Surface Water Mapping Research Articles

A surface water mapping framework combining optical and radar remote sensing and its application in China

Satellite remote sensing provides an efficient pathway to map inland surface water extent across different spatial and temporal scales. However, how to monitor the surface water distribution and its spatiotemporal variability via combining optical and radar remote sensing datasets still faces substantial challenges. In this study, we propose a Seamless Surface Water Mapping Framework (SSWMF) which synergizes both optical (MODIS, Landsat 8, Sentinel-2) and SAR (Sentinel-1) imageries. The validity of SSWMF was first proved over the middle and lower reaches of the Yangtze River of China with abundant lake resources, showing an overall accuracy of 90.72%, and the results indicate that SSWMF can provide surface water map with higher spatial and temporal continuity compared to the Joint Research Centre Global Surface Water dataset. Multi-source validation showed that the SSWMF-derived surface water maps can well capture the temporal fluctuation and spatial heterogeneity of water resources over China, with an overall accuracy of 92.39%. Overall, our results suggest that the proposed water mapping framework is promising and is readily applicable to large-scale water resource management and drought/flood monitoring.

Using Landsat and MODIS satellite collections to examine extent, timing, and potential impacts of surface water inundation in California croplands

The state of California, United States of America produces many crop products that are both utilized domestically and exported throughout the world. With nearly 39,000 km2 of croplands, monitoring unintentional and intentional surface water inundation is important for water resource management and flood hazard readiness. We examine inundation dynamics in California croplands from 2003 to 2020 by intersecting monthly surface water maps (n = 216 months) derived using two satellite remote sensing platforms (Landsat and Moderate Resolution Imaging Spectroradiometer [MODIS]) with a high-quality cropland map generated by the California Department of Water Resources. Surface water maps were produced using the Dynamic Surface Water Extent model, in which satellite image pixels are classified into different levels of detection confidence. Our analysis focused on calculating monthly and annual occurrence of “high confidence” water for each satellite collection across eight cropland types and 58 counties. Results indicate that 49.9% (MODIS) to 56.4% (Landsat) of croplands were inundated at least once during the 18-year timespan. Rice crops, due to their unique need of consistent surface water and dominance as a crop type in CA, had the highest proportion of and mean annual inundation area, while citrus crops had the lowest. Mean monthly inundation patterns in most croplands followed California's precipitation patterns with high inundation during the winter and spring rainy season. At the county level, croplands in the southern Central Valley typically had high occurrences of inundation in conjunction with large crop areas. Exposure and sensitivity of inundation for three crop types (citrus, deciduous, and vineyards) that are typically less associated with intentional inundation were geographically variable, but overall were generally highest in counties in the southern Central Valley, California's primary agricultural region. Flood and precipitation related crop insurance claims indicated that rice had the highest mean indemnity payout for any month with damages typically occurring in March and April. Insurance claims were also high in deciduous fruit and nut crops, which had peak damages in February. A comparison between inundation results and insurance claims suggests that the inundation mapped by our process coincides with claim activity. These data elucidate water inundation patterns across the state that can serve to inform farmers, insurers, decision makers, resource managers, and flood mitigation professionals.

Water Surface Mapping from Sentinel-1 Imagery Based on Attention-UNet3+: A Case Study of Poyang Lake Region

The mapping of water surfaces is important for water resource and flood monitoring. Synthetic Aperture Radar (SAR) images can be used to monitor water bodies and detect floods over large areas. To address the problem of low identification accuracy in different time phases and different scales of water area, a water surface mapping method based on Attention-UNet3+ with SAR images is proposed in this paper. In the model, full-scale skip connections are introduced for combining feature maps from different scales and improving the accuracy of narrow water identification; the spatial attention module is used to obtain the importance of each connected feature, which can reduce the number of false alarms caused by speckle noise and water shadows in SAR data; the deep supervision module is used to learn hierarchical representative features from comprehensive aggregated feature maps to provide the periodic output capability of the model and meet the needs of rapid and large-scale water identification. The effectiveness of Attention-UNet3+ is verified by experiments in the Poyang Lake region with Sentinel-1 SAR images. The results show that the proposed Attention-UNet3+ outperforms the conventional threshold segmentation and deep learning models such as UNet, Deepvlabv3+, and SegNet, with an average IOU/Kappa value of 0.9502/0.9698. Multitemporal Sentinel-1 images in 2021 covering Poyang Lake are used for time series water surface mapping with the proposed method, and it is found that the detected water area of Poyang Lake has a good correlation with the corresponding water level values at observation stations. The Pearson coefficients are about 0.96. The above results indicate that the proposed method achieves good water surface mapping performance.

Changes in extent of open-surface water bodies in China's Yellow River Basin (2000–2020) using Google Earth Engine cloud platform

Dynamics of open surface water is of great significance to understand the impacts of global changes and human activities on water resources. However, long-term changes of open-surface water bodies in the Yellow River Basin have remained poorly characterized. In the study, 26,681 high-quality Landsat images were processed using the Google Earth Engine (GEE) cloud platform to study the long-term changes of the open water bodies in the Yellow River Basin from 2000 to 2020. A percentile-based image synthesis method was used to collect training samples. The multi-index water body extraction rule (MIWER) was proposed to rapidly extract surface water bodies. The results show that (1) The MIWER is suitable for long-term and large-scale surface water mapping based on Landsat images; (2) The average area of permanent and seasonal water in the last 20 years is 9062.59 km 2 and 6918.83 km 2 , respectively, accounting for 56.7% and 43.3% of the total surface water in the Yellow River Basin, respectively; (3) From 2000–2020, both permanent and seasonal water in the whole Yellow River Basin and sub-basins have increased. In general, the total open surface water shows an increasing trend with obvious spatial heterogeneity; (4) The changes of surface water in the Yellow River Basin are related to climate change and intensive human activities, but the influencing factors vary in different regions. The findings of this study demonstrate a novel robust, low-cost water extraction method that satisfies the requirements of terrestrial water inundation mapping and management across the Yellow River Basin. The methodology of this study is expected to be extended to global surface waters, providing valuable information for global surface water resource planning and management.

Cloud-based large-scale data retrieval, mapping, and analysis for land monitoring applications with Google Earth Engine (GEE)

Cloud-based computing systems are linked with analytical tools for large-scale flood monitoring to solve these problems. Researchers want an exceptionally efficient and resilient geospatial framework with advanced algorithms for immediate results from the examination of large datasets. The study uses web-based analysis to demonstrate the potential of Google Earth Engine (GEE) for geospatial-analytical processes in flood-affected areas and to comprehend the socio-demographic ramifications. Surface water mapping is done using a histogram-based threshold method. The study examines how to analyse Sentinel-1 SAR data for automated flood mapping and how to validate results using data from the optical sensor Sentinel-2. Furthermore, using the Google Earth Engine platform, this study focuses on cloud-based large-scale flood data mapping. The research combines geographic information with advanced data processing techniques, algorithms, and web-based platforms to produce encouraging results and monitor real-time flooding occurrences for significant planning and decision-making. The research effectively assesses the importance of cloud-based data processing for the performance evaluation of algorithms in a cloud-based platform for monitoring real-time issues. The study's findings are useful for analysing surface water mapping applications.

Monitoring Surface Water Inundation of Poyang Lake and Dongting Lake in China Using Sentinel-1 SAR Images

High-temporal-resolution inundation maps play an important role in surface water monitoring, especially in lake sites where water bodies change tremendously. Synthetic Aperture Radar (SAR) that guarantees a full time-series in monitoring surface water due to its cloud-penetrating capability is preferred in practice. To date, the methods of extracting and analyzing inundation maps of lake sites have been widely discussed, but the method of extracting surface water maps refined by inundation frequency map and the distinction of inundation frequency map from different datasets have not been fully explored. In this study, we leveraged the Google Earth Engine platform to compare and evaluate the effects of a method combining a histogram-based algorithm with a temporal-filtering algorithm in order to obtain high-quality surface water maps. Both algorithms were conducted on Sentinel-1 images over Poyang Lake and Dongting Lake, the two largest lakes in China, respectively. High spatiotemporal time-series analyses of both lakes were implemented between 2017 and 2021, while the inundation frequency maps extracted from Sentinel-1 data were compared with those extracted from Landsat images. It was found that Sentinel-1 can monitor water inundation with a substantially higher accuracy, although minor differences were found between the two sites, with the overall accuracy for Poyang Lake (95.38–98.69%) being higher than that of Dongting Lake (95.05–97.5%). The minimum and maximum water areas for five years were 1232.96 km2 and 3828.36 km2 in Poyang Lake, and 624.7 km2 and 2189.17 km2 in Dongting Lake. Poyang Lake was frequently inundated with 553.03 km2 of permanent water and 3361.39 km2 of seasonal water while Dongting Lake was less frequently inundated with 320.09 km2 of permanent water and 2224.53 km2 of seasonal water. The inundation frequency maps from different data sources had R2 values higher than 0.8, but there were still significant differences between them. The overall inundation frequency values of the Sentinel-1 inundation frequency maps were lower than those of the Landsat inundation frequency maps due to the severe contamination from cloud cover in Landsat imagery, which should be paid attention in practical application.

Detecting, extracting, and mapping of inland surface water using Landsat 8 Operational Land Imager: A case study of Pune district, India.

Background: Recent developments in optical satellite remote sensing have led to a new era in the detection of surface water with its changing dynamics. This study presents the creation of surface water inventory for a part of Pune district (an administrative area), in India using the Landsat 8 Operational Land Imager (OLI) and a multi spectral water indices method. Methods: A total of 13 Landsat 8 OLI cloud free images were analyzed for surface water detection. Modified Normalized Difference Water Index (MNDWI) spectral index method was employed to enhance the water pixels in the image. Water and non-water areas in the map were discriminated using the threshold slicing method with a trial and error approach. The accuracy analysis based on kappa coefficient and percentage of the correctly classified pixels was presented by comparing MNDWI maps with corresponding Joint Research Centre (JRC) Global Surface Water Explorer (GSWE) images. The changes in the surface area of eight freshwater reservoirs within the study area (Bhama Askhed, Bhatghar, Chaskaman, Khadakwasala, Mulashi, Panshet, Shivrata, and Varasgaon) for the year 2016 were analyzed and compared to GSWE time series water databases for accuracy assessment. The annual water occurrence map with percentage water occurrence on a yearly basis was also prepared. Results: The kappa coefficient agreement between MNDWI images and GSWE images is in the range of 0.56 to 0.96 with an average agreement of 0.82 indicating a strong level of agreement. Conclusions: MNDWI is easy to implement and is a sufficiently accurate method to separate water bodies from satellite images. The accuracy of the result depends on the clarity of image and selection of an optimum threshold method. The resulting accuracy and performance of the proposed algorithm will improve with implementation of automatic threshold selection methods and comparative studies for other spectral indices methods.

AUnet: A Deep Learning Framework for Surface Water Channel Mapping Using Large-Coverage Remote Sensing Images and Sparse Scribble Annotations from OSM Data

Water is a vital component of life that exists in a variety of forms, including oceans, rivers, ponds, streams, and canals. The automated methods for detecting, segmenting, and mapping surface water have improved significantly with the advancements in satellite imagery and remote sensing. Many strategies and techniques to segment water resources have been presented in the past. However, due to the variant width and complex appearance, the segmentation of the water channel remains challenging. Moreover, traditional supervised deep learning frameworks have been restricted by the scarcity of water channel datasets that include precise water annotations. With this in mind, this research presents the following three main contributions. Firstly, we curated a new dataset for water channel mapping in the Pakistani region. Instead of employing pixel-level water channel annotations, we used a weakly trained method to extract water channels from VHR pictures, relying only on OpenStreetMap (OSM) waterways to create sparse scribbling annotations. Secondly, we benchmarked the dataset on state-of-the-art semantic segmentation frameworks. We also proposed AUnet, an atrous convolution inspired deep learning network for precise water channel segmentation. The experimental results demonstrate the superior performance of the proposed AUnet model for segmenting using weakly supervised labels, where it achieved a mean intersection over union score of 0.8791 and outperformed state-of-the-art approaches by 5.90% for the extraction of water channels.

A Novel Approach for Automatic Urban Surface Water Mapping with Land Surface Temperature (AUSWM)

The principal difficulty in extracting urban surface water using remote-sensing techniques is the influence of noise from complex urban environments. Although various methods exist, there are still many sources of noise interference when extracting urban surface water, and automatic cartographic methods with long time series are especially scarce. Here, we construct an automatic urban surface water extraction method from the combination of traditional water index, urban shadow index (USI), and land surface temperature (LST) by using the Google Earth Engine cloud computing platform and Landsat imagery. The three principal findings derived from the application of the method were as follows. (i) In comparison with autumn and winter, LST in spring and summer could better distinguish water from high-reflection ground objects, shadows, and roads and roofs covered by asphalt. (ii) The overall accuracy of Automated Water Extraction Index (AWEIsh) in Zhengzhou was 77.5% and the Kappa coefficient was 0.55; with consideration of the USI and LST, the overall accuracy increased to 96.0% and the Kappa coefficient increased to 0.92. (iii) During 1990–2020, the area of urban surface water in Zhengzhou increased, with an evident trend in expansion from 11.51 km2 in 2008 to 49.28 km2 in 2020. Additionally, possible omissions attributable to using 30m-resolution imagery to extract urban water areas were also discussed. The method proposed in this study was proven effective in eliminating the influence of noise in urban areas, and it could be used as a general method for high-accuracy long-term mapping of urban surface water.

ReaLSAT, a global dataset of reservoir and lake surface area variations

Lakes and reservoirs, as most humans experience and use them, are dynamic bodies of water, with surface extents that increase and decrease with seasonal precipitation patterns, long-term changes in climate, and human management decisions. This paper presents a new global dataset that contains the location and surface area variations of 681,137 lakes and reservoirs larger than 0.1 square kilometers (and south of 50 degree N) from 1984 to 2015, to enable the study of the impact of human actions and climate change on freshwater availability. Within its scope for size and region covered, this dataset is far more comprehensive than existing datasets such as HydroLakes. While HydroLAKES only provides a static shape, the proposed dataset also has a timeseries of surface area and a shapefile containing monthly shapes for each lake. The paper presents the development and evaluation of this dataset and highlights the utility of novel machine learning techniques in addressing the inherent challenges in transforming satellite imagery to dynamic global surface water maps.

Dynamic surface water maps of Canada from 1984 to 2019 Landsat satellite imagery

Knowledge of the location of surface water in time and space is critical to inform policy on the environment, wildlife, and human welfare. Dynamic surface water maps have been created at continental to global scales from medium and coarse resolution satellite image archives, most notably by Pekel et al. (2016), who mapped global water dynamics from 1984 to 2015 Landsat data. Their occurrence layer, depicting the percentage of time that water was observed in each 30 m resolution cell, has been relied upon heavily by Natural Resources Canada's (NRCan) Emergency Geomatics Service (EGS) to map surface water during flood events. To generate dynamic surface water maps that are optimized for Canada's unique geography, the fully automated EGS surface water mapping methodology was applied in a cloud environment to the 1984–2019 Landsat archive over Canada. National-scale surface water maps and derived inundation frequency akin to Pekel's occurrence, as well as inter-annual wetting and drying trends calculated using per-pixel logistic regression, were produced to form the complete dataset. Separate comparisons of our frequency layer with Pekel's occurrence layer and Canada's water base layer from the National Hydrographic Network (NHN) were conducted. The comparison with Pekel's occurrence showed that our product contains a large number of unique water objects, the majority of which are correct when assessed against Google Earth (GE) imagery. Comparison with the NHN indicated that the NHN contained a large number of permanent water objects that were mapped as ephemeral water objects in our product, with the majority of these being verified in GE as true ephemeral water objects such as floodplains and wetlands. Wetting trends were found to be more than five times greater than drying trends across Canada, with notable wetting in the Prairie Pothole region and Low Arctic verified with examples of statistically significant wetting and drying features. The dataset will enhance EGS flood and river ice mapping operations, provide information on floodplain location and extent, and give insight into the effects of climate change on surface water availability.

Analysis of Surface Water Trends for the Conterminous United States Using MODIS Satellite Data, 2003–2019

AbstractSatellite imagery is commonly used to map surface water extents over time, but many approaches yield discontinuous records resulting from cloud obstruction or image archive gaps. We applied the Dynamic Surface Water Extent (DSWE) model to downscaled (250‐m) daily Moderate Resolution Imaging Spectroradiometer (MODIS) data in Google Earth Engine to generate monthly surface water maps for the conterminous United States (US) from 2003 through 2019. The aggregation of daily observations to monthly maps of maximum water extent produced records with diminished cloud and cloud shadow effects across most of the country. We used the continuous monthly record to analyze spatiotemporal surface water trends stratified within Environmental Protection Agency Ecoregions. Although not all ecoregion trends were significant (p < 0.05), results indicate that much of the western and eastern US underwent a decline in surface water over the 17‐year period, while many ecoregions in the Great Plains had positive trends. Trends were also generated from monthly streamgage discharge records and compared to surface water trends from the same ecoregion. These approaches agreed on the directionality of trend detected for 54 of 85 ecoregions, particularly across the Great Plains and portions of the western US, whereas trends were not congruent in select western deserts, the Great Lakes region, and the southeastern US. By describing the geographic distribution of surface water over time and comparing these records to instrumented discharge data across the conterminous US, our findings demonstrate the efficacy of using satellite imagery to monitor surface water dynamics and supplement traditional instrumented monitoring.

Google earth engine based computational system for the earth and environment monitoring applications during the COVID-19 pandemic using thresholding technique on SAR datasets

Observing the earth and environmental conditions during the COVID-19 pandemic lockdown along with travel restrictions headed to worse circumstance. These scenarios amplified the hurdles of flood management. In order to resolves these issues, an efficient and resilient geospatial framework with unconventional systems is also required for the generation of instantaneous results. Hence to avoid these deficiencies, the google earth engine based computational system integrated with analytical tools for large-scale data handling is introduced for the earth and environmental monitoring applications. The present study proposes a working model for geospatial data processing to understand socio-demographic implications with a web-based analytical interface. The research introduces a histogram-based thresholding approach for real-time surface water mapping along with precise data processing and analysis for automated monitoring. The study integrates geospatial datasets to a enhanced data processing methods in a web-based platform to deliver the required results for extensive planning and decision making. Furthermore, a similar type of work can be undertaken for other disaster management applications.

LONG-TERM LAND SURFACE WATER MONITORING IN THE YELLOW RIVER BASIN OF CHINA BASED ON LANDSAT IMAGERY ON THE GOOGLE EARTH ENGINE

Abstract. Land surface water is the most active part of the earth's surface layer and plays an important role in material balance and energy transformation. The monitoring of dynamic changes in surface open water helps to grasp the distribution and changes of water resources, which is important for understanding the impact of human activities on water resources and safeguarding the ecological security of the basin. In the study, 72,520 Landsat images were processed using the Google Earth Engine (GEE) cloud platform to study the long-term changes of the open surface water in the Yellow River Basin (YRB) from 2000 to 2020. The results show that (1) The multi-index water body extraction rules (MIWER) is suitable for long-term and large-scale surface water mapping based on Landsat images; (2) From 2000 to 2020, both permanent and seasonal water in the whole YRB and sub-basins have increased. In general, the total open surface water in the YRB show an increasing trend with obvious spatial heterogeneity; (3) The changes of open surface water in the YRB are related to climate change and intensive human activities, but the influencing factors vary in different regions and need to be further studied in the future. The findings of this study can be used to help the policy-makers understand changing water resources and their driving mechanism and provide a reference for water resources management, agricultural irrigation, and ecological protection.

Surface Water Dynamics from Space: A Round Robin Intercomparison of Using Optical and SAR High-Resolution Satellite Observations for Regional Surface Water Detection

Climate change, increasing population and changes in land use are all rapidly driving the need to be able to better understand surface water dynamics. The targets set by the United Nations under Sustainable Development Goal 6 in relation to freshwater ecosystems also make accurate surface water monitoring increasingly vital. However, the last decades have seen a steady decline in in situ hydrological monitoring and the availability of the growing volume of environmental data from free and open satellite systems is increasingly being recognized as an essential tool for largescale monitoring of water resources. The scientific literature holds many promising studies on satellite-based surface-water mapping, but a systematic evaluation has been lacking. Therefore, a round robin exercise was organized to conduct an intercomparison of 14 different satellite-based approaches for monitoring inland surface dynamics with Sentinel-1, Sentinel-2, and Landsat 8 imagery. The objective was to achieve a better understanding of the pros and cons of different sensors and models for surface water detection and monitoring. Results indicate that, while using a single sensor approach (applying either optical or radar satellite data) can provide comprehensive results for very specific localities, a dual sensor approach (combining data from both optical and radar satellites) is the most effective way to undertake largescale national and regional surface water mapping across bioclimatic gradients.

Large-Scale Surface Water Mapping Based on Landsat and Sentinel-1 Images

Surface water is a highly dynamical object on the earth’s surface. At present, satellite remote sensing is the most effective way to accurately depict the temporal and spatial variation characteristics of surface water on a large scale. In this study, a region-adaptive random forest algorithm is designed on the Google Earth Engine (GEE) for automatic surface water mapping by using data from multi-sensors such as Landsat 7 ETM+, Landsat 8 OLI, and Sentinel-1 SAR images as source data, and China as a case study region. The visual comparison of the mapping results with the original images under different landform areas shows that the extracted water body boundary is consistent with the water range in the image. The cross-validation with the JRC GSW validation samples shows a very high precision that the average producer’s accuracy and average user’s accuracy of water is 0.933 and 0.998, respectively. The average overall accuracy and average kappa is 0.966 and 0.931, respectively. The independent verification results of lakes with different areas also prove the high accuracy for our method, with a maximum average error of 3.299%. These results show that the method is an ideal way for large-scale surface water mapping with a high spatial–temporal resolution.

AHSWFM: Automated and Hierarchical Surface Water Fraction Mapping for Small Water Bodies Using Sentinel-2 Images

Accurately mapping surface water fractions is essential to understanding the distribution and area of small water bodies (SWBs), which are numerous and widespread. Traditional spectral unmixings based on the linear mixture model require high-quality prior endmember information, and are not appropriate in situations such as dealing with multiple scattering effects. To overcome difficulties with unknown mixing mechanisms and parameters, a novel automated and hierarchical surface water fraction mapping (AHSWFM) for mapping SWBs from Sentinel-2 images was proposed. AHSWFM is automated, requires no endmember prior knowledge and uses self-trained regression using scalable algorithms and random forest to construct relationships between the multispectral data and water fractions. AHSWFM uses a hierarchical structure that divides pixels into pure water, pure land and mixed water-land pixels, and predicts their water fractions separately to avoid overestimating water fractions for pure land pixels and underestimating water fractions for pure water pixels. Results show that using the hierarchical strategy can increase the accuracy in estimating SWB areas. AHSWFM predicted SWB areas with a root mean square error of approximately 0.045 ha in a region using more than 1200 SWB samples that were mostly smaller than 0.75 ha.

Quantification of Off-Channel Inundated Habitat for Pacific Chinook Salmon (Oncorhynchus tshawytscha) along the Sacramento River, California, Using Remote Sensing Imagery

Off-channel areas are one of the most impacted aquatic habitats by humans globally, as extensive agricultural and urban development has limited them to roughly 10% of historical extent. This is also true for California’s Sacramento River Valley, where historically frequent widespread inundation has been reduced to a few off-channel water bodies along the mid-Sacramento River. This remaining shallow-water habitat provides crucial ecological benefits to multiple avian and fish species, but especially to floodplain-adapted species such as Chinook salmon (Oncorhynchus tshawytscha). Characterizing spatiotemporal off-channel dynamics, including inundation extent and residence time, is fundamental to better understanding the intrinsic value of such habitats and their potential to support recovery actions. Remote sensing techniques have been increasingly used to map surface water at regional and local scales, with improved resolutions. As such, this study maps off-channel inundation areas and describes their temporal dynamics by analyzing pixel-based time- series of multiple water indices, modified Normalized Difference Water Index (mNDWI) and the Automated Water Extraction Index (AWEI), generated from LandSat-8 and Sentinel-2 data between 2013–2021. Quantified off-channel area was similar with each water index and method used, but improved performance was associated with Sentinel-2 products and AWEI index to identify wetted areas under lower mainstem discharges. Results indicate an uneven distribution of off-channel habitat in the study area, with limited inundated areas in upstream reaches (<16% of total off-channel area for greater flows). In addition, much less habitat exists for flows under 400 m3/s, an important migration cue for endangered winter-run Chinook salmon, limiting juvenile access to areas with enhanced rearing conditions. Off-channel habitat residence times averaged between 7 and 16 days, primarily defined by the rate of receding flows, with rapid flow recession providing marginal off-channel habitat. This study shows reasonable performance of moderate resolution LandSat-8 and Sentinel-2 remote sensing imagery to characterize shallow-water inundated habitat in higher-order rivers, and as a method to inform restoration and native fish recovery efforts.

Spatio-Temporal Extraction of Surface Waterbody and Its Response of Extreme Climate along the Upper Huaihe River

The upper Huaihe River is the water-producing area of the Huaihe River Basin and the major grain and oil-producing area in China. The changing global climate over the recent years has increased the frequency of extreme weather in the upper reaches of the Huaihe River. Research on the responses of surface water bodies to extreme climates has become increasingly important. Based on all utilizable Landsat 4–8 T1–SR data and frequency mapping, the spatio-temporal extraction of surface water and its response to extreme climate were studied. We generated high-precision frequency maps of surface water, and a comparison of cartographic accuracy evaluation indices and spatial consistency was also carried out. The high-precision interpretation of small waterbodies constructs a surface water distribution with better continuity and integrity. Furthermore, we investigated the effect of El Niño/La Niña events on precipitation, temperature, and surface water along the upper Huaihe River, using the Mann–Kendall mutation tests. The results show: in 1987–2018, periods of abrupt changes in precipitation coincide with EI Niño/La Niña events, indicating that the precipitation was sensitive to EI Niño/La Niña events, which also strongly correlated with surface water area during wet and dry years. The effect of extreme events on seasonal water was smaller than permanent water. Surface water area showed an insignificant declining trend after 1999 and a significant drop in 2012. The phenomenon of topographic enhancement of precipitation controlled the spatial distribution of permanent water, with human activities having a substantial effect on the landscape pattern of seasonal water. Finally, discussions and applications related to the Markov Chain probability calculation theory in the paper contributed to enriching the theories on frequency mapping. The relevant results provide a theoretical basis and case support for the formulation of long-term water resources utilization and allocation policies.

Development of a Multi-Index Method Based on Landsat Reflectance Data to Map Open Water in a Complex Environment

Mapping surface water extent is important for managing water supply for agriculture and the environment. Remote sensing technologies, such as Landsat, provide an affordable means of capturing surface water extent with reasonable spatial and temporal coverage suited to this purpose. Many methods are available for mapping surface water including the modified Normalised Difference Water Index (mNDWI), Fisher’s water index (FWI), Water Observations from Space (WOfS), and the Tasseled Cap Wetness index (TCW). While these methods can discriminate water, they have their strengths and weaknesses, and perform at their best in different environments, and with different threshold values. This study combines the strengths of these indices by developing rules that applies an index to the environment where they perform best. It compares these indices across the Murray-Darling Basin (MDB) in southeast Australia, to assess performance and compile a heuristic rule set for accurate application across the MDB. The results found that all single indices perform well with the Kappa statistic showing strong agreement, ranging from 0.78 for WOfS to 0.84 for TCW (with threshold −0.035), with improvement in the overall output when the index best suited for an environment was selected. mNDWI (using a threshold of −0.3) works well within river channels, while TCW (with threshold −0.035) is best for wetlands and flooded vegetation. FWI and mNDWI (with threshold 0.63 and 0, respectively) work well for remaining areas. Selecting the appropriate index for an environment increases the overall Kappa statistic to 0.88 with a water pixel accuracy of 90.5% and a dry pixel accuracy of 94.8%. An independent assessment illustrates the benefit of using the multi-index approach, making it suitable for regional-scale multi-temporal analysis.