THE USE OF SYNTHETIC APERTURE RADAR (SAR) DATA IN THE ANALYSIS OF INUNDATED AREAS DURING THE SPRING FLOOD

The paper discusses the opportunities of remote sensing data application as one of the main sources of information for monitoring river floods. Effective operation of flood forecasting systems requires reliable real-time data on inundation areas for timely calibration and verification of the used hydrodynamic models. The opportunity to obtain data from optical sensors might be limited because of dense cloud cover. Synthetic aperture radar (SAR) techniques are increasingly used today due to ability to operate independently of the surface illumination and the state of cloud cover receiving high spatial resolution data in near real-time mode. An important feature of SAR from space today is the increase in the number of freely distributed space data, in particular — images from Sentinel satellites developed by the European Space Agency. For instance, for the territory of Russia Sentinel-1 performs SAR imaging with 2–3 days coverage frequency. Within the framework of the project carried out by the authors, the research area is the city of Velikiy Ustuyg (Russia) located at the confluence of rivers Suhona and Ug. To identify flooded areas the RADARSAT-2 and Sentinel-1 images classification based on thresholding was carried out in open-source software. The visualization of the results was performed on the basis of information analytical system “Prostor”. The results of SAR data processing were compared with contours obtained on the basis of the calculation of the NDWI index from optical data from the Sentinel-2 and Resurs-P satellites. According to the spatial resolution of the data and the selected processing technology, it is possible to achieve high accuracy of flood mapping in open areas with low urbanization. The result confirms that SAR data can be successfully applied for operational flood forecasting.

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Multi-Temporal Synthetic Aperture Radar Data for Paddy Crop Area Estimation in Eastern part of Godavari Delta, Andhra Pradesh, India.

Current Agriculture Research Journal ◽

10.12944/carj.8.3.06 ◽

2020 ◽

Vol 8 (3) ◽

pp. 208-218

Author(s):

S.K. Tiwari ◽

Prasada Rao G

Keyword(s):

Synthetic Aperture Radar ◽

Cloud Cover ◽

Andhra Pradesh ◽

Synthetic Aperture ◽

Crop Acreage ◽

The Third ◽

Godavari Delta ◽

Sar Data ◽

Paddy Crop ◽

Aperture Radar

In the present study, an attempt is made to estimate the area under paddy crop during Rabi, 2013-14 using Microwave satellite data in the eastern part of Godavari delta. Clouds veil nearly the entire sky in both (Kharif & Rabi) seasons of Andhra Pradesh and hinder the estimation of crop acreage through optical satellite sensors. Microwaves can penetrate clouds and be used to detect crops during the day and night, regardless of cloud cover. Radar Imaging SATellite-1 (RISAT-1), microwave sensor, dual-polarization Horizontal-Horizontal (HH), Horizontal-Vertical (HV), Medium Resolution scanSAR Mode (MRS) data (18 m pixel spacing and 37° incidence angle) of three different dates (in December, January, and February) with 25 days interval was used. The backscatter (dB) values of the early, mid, and late-season transplanted stages of paddy crop were used to estimate the paddy crop acreage coupled with ground truth information during different stages of the crop. It was observed that the dB values at the transplanting stage rapidly increased with plant growth in the early season sown areas and mid-season sowed paddy illustrate a dip in dB values in the second date due to change in transplantation and increased backscatter coefficient values in the third date because of crop growth after transplantation. The backscatter signature value of late sowing paddy crop showed first and second dates with high backscatter due to previous crop/vegetation and then a sudden dip in the third date as submerged field ready for transplantation. The dB values of the above stages were used in decision-based classifier to estimate paddy crop acreage. The paddy area was compared at Mandal (sub-district level) estimates observed the significant coefficient of determination (R² = 0.89) between traditional estimates and Synthetic Aperture Radar (SAR) data assessment. This study robustly suggests the utilization of SAR data in agricultural crop monitoring during cloud cover.

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Artificial Neural Network Modeling of High Arctic Phytomass Using Synthetic Aperture Radar and Multispectral Data

10.32920/14637897.v1 ◽

2021 ◽

Author(s):

Adam Collingwood ◽

Paul Treitz ◽

Francois Charbonneau ◽

David M. Atkinson

Keyword(s):

Synthetic Aperture Radar ◽

High Arctic ◽

Synthetic Aperture ◽

The Arctic ◽

Optical Data ◽

Neural Network Modeling ◽

Artificial Neural ◽

Sar Data ◽

The Relationship ◽

Aperture Radar

Vegetation in the Arctic is often sparse, spatially heterogeneous, and difficult to model. Synthetic Aperture Radar (SAR) has shown some promise in above-ground phytomass estimation at sub-arctic latitudes, but the utility of this type of data is not known in the context of the unique environments of the Canadian High Arctic. In this paper, Artificial Neural Networks (ANNs) were created to model the relationship between variables derived from high resolution multi-incidence angle RADARSAT-2 SAR data and optically-derived (GeoEye-1) Soil Adjusted Vegetation Index (SAVI) values. The modeled SAVI values (i.e., from SAR variables) were then used to create maps of above-ground phytomass across the study area. SAVI model results for individual ecological classes of polar semi-desert, mesic heath, wet sedge, and felsenmeer were reasonable, with r2 values of 0.43, 0.43, 0.30, and 0.59, respectively. When the outputs of these models were combined to analyze the relationship between the model output and SAVI as a group, the r2 value was 0.60, with an 8% normalized root mean square error (% of the total range of phytomass values), a positive indicator of a relationship. The above-ground phytomass model also resulted in a very strong relationship (r2 = 0.87) between SAR-modeled and field-measured phytomass. A positive relationship was also found between optically derived SAVI values and field measured phytomass (r2 = 0.79). These relationships demonstrate the utility of SAR data, compared to using optical data alone, for modeling above-ground phytomass in a high arctic environment possessing relatively low levels of vegetation.

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Combined Use of Optical and Synthetic Aperture Radar Data for REDD+ Applications in Malawi

Land ◽

10.3390/land7040116 ◽

2018 ◽

Vol 7 (4) ◽

pp. 116 ◽

Cited By ~ 7

Author(s):

Manuela Hirschmugl ◽

Carina Sobe ◽

Janik Deutscher ◽

Mathias Schardt

Keyword(s):

Time Series ◽

Land Cover ◽

Synthetic Aperture Radar ◽

Data Availability ◽

Synthetic Aperture ◽

Optical Data ◽

Series Data ◽

Forest Mapping ◽

Sar Data ◽

Aperture Radar

Recent developments in satellite data availability allow tropical forest monitoring to expand in two ways: (1) dense time series foster the development of new methods for mapping and monitoring dry tropical forests and (2) the combination of optical data and synthetic aperture radar (SAR) data reduces the problems resulting from frequent cloud cover and yields additional information. This paper covers both issues by analyzing the possibilities of using optical (Sentinel-2) and SAR (Sentinel-1) time series data for forest and land cover mapping for REDD+ (Reducing Emissions from Deforestation and Forest Degradation) applications in Malawi. The challenge is to combine these different data sources in order to make optimal use of their complementary information content. We compare the results of using different input data sets as well as of two methods for data combination. Results show that time-series of optical data lead to better results than mono-temporal optical data (+8% overall accuracy for forest mapping). Combination of optical and SAR data leads to further improvements: +5% in overall accuracy for land cover and +1.5% for forest mapping. With respect to the tested combination methods, the data-based combination performs slightly better (+1% overall accuracy) than the result-based Bayesian combination.

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Artificial Neural Network Modeling of High Arctic Phytomass Using Synthetic Aperture Radar and Multispectral Data

10.32920/14637897 ◽

2021 ◽

Author(s):

Adam Collingwood ◽

Paul Treitz ◽

Francois Charbonneau ◽

David M. Atkinson

Keyword(s):

Synthetic Aperture Radar ◽

High Arctic ◽

Synthetic Aperture ◽

The Arctic ◽

Optical Data ◽

Neural Network Modeling ◽

Artificial Neural ◽

Sar Data ◽

The Relationship ◽

Aperture Radar

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Quantification of Mangrove Extent using a Combination of Optical and Radar Images in Google Earth Engine Platform: The case of Anlo Beach Wetland Complex, Shama District, Western Region, Ghana

10.21203/rs.3.rs-398182/v1 ◽

2021 ◽

Author(s):

Daniel Aja ◽

Michael Miyittah ◽

Donatus Bapentire Angnuureng

Keyword(s):

Synthetic Aperture Radar ◽

Google Earth ◽

Kappa Coefficient ◽

Synthetic Aperture ◽

Optical Data ◽

Radar Images ◽

Google Earth Engine ◽

Sar Data ◽

Aperture Radar

Abstract Mangrove Forest classification in tropical coastal zones based on only passive remote sensing methods is hampered by Mangrove complexities, topographic considerations and cloud cover effects among other things. This paper reports on a novel approach that combines Optical Satellite images and Synthetic Aperture Radar alongside their derived parameters to overcome the challenges of distinguishing Mangrove stand in cloud prone regions. Google Earth Engine (GEE) cloud-based geospatial processing platform was used to extract several scenes of Landsat Surface Reflectance Tier1 and synthetic aperture radar (C-band and L-band). The imageries were enhanced by creating a function that masks out clouds from the optical satellite image and by using speckle filter to remove noise from the radar data. The random forest algorithm proved to be a robust and accurate machine learning approach for mangrove classification and assessment. Our result show that about 16% of the mangrove extent was lost in the last decade. The accuracy was assessed based on three classification scenarios: classification of optical data only, classification of SAR data only, and combination of both optical and SAR data. The overall accuracies were 99.1% (Kappa Coefficient =0.797), 84.6% (Kappa Coefficient = 0.687) and 98.9% (Kappa Coefficient = 0.828) respectively. This case study demonstrates how mangrove mapping can help focus conservation practices locally in climate change setting, coupled with sea level rise and related threats to coastal ecosystems.

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Forest Fuel Loads Estimation from Landsat ETM+ and ALOS PALSAR Data

Remote Sensing ◽

10.3390/rs13061189 ◽

2021 ◽

Vol 13 (6) ◽

pp. 1189

Author(s):

Yanxi Li ◽

Xingwen Quan ◽

Zhanmang Liao ◽

Binbin He

Keyword(s):

Synthetic Aperture Radar ◽

Synthetic Aperture ◽

Optical Data ◽

Fuel Load ◽

Load Estimation ◽

Fuel Loads ◽

Different Types ◽

L Band ◽

Sar Data ◽

Aperture Radar

Fuel load is the key factor driving fire ignition, spread and intensity. The current literature reports the light detection and ranging (LiDAR), optical and airborne synthetic aperture radar (SAR) data for fuel load estimation, but the optical and SAR data are generally individually explored. Optical and SAR data are expected to be sensitive to different types of fuel loads because of their different imaging mechanisms. Optical data mainly captures the characteristics of leaf and forest canopy, while the latter is more sensitive to forest vertical structures due to its strong penetrability. This study aims to explore the performance of Landsat Enhanced Thematic Mapper Plus (ETM+) and Advanced Land Observing Satellite (ALOS) Phased Arrayed L-band Synthetic Aperture Radar (PALSAR) data as well as their combination on estimating three different types of fuel load—stem fuel load (SFL), branch fuel load (BFL) and foliage fuel load (FFL). We first analyzed the correlation between the three types of fuel load and optical and SAR data. Then, the partial least squares regression (PLSR) was used to build the fuel load estimation models based on the fuel load measurements from Vindeln, Sweden, and variables derived from optical and SAR data. Based on the leave-one-out cross-validation (LOOCV) method, results show that L-band SAR data performed well on all three types of fuel load (R2 = 0.72, 0.70, 0.72). The optical data performed best for FFL estimation (R2 = 0.66), followed by BFL (R2 = 0.56) and SFL (R2 = 0.37). Further improvements were found for the SFL, BFL and FFL estimation when integrating optical and SAR data (R2 = 0.76, 0.81, 0.82), highlighting the importance of data selection and combination for fuel load estimation.

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