A Combined Approach for Monitoring Monthly Surface Water/Ice Dynamics of Lesser Slave Lake Via Earth Observation Data

Dastour, Hatef; Ghaderpour, Ebrahim; Hassan, Quazi K.

doi:10.1109/jstars.2022.3196611

Cited by 13 publications

(10 citation statements)

References 32 publications

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“…In this sense, the use of radar images can be combined with optical images to have a continuous database for water monitoring (e.g., Refs. 82–85). Differences in intra-annual and seasonal variability were observed among reservoirs.…”

Section: Discussionmentioning

confidence: 99%

Spatio-temporal water quality variability and regionalization of tropical reservoirs based on Landsat imagery

Carvalho

Figueira²,

Fortunato³

2023

J. Appl. Rem. Sens.

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Reservoirs are aquatic environments with complex interactions between biochemical and physical components. Many of those parameters can be estimated from remote sensing tools, especially since increasing technological progress allows the analysis of those environments in high spatial and temporal resolutions. This work aimed to analyze the water quality of four reservoirs, with high energy productivity, located in the center-southern region of Brazil from bio-optical and thermal water parameters. Therefore, hundreds of Landsat 5 (TM), 7 (ETM+), and 8 (OLI) satellite images were processed by the Google Earth Engine cloud computing platform to estimate water surface temperature, chlorophyll-a density (chl-a), Secchi depth (SD), and trophic state index (TSI). To calibrate the equations, 11 in situ monitoring points were used in 27 scenes from the TM and ETM+ sensors and 30 from the OLI sensor, within 6 days interval between the monitoring date and the image acquisition. Additionally, records from two weather stations near the reservoirs were analyzed to evaluate meteorological influences on water parameters. SD has influenced TSI in three reservoirs, while chl-a has acted as a proxy in only one. Moderate correlations were observed between air temperature, rainfall, and global radiation and the bio-optical and thermal water parameters. Remote sensing has detected a clear spatio-temporal variation, which allowed regionalizing the reservoirs. Reservoir regionalization based on the mapped parameters may be able not only to visualize the spatial dynamics of the waters but also as a tool for the management of water bodies with multiple users and issues.

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Section: Discussionmentioning

confidence: 99%

Spatio-temporal water quality variability and regionalization of tropical reservoirs based on Landsat imagery

Carvalho

Figueira²,

Fortunato³

2023

J. Appl. Rem. Sens.

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“…To assess the quantitative performance, several benchmark metrics were used: precision, recall, f1-score (F1), overall accuracy score (OA), and intersection over union (IoU) 57 , 58 . These metrics are defined as precision=TPTP+FP,recall=TPTP+FN,F1=2×precision×recallprecision+recall,OA=TP+TNTP+FN+FP+TN,IoU=TPTP+FP+FN.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Section: Comparative Methods and Evaluation Metricsmentioning

confidence: 99%

U-shaped contourlet network for high-spatial-resolution remote sensing images segmentation

Zhao,

Cao,

Dong

2023

J. Appl. Rem. Sens.

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.Accurate semantic segmentation of images has long been a research priority in remote sensing. However, the presence of geometrically complex and spatially diverse objects increases the difficulty in simultaneously obtaining coherent and accurate labeling result. To solve this challenge, our study combined multiscale geometric feature extraction with convolutional neural network and proposed a new U-shaped contourlet network (USCNet) for segmentation from high-spatial-resolution remote sensing images (HSRRSIs). This network is designed to learn and characterize the geometric features present in HSRRSIs. The USCNet first transforms the original dataset into a pyramidal structure containing multiscale and multidirectional geometric information and then fuses the spatial and geometric features to extract high-level semantic information. This network has two advantages: (1) coarse-to-fine spatial features are learned efficiently using a hierarchical learning structure and (2) the multiscale learning scheme captures geometric information in different directions. The results of extensive experiments conducted on two remote sensing datasets (the International Society for Photogrammetry and Remote Sensing Vaihingen and Potsdam challenge datasets) show that the proposed approach outperforms several state-of-the-art semantic segmentation methods.

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“…Furthermore, the integration of machine learning techniques with SAR and spectrally rich optical data has significantly enhanced crop classification accuracy [30]. The effective extraction of surface water and dynamic water changes has been achieved through a combination of VH polarization and a modified normalized difference water index [31,32]. Felix Lobert had accurately predicted the harvest date of winter wheat using an intensive Sentinel-1, Sentinel-2, and Landsat-8 time series [33].…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Ability of the Sentinel-1 Cross-Polarization Ratio to Detect Spring Maize Phenology Using Adaptive Dynamic Threshold

Ma,

Jiang,

Huang

et al. 2024

Remote Sensing

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Accurate, timely, and fine-resolution crop phenology is essential for determining the optimal timing of agronomic management practices supporting precision agriculture and food security. Synthetic Aperture Radar (SAR) methods, unaffected by cloud occlusion, have been widely applied in monitoring maize phenology. Nonetheless, their reliance on manual threshold settings, which depend on the user’s expertise, limits their applicability. Furthermore, the neglect of SAR’s potential for monitoring other phenological periods (e.g., seven-leaves date (V7), jointing date (JD), tassel date (TD), and milky date (MID)) hinders their robustness, particularly for regional-scale applications. To address these issues, this study used an adaptive dynamic threshold to evaluate the ability of the Sentinel-1 cross-polarization ratio (CR) in detecting the three-leaves date (V3), V7, JD, TD, MID, and maturity date (MD) of maize. We analyzed the effect of incidence angle, precipitation, and wind speed on Sentinel-1 features to identify the optimal feature for time series fitting. Then, we employed linear regression to determine the optimal threshold and developed an adaptive dynamic threshold for phenology detection. This approach effectively mitigated the speckle noise of Sentinel-1 and minimized artificial interference caused by customary conventional thresholds. Finally, we mapped phenology across 8.3 million ha in Heilongjiang Province. The results indicated that the approach has a higher ability to detect JD (RMSE = 11.10 d), MID (RMSE = 10.31 d), and MD (RMSE = 9.41 d) than that of V3 (RMSE = 32.07 d), V7 (RMSE = 56.37 d), and TD (RMSE = 43.33 d) in Sentinel-1. Compared with Sentinel-2, the average RMSE of JD, MID, and MD decreased by 4.14%, 35.28%, and 26.48%. Moreover, when compared to different thresholds, the adaptive dynamic threshold can quickly determine the optimal threshold for detecting each phenological stage. CR is least affected by incident angle, precipitation, and wind speed, effectively suppressing noise to reflect phenological development better. This approach supports the rapid and feasible mapping of maize phenology across broad spatial regions with a few samples.

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A Combined Approach for Monitoring Monthly Surface Water/Ice Dynamics of Lesser Slave Lake Via Earth Observation Data

Cited by 13 publications

References 32 publications

Spatio-temporal water quality variability and regionalization of tropical reservoirs based on Landsat imagery

Spatio-temporal water quality variability and regionalization of tropical reservoirs based on Landsat imagery

U-shaped contourlet network for high-spatial-resolution remote sensing images segmentation

Evaluating the Ability of the Sentinel-1 Cross-Polarization Ratio to Detect Spring Maize Phenology Using Adaptive Dynamic Threshold

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