Digital elevation model (DEM) plays a vital role in hydrological modelling and environmental studies. Many essential layers can be extracted from this land surface information, including slope, aspect, rivers, and curvature. Therefore, DEM quality and accuracy will affect the extracted features and the whole process of modeling. Despite freely available DEMs from various sources, many researchers generate this information for their areas from various observations. Sentinal-1 synthetic aperture radar (SAR) images are among the best Earth observations for DEM generation thanks to their availabilities, high-resolution, and C-band sensitivity to surface structure. This paper presents a comparative study, from a hydrological point of view, on the quality and reliability of the DEMs generated from Sentinel-1 data and DEMs from other sources such as AIRSAR, ALOS-PALSAR, TanDEM-X, and SRTM. To this end, pair of Sentinel-1 data were acquired and processed using the SAR interferometry technique to produce a DEM for two different study areas of a part of the Cameron Highlands, Pahang, Malaysia, a part of Sanandaj, Iran. Based on the estimated linear regression and standard errors, generating DEM from Sentinel-1 did not yield promising results. The river streams for all DEMs were extracted using geospatial analysis tool in a geographic information system (GIS) environment. The results indicated that because of the higher spatial resolution (compared to SRTM and TanDEM-X), more stream orders were delineated from AIRSAR and Sentinel-1 DEMs. Due to the shorter perpendicular baseline, the phase decorrelation in the created DEM resulted in a lot of noise. At the same time, results from ground control points (GCPs) showed that the created DEM from Sentinel-1 is not promising. Therefore, other DEMs' performance, such as 90-meters' TanDEM-X and 30-meters' SRTM, are better than Sentinel-1 DEM (with a better spatial resolution).
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7767291 | PMC |
| http://dx.doi.org/10.3390/s20247214 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Remote sensing-based mangrove blue carbon assessment in the Asia-Pacific: A systematic review.
Sci Total Environ
August 2024
Ecoresolve, San Francisco, CA, United States; Morobe Development Foundation (via United Nations Volunteering Program), Lae, Papua New Guinea; BlueForests, San Francisco, CA, United States; Department of Civil Engineering, College of Engineering, American University of Sharjah (AUS), Sharjah, United Arab Emirates; Earth Observation Center, Institute of Climate Change, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia; Department of Geography, University of California - Berkeley, Berkeley, CA, United States. Electronic address:
Accurate measuring, mapping, and monitoring of mangrove forests support the sustainable management of mangrove blue carbon in the Asia-Pacific. Remote sensing coupled with modeling can efficiently and accurately estimate mangrove blue carbon stocks at larger spatiotemporal extents. This study aimed to identify trends in remote sensing/modeling employed in estimating mangrove blue carbon, attributes/variations in mangrove carbon sequestration estimated using remote sensing, and to compile research gaps and opportunities, followed by providing recommendations for future research.
View Article and Find Full Text PDFExtraction of Planting Information Based on Deep Learning and Multi-Source Data.
Sensors (Basel)
February 2024
College of Geography and Environmental Science, Northwest Normal University, Lanzhou 730070, China.
Accurate extraction of crop acreage is an important element of digital agriculture. This study uses Sentinel-2A, Sentinel-1, and DEM as data sources to construct a multidimensional feature dataset encompassing spectral features, vegetation index, texture features, terrain features, and radar features. The Relief-F algorithm is applied for feature selection to identify the optimal feature dataset.
View Article and Find Full Text PDFCoastal Vulnerability Index sensitivity to shoreline position and coastal elevation parameters in the Niger Delta region, Nigeria.
Sci Total Environ
April 2024
Lancaster Environment Centre (LEC), Lancaster University, Lancaster LA1 4YQ, UK; Department of Estate Management, University of Benin, Benin City 300287, Nigeria.
It is imperative to assess coastal vulnerability to safeguard coastal areas against extreme events and sea-level rise. In the Niger Delta region, coastal vulnerability index assessment in the past focused on open-access parameters without comparing the open-access parameters, especially coastal elevation and shoreline change. This sensitivity to the shoreline method and open-access coastal elevation limits the information for the planning of coastal adaptation.
View Article and Find Full Text PDFSmall- and medium-sized rice fields identification in hilly areas using all available sentinel-1/2 images.
Plant Methods
February 2024
Department of Geography and Spatial Information Techniques, Center for Land and Marine Spatial Utilization and Governance Research, Ningbo University, Ningbo, 315211, China.
Background: Mastering the spatial distribution and planting area of paddy can provide a scientific basis for monitoring rice production, and planning grain production layout. Previous remote sensing studies on paddy concentrated in the plain areas with large-sized fields, ignored the fact that rice is also widely planted in vast hilly regions. In addition, the land cover types here are diverse, rice fields are characterized by a scattered and fragmented distribution with small- or medium-sized, which pose difficulties for high-precision rice recognition.
View Article and Find Full Text PDFImproving SMAP soil moisture spatial resolution in different climatic conditions using remote sensing data.
Environ Monit Assess
November 2023
Department of Electronics and Telecommunication Engineering, School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran.
Soil moisture (SM) at the interface between the land surface and atmosphere is one of the major environmental parameters which plays an important role in hydrological applications. In this article, the SM measured by Soil Moisture Active Passive (SMAP) is downscaled from 3- to 1-km spatial resolution. The main purpose is to evaluate the performance of two different downscaling methods over a variety of climatic conditions and land cover types.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!