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Special Issue "AI-Driven Satellite Data for Global Environment Monitoring"

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "AI Remote Sensing".

Deadline for manuscript submissions: 31 December 2023 | Viewed by 5738

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Special Issue Editor

Prof. Dr. Yang-Won Lee

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Department of Spatial Information Engineering, Pukyong National University, Busan 48513, Korea
Interests: artificial intelligence; semantic segmentation; remote sensing of disaster; applications in agriculture, forest, hydrology, and meteorology
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Special Issue Information

Dear Colleagues,

The acceleration of environmental changes on Earth may significantly affect the global atmosphere, oceans, agriculture, forests, and water. Indeed, the Earth belongs to our descendants, not us, so we must deliver a safe and clean Earth to them. Satellite remote-sensing data is the essential material for spatially and temporally continuous observation of the Earth. Moreover, recent technological developments ensure higher resolution and broader coverage to monitor disasters, meteorology, air quality, vegetation, hydrology, and polar regions. AI is a powerful tool for creating high-quality satellite images and for observation of the Earth’s environmental phenomena using advanced computing power. In addition to the classical algorithms, various state-of-the-art models can help improve AI-driven satellite data for global environmental monitoring. We invite colleagues' insights and contributions to various research areas involving remote sensing combined with an AI approach. Papers can be focused on, but are not limited to, the following:

Deep-learning-based object detection from satellite images for environmental monitoring of Earth;
Semantic segmentation of satellite images for environmental monitoring of Earth;
Super-resolution techniques for environmental monitoring of Earth;
AI-based spatiotemporal image fusion for environmental monitoring of Earth;
AI-based change detection for environmental monitoring of Earth;
Satellite-based disaster management using AI models;
AI-based retrieval algorithm for the satellite products in atmosphere, meteorology, ocean, and air quality;
AI-based retrieval algorithm for the satellite products in agriculture, forests, hydrology, and ecology;
AI-driven novel methods for Earth’s environmental monitoring with satellite images.

Prof. Dr. Yang-Won Lee
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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Research

21 pages, 16048 KiB

Open AccessArticle

Tree-Structured Parzan Estimator–Machine Learning–Ordinary Kriging: An Integration Method for Soil Ammonia Spatial Prediction in the Typical Cropland of Chinese Yellow River Delta with Sentinel-2 Remote Sensing Image and Air Quality Data

and

Remote Sens. 2023, 15(17), 4268; https://doi.org/10.3390/rs15174268 - 30 Aug 2023

Viewed by 573

Abstract

Spatial prediction of soil ammonia (NH₃) plays an important role in monitoring climate warming and soil ecological health. However, traditional machine learning (ML) models do not consider optimal parameter selection and spatial autocorrelation. Here, we present an integration method (tree-structured Parzen estimator–machine learning–ordinary kriging (TPE–ML–OK)) to predict spatial variability of soil NH₃ from Sentinel-2 remote sensing image and air quality data. In TPE–ML–OK, we designed the TPE search algorithm, which encourages gradient boosting decision tree (GBDT), random forest (RF), and extreme gradient boosting (XGB) models to pay more attention to the optimal hyperparameters’ high-possibility range, and then the residual ordinary kriging model is used to further improve the prediction accuracy of soil NH₃ flux. We found a weak linear correlation between soil NH₃ flux and environmental variables using scatter matrix correlation analysis. The optimal hyperparameters from the TPE search algorithm existed in the densest iteration region, and the TPE–XGB–OK method exhibited the highest predicted accuracy (R² = 85.97%) for soil NH₃ flux in comparison with other models. The spatial mapping results based on TPE–ML–OK methods showed that the high fluxes of soil NH₃ were concentrated in the central and northeast areas, which may be influenced by rivers or soil water. The analysis result of the SHapley additive explanation (SHAP) algorithm found that the variables with the highest contribution to soil NH₃ were O₃, SO₂, PM₁₀, CO, and NDWI. The above results demonstrate the powerful linear–nonlinear interpretation ability between soil NH₃ and environmental variables using the integration method, which can reduce the impact on agricultural nitrogen deposition and regional air quality. Full article

(This article belongs to the Special Issue AI-Driven Satellite Data for Global Environment Monitoring)

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21 pages, 4130 KiB

Open AccessArticle

Image Texture Analysis Enhances Classification of Fire Extent and Severity Using Sentinel 1 and 2 Satellite Imagery

Rebecca Kate Gibson

Anthea Mitchell

and

Hsing-Chung Chang

Remote Sens. 2023, 15(14), 3512; https://doi.org/10.3390/rs15143512 - 12 Jul 2023

Cited by 2 | Viewed by 761

Abstract

Accurate and reliable mapping of fire extent and severity is critical for assessing the impact of fire on vegetation and informing post-fire recovery trajectories. Classification approaches that combine pixel-wise and neighbourhood statistics including image texture derived from high-resolution satellite data may improve on current methods of fire severity mapping. Texture is an innate property of all land cover surfaces that is known to vary between fire severity classes, becoming increasingly more homogenous as fire severity increases. In this study, we compared candidate backscatter and reflectance indices derived from Sentinel 1 and Sentinel 2, respectively, together with grey-level-co-occurrence-matrix (GLCM)-derived texture indices using a random forest supervised classification framework. Cross-validation (for which the target fire was excluded in training) and target-trained (for which the target fire was included in training) models were compared to evaluate performance between the models with and without texture indices. The results indicated that the addition of texture indices increased the classification accuracies of severity for both sensor types, with the greatest improvements in the high severity class (23.3%) for the Sentinel 1 and the moderate severity class (17.4%) for the Sentinel 2 target-trained models. The target-trained models consistently outperformed the cross-validation models, especially with regard to Sentinel 1, emphasising the importance of local training data in capturing post-fire variation in different forest types and severity classes. The Sentinel 2 models more accurately estimated fire extent and were improved with the addition of texture indices (3.2%). Optical sensor data yielded better results than C-band synthetic aperture radar (SAR) data with respect to distinguishing fire severity and extent. Successful detection using C-band data was linked to significant structural change in the canopy (i.e., partial-complete canopy consumption) and is more successful over sparse, low-biomass forest. Future research will investigate the sensitivity of longer-wavelength (L-band) SAR regarding fire severity estimation and the potential for an integrated fire-mapping system that incorporates both active and passive remote sensing to detect and monitor changes in vegetation cover and structure. Full article

(This article belongs to the Special Issue AI-Driven Satellite Data for Global Environment Monitoring)

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19 pages, 7409 KiB

Open AccessArticle

Deep Learning-Based Framework for Soil Moisture Content Retrieval of Bare Soil from Satellite Data

and

Remote Sens. 2023, 15(7), 1916; https://doi.org/10.3390/rs15071916 - 03 Apr 2023

Cited by 3 | Viewed by 1340

Abstract

Machine learning (ML) is a branch of artificial intelligence (AI) that has been successfully applied in a variety of remote sensing applications, including geophysical information retrieval such as soil moisture content (SMC). Deep learning (DL) is a subfield of ML that uses models with complex structures to solve prediction problems with higher performance than traditional ML. In this study, a framework based on DL was developed for SMC retrieval. For this purpose, a sample dataset was built, which included synthetic aperture radar (SAR) backscattering, radar incidence angle, and ground truth data. Herein, the performance of five optimized ML prediction models was evaluated in terms of soil moisture prediction. However, to boost the prediction performance of these models, a DL-based data augmentation technique was implemented to create a reconstructed version of the available dataset. This includes building a sparse autoencoder DL network for data reconstruction. The Bayesian optimization strategy was employed for fine-tuning the hyperparameters of the ML models in order to improve their prediction performance. The results of our study highlighted the improved performance of the five ML prediction models with augmented data. The Gaussian process regression (GPR) showed the best prediction performance with 4.05% RMSE and 0.81 R² on a 10% independent test subset. Full article

(This article belongs to the Special Issue AI-Driven Satellite Data for Global Environment Monitoring)

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23 pages, 3374 KiB

Open AccessArticle

Creation of a Walloon Pasture Monitoring Platform Based on Machine Learning Models and Remote Sensing

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Remote Sens. 2023, 15(7), 1890; https://doi.org/10.3390/rs15071890 - 31 Mar 2023

Viewed by 974

Abstract

The use of remote sensing data and the implementation of machine learning (ML) algorithms is growing in pasture management. In this study, ML models predicting the available compressed sward height (CSH) in Walloon pastures based on Sentinel-1, Sentinel-2, and meteorological data were developed to be integrated into a decision support system (DSS). Given the area covered (>4000 km² of pastures of 100 m² pixels), the consequent challenge of computation time and power requirements was overcome by the development of a platform predicting CSH throughout Wallonia. Four grazing seasons were covered in the current study (between April and October from 2018 to 2021, the mean predicted CSH per parcel per date ranged from 48.6 to 67.2 mm, and the coefficient of variation from 0 to 312%, suggesting a strong heterogeneity of variability of CSH between parcels. Further exploration included the number of predictions expected per grazing season and the search for temporal and spatial patterns and consistency. The second challenge tackled is the poor data availability for concurrent acquisition, which was overcome through the inclusion of up to 4-day-old data to fill data gaps up to the present time point. For this gap filling methodology, relevancy decreased as the time window width increased, although data with 4-day time lag values represented less than 4% of the total data. Overall, two models stood out, and further studies should either be based on the random forest model if they need prediction quality or on the cubist model if they need continuity. Further studies should focus on developing the DSS and on the conversion of CSH to actual forage allowance. Full article

(This article belongs to the Special Issue AI-Driven Satellite Data for Global Environment Monitoring)

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15 pages, 8585 KiB

Open AccessArticle

A Novel Framework for Correcting Satellite-Based Precipitation Products for Watersheds with Discontinuous Observed Data, Case Study in Mekong River Basin

Giha Lee

Duc Hai Nguyen

and

Xuan-Hien Le

Remote Sens. 2023, 15(3), 630; https://doi.org/10.3390/rs15030630 - 20 Jan 2023

Cited by 2 | Viewed by 1179

Abstract

Satellite-based precipitation (SP) data are gaining scientific interest due to their advantage in producing high-resolution products with quasi-global coverage. However, since the major reliance of precipitation data is on the distinctive geographical features of each location, they remain at a considerable distance from station-based data. This paper examines the effectiveness of a convolutional autoencoder (CAE) architecture in pixel-by-pixel bias correction of SP products for the Mekong River Basin (MRB). Two satellite-based products (TRMM and PERSIANN-CDR) and a gauge-based product (APHRODITE) are gridded rainfall products mined in this experiment. According to the estimated statistical criteria, the CAE model was effective in reducing the gap between SP products and benchmark data both in terms of spatial and temporal correlations. The two corrected SP products (CAE_TRMM and CAE_CDR) performed competitively, with CAE TRMM appearing to have a slight advantage over CAE CDR, however, the difference was minor. This study’s findings proved the effectiveness of deep learning-based models (here CAE) for bias correction of SP products. We believe that this technique will be a feasible alternative for delivering an up-to-current and reliable dataset for MRB studies, given that the sole available gauge-based dataset for this area has been out of date for a long time. Full article

(This article belongs to the Special Issue AI-Driven Satellite Data for Global Environment Monitoring)

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