Editorial for the Special Issue “Advanced Machine Learning for Time Series Remote Sensing Data Analysis”

Jeon, Gwanggil; Bellandi, Valerio; Chehri, Abdellah

doi:10.3390/rs12172815

Cited by 3 publications

(3 citation statements)

References 8 publications

(8 reference statements)

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“…Machine learning algorithms have been widely studied in the field of remote sensing and have shown excellent performance in solving nonlinear relationship problems [60]. Machine learning algorithms such as RF and SVM have been extensively applied to soil moisture prediction due to their high accuracy and stability [61,62]. He et al [63] integrated the "trapezoid" model and multiple learning techniques (RF and XGBoost) to estimate soil moisture on the Tibetan Plateau based on MODIS data.…”

Section: Multisource Remote Sensing Data and Machine Learning Model H...mentioning

confidence: 99%

Estimation of Soil Moisture Using Multi-Source Remote Sensing and Machine Learning Algorithms in Farming Land of Northern China

Liu,

Wu,

Cui

et al. 2023

Remote Sensing

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Soil moisture is a key parameter for the circulation of water and energy exchange between surface and the atmosphere, playing an important role in hydrology, agriculture, and meteorology. Traditional methods for monitoring soil moisture suffer from spatial discontinuity, time-consuming processes, and high costs. Remote sensing technology enables the non-destructive and efficient retrieval of land information, allowing rapid soil moisture monitoring to schedule crop irrigation and evaluate the irrigation efficiency. Satellite data with different resolutions provide different observation scales. Evaluating the accuracy of estimating soil moisture based on open and free satellite data, as well as exploring the comprehensiveness and adaptability of different satellites for soil moisture temporal and spatial observations, are important research contents of current soil moisture monitoring. The study utilized three types of satellite data, namely GF-1, Landsat-8, and GF-4, with respective temporal and spatial resolutions of 16 m (every 4 days), 30 m (every 16 days), and 50 m (daily). The gray relational analysis (GRA) was employed to identify vegetation indices that selected sensitivity to soil moisture at varying depths (3 cm, 10 cm, and 20 cm). Then, this study employed random forest (RF), Extra Tree (ETr), and linear regression (LR) algorithms to estimate soil moisture at different depths with optical satellite data sources. The results showed that the accuracy of soil moisture estimation was different at different growth stages. The model accuracy exhibited an upward trend during the middle and late growth stages, coinciding with higher vegetation coverage; however, it demonstrated a decline in accuracy during the early and late growth stages due to either the absence or limited presence of vegetation. Among the three satellite images, the vegetation indices derived from GF-1 exhibited were more sensitive to vegetation characteristics and demonstrated superior soil moisture estimation accuracy (with R2 ranging 0.129–0.928, RMSE ranging 0.017–0.078), followed by Landsat-8 (with R2 ranging 0.117–0.862, RMSE ranging 0.017–0.088). The soil moisture estimation accuracy of GF-4 was the worst (with R2 ranging 0.070–0.921, RMSE ranging 0.020–0.140). Thus, GF-1 is suitable for vegetated areas. In addition, the ETr model outperformed the other models in both accuracy and stability (ETr model: R2 ranging from 0.117 to 0.928, RMSE ranging from 0.021 to 0.091; RF model: R2 ranging from 0.225 to 0.926, RMSE ranging from 0.019 to 0.085; LR model: R2 ranging from 0.048 to 0.733, RMSE ranging from 0.030 to 0.144). Utilizing GF-1 is recommended to construct the ETr model for assessing soil moisture variations in the farming land of northern China. Therefore, in cases where there are limited ground sample data, it is advisable to utilize high-spatiotemporal-resolution remote sensing data, along with machine learning algorithms such as ETr and RF, which are suitable for small samples, for soil moisture estimation.

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Section: Multisource Remote Sensing Data and Machine Learning Model H...mentioning

confidence: 99%

Estimation of Soil Moisture Using Multi-Source Remote Sensing and Machine Learning Algorithms in Farming Land of Northern China

Liu,

Wu,

Cui

et al. 2023

Remote Sensing

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“…Deep learning has been widely used in recent years for time series classification (TSC) data processing [47][48][49][50][51][52]. AE data are typically time series data, and applying deep learning to solve acoustic emission time series classification problems has not yet been reported upon or studied.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Approach for Damage Classification Based on Acoustic Emission Data in Composite Materials

Fu-ping

Jiang

et al. 2022

Materials

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Damage detection and the classification of carbon fiber-reinforced composites using non-destructive testing (NDT) techniques are of great importance. This paper applies an acoustic emission (AE) technique to obtain AE data from three tensile damage tests determining fiber breakage, matrix cracking, and delamination. This article proposes a deep learning approach that combines a state-of-the-art deep learning technique for time series classification: the InceptionTime model with acoustic emission data for damage classification in composite materials. Raw AE time series and frequency-domain sequence data are used as the input for the InceptionTime network, and both obtain very high classification performances, achieving high accuracy scores of about 99%. The InceptionTime network produces better training, validation, and test accuracy with the raw AE time series data than it does with the frequency-domain sequence data. Simultaneously, the InceptionTime model network shows its potential in dealing with data imbalances.

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“…These advanced techniques have now improved researcher's abilities to analyze GEC across the realms of Earth's natural systems, and tools are already available in a variety of programming languages and software packages (Lary et al, 2016;Maxwell et al, 2018;Yuan et al, 2020). The introduction of AI and DL in remote sensing has prompted the proposal of three major directions in time series research, including new methods for constructing time series datasets, data extraction, and environmental applications for time series analysis (Ma et al, 2019;Jeon et al, 2020).…”

mentioning

confidence: 99%

Specialty Grand Challenge: Remote Sensing Time Series Analysis

Southworth

Muir

2021

Front. Remote Sens.

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The globe is currently undergoing a range of alarming changes related to social and environmental systems, and the links between the two. Our ability as researchers to study the dynamics of these ongoing processes is essential for real-world understanding and application of management strategies that can mitigate potentially negative outcomes. The scale of change and its associated impact generated by natural and anthropogenic drivers varies across the landscape, such as local degradation of ecosystem services, regional deforestation, large scale urbanization, and widespread yet geographically specific changes yielded by vagaries in climate. Understanding such critical changes is of paramount importance for the future wellbeing of the coupled human-natural systems that we are all a part of and on which we all depend.Historically, one of the greatest limitations in our ability to study these systems with remote sensing technology has been inadequate availability of time series datasets that provide fine enough spatial and temporal resolution capable of identifying processes of global environmental change (GEC). However, with the advances in sensors used for environmental remote sensing, as well as the improvements in data storage and distribution, we now have the capacity to employ time series techniques for detecting GEC and addressing the multitude of questions surrounding its impacts. Currently, many of the time series methodologies being applied to examine this suite of issues are still in development, and as such, there is significant space for growth, innovation, and exploration in the field of time series remote sensing analysis (TSRSA).Only a few decades ago, what was considered a detailed TSRSA may have involved three or more Landsat images and corresponding land cover classifications with a set time interval, such as a decadal study (

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Editorial for the Special Issue “Advanced Machine Learning for Time Series Remote Sensing Data Analysis”

Cited by 3 publications

References 8 publications

Estimation of Soil Moisture Using Multi-Source Remote Sensing and Machine Learning Algorithms in Farming Land of Northern China

Estimation of Soil Moisture Using Multi-Source Remote Sensing and Machine Learning Algorithms in Farming Land of Northern China

Deep Learning Approach for Damage Classification Based on Acoustic Emission Data in Composite Materials

Specialty Grand Challenge: Remote Sensing Time Series Analysis

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