Evaluation of GSMaP Daily Rainfall Satellite Data for Flood Monitoring: Case Study—Kyushu Japan

In this paper, the Global Satellite Mapping of Precipitation Moving Vector with Kalman filter (GSMaP_MVK) was evaluated and corrected at daily time scales with a spatial resolution of 0.1°;latitude/longitude. The reference data came from thirty-four rain gauges on Kyushu Island, Japan. This study focused on the GSMaP_MVK’s ability to detect heavy rainfall patterns that may lead to flooding. Statistical analysis was used to evaluate the GSMaP_MVK data both quantitatively and qualitatively. The statistical analysis included the relative bias (B), the mean error (E), the Nash-Sutcliffe coefficient (CNS), the Root Mean Square Error (RMSE) and the correlation coefficient (r). In addition, Generalized Additive Models (GAMs) were used to conduct GSMaP_MVK data correction. The results of these analyses indicate that GSMaP_MVK data have lower values than observed data and may be significantly underestimated during heavy rainfall. By applying GAM to bias correction, GSMaP_MVK’s ability to detect heavy rainfall was improved. In addition, GAM for bias correction could effectively be applied for significant underestimates of GSMaP_ MVK (i.e., bias of more than 55%). GAM is a new approach to predict rainfall amount for flood and landslide monitoring of satellite base precipitation, especially in areas where rain gauge data are limited.

SAR image water extraction using the attention U-net and multi-scale level set method:flood monitoring in South China in 2020 as a test case

Level set method has been extensively used for image segmentation,which is a key technology of water extraction.However,one of the problems of the level-set method is how to find the appropriate initial surface parameters,which will affect the accuracy and speed of level set evolution.Recently,the semantic segmentation based on deep learning has opened the exciting research possibilities.In addition,the Convolutional Neural Network(CNN)has shown a strong feature representation capability.Therefore,in this paper,the CNN method is used to obtain the initial SAR image segmentation map to provide deep a priori information for the zero-level set curve,which only needs to describe the general outline of the water body,rather than the accurate edges.Compared with the traditional circular and rectangular zero-level set initialization method,this method can converge to the edge of the water body faster and more precisely;it will not fall into the local minimum value and be able to obtain accurate segmentation results.The effectiveness of the proposed method is demonstrated by the experimental results of flood disaster monitoring in South China in 2020.

Flood disaster monitoring based on Sentinel-1 data:A case study of Sihu Basin and Huaibei Plain,China

Summer floods occur frequently in many regions of China,affecting economic development and social stability.Remote sensing is a new technique in disaster monitoring.In this study,the Sihu Basin in Hubei Province of China and the Huaibei Plain in Anhui Province of China were selected as the study areas.Thresholds of backscattering coefficients in the decision tree method were calculated with the histogram analysis method,and flood disaster monitoring in the two study areas was conducted with the threshold method using Sentinel-1 satellite images.Through satellite-based flood disaster monitoring,the flooded maps and the areas of expanded water bodies and flooded crops were derived.The satellite-based monitoring maps were derived by comparing the expanded area of images during a flood disaster with that before the disaster.The difference in spatiotemporal distribution of flood disasters in these two regions was analyzed.The results showed that flood disasters in the Sihu Basin occurred frequently in June and July,and flood disasters in the Huaibei Plain mostly occurred in August,with a high interannual vari-ability.Flood disasters in the Sihu Basin were usually widespread,and the affected area was between Changhu and Honghu lakes.The Huaibei Plain was affected by scattered disasters.The annual mean percentages of flooded crop area were 14.91%and 3.74% in the Sihu Basin and Huaibei Plain,respectively.The accuracies of the extracted flooded area in the Sihu Basin in 2016 and 2017 were 96.20% and 95.19%,respectively.

G-WADI PERSIANN-CCS GeoServer for extreme precipitation event monitoring

The Center for Hydrometeorology and Remote Sensing at the University of California, Irvine （CHRS） has been collaborating with UNESCO＇s International Hydrological Program （IHP） to build a facility for forecasting and mitigating hydrological disasters. This collaboration has resulted in the development of the Water and Development Information for Arid Lands-- a Global Network （G-WADI） PERSIANN-CCS GeoServer, a near real-time global precipitation visualization and data service. This GeoServer pro- vides to end-users the tools and precipitation data needed to support operational decision making, research and sound water man- agement. This manuscript introduces and demonstrates the practicality of the G-WADI PERSIANN-CCS GeoServer for monitor- ing extreme precipitation events even over regions where ground measurements are sparse. Two extreme events are analyzed. The first event shows an extreme precipitation event causing widespread flooding in Beijing, China and surrotmding districts on July 21, 2012. The second event shows tropical storm Nock-Ten that occurred in late July of 2011 causing widespread flooding in Thailand. Evaluation of PERSIANN-CCS precipitation over Thailand using a rain gauge network is also conducted and discussed.

基于DEM的洪涝灾害监测模型与应用(英文)

In order to assess the flood damage rapidly and accurately,this paper proposed a practical method of flood disaster monitoring based on meso-scale automatic weather stations rainfall data and 1:5 million high-precision DEM (digital elevation model) data.It can predict roughly areas by the automatic weather station rainfall analysis and processing when the floods happen.Using partitions 'horizontal' approximation methods,the model of DEM flooding disaster's monitoring has been constructed based on 1:5 million high-precision DEM.And the technical methods applied to the analysis of experimental area.The result of flood disaster's monitoring is carried on comparison and the analysis through the verification by CBERS-02B.It finds that the area of floods is very consistent by the model of DEM and CBERS-02B flooding disaster's monitoring.So the method of flood disaster's motoring based on DEM can be real-time,dynamic,and can monitor the flood zone accurately and effectively.It also can provide the decision making department with present and assisting scheme of policy making.

Flood Duration Estimation Based on Multisensor,Multitemporal Remote Sensing:The Sardoba Reservoir Flood

Single-sensor monitoring of flood events at high spatial and temporal resolutions is difficult because of the lack of data owing to instrument defects,cloud contamination,imaging geometry.However,combining multisensor data provides an impressive solution to this problem.In this study,11 synthetic aperture radar(SAR)images and 13 optical images were collected from the Google Earth Engine(GEE)platform during the Sardoba Reservoir flood event to constitute a time series dataset.Threshold-based and indices-based methods were used for SAR and optical data,respectively,to extract the water extent.The final sequential flood water maps were obtained by fusing the results from multisensor time series imagery.Experiments show that,when compare with the Global Surface Water Dynamic(GSWD)dataset,the overall accuracy and Kappa coefficient of the water body extent extracted by our methods range from 98.8%to 99.1%and 0.839 to 0.900,respectively.The flooded extent and area increased sharply to a maximum between May 1 and May 4,and then experienced a sustained decline over time.The flood lasted for more than a month in the lowland areas in the north,indicating that the northern region is severely affected.Land cover changes could be detected using the temporal spectrum analysis,which indicated that detailed temporal information benefiting from the multisensor data is highly important for time series analyses.

Application of Fengyun-4 Satellite to Flood Disaster Monitoring through a Rapid Multi-Temporal Synthesis Approach

Fengyun-4 A(FY-4 A) belongs to the second generation of geostationary meteorological satellite series in China. Its observations with high frequency and resolution provide a better data basis for monitoring of extreme weather such as sudden flood disasters. In this study, the flood disasters occurred in Bangladesh, India, and some other areas of South Asia in August 2018 were investigated by using a rapid multi-temporal synthesis approach for the first time for removal of thick clouds in FY-4 A images. The maximum between-class variance algorithm(OTSU;developed by Otsu in 2007) and linear spectral unmixing methods are used to extract the water area of flood disasters. The accuracy verification shows that the water area of flood disasters extracted from FY-4 A is highly correlated with that from the high-resolution satellite datasets Gaofen-1(GF-1) and Sentinel-1 A, with the square correlation coefficient R2 reaching 0.9966. The average extraction accuracy of FY-4 A is over 90%. With the rapid multi-temporal synthesis approach used in flood disaster monitoring with FY-4 A satellite data, advantages of the wide coverage, fast acquisition,and strong timeliness with geostationary meteorological satellites are effectively combined. Through the synthesis of multi-temporal images of the flood water body, the influence of clouds is effectively eliminated, which is of great significance for the real-time flood monitoring. This also provides an important service guarantee for the disaster prevention and reduction as well as economic and social development in China and the Asia-Pacific region.