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1. A Submonthly Surface Water Classification Framework via Gap-Fill Imputation and Random Forest Classifiers of Landsat Imagery

   https://doi.org/10.3390/rs13091742  

   Labuzzetta, Charles; Zhu, Zhengyuan; Chang, Xinyue; Zhou, Yuyu (May 2021, Remote Sensing)

   null (Ed.)

   Global surface water classification layers, such as the European Joint Research Centre’s (JRC) Monthly Water History dataset, provide a starting point for accurate and large scale analyses of trends in waterbody extents. On the local scale, there is an opportunity to increase the accuracy and temporal frequency of these surface water maps by using locally trained classifiers and gap-filling missing values via imputation in all available satellite images. We developed the Surface Water IMputation (SWIM) classification framework using R and the Google Earth Engine computing platform to improve water classification compared to the JRC study. The novel contributions of the SWIM classification framework include (1) a cluster-based algorithm to improve classification sensitivity to a variety of surface water conditions and produce approximately unbiased estimation of surface water area, (2) a method to gap-fill every available Landsat image for a region of interest to generate submonthly classifications at the highest possible temporal frequency, (3) an outlier detection method for identifying images that contain classification errors due to failures in cloud masking. Validation and several case studies demonstrate the SWIM classification framework outperforms the JRC dataset in spatiotemporal analyses of small waterbody dynamics with previously unattainable sensitivity and temporal frequency. Most importantly, this study shows that reliable surface water classifications can be obtained for all pixels in every available Landsat image, even those containing cloud cover, after performing gap-fill imputation. By using this technique, the SWIM framework supports monitoring water extent on a submonthly basis, which is especially applicable to assessing the impact of short-term flood and drought events. Additionally, our results contribute to addressing the challenges of training machine learning classifiers with biased ground truth data and identifying images that contain regions of anomalous classification errors. 

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2. Comparison of Cloud-Filling Algorithms for Marine Satellite Data

   https://doi.org/10.3390/rs12203313  

   Stock, Andy; Subramaniam, Ajit; Van Dijken, Gert L.; Wedding, Lisa M.; Arrigo, Kevin R.; Mills, Matthew M.; Cameron, Mary A.; Micheli, Fiorenza (October 2020, Remote Sensing)

   null (Ed.)

   Marine remote sensing provides comprehensive characterizations of the ocean surface across space and time. However, cloud cover is a significant challenge in marine satellite monitoring. Researchers have proposed various algorithms to fill data gaps “below the clouds”, but a comparison of algorithm performance across several geographic regions has not yet been conducted. We compared ten basic algorithms, including data-interpolating empirical orthogonal functions (DINEOF), geostatistical interpolation, and supervised learning methods, in two gap-filling tasks: the reconstruction of chlorophyll a in pixels covered by clouds, and the correction of regional mean chlorophyll a concentrations. For this purpose, we combined tens of cloud-free images with hundreds of cloud masks in four study areas, creating thousands of situations in which to test the algorithms. The best algorithm depended on the study area and task, and differences between the best algorithms were small. Ordinary Kriging, spatiotemporal Kriging, and DINEOF worked well across study areas and tasks. Random forests reconstructed individual pixels most accurately. We also found that high levels of cloud cover led to considerable errors in estimated regional mean chlorophyll a concentration. These errors could, however, be reduced by about 50% to 80% (depending on the study area) with prior cloud-filling. 

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3. CloudNet: A Deep Learning Approach for Mitigating Occlusions in Landsat-8 Imagery using Data Coalescence

   https://doi.org/10.1109/eScience55777.2022.00026  

   Khandelwal, Paahuni; Armstrong, Samuel; Matin, Abdul; Pallickara, Shrideep; Pallickara, Sangmi Lee (October 2022, 2022 IEEE 18th International Conference on e-Science (e-Science))

   Multi-spectral satellite images that remotely sense the Earth's surface at regular intervals are often contaminated due to occlusion by clouds. Remote sensing imagery captured via satellites, drones, and aircraft has successfully influenced a wide range of fields such as monitoring vegetation health, tracking droughts, and weather forecasting, among others. Researchers studying the Earth's surface are often hindered while gathering reliable observations due to contaminated reflectance values that are sensitive to thin, thick, and cirrus clouds, as well as their shadows. In this study, we propose a deep learning network architecture, CloudNet, to alleviate cloud-occluded remote sensing imagery captured by Landsat-8 satellite for both visible and non-visible spectral bands. We propose a deep neural network model trained on a distributed storage cluster that leverages historical trends within Landsat-8 imagery while complementing this analysis with high-resolution Sentinel-2 imagery. Our empirical benchmarks profile the efficiency of the CloudNet model with a range of cloud-occluded pixels in the input image. We further compare our CloudNet's performance with state-of-the-art deep learning approaches such as SpAGAN and Resnet. We propose a novel method, dynamic hierarchical transfer learning, to reduce computational resource requirements while training the model to achieve the desired accuracy. Our model regenerates features of cloudy images with a high PSNR accuracy of 34.28 dB. 

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4. Land Cover Change in the Lower Yenisei River Using Dense Stacking of Landsat Imagery in Google Earth Engine

   https://doi.org/10.3390/rs10081226  

   E. Nyland, Kelsey; E. Gunn, Grant; I. Shiklomanov, Nikolay; N. Engstrom, Ryan; A. Streletskiy, Dmitry (August 2018, Remote Sensing)

   Climate warming is occurring at an unprecedented rate in the Arctic due to regional amplification, potentially accelerating land cover change. Measuring and monitoring land cover change utilizing optical remote sensing in the Arctic has been challenging due to persistent cloud and snow cover issues and the spectrally similar land cover types. Google Earth Engine (GEE) represents a powerful tool to efficiently investigate these changes using a large repository of available optical imagery. This work examines land cover change in the Lower Yenisei River region of arctic central Siberia and exemplifies the application of GEE using the random forest classification algorithm for Landsat dense stacks spanning the 32-year period from 1985 to 2017, referencing 1641 images in total. The semiautomated methodology presented here classifies the study area on a per-pixel basis utilizing the complete Landsat record available for the region by only drawing from minimally cloud- and snow-affected pixels. Climatic changes observed within the study area’s natural environments show a statistically significant steady greening (~21,000 km2 transition from tundra to taiga) and a slight decrease (~700 km2) in the abundance of large lakes, indicative of substantial permafrost degradation. The results of this work provide an effective semiautomated classification strategy for remote sensing in permafrost regions and map products that can be applied to future regional environmental modeling of the Lower Yenisei River region. 

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5. Integrating Satellite Imagery and Ground-Based Measurements with a Machine Learning Model for Monitoring Lake Dynamics over a Semi-Arid Region

   https://doi.org/10.3390/hydrology10040078  

   Ekpetere, Kenneth; Abdelkader, Mohamed; Ishaya, Sunday; Makwe, Edith; Ekpetere, Peter (April 2023, Hydrology)

   The long-term variability of lacustrine dynamics is influenced by hydro-climatological factors that affect the depth and spatial extent of water bodies. The primary objective of this study is to delineate lake area extent, utilizing a machine learning approach, and to examine the impact of these hydro-climatological factors on lake dynamics. In situ and remote sensing observations were employed to identify the predominant explanatory pathways for assessing the fluctuations in lake area. The Great Salt Lake (GSL) and Lake Chad (LC) were chosen as study sites due to their semi-arid regional settings, enabling the testing of the proposed approach. The random forest (RF) supervised classification algorithm was applied to estimate the lake area extent using Landsat imagery that was acquired between 1999 and 2021. The long-term lake dynamics were evaluated using remotely sensed evapotranspiration data that were derived from MODIS, precipitation data that were sourced from CHIRPS, and in situ water level measurements. The findings revealed a marked decline in the GSL area extent, exceeding 50% between 1999 and 2021, whereas LC exhibited greater fluctuations with a comparatively lower decrease in its area extent, which was approximately 30% during the same period. The framework that is presented in this study demonstrates the reliability of remote sensing data and machine learning methodologies for monitoring lacustrine dynamics. Furthermore, it provides valuable insights for decision makers and water resource managers in assessing the temporal variability of lake dynamics. 

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