FLOOD MONITORING USING SENTINEL-1 SAR DATA: A CASE STUDY BASED ON AN EVENT OF 2018 AND 2019 SOUTHERN PART OF KERALA

Abstract. Flood is a natural hazard influenced by rainfall and dam collapse, which propels release of enormous amount of water. In the last two decades flood is the second largest natural hazards occurred worldwide, which caused serious damage to life properties, settlements and economic activities. Flood mapping is a process that is useful for assessment and reduces the risk factor during the flood. An effective monitoring of flood prone area is necessary to handle GIS techniques and without remote sensing data it is difficult to identify the flooded area in this study Microwave remote sensing plays a lead role in natural hazards, here Synthetic Aperture Radar (SAR) data is the best way for monitoring flood hazards. In this study Southern part of the Kerala is chosen as the study area, In August 2018, during the south west monsoon due to heavy rainfall a severe flood affected the southern part of Kerala which saw a 37% increase in the rate of normal rainfall. The objective of the study is to find the flood zone area using SAR data and estimate the flood occurrence over a period of time. However a satellite imagery of optical data is used to analyse the pre and post event of flood, but during a heavy rainfall, cloud may interrupt the data acquisition. SAR satellite imagery fromSentinel-1A is a cloud penetrating data available in all kind of weather conditions during day and night time, which provides a good source of high resolution data sets. To identify the flood affected area an adapted technology of threshold methodology developed by using SAR data and change detection for the year 2018 and 2019, will illustrate flood extended part in southern part of Kerala. The result shows the estimation of flood extended part of the study area and the damages occurred during a flooded time period of post and pre event, vulnerability assessing of crop and agriculture is to obtain an intensity of the damaged areas which is closely associated with the river channel, the Polarization displays a similar sequence for amount of flooding. The study helps to find the reason of flood extent and to equip with better planning for risk reduction and management during a flooding period.

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Calibration of the depth invariant algorithm to monitor the tidal action of Rabigh City at the Red Sea Coast, Saudi Arabia

Open Geosciences ◽

10.1515/geo-2020-0217 ◽

2020 ◽

Vol 12 (1) ◽

pp. 1666-1678

Author(s):

Mohammed H. Aljahdali ◽

Mohamed Elhag

Keyword(s):

Remote Sensing ◽

Saudi Arabia ◽

Wind Speed ◽

Red Sea ◽

Remote Sensing Data ◽

Microwave Remote Sensing ◽

Optical Data ◽

Mean Values ◽

Sensing Data ◽

The Mean

AbstractRabigh is a thriving coastal city located at the eastern bank of the Red Sea, Saudi Arabia. The city has suffered from shoreline destruction because of the invasive tidal action powered principally by the wind speed and direction over shallow waters. This study was carried out to calibrate the water column depth in the vicinity of Rabigh. Optical and microwave remote sensing data from the European Space Agency were collected over 2 years (2017–2018) along with the analog daily monitoring of tidal data collected from the marine station of Rabigh. Depth invariant index (DII) was implemented utilizing the optical data, while the Wind Field Estimation algorithm was implemented utilizing the microwave data. The findings of the current research emphasis on the oscillation behavior of the depth invariant mean values and the mean astronomical tides resulted in R2 of 0.75 and 0.79, respectively. Robust linear regression was established between the astronomical tide and the mean values of the normalized DII (R2 = 0.81). The findings also indicated that January had the strongest wind speed solidly correlated with the depth invariant values (R2 = 0.92). Therefore, decision-makers can depend on remote sensing data as an efficient tool to monitor natural phenomena and also to regulate human activities in fragile ecosystems.

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Discriminating Wet Snow and Firn for Alpine Glaciers Using Sentinel-1 Data: A Case Study at Rofental, Austria

Geosciences ◽

10.3390/geosciences9020069 ◽

2019 ◽

Vol 9 (2) ◽

pp. 69 ◽

Cited By ~ 2

Author(s):

Achim Heilig ◽

Anna Wendleder ◽

Andreas Schmitt ◽

Christoph Mayer

Keyword(s):

Remote Sensing ◽

Remote Sensing Data ◽

Optical Data ◽

Optical Remote Sensing ◽

Mountain Ranges ◽

Snow Covered Area ◽

Covered Area ◽

Wet Snow ◽

Sar Data ◽

Sar Analysis

Continuous monitoring of glacier changes supports our understanding of climate related glacier behavior. Remote sensing data offer the unique opportunity to observe individual glaciers as well as entire mountain ranges. In this study, we used synthetic aperture radar (SAR) data to monitor the recession of wet snow area extent per season for three different glacier areas of the Rofental, Austria. For four glaciological years (GYs, 2014/2015–2017/2018), Sentinel-1 (S1) SAR data were acquired and processed. For all four GYs, the seasonal snow retreated above the elevation range of perennial firn. The described processing routine is capable of discriminating wet snow from firn areas for all GYs with sufficient accuracy. For a short in situ transect of the snow—firn boundary, SAR derived wet snow extent agreed within an accuracy of three to four pixels or 30–40 m. For entire glaciers, we used optical remote sensing imagery and field data to assess reliability of derived wet snow covered area extent. Differences in determination of snow covered area between optical data and SAR analysis did not exceed 10% on average. Offsets of SAR data to results of annual field assessments are below 10% as well. The introduced workflow for S1 data will contribute to monitoring accumulation area extent for remote and hazardous glacier areas and thus improve the data basis for such locations.

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crops planting area identification and analysis based on multi-source high resolution remote sensing data

10.5194/egusphere-egu2020-13227 ◽

2020 ◽

Author(s):

Lei Wang ◽

Haoran Sun ◽

Wenjun Li ◽

Liang Zhou

Keyword(s):

Remote Sensing ◽

Random Forest ◽

Classification Accuracy ◽

Kappa Coefficient ◽

Optical Data ◽

Accurate Estimation ◽

Support Vector ◽

Random Forest Method ◽

Multi Temporal ◽

Sar Data

Crop planting structure is of great significance to the quantitative management of agricultural water and the accurate estimation of crop yield. With the increasing spatial and temporal resolution of remote sensing optical and SAR(Synthetic Aperture Radar) images, &#160;efficient crop mapping in large area becomes possible and the accuracy is improved. In this study, Qingyijiang Irrigation District in southwest of China is selected for crop identification methods comparison, which has heterogeneous terrain and complex crop structure . Multi-temporal optical (Sentinel-2) and SAR (Sentinel-1) data were used to calculate NDVI and backscattering coefficient as the main classification indexes. The multi-spectral and SAR data showed significant change in different stages of the whole crop growth period and varied with different crop types. Spatial distribution and texture analysis was also made. Classification using different combinations of indexes were performed using neural network, support vector machine and random forest method. The results showed that, the use of multi-temporal optical data and SAR data in the key growing periods of main crops can both provide satisfactory classification accuracy. The overall classification accuracy was greater than 82% and Kappa coefficient was greater than 0.8. SAR data has high accuracy and much potential in rice identification. However optical data had more accuracy in upland crops classification. In addition, the classification accuracy can be effectively improved by combination of classification indexes from optical and SAR data, the overall accuracy was up to 91.47%. The random forest method was superior to the other two methods in terms of the overall accuracy and the kappa coefficient.

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COMBINED ANALYSIS OF RADARSAT-2 SAR AND SENTINEL-2 OPTICAL DATA FOR IMPROVED MONITORING OF TUBER INITIATION STAGE OF POTATO

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-5-275-2018 ◽

2018 ◽

Vol XLII-5 ◽

pp. 275-279 ◽

Cited By ~ 1

Author(s):

D. Mandal ◽

V. Kumar ◽

Y. S. Rao ◽

A. Bhattacharya ◽

S. Bera ◽

...

Keyword(s):

Remote Sensing ◽

Temporal Dynamics ◽

Potato Production ◽

Tuber Initiation ◽

Optical Data ◽

Growth Monitoring ◽

Tuber Bulking ◽

Potato Tuberization ◽

Sar Data ◽

Sentinel 2

Abstract. Tuber initiation and tuber bulking stages are critical part of various phenological phases for potato production. Tuber initiation covers the period from the formation of spherical rhizome ends, the flowering and the start of tuber bulking. In general, the tuberization spans from 3 to 5 weeks after emergence and ends with the row closer i.e. canopies in adjacent rows touch each other across the furrow. Hence, this rapid growth seeks critical agronomic management practices such as irrigation and fertilization. It majorly influences the growth of stems, foliar area, dry weight and number of tubers particularly at the phase of tuber initiation. During these phenological stages, potato crops are susceptible to the infestation of late blight diseases caused by Phytophthora infestans and largely affects the potato production. Thus identifying the crop risk using remote sensing approaches can provide an efficient potato growth monitoring framework. In the context of monitoring crop dynamics, quad-pol Synthetic Aperture Radar (SAR) data has proven to be effective due to its sensitivity towards dielectric and geometric properties. In addition to SAR data, optical remote sensing data derived vegetation information can provide an improved insight into crop growth when combined with SAR data. In this research, quad-pol RADARSAT-2 and Sentinel-2 optical data are analyzed to monitor potato tuberization phase over Bardhaman district in the state ofWest Bengal, which is one of the major potato growing regions in India. The proposed approach uses polarimetric parameters such as backscatter intensities, ratio (HH/VV, VH/VV, linear depolarization ratio), and co-pol correlation (&rho;HH–VV) along with the vegetation indices derived from the Sentinel-2 data for understanding the spatio-temporal dynamics. The initial results show a promising accuracy in monitoring the dynamics of potato tuberization. Integration of such earth observation (EO) data, in conjunction with in-situ field measurements, might significantly enhance the current capabilities for crop monitoring.

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Remote sensing of terrestrial non-photosynthetic vegetation using hyperspectral, multispectral, SAR, and LiDAR data

Progress in Physical Geography Earth and Environment ◽

10.1177/0309133315582005 ◽

2015 ◽

Vol 40 (2) ◽

pp. 276-304 ◽

Cited By ~ 24

Author(s):

Zhaoqin Li ◽

Xulin Guo

Keyword(s):

Remote Sensing ◽

Remote Sensing Data ◽

Wildlife Habitat ◽

Optical Data ◽

Lidar Data ◽

Natural Fire ◽

Sensing Data ◽

Advantages And Disadvantages ◽

Sar Data ◽

Ecological Importance

Quantifying non-photosynthetic vegetation (NPV) is important for ecosystem management and studies on climate change, ecology, and hydrology because it controls uptake of carbon, water, and nutrients together with frequency and intensity of natural fire, and serves as wildlife habitat. The ecological importance of NPV has driven considerable research on quantitatively estimating NPV in diverse ecosystems including croplands, forests, grasslands, savannah, and shrublands using remote sensing data. However, a comprehensive review is not available. This review highlights the theoretical bases and the critical elements of remote sensing for NPV estimation, and summarizes research on estimating fractional cover of NPV (NPV cover) and biomass using passive optical hyperspectral and multispectral remote sensing data, active synthetic aperture radar (SAR) and light detection and ranging (LiDAR), and integrated multi-sensorial data. We also discuss advantages and disadvantages of optical, LiDAR, and SAR data and pinpoint future direction on NPV estimation using remote sensing data. Currently, most NPV research has been mainly focused on NPV cover, not NPV biomass, using passive optical data, while a few studies have used LiDAR data to quantify NPV biomass in forests and SAR data on NPV estimation in croplands and grasslands. In the future, more efforts should be made to estimate NPV biomass and to investigate the best use of hyperspectral, LiDAR, SAR data, and their integration. The upcoming new optical sensor on Sentinel-2 satellites, Radarsat-2 constellation and NovaSAR, technological innovation in hyperspectral, LiDAR, and SAR, and improvements on methodology for information extraction and combining multi-sensorial data will provide more opportunities for NPV estimation.

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AN UNSUPERVISED SOIL MOISTURE ESTIMATION MODELLING APPROACH USING C-BAND DUAL PolSAR DATA

10.5194/egusphere-egu2020-12870 ◽

2020 ◽

Author(s):

Reet Kamal Tiwari ◽

Akshar Tripathi

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

Field Data ◽

Microwave Remote Sensing ◽

Data Availability ◽

Series Data ◽

Soil Conditions ◽

Decomposition Models ◽

Sar Data ◽

Moisture Estimation

With the advent of remote sensing and its widescale implementation in the field of agriculture and soil studies, today remote sensing has become an integral non-evasive analysis and research tool. After decades of research with conventional optical remote sensing, both airborne and spaceborne, a need was felt to have an all-weather remote sensing data availability. Spaceborne SAR (Synthetic Aperture RADAR) or microwave remote sensing with its all-weather availability and high temporal resolution, owing to its penetration capabilities has been found highly suitable for the soil and crop health studies. Since, SAR remote sensing is highly sensitive to surface roughness and dielectrics in dry and moist soil conditions respectively, it becomes highly important to study and observe the variations of these properties in various polarisation channels. PolSAR (Polarimetric SAR) data with its different decomposition models has an advantage over conventional SAR data since it uses more than one polarisation channels and polarimetric decomposition models which consider several soil and crop parameters. This helps to study the RADAR wave interaction with the target easier. This helps in the proper and better study and understanding of retrieval of soil moisture and analysis of its variation over time. This study makes use of C-band Sentinel 1A satellite dual PolSAR, time series data of VV and VH polarisations. The datasets used are that of pre-monsoon and monsoon period of 2019, February to May respectively for Rupnagar area. In this study it has been aimed to model for retrieval of soil moisture based on RADAR backscatter values and Normalised Differential Moisture Indices values from Sentinel-1A and Sentinel 2 satellite imageries respectively. The process has been performed on both VV and VH polarisations and the results are analysed for both the time periods. Theoretically, it has been observed that VH polarisation yields better and nearer to ground truth results with least Root Mean Squared Error (RMSE) of 0.05 and high R2-Squared statistics of 0.72 (72%) in training and testing. This study aims at unsupervised modelling using satellite datasets for model development, training and validation and without the input of field data. The results though not very good yet give an idea of soil moisture estimation and is highly beneficial for areas and conditions when field validations and data collection is difficult or not possible. This study also aims at reducing field validation dependence. Once integrated with field data, accuracy is expected to increase.

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The Flood Resilience Dashboard

10.5194/egusphere-egu2020-21602 ◽

2020 ◽

Author(s):

Ian McCallum ◽

Stefan Velev ◽

Finn Laurien ◽

Reinhard Mechler ◽

Adriana Keating ◽

...

Keyword(s):

Remote Sensing ◽

Satellite Imagery ◽

Spatial Data ◽

Forest Cover ◽

Previous Experience ◽

Capacity Assessment ◽

Night Time ◽

Entry Barrier ◽

Web Map ◽

Crowdsourced Data

The purpose of the &#8220;Flood Resilience Dashboard&#8221; is to put geo-spatial flood resilience data into the hands of practitioners. The idea is to provide an intuitive platform that combines as much open, peer-reviewed flood resilience related spatial data as possible with available related spatial data from the Flood Resilience Alliance, which in turn can be used to inform decisions. This data will include among others the Zurich Flood Resilience Measurement for Communities (FRMC) data, Vulnerability Capacity Assessment (VCA) maps, remote sensing derived information on flooding and other biophysical datasets (e.g. forest cover, water extent), modelled risk information, satellite imagery (e.g. night-time lights), crowdsourced data and more.&#160;The Dashboard will, as much as possible, lower the entry barrier for non-technical users, providing a simple login experience for the users. Users should be able to explore the Dashboard using standard web map navigation tools. The various charts and tables on the Dashboard dynamically refresh as features on the map are selected or the map extent is changed. No previous experience or understanding of geo-spatial data is required, beyond basic web-map navigation.

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ROLE OF SAR DATA IN WATER BODY MAPPING AND RESERVOIR SEDIMENTATION ASSESSMENT

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iv-5-151-2018 ◽

2018 ◽

Vol IV-5 ◽

pp. 151-158 ◽

Cited By ~ 3

Author(s):

N. R. Prasad ◽

V. Garg ◽

P. K. Thakur

Keyword(s):

Remote Sensing ◽

Storage Capacity ◽

Optical Data ◽

Reservoir Sedimentation ◽

Reservoir Level ◽

Body Mapping ◽

Spread Area ◽

Sar Data ◽

Water Spread ◽

Microwave Data

Abstract. Reservoir sedimentation is the major problem, due to it every year the reservoir capacity is lost to considerable amount. Surveying for assessment of the reservoir by conventional approach is time and money consuming. Geospatial technology provides ample opportunity in this field through the availability of high resolution satellite data from sensors such as Sentinel, Indian Remote Sensing Satellite, Landsat, and SPOT have been used to calculate the water spread area of the reservoir. However, due to presence of cloud in most of the optical data during onset of monsoon, the water spread at the lowest reservoir level could not be mapped. In turn the revised capacity or sedimentation is generally assessed between either below full reservoir level (FRL) or above maximum draw down level (MDDL). Nowadays, the microwave synthetic aperture radar (SAR) data at reasonable spatial resolution is available freely in public domain. Moreover, microwave data has capability to penetrate cloud and the information below cloud can easily be retrieved. To overcome the issues related to optical data, in the present study, the reservoir sedimentation for Ghataprabha reservoir was estimated using SAR data. Sentinel-1A data was used to delineate the water spread area for the water year of 2016–17. The original live storage capacity (1974) was estimated to be 1434.14Mm3 at FRL 662.940m by the authorities using the hydrographic survey during the commissioning of the reservoir in the year 1974. The live storage capacity was found out to be 1366.14Mm3 at FRL, however, as per original elevation-area-capacity curve the live capacity is around 1262.404Mm3 at 660.50m. Estimated live storage capacity from Remote sensing approach (2016–17) was assesses as 1182.5Mm3 at 660.51m. The storage capacity has reduced from 1262.40Mm3 (1974) to 1182.51Mm3 i.e. around 171.732Mm3. As per present analysis the rate of sedimentation is around 4Mm3/yr. It was realized that using the SAR microwave data, the revised capacity of the reservoir from its near MDDL to FRL could be assessed through remote sensing approach.

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DOWNSCALING OF SMAP SOIL MOISTURE PRODUCT BY DATA FUSION WITH VIIRS LST/EVI PRODUCT

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlvi-4-w5-2021-355-2021 ◽

2021 ◽

Vol XLVI-4/W5-2021 ◽

pp. 355-360

Author(s):

X. Lei ◽

Y. Wang ◽

T. Guo

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

Data Fusion ◽

Large Scale ◽

Microwave Remote Sensing ◽

Global Scale ◽

Moisture Distribution ◽

Optical Data ◽

Hydrological Simulation ◽

Original Dataset

Abstract. Soil moisture is an essential variable of environment and climate change, which affects the energy and water exchange between soil and atmosphere. The estimation of soil moisture is thus very important in geoscience, while at same time also challenging. Satellite remote sensing provides an efficient way for large-scale soil moisture distribution mapping, and microwave remote sensing satellites/sensors, such as Soil Moisture and Ocean Salinity (SMOS), Advanced Microwave Scanning Radiometer (AMSR), and Soil Moisture Active Passive (SMAP) satellite, are widely used to retrieve soil moisture in a global scale. However, most microwave products have relatively coarse resolution (tens of kilometres), which limits their application in regional hydrological simulation and disaster prevention. In this study, the SMAP soil moisture product with spatial resolution of 9km is downscaled to 750m by fusing with VIIRS optical products. The LST-EVI triangular space pattern provides the physical foundation for the microwave-optical data fusion, so that the downscaled soil moisture product not only matches well with the original SMAP product, but also presents more detailed distribution patterns compared with the original dataset. The results show a promising prospect to use the triangular method to produce finer soil moisture datasets (within 1 km) from the coarse soil moisture product.

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