PRODUCTION OF GLOBAL SURFACE WATER MAP BY MULTIPLE MICROWAVE RADIOMETERS AND ITS FUNDAMENTAL VALIDATION

2018 ◽  
Vol 74 (5) ◽  
pp. I_67-I_72
Author(s):  
Shinta SETO ◽  
Hironori MINE
Keyword(s):  
Surface Water ◽  
Microwave Radiometers ◽  
Global Surface

Observing Global Surface Water Flood Dynamics

2013 ◽  
pp. 839-852
Author(s):  
Paul D. Bates ◽  
Jefferey C. Neal ◽  
Douglas Alsdorf ◽  
Guy J.-P. Schumann
Keyword(s):  
Surface Water ◽  
Flood Dynamics ◽  
Global Surface

scholarly journals Construction of the Long-Term Global Surface Water Extent Dataset Based on Water-NDVI Spatio-Temporal Parameter Set

2020 ◽  
Vol 12 (17) ◽  
pp. 2675
Author(s):  
Qianqian Han ◽  
Zhenguo Niu
Keyword(s):  
Surface Water ◽  
Water Area ◽  
Temporal Parameter ◽  
Modis Ndvi ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Spatio Temporal ◽  
Global Surface ◽  
Global Water

Inland surface water is highly dynamic, seasonally and inter-annually, limiting the representativity of the water coverage information that is usually obtained at any single date. The long-term dynamic water extent products with high spatial and temporal resolution are particularly important to analyze the surface water change but unavailable up to now. In this paper, we construct a global water Normalized Difference Vegetation Index (NDVI) spatio-temporal parameter set based on the Moderate-resolution Imaging Spectroradiometer (MODIS) NDVI. Employing the Google Earth Engine, we construct a new Global Surface Water Extent Dataset (GSWED) with coverage from 2000 to 2018, having an eight-day temporal resolution and a spatial resolution of 250 m. The results show that: (1) the MODIS NDVI-based surface water mapping has better performance compared to other water extraction methods, such as the normalized difference water index, the modified normalized difference water index, and the OTSU (maximal between-cluster variance method). In addition, the water-NDVI spatio-temporal parameter set can be used to update surface water extent datasets after 2018 as soon as the MODIS data are updated. (2) We validated the GSWED using random water samples from the Global Surface Water (GSW) dataset and achieved an overall accuracy of 96% with a kappa coefficient of 0.9. The producer’s accuracy and user’s accuracy were 97% and 90%, respectively. The validated comparisons in four regions (Qinghai Lake, Selin Co Lake, Utah Lake, and Dead Sea) show a good consistency with a correlation value of above 0.9. (3) The maximum global water area reached 2.41 million km2 between 2000 and 2018, and the global water showed a decreasing trend with a significance of P = 0.0898. (4) Analysis of different types of water area change regions (Selin Co Lake, Urmia Lake, Aral Sea, Chiquita Lake, and Dongting Lake) showed that the GSWED can not only identify the seasonal changes of the surface water area and abrupt changes of hydrological events but also reflect the long-term trend of the water changes. In addition, GSWED has better performance in wetland areas and shallow areas. The GSWED can be used for regional studies and global studies of hydrology, biogeochemistry, and climate models.

scholarly journals Changes in Net Global Surface Water Area Since 1985

Proceedings ◽  
2020 ◽  
Vol 30 (1) ◽  
pp. 69
Author(s):  
Zahra Kalantari ◽  
Sonia Borja ◽  
Georgia Destouni
Keyword(s):  
Surface Water ◽  
Ecosystem Functioning ◽  
Water Allocation ◽  
Land Use Changes ◽  
Water Area ◽  
Aral Sea ◽  
Surface Water Resources ◽  
Global Surface ◽  
Spatial And Temporal Characteristics

Spatial and temporal characteristics of surface water resources (e.g., extension, connectivity, seasonality) are key elements in water allocation, climate and hydrological regulation, ecosystem functioning, and the food-energy-water nexus. Changes in surface water area due to losses/gains to land could strongly affect these processes on different scales. Previous findings on changes in the Earth’s surface water area are contradictory. Based on water–land year classification datasets, we estimated global surface water area changes between 1985–2000 and 2001–2015. We found a net global gain in surface water of 100,454 km2, attributable to a large net gain in seasonal water (83,329 km2) and a small net gain in permanent water (17,125 km2). In general, net changes were highly heterogeneous in space, with local exceptions of clear drying and wetting trends, e.g., the Aral Sea and Quill Lakes, respectively. These findings raise multiple questions as to why seasonal water gains dominate and how different intertwined drivers (e.g., hydroclimate and human-induced water–land use changes) shape the distribution of the Earth’s surface water. Understanding these long-term changes is essential to predicting water-related pressures and prioritizing management decisions.

scholarly journals Identifikasi Genangan Banjir di Wilayah DKI Jakarta Menggunakan Citra Satelit Sentinel-1

2021 ◽  
Vol 4 (2) ◽  
pp. 95
Author(s):  
Ana Mardhiyana Alawiyah ◽  
Harintaka Harintaka
Keyword(s):  
Surface Water ◽  
Change Detection ◽  
Ratio Image ◽  
Global Surface ◽  
Optimum Threshold

Provinsi DKI Jakarta merupakan wilayah dengan karakteristik topografi yang berada pada kemiringan lereng relatif landai dan terletak pada dataran rendah. Hal ini menyebabkan wilayah Jakarta menjadi semakin rentan tergenang air dan banjir pada musim hujan. Pada awal tahun 2020, Jakarta mengalami banjir yang diakibatkan oleh hujan lebat yang turun sejak 31 Desember 2019 hingga 1 Januari 2020. Untuk keperluan antisipasi dan mitigasi bencana terkait banjir tersebut diperlukan ketersediaan data tentang luas genangan banjir dan letaknya secara cepat. Salah satu teknologi yang potensial untuk diterapkan adalah menggunakan citra satelit Sentinel-1. Berdasarkan kondisi tersebut, telah dilakukan kajian untuk mendeteksi daerah terdampak genangan banjir dari citra satelit Sentinel-1. Adapun teknik yang digunakan adalah metode change detection dan thresholding. Pada kajian ini digunakan citra Sentinel-1 perekaman tahun 2019 dan 2020, DEMNAS, Global Surface Water dan batas administrasi wilayah DKI Jakarta. Adapun tahapan pelaksanaan kajian mencakup proses change detection dengan ratio image dari dua citra Sentinel-1 GRD sebelum dan saat banjir, perhitungan nilai optimum threshold untuk menentukan banjir dan tidak banjir, masking banjir, dan perhitungan luas genangan. Berdasarkan hasil kajian diperoleh luas total daerah yang terkena banjir sekitar 1.156,84 hektar, sedangkan luas area banjir dari data Pemprov DKI sekitar 12.896,35 hektar. Hasil validasi menggunakan data Pemprov DKI dengan membandingan antara citra Sentinel-1 tahun 2019 dan 2020, diperoleh hasil 61 lokasi atau sekitar 28,96% termasuk ke dalam hasil interpretasi kelas banjir dan terdapat 157 lokasi atau sekitar 71,04% termasuk ke dalam hasil interpretasi kelas tidak banjir.

scholarly journals An Assessment of Surface Water Detection Methods for Water Resource Management in the Nigerien Sahel

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 431 ◽  
Author(s):  
Kelsey Herndon ◽  
Rebekke Muench ◽  
Emil Cherrington ◽  
Robert Griffin
Keyword(s):  
Remote Sensing ◽  
Surface Water ◽  
Decision Tree ◽  
Water Bodies ◽  
Spectral Indices ◽  
Dynamic Surface ◽  
Tree Methods ◽  
Global Surface ◽  
Decision Tree Methods ◽  
Semi Arid

Water is a scarce, but essential resource in the Sahel. Rainfed ephemeral ponds and lakes that dot the landscape are necessary to the livelihoods of smallholder farmers and pastoralists who rely on these resources to irrigate crops and hydrate cattle. The remote location and dispersed nature of these water bodies limits typical methods of monitoring, such as with gauges; fortunately, remote sensing offers a quick and cost-effective means of regularly measuring surface water extent in these isolated regions. Dozens of operational methods exist to use remote sensing to identify waterbodies, however, their performance when identifying surface water in the semi-arid Sahel has not been well-documented and the limitations of these methods for the region are not well understood. Here, we evaluate two global dynamic surface water datasets, fifteen spectral indices developed to classify surface water extent, and three simple decision tree methods created specifically to identify surface water in semi-arid environments. We find that the existing global surface water datasets effectively minimize false positives, but greatly underestimate the presence and extent of smaller, more turbid water bodies that are essential to local livelihoods, an important limitation in their use for monitoring water availability. Three of fifteen spectral indices exhibited both high accuracy and threshold stability when evaluated over different areas and seasons. The three simple decision tree methods had mixed performance, with only one having an overall accuracy that compared to the best performing spectral indices. We find that while global surface water datasets may be appropriate for analysis at the global scale, other methods calibrated to the local environment may provide improved performance for more localized water monitoring needs.

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