A Multi-Sensor Comparative Analysis on the Suitability of Generated DEM from Sentinel-1 SAR Interferometry Using Statistical and Hydrological Models

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).

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Static and Dynamic Analysis of Motor Cycle Wheel Designs

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.813-814.915 ◽

2015 ◽

Vol 813-814 ◽

pp. 915-920 ◽

Cited By ~ 2

Author(s):

A. Eswara Kumar ◽

M. Naga Raju ◽

Navuri Karteek ◽

Daggupati Prakash

Keyword(s):

Vital Role ◽

Point Of View ◽

Al Alloy ◽

Analysis Tool ◽

Structural Stiffness ◽

Element Analysis ◽

Static And Dynamic Analysis ◽

Wheel Rim ◽

Strength And Stiffness ◽

Von Mises

The wheel of a vehicle plays a vital role to bear the load applies on it. Generally spokes acts as the supports between the wheel rim and hub. These spokes must have sufficient strength and stiffness to avoid the failure of the wheel. In present days these wheels are made up of aluminum alloy, magnesium alloy and steel. To reduce the weight of the wheel many wheel designs are implemented and applied for different vehicles. In this paper three different wheel designs are chosen, those are inclined spokes, curved spokes and Y shaped spokes made up of Al alloy, Mg alloy and Steel. Static structural analysis subjected to pressure on the wheel rim and free vibrational analyses are performed by using finite element analysis tool Ansys 12. The objective of the present work is to observe the best design which contains higher structural stiffness, specific structural stiffness with lower von mises stresses under static load conditions. It is observed that curve shaped spoke designs are better in for manufacturing of wheel in both static and dynamic point of view.

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Reconstruction of High-Temporal- and High-Spatial-Resolution Reflectance Datasets Using Difference Construction and Bayesian Unmixing

Remote Sensing ◽

10.3390/rs12233952 ◽

2020 ◽

Vol 12 (23) ◽

pp. 3952

Author(s):

Lei Yang ◽

Jinling Song ◽

Lijuan Han ◽

Xin Wang ◽

Jing Wang

Keyword(s):

Time Series ◽

Spatial Resolution ◽

Land Surface ◽

High Spatial Resolution ◽

Vegetation Indices ◽

Vital Role ◽

Dynamic Monitoring ◽

Fusion Model ◽

Spatiotemporal Resolution ◽

Reconstruction Methods

High-temporal- and high-spatial-resolution reflectance datasets play a vital role in monitoring dynamic changes at the Earth’s land surface. So far, many sensors have been designed with a trade-off between swath width and pixel size; thus, it is difficult to obtain reflectance data with both high spatial resolution and frequent coverage from a single sensor. In this study, we propose a new Reflectance Bayesian Spatiotemporal Fusion Model (Ref-BSFM) using Landsat and MODIS (Moderate Resolution Imaging Spectroradiometer) surface reflectance, which is then used to construct reflectance datasets with high spatiotemporal resolution and a long time series. By comparing this model with other popular reconstruction methods (the Flexible Spatiotemporal Data Fusion Model, the Spatial and Temporal Adaptive Reflectance Fusion Model, and the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model), we demonstrate that our approach has the following advantages: (1) higher prediction accuracy, (2) effective treatment of cloud coverage, (3) insensitivity to the time span of data acquisition, (4) capture of temporal change information, and (5) higher retention of spatial details and inconspicuous MODIS patches. Reflectance time-series datasets generated by Ref-BSFM can be used to calculate a variety of remote-sensing-based vegetation indices, providing an important data source for land surface dynamic monitoring.

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Sustaining Rice Production through Biofertilization with N2-Fixing Cyanobacteria

Applied Sciences ◽

10.3390/app11104628 ◽

2021 ◽

Vol 11 (10) ◽

pp. 4628

Author(s):

Macarena Iniesta-Pallarés ◽

Consolación Álvarez ◽

Francisco M. Gordillo-Cantón ◽

Carmen Ramírez-Moncayo ◽

Pilar Alves-Martínez ◽

...

Keyword(s):

Agricultural Practices ◽

Vital Role ◽

Rice Production ◽

Point Of View ◽

Paddy Fields ◽

Nitrogen Fertilizers ◽

Trophic Chain ◽

Agricultural Practice ◽

Biotechnological Potential

Current agricultural productivity depends on an exogenous nutrient supply to crops. This is of special relevance in cereal production, a fundamental part of the trophic chain that plays a vital role in the human diet. However, our agricultural practices entail highly detrimental side-effects from an environmental point of view. Long-term nitrogen fertilization in croplands results in degradation of soil, water, and air quality, producing eutrophication and subsequently contributing to global warming. In accordance with this, there is a biotechnological interest in using nitrogen-fixing microorganisms to enhance crop growth without adding chemically synthesized nitrogen fertilizers. This is particularly beneficial in paddy fields, where about 60% of the synthetic fertilizer that has been applied is dissolved in the water and washed away. In these agricultural systems, N2-fixing cyanobacteria show a promising biotechnological potential as biofertilizers, improving soil fertility while reducing the environmental impact of the agricultural practice. In the current study, Andalusian paddy fields have been explored to isolate N2-fixing cyanobacteria. These endogenous microorganisms have been subsequently re-introduced in a field trial in order to enhance rice production. Our results provide valuable insights regarding the use of an alternative natural source of nitrogen for rice production.

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A Response of Snow Cover to the Climate in the Northwest Himalaya (NWH) Using Satellite Products

Remote Sensing ◽

10.3390/rs13040655 ◽

2021 ◽

Vol 13 (4) ◽

pp. 655

Author(s):

Animesh Choudhury ◽

Avinash Chand Yadav ◽

Stefania Bonafoni

Keyword(s):

Snow Cover ◽

Land Surface ◽

Significant Negative Correlation ◽

Shuttle Radar Topography Mission ◽

Vital Role ◽

Snow Cover Area ◽

Study Region ◽

Northwest Himalaya ◽

The Past ◽

Average Annual Temperature

The Himalayan region is one of the most crucial mountain systems across the globe, which has significant importance in terms of the largest depository of snow and glaciers for fresh water supply, river runoff, hydropower, rich biodiversity, climate, and many more socioeconomic developments. This region directly or indirectly affects millions of lives and their livelihoods but has been considered one of the most climatically sensitive parts of the world. This study investigates the spatiotemporal variation in maximum extent of snow cover area (SCA) and its response to temperature, precipitation, and elevation over the northwest Himalaya (NWH) during 2000–2019. The analysis uses Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra 8-day composite snow Cover product (MOD10A2), MODIS/Terra/V6 daily land surface temperature product (MOD11A1), Climate Hazards Infrared Precipitation with Station data (CHIRPS) precipitation product, and Shuttle Radar Topography Mission (SRTM) DEM product for the investigation. Modified Mann-Kendall (mMK) test and Spearman’s correlation methods were employed to examine the trends and the interrelationships between SCA and climatic parameters. Results indicate a significant increasing trend in annual mean SCA (663.88 km2/year) between 2000 and 2019. The seasonal and monthly analyses were also carried out for the study region. The Zone-wise analysis showed that the lower Himalaya (184.5 km2/year) and the middle Himalaya (232.1 km2/year) revealed significant increasing mean annual SCA trends. In contrast, the upper Himalaya showed no trend during the study period over the NWH region. Statistically significant negative correlation (−0.81) was observed between annual SCA and temperature, whereas a nonsignificant positive correlation (0.47) existed between annual SCA and precipitation in the past 20 years. It was also noticed that the SCA variability over the past 20 years has mainly been driven by temperature, whereas the influence of precipitation has been limited. A decline in average annual temperature (−0.039 °C/year) and a rise in precipitation (24.56 mm/year) was detected over the region. The results indicate that climate plays a vital role in controlling the SCA over the NWH region. The maximum and minimum snow cover frequency (SCF) was observed during the winter (74.42%) and monsoon (46.01%) season, respectively, while the average SCF was recorded to be 59.11% during the study period. Of the SCA, 54.81% had a SCF above 60% and could be considered as the perennial snow. The elevation-based analysis showed that 84% of the upper Himalaya (UH) experienced perennial snow, while the seasonal snow mostly dominated over the lower Himalaya (LH) and the middle Himalaya (MH).

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Combination of GF-2 high spatial resolution imagery and land surface factors for predicting soil salinity of muddy coasts

CATENA ◽

10.1016/j.catena.2021.105304 ◽

2021 ◽

Vol 202 ◽

pp. 105304

Author(s):

Yufeng Li ◽

Cheng Wang ◽

Alan Wright ◽

Hongyu Liu ◽

Huabing Zhang ◽

...

Keyword(s):

Spatial Resolution ◽

Soil Salinity ◽

Land Surface ◽

High Spatial Resolution ◽

High Spatial Resolution Imagery

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Unsupervised Sub-Pixel Water Body Mapping with Sentinel-3 OLCI Image

Remote Sensing ◽

10.3390/rs11030327 ◽

2019 ◽

Vol 11 (3) ◽

pp. 327 ◽

Cited By ~ 8

Author(s):

Xia Wang ◽

Feng Ling ◽

Huaiying Yao ◽

Yaolin Liu ◽

Shuna Xu

Keyword(s):

Surface Water ◽

Real Time ◽

Spatial Resolution ◽

Water Body ◽

Land Surface ◽

Clustering Algorithm ◽

Body Mapping ◽

Water Index ◽

Land Surface Water ◽

Body Map

Mapping land surface water bodies from satellite images is superior to conventional in situ measurements. With the mission of long-term and high-frequency water quality monitoring, the launch of the Ocean and Land Colour Instrument (OLCI) onboard Sentinel-3A and Sentinel-3B provides the best possible approach for near real-time land surface water body mapping. Sentinel-3 OLCI contains 21 bands ranging from visible to near-infrared, but the spatial resolution is limited to 300 m, which may include lots of mixed pixels around the boundaries. Sub-pixel mapping (SPM) provides a good solution for the mixed pixel problem in water body mapping. In this paper, an unsupervised sub-pixel water body mapping (USWBM) method was proposed particularly for the Sentinel-3 OLCI image, and it aims to produce a finer spatial resolution (e.g., 30 m) water body map from the multispectral image. Instead of using the fraction maps of water/non-water or multispectral images combined with endmembers of water/non-water classes as input, USWBM directly uses the spectral water index images of the Normalized Difference Water Index (NDWI) extracted from the Sentinel-3 OLCI image as input and produces a water body map at the target finer spatial resolution. Without the collection of endmembers, USWBM accomplished the unsupervised process by developing a multi-scale spatial dependence based on an unsupervised sub-pixel Fuzzy C-means (FCM) clustering algorithm. In both validations in the Tibet Plate lake and Poyang lake, USWBM produced more accurate water body maps than the other pixel and sub-pixel based water body mapping methods. The proposed USWBM, therefore, has great potential to support near real-time sub-pixel water body mapping with the Sentinel-3 OLCI image.

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A Random Forest-Based Data Fusion Method for Obtaining All-Weather Land Surface Temperature with High Spatial Resolution

Remote Sensing ◽

10.3390/rs13112211 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2211

Author(s):

Shuo Xu ◽

Jie Cheng ◽

Quan Zhang

Keyword(s):

Random Forest ◽

Surface Temperature ◽

Spatial Resolution ◽

Land Surface Temperature ◽

Land Surface ◽

High Spatial Resolution ◽

Learning Algorithm ◽

Mainland China ◽

Fusion Method ◽

Moisture Condition

Land surface temperature (LST) is an important parameter for mirroring the water–heat exchange and balance on the Earth’s surface. Passive microwave (PMW) LST can make up for the lack of thermal infrared (TIR) LST caused by cloud contamination, but its resolution is relatively low. In this study, we developed a TIR and PWM LST fusion method on based the random forest (RF) machine learning algorithm to obtain the all-weather LST with high spatial resolution. Since LST is closely related to land cover (LC) types, terrain, vegetation conditions, moisture condition, and solar radiation, these variables were selected as candidate auxiliary variables to establish the best model to obtain the fusion results of mainland China during 2010. In general, the fusion LST had higher spatial integrity than the MODIS LST and higher accuracy than downscaled AMSR-E LST. Additionally, the magnitude of LST data in the fusion results was consistent with the general spatiotemporal variations of LST. Compared with in situ observations, the RMSE of clear-sky fused LST and cloudy-sky fused LST were 2.12–4.50 K and 3.45–4.89 K, respectively. Combining the RF method and the DINEOF method, a complete all-weather LST with a spatial resolution of 0.01° can be obtained.

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NOAA Satellite Soil Moisture Operational Product System (SMOPS) Version 3.0 Generates Higher Accuracy Blended Satellite Soil Moisture

Remote Sensing ◽

10.3390/rs12172861 ◽

2020 ◽

Vol 12 (17) ◽

pp. 2861

Author(s):

Jifu Yin ◽

Xiwu Zhan ◽

Jicheng Liu

Keyword(s):

Soil Moisture ◽

Real Time ◽

Land Surface ◽

Vital Role ◽

Product System ◽

Global Soil ◽

One Stop ◽

Satellite Sensors ◽

Blended Data

Soil moisture plays a vital role for the understanding of hydrological, meteorological, and climatological land surface processes. To meet the need of real time global soil moisture datasets, a Soil Moisture Operational Product System (SMOPS) has been developed at National Oceanic and Atmospheric Administration to produce a one-stop shop for soil moisture observations from all available satellite sensors. What makes the SMOPS unique is its near real time global blended soil moisture product. Since the first version SMOPS publicly released in 2010, the SMOPS has been updated twice based on the users’ feedbacks through improving retrieval algorithms and including observations from new satellite sensors. The version 3.0 SMOPS has been operationally released since 2017. Significant differences in climatological averages lead to remarkable distinctions in data quality between the newest and the older versions of SMOPS blended soil moisture products. This study reveals that the SMOPS version 3.0 has overwhelming advantages of reduced data uncertainties and increased correlations with respect to the quality controlled in situ measurements. The new version SMOPS also presents more robust agreements with the European Space Agency’s Climate Change Initiative (ESA_CCI) soil moisture datasets. With the higher accuracy, the blended data product from the new version SMOPS is expected to benefit the hydrological, meteorological, and climatological researches, as well as numerical weather, climate, and water prediction operations.

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Permafrost temperature and active-layer thickness of Yakutia with 0.5-degree spatial resolution for model evaluation

Earth System Science Data ◽

10.5194/essd-5-305-2013 ◽

2013 ◽

Vol 5 (2) ◽

pp. 305-310 ◽

Cited By ~ 13

Author(s):

C. Beer ◽

A. N. Fedorov ◽

Y. Torgovkin

Keyword(s):

Layer Thickness ◽

Spatial Resolution ◽

Active Layer ◽

Land Surface ◽

Grid Cell ◽

Active Layer Thickness ◽

East Siberia ◽

Permafrost Temperature ◽

Surface Models ◽

The Mean

Abstract. Based on the map of landscapes and permafrost conditions in Yakutia (Merzlotno-landshaftnaya karta Yakutskoi0 ASSR, Gosgeodeziya SSSR, 1991), rasterized maps of permafrost temperature and active-layer thickness of Yakutia, East Siberia were derived. The mean and standard deviation at 0.5-degree grid cell size are estimated by assigning a probability density function at 0.001-degree spatial resolution. The gridded datasets can be accessed at the PANGAEA repository (doi:10.1594/PANGAEA.808240). Spatial pattern of both variables are dominated by a climatic gradient from north to south, and by mountains and the soil type distribution. Uncertainties are highest in mountains and in the sporadic permafrost zone in the south. The maps are best suited as a benchmark for land surface models which include a permafrost module.

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River parametrisation of the JULES land surface model for improved runoff routing at the global scale.

10.5194/egusphere-egu21-3444 ◽

2021 ◽

Author(s):

Aristeidis Koutroulis ◽

Manolis Grillakis ◽

Camilla Mathison ◽

Eleanor Burke

Keyword(s):

Spatial Resolution ◽

Land Surface ◽

River Flow ◽

Land Surface Model ◽

Flow Simulation ◽

Surface Model ◽

Routing Scheme ◽

Basin Scale ◽

River Flow Simulation ◽

River Routing

The JULES land surface model has a wide ranging application in studying different processes of the earth system including hydrological modeling [1]. Our aim is to tune the existing configuration of the global river routing scheme at 0.5o spatial resolution [2] and improve river flow simulation performance at finer temporal scales. To do so, we develop a factorial experiment of varying effective river velocity and meander coefficient, components of the Total Runoff Integrating Pathways (TRIP) river routing scheme. We test and adjust best performing configurations at the basin scale based on observations from GRDC 230 stations that exhibiting a variety of hydroclimatic and physiographic conditions. The analysis was focused on watersheds of near-natural conditions [3] to avoid potential influences of human management on river flow. The HydroATLAS database [4] was employed to identify basin scale descriptive hydro-environmental indicators that could be associated with the components of the TRIP. These indicators summarize hydrologic and physiographic characteristics of the drainage area of each flow gauge. For each basin we select the best performing set of TRIP parameters per basin resulting to the optimal efficiency of river flow simulation based on the Nash&#8211;Sutcliffe and Kling&#8211;Gupta efficiency metrics. We find that better performance is driven predominantly by characteristics related to the stream gradient and terrain slope. These indicators can serve as descriptors for extrapolating the adjustment of TRIP parameters for global land configurations at 0.5o spatial resolution using regression models.&#160;[1] Papadimitriou et al 2017, Hydrol. Earth Syst. Sci., 21, 4379&#8211;4401[2] Falloon et al 2007. Hadley Centre Tech. Note 72, 42 pp.[3] Fang Zhao et al 2017 Environ. Res. Lett. 12 075003[4] Linke et al 2019, Scientific Data 6: 283.

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