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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. Joint Inversion of GNSS and GRACE for Terrestrial Water Storage Change in California

   https://doi.org/10.1029/2021JB023135  

   Carlson, G. ; Werth, S. ; Shirzaei, M. ( March 2022 , Journal of Geophysical Research: Solid Earth)

   Global Navigation Satellite System (GNSS) vertical displacements measuring the elastic response of Earth's crust to changes in hydrologic mass have been used to produce terrestrial water storage change (∆TWS) estimates for studying both annual ∆TWS as well as multi‐year trends. However, these estimates require a high observation station density and minimal contamination by nonhydrologic deformation sources. The Gravity Recovery and Climate Experiment (GRACE) is another satellite‐based measurement system that can be used to measure regional TWS fluctuations. The satellites provide highly accurate ∆TWS estimates with global coverage but have a low spatial resolution of ∼400 km. Here, we put forward the mathematical framework for a joint inversion of GNSS vertical displacement time series with GRACE ∆TWS to produce more accurate spatiotemporal maps of ∆TWS, accounting for the observation errors, data gaps, and nonhydrologic signals. We aim to utilize the regional sensitivity to ∆TWS provided by GRACE mascon solutions with higher spatial resolution provided by GNSS observations. Our approach utilizes a continuous wavelet transform to decompose signals into their building blocks and separately invert for long‐term and short‐term mass variations. This allows us to preserve trends, annual, interannual, and multi‐year changes in TWS that were previously challenging to capture by satellite‐based measurement systems or hydrological models, alone. We focus our study in California, USA, which has a dense GNSS network and where recurrent, intense droughts put pressure on freshwater supplies. We highlight the advantages of our joint inversion results for a tectonically active study region by comparing them against inversion results that use only GNSS vertical deformation as well as with maps of ∆TWS from hydrological models and other GRACE solutions. We find that our joint inversion framework results in a solution that is regionally consistent with the GRACE ∆TWS solutions at different temporal scales but has an increased spatial resolution that allows us to differentiate between regions of high and low mass change better than using GRACE alone.

    

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3. Mapping Potentially Acid Generating Material on Abandoned Mine Lands Using Remotely Piloted Aerial Systems

   https://doi.org/10.3390/min11040365  

   Cramer, Alison S. ; Calvin, Wendy M. ; McCoy, Scott W. ; Breitmeyer, Ronald J. ; Haagsma, Marja ; Kratt, Christopher ( April 2021 , Minerals)

   Weathering and transport of potentially acid generating material (PAGM) at abandoned mines can degrade downstream environments and contaminate water resources. Monitoring the thousands of abandoned mine lands (AMLs) for exposed PAGM using field surveys is time intensive. Here, we explore the use of Remotely Piloted Aerial Systems (RPASs) as a complementary remote sensing platform to map the spatial and temporal changes of PAGM across a mine waste rock pile on an AML. We focus on testing the ability of established supervised and unsupervised classification algorithms to map PAGM on imagery with very high spatial resolution, but low spectral sampling. At the Perry Canyon, NV, USA AML, we carried out six flights over a 29-month period, using a RPAS equipped with a 5-band multispectral sensor measuring in the visible to near infrared (400–1000 nm). We built six different 3 cm resolution orthorectified reflectance maps, and our tests using supervised and unsupervised classifications revealed benefits to each approach. Supervised classification schemes allowed accurate mapping of classes that lacked published spectral libraries, such as acid mine drainage (AMD) and efflorescent mineral salts (EMS). The unsupervised method produced similar maps of PAGM, as compared to supervised schemes, but with little user input. Our classified multi-temporal maps, validated with multiple field and lab-based methods, revealed persistent and slowly growing ‘hotspots’ of jarosite on the mine waste rock pile, whereas EMS exhibit more rapid fluctuations in extent. The mapping methods we detail for a RPAS carrying a broadband multispectral sensor can be applied extensively to AMLs. Our methods show promise to increase the spatial and temporal coverage of accurate maps critical for environmental monitoring and reclamation efforts over AMLs. 

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4. Detecting Vegetation to Open Water Transitions in a Subtropical Wetland Landscape from Historical Panchromatic Aerial Photography and Multispectral Satellite Imagery

   https://doi.org/10.3390/rs14163976  

   Lamb, Lukas M. ; Gann, Daniel ; Velazquez, Jesse T. ; Troxler, Tiffany G. ( August 2022 , Remote Sensing)

   Over the last century, direct human modification has been a major driver of coastal wetland degradation, resulting in widespread losses of wetland vegetation and a transition to open water. High-resolution satellite imagery is widely available for monitoring changes in present-day wetlands; however, understanding the rates of wetland vegetation loss over the last century depends on the use of historical panchromatic aerial photographs. In this study, we compared manual image thresholding and an automated machine learning (ML) method in detecting wetland vegetation and open water from historical panchromatic photographs in the Florida Everglades, a subtropical wetland landscape. We compared the same classes delineated in the historical photographs to 2012 multispectral satellite imagery and assessed the accuracy of detecting vegetation loss over a 72 year timescale (1940 to 2012) for a range of minimum mapping units (MMUs). Overall, classification accuracies were >95% across the historical photographs and satellite imagery, regardless of the classification method and MMUs. We detected a 2.3–2.7 ha increase in open water pixels across all change maps (overall accuracies > 95%). Our analysis demonstrated that ML classification methods can be used to delineate wetland vegetation from open water in low-quality, panchromatic aerial photographs and that a combination of images with different resolutions is compatible with change detection. The study also highlights how evaluating a range of MMUs can identify the effect of scale on detection accuracy and change class estimates as well as in determining the most relevant scale of analysis for the process of interest. 

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5. Spatial comparison of inland water observations from CYGNSS, MODIS, Landsat, and commercial satellite imagery

   https://doi.org/10.1186/s40562-024-00321-1  

   Pavur, G. K. ; Kim, H. ; Fang, B. ; Lakshmi, V. ( March 2024 , Geoscience Letters)

   Accurate and timely inland waterbody extent and location data are foundational information to support a variety of hydrological applications and water resources management. Recently, the Cyclone Global Navigation Satellite System (CYGNSS) has emerged as a promising tool for delineating inland water due to distinct surface reflectivity characteristics over dry versus wet land which are observable by CYGNSS’s eight microsatellites with passive bistatic radars that acquire reflected L-band signals from the Global Positioning System (GPS) (i.e., signals of opportunity). This study conducts a baseline 1-km comparison of water masks for the contiguous United States between latitudes of 24°N-37°N for 2019 using three Earth observation systems: CYGNSS (i.e., our baseline water mask data), the Moderate Resolution Imaging Spectroradiometer (MODIS) (i.e., land water mask data), and the Landsat Global Surface Water product (i.e., Pekel data). Spatial performance of the 1-km comparison water mask was assessed using confusion matrix statistics and optical high-resolution commercial satellite imagery. When a mosaic of binary thresholds for 8 sub-basins for CYGNSS data were employed, confusion matrix statistics were improved such as up to a 34% increase in F1-score. Further, a performance metric of ratio of inland water to catchment area showed that inland water area estimates from CYGNSS, MODIS, and Landsat were within 2.3% of each other regardless of the sub-basin observed. Overall, this study provides valuable insight into the spatial similarities and discrepancies of inland water masks derived from optical (visible) versus radar (Global Navigation Satellite System Reflectometry, GNSS-R) based satellite Earth observations.

    

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