map.

Upstream and downstream morphodynamic influence in simulated and real meandering rivers

2021 ◽  
Author(s):  
Hossein amini ◽  
Guido Zolezzi ◽  
Federico Monegaglia ◽  
Emanuele Olivetti ◽  
Marco Tubino
Keyword(s):  
Migration Rate ◽  
Machine Learning Techniques ◽  
Stochastic Gradient Descent ◽  
Support Vector ◽  
Lateral Migration ◽  
Remotely Sensed Data ◽  
Meandering Rivers ◽  
Meandering River ◽  
Migration Rates ◽  
Meander Migration

<p>This study investigates the dependency of meander lateral migration rates on the spatial distribution of channel centerline curvature in both synthetic and real meandering rivers. It employs Machine Learning techniques (hereafter ML) to relate observed local lateral meander migration rates with the local and the upstream/downstream values of the centerline curvature. To achieve this goal, it was primarily essential to identify the feasibility of using ML in the meandering river's morphodynamics. We then determined the ability of ML to predict the excess near bank velocity based a set of input data using different regression techniques (linear and polynomial, Stochastic Gradient Descent, Multi-Layer Perceptron, and Support Vector Machine). We then moved forward to study the upstream-downstream influence on local migration rate. Synthetic meandering river planforms, as obtained through the planform evolution model of Bogoni et al. (2017), which is based on Zolezzi and Seminara (2001) meander flow model, were used as test cases for the calibration and check of the different adopted ML algorithms. The calibrated algorithms were then applied to multi-temporal information on meander planform dynamics obtained through the PyRiS software (Monegaglia et al., 2018), to quantify to which extent the upstream and downstream distribution of meander centerline curvature affects the local meander migration rate in real rivers.</p><p>References </p><p>1- Zolezzi, G., & Seminara, G. (2001b). Downstream and upstream influence in river meandering. Part 1. General theory and application overdeepening. Journal of Fluid Mechanics, 438(September 2015), 183–211. https://doi.org/10.1017/S002211200100427X</p><p>2- Monegaglia, F., Zolezzi, G., Güneralp, I., Henshaw, A. J., & Tubino, M. (2018). Automated extraction of meandering river morphodynamics from multitemporal remotely sensed data. In Environmental Modelling & Software (Vol. 105, pp. 171–186). https://doi.org/10.1016/j.envsoft.2018.03.028</p><p>3- Bogoni, M., Putti, M., & Lanzoni, S. (2017). Modeling meander morphodynamics over self-formed heterogeneous floodplains. In Water Resources Research (Vol. 53, Issue 6, pp. 5137–5157). https://doi.org/10.1002/2017wr020726</p><p>4- Benozzo, D.,  Olivetti, E., Avesani, P. (2017). Supervised Estimation of Granger-Based Causality between Time series. In Frontiers in Neuroinformatics. </p><p>https://doi.org/10.3389/fninf.2017.00068 </p><p>5- Sharma A., Kiciman, E. (2020). DoWhy: An End-to-End library for Causal Inference. arXiv preprint arXiv:2011.04216. </p><p>https://arxiv.org/abs/2011.04216</p>

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