Low-cost, high-density water quality data in combination with new high-fidelity imaging satellites can enable high-accuracy simplified remote sensing of inland drinking water sources for sustainability and resilience at the local level. Remote Sensing for sustainability and resilience requires affordability and simplicity for implementation at the local level for drinking water monitoring and protection. United Nations Environment Program (UNEP) studies focused on implementing Integrated Water Resources Management (IWRM) within UN Sustainable Development Goal (SDG) 6.5 indicate that water pollution has worsened over the last three decades in the majority of rivers in Latin America, Africa and Asia [1] with all three areas having relatively limited information on water quality, especially for routine water management. UNEP has suggested the use of satellite remote sensing of several basic water parameters including temperature, chlorophyll a, Secchi Disk depth/transparency, turbidity and sediment dynamics with focused "water-truthing" to fill this information gap.

Previous studies have recognized that traditional empirical water quality remote sensing models are region-specific and lack spatial transferability and temporal repeatability, which prevents the mapping and monitoring inland water quality at a large regional or basin scale. This research evaluates the spatial transferability and temporal repeatability of our multi-predictor ensemble learning model in comparison with traditional empirical models. Our evaluations show that the multi-predictor ensemble model not only substantially improves the prediction accuracy in comparison with traditional individual empirical models, but also has strong spatial and temporal extensibility. The multi-predictor ensemble model calibrated at one specific place and at one certain time can be transferred and re-used in other places and in different time periods with reliable and accurate predictions. The strong spatial and temporal extensibility of the multi-predictor ensemble model is largely attributed to the selective strategy for combining component model results according to the spectral-space partition, which makes the ensemble model dynamically adaptable to a wide range of water conditions over space and time.

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) has been collecting elevation measurements of the Earth’s surface since its launch in September 2018. Although ICESat-2 was not designed for urban applications, its excellent altimetry capabilities over the globe make it possible to obtain urban height information. However, few studies have been conducted to validate ICESat-2 height measurements in urban areas. In this letter, we evaluate the heights retrieved from 20 months of ATL03 and ATL08 data using airborne LiDAR data collected in New York City (NYC). The results indicated that the heights from ATL03 product have a moderate agreement with airborne LiDAR data with vertical errors around −1.49 m (root mean squared error [RMSE] = 2.89 m, mean absolute error [MAE] = 2.11 m,$R^{2} = 0.98$, and observations = 910 497). The ATL08 product also performed a fine accuracy in terrain heights estimating (mean error [ME] = −0.01 m, RMSE = 3.63 m, MAE = 2.04 m, and$R^{2} = 0.94$). In the three categories of urban environments, ATL03 performs best in urban high-rise dense area, with an average residual error of −1.44 m, followed by urban non-high-rise dense area with an average residual error of −1.49 m. In urban forest area, ATL08 shows its performance in measuring terrain height with an RMSE of 1.78 m. We demonstrated that ICESat-2 can provide a useful source of urban heights and holds great potential to light up more urban applications related to urban change monitoring and 3-D morphology.

In previous studies, empirical models have been widely used to map water quality parameters for inland waters using remotely sensed imagery. Due to the complex optical properties of inland waters, empirical models often have limited performances and they cannot be extended or reused across space and time. To overcome the limitations of traditional empirical models, this article further explores the ensemble method for water quality parameter retrieval. Based on the Sentinel-2 multispectral images and extensive coincidentin situwater quality data, we examined different implementation strategies and evaluated the extensibility of a multipredictor ensemble model for retrieving chlorophyll-a (Chl-a) across space and time. Our analysis shows that the multipredictor ensemble model can improve the Chl-a prediction accuracy by 46% compared with the best traditional empirical model. Our experiments suggest that the selection of heterogeneous component models improves the performance of the ensemble model and the new iterative$K$-fold calibration approach results in a considerably better ensemble model than the global calibration approach. To evaluate the spatial extensibility, our ensemble model calibrated for Harsha Lake was applied to Caesar Creek Lake and Brookville Lake. This ensemble model calibrated with the data sets acquired on October 7, 2016 was also applied to Sentinel-2 images of Harsha Lake acquired on October 2, 2017 and August 23, 2018, to evaluate its extensibility for different years. Our evaluation results show that the ensemble model can be transferred and reused across space and time to provide reliable and accurate Chl-a estimates.

Characterizing landscape patterns and revealing their underlying processes are critical for studying climate change and environmental problems. Previous methods for mapping land cover changes largely focused on the classification of remote sensing images. Therefore, they could not provide information about the evolutionary process of land cover changes. In this paper, we developed a spatiotemporal structural graph (STSG) technique for a comprehensive analysis of land cover changes. First, a land cover neighborhood graph was generated for each snapshot to quantify the spatial relationship between adjacent land cover objects. Then, an object-based temporal tracking algorithm was designed to monitor the temporal changes between land cover objects over time. Finally, land cover evolutionary trajectories, pixel-level land cover change trajectories, and node-wise connectivity changes over time were characterized. We applied the proposed method to analyze land cover changes in Suffolk County, New York from 1996 to 2010. The results demonstrated that STSG can not only characterize and visualize detailed land cover changes spatially but also maintain the temporal sequence and relations of land cover objects in an integrated space-time environment. The proposed STSG provides a useful framework for analyzing land cover changes and can be adapted to characterize and quantify other spatiotemporal phenomena.

The Geoscience Laser Altimeter System onboard the NASA Ice, Cloud, and land Elevation Satellite (ICESat/GLAS) provided elevation measurements of Earth's surface between 2003 and 2009. The centroid and maximum-amplitude-peak (MAP) retracking methods have been designed and applied to process the returned laser waveforms for elevation measurements. Although these two methods work well in general, they may generate erroneous measurements when the returned waveform was complicated by adverse atmospheric conditions (clouds, ice fogs, blowing snow, and dust storms). The centroid retracking method is often more severely affected when compared with the MAP retracking method. In this study, we present a new retracking method that exploits the spatial contextual information from neighboring footprints along the satellite ground track, in addition to the single return waveform shape information. Our method uses a probabilistic relaxation (PR) algorithm to integrate the spatial contextual information and the waveform shape information to identify the waveform peak that most likely represents the true surface elevation, rather than simply detecting the peak with the maximum magnitude. For different types of land surfaces, such as inland lakes, polar tundra, ice sheet, and sand deserts, we demonstrate that our new PR retracking method is able to produce more reliable, consistent, and accurate elevation measurements than the standard NASA ICESat/GLAS data products. The root mean squares error (RMSE) is reduced from 0.85 to 0.17 m for inland lake, from 0.81 to 0.23 m for polar tundra, from 1.25 to 0.33 m for ice sheet, and from 2.48 to 2.34 m for sand desert.

Various empirical algorithms have been developed to retrieve chlorophyll-a (Chl-a) from multispectral and hyperspectral images as a proxy variable for algal blooms in inland waters. In most previous studies, a single empirical model (global model) was calibrated for the entire water body under study. Our analysis shows that the performance of a global model is limited for optically complex inland waters. We discovered that the global model tends to overestimate in some regions and underestimate in other regions, and that the model residuals (errors) display an apparent spatial autocorrelation pattern. To address the inadequacy of the global empirical model, this paper presents regionally or locally adaptive models to better estimate Chl-a concentrations for the first time. We collected two dense sets of Chl-a measurements over Harsha Lake in Ohio during a Sentinel-2A satellite overpass and a dedicated airborne hyperspectral flight. Based on the atmospherically corrected multispectral and hyperspectral images and concurrent in situ measurements, we implemented and evaluated the performance of regionally and locally adaptive models in comparison with the single global model. Among a number of candidate empirical algorithms, the two-band algorithm produces the best global model for Chl-a retrievals for both the multispectral and hyperspectral image sources. By subdividing the water body under investigation into several regions or a set of local areas, we demonstrate that regionally and locally adaptive models can improve Chl-a estimate accuracy by 13%-28% for the multispectral image and by 33%-47% for the hyperspectral image, in comparison with the best global Chl-a model.

ICESat/GLAS provided measurements of Earth surface elevation and its dynamic changes between 2003 and 2009. In this paper, we present a new retracking method that exploits the spatial contextual information from neighboring footprints along the satellite ground track, in addition to the waveform information as in the standard MAP retracking method. Our method utilizes a probabilistic relaxation algorithm to integrate the spatial contextual information with the waveform geometric information to identify the waveform peak that is most likely represent the true surface elevation, rather than simply detecting the peak with the largest magnitude. Our analysis demonstrates that the new probabilistic relaxation retracking method is able to correct the measurement errors of the standard MAP retracking method and produce much more reasonable and consistent elevation measurements for different types of land surfaces (i.e., inland lakes, coastal zones/beaches, deserts, ice sheet, etc.).

Previous studies have demonstrated urban built-up areas can be derived from nighttime light satellite (DMSP-OLS) images at the national or continent scale. This paper presents a novel object-based method for detecting and characterizing urban spatial clusters from nighttime light satellite images automatically. First, urban built-up areas, derived from the regionally adaptive thresholding of DMSP-OLS nighttime light data, are represented as discrete urban objects. These urban objects are treated as basic spatial units and quantified in terms of geometric and shape attributes and their spatial relationships. Next, a spatial cluster analysis is applied to these basic urban objects to form a higher level of spatial units – urban spatial clusters. The Minimum Spanning Tree (MST) is used to represent spatial proximity relationships among urban objects. An algorithm based on competing propagation of objects is proposed to construct the MST of urban objects. Unlike previous studies, the distance between urban objects (i.e., the boundaries of urban built-up areas) is adopted to quantify the edge weight in MST. A Gestalt Theory-based method is employed to partition the MST of urban objects into urban spatial clusters. The derived urban spatial clusters are geographically delineated through mathematical morphology operation and construction of minimum convex hull. A series of landscape ecologic and statistical attributes are defined and calculated to characterize these clusters. Our method has been successfully applied to the analysis of urban landscape of China at the national level, and a series of urban clusters have been delimited and quantified.