European Remote Sensing: progress, challenges, and opportunities

Ban, Yifang; Marullo, Salvatore; Eklundh, Lars

doi:10.1080/01431161.2017.1291484

Cited by 6 publications

(4 citation statements)

References 17 publications

(14 reference statements)

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“…Since lakes are located within a river basin (although lakes have their own lake basins). It is a normal practice, the analysis on lake water quality is executed based on the DOE's WQI classification, DOE Water Quality Classification based on Water Quality Index and National Water Quality Standards for Malaysia [15]. However, Putrajaya has developed its own standards and the details can be found in [4], [7].…”

Section: Introductionmentioning

confidence: 99%

Seasonal-Spatial of Putrajaya Lake Water Quality Parameter (WQP) Concentration Using Geographic Information System (GIS)

Asmat

Hazali

Nor

et al. 2018

IJET

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This study presents the assessment of selected water quality parameters (WQP) distribution at Putrajaya Lake during the dry and wet season using spatial analysis. The WQP used in this study were dissolved oxygen (DO), biochemical oxygen demand (BOD), chemical oxygen demand (COD), ammonia-nitrogen (NH3-N), total suspended solid (TSS), pH and temperature. Total thirteen water samples were obtained during both dry and wet seasons from May 2015 until March 2016 distributed throughout the areas. To explore the WQP distribution, Inverse Distance Weighted (IDW) was employed. The results have shown that mean values of WQ concentration during dry and wet season are varies accordingly. The highest value parameters of water quality during dry season which are COD (17.2 mg/L), DO (7.11 mg/L) and NH3N (0.94 mg/L). Meanwhile on the wet season, the highest values are recorded for BOD (5.27 mg/L), pH (7.62 mg/L) and TSS (18.4 mg/L) respectively. This has indicated that the concentration of WQP in the lake changed seasonally. Furthermore, among the WQPs; DO, TSS and COD have exhibited significant relationship between others for both seasons.

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Section: Introductionmentioning

confidence: 99%

Seasonal-Spatial of Putrajaya Lake Water Quality Parameter (WQP) Concentration Using Geographic Information System (GIS)

Asmat

Hazali

Nor

et al. 2018

IJET

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“…In the past, quite a few active and passive missions retrieved soil moisture, which mainly includes Soil Moisture and Ocean Salinity (SMOS) [8], Soil Moisture Active Passive (SMAP) [9], Advanced Scatterometer (ASCAT) [10], European Remote Sensing (ERS) [11], Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), and Advanced Microwave Scanning Radiometer 2 (AMSR2) [12,13]. Global Change Observation Mission First-Water (GCOM-W1) launched on 17 May 2012 carries AMSR2 and is an improved design of AMSR-E NASA's Aqua Satellite with enhancement in the reflector, C-band frequency and improved calibration system.…”

Section: Introductionmentioning

confidence: 99%

Satellite Soil Moisture Validation Using Hydrological SWAT Model: A Case Study of Puerto Rico, USA

Nilawar¹,

Calderella

Lakhankar

et al. 2017

Hydrology

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Soil moisture is placed at the interface between land and atmosphere which influences water and energy flux. However, soil moisture information has a significant importance in hydrological modelling and environmental processes. Recent advances in acquiring soil moisture from the satellite and its effective utilization provide an alternative to the conventional soil moisture methods. In this study, an attempt is made to apply physically based, distributed-parameter, Soil and Water Assessment Tool (SWAT) to validate Advanced Microwave Scanning Radiometer (AMSR2) soil moisture in parts of Puerto Rico. For this, calibration is performed for the years 2010 to 2012 with known observed discharge sites, Rio Guanajibo and Rio Grande de Añasco in Puerto Rico and validation, with the observed stream flow for the year 2013 using the AMSR2 soil moisture. Moreover, the SWAT and AMSR2 soil moisture outcome are compared on a monthly basis. The model capability and performance in simulating the stream flow are evaluated utilizing the statistical method. The results indicated a negligible difference in SWAT soil moisture and AMSR2 soil moisture for stream flow estimation. Finally, the model retrievals show a satisfactory agreement between observed and simulated streamflow.

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“…In the ocean color community, fusion methods and machine learning are emerging topics. Potential benefits of these methods include filling data gaps caused by cloud cover, inferring vertical distributions of biogeochemical variables observed at sea surface, emulate models of the upper ocean dynamics at high spatial resolution and developing relationships between environmental variables [9][10][11][12]. Further applications can be found in carbon dynamics [13][14][15], coastal turbidity [16,17], phytoplankton blooms [18][19][20][21], harmful algal blooms [22,23], and primary production [24].…”

Section: Introductionmentioning

confidence: 99%

Construction of Multi-Year Time-Series Profiles of Suspended Particulate Inorganic Matter Concentrations Using Machine Learning Approach

et al. 2017

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Hydro-sedimentary numerical models have been widely employed to derive suspended particulate matter (SPM) concentrations in coastal and estuarine waters. These hydro-sedimentary models are computationally and technically expensive in nature. Here we have used a computationally less-expensive, well-established methodology of self-organizing maps (SOMs) along with a hidden Markov model (HMM) to derive profiles of suspended particulate inorganic matter (SPIM). The concept of the proposed work is to benefit from all available data sets through the use of fusion methods and machine learning approaches that are able to process a growing amount of available data. This approach is applied to two different data sets entitled "Hidden" and "Observable". The hidden data are composed of 15 months (27 September 2007 to 30 December 2008) of hourly SPIM profiles extracted from the Regional Ocean Modeling System (ROMS). The observable data include forcing parameter variables such as significant wave heights (Hs and Hs50 (50 days)) from the Wavewatch 3-HOMERE database and barotropic currents (Ubar and Vbar) from the Iberian-Biscay-Irish (IBI) reanalysis data. These observable data integrate hourly surface samples from 1 February 2002 to 31 December 2012. The time-series profiles of the SPIM have been derived from four different stations in the English Channel by considering 15 months of output hidden data from the ROMS as a statistical representation of the ocean for ≈11 years. The derived SPIM profiles clearly show seasonal and tidal fluctuations in accordance with the parent numerical model output. The surface SPIM concentrations of the derived model have been validated with satellite remote sensing data. The time series of the modeled SPIM and satellite-derived SPIM show similar seasonal fluctuations. The ranges of concentrations for the four stations are also in good agreement with the corresponding satellite data. The high accuracy of the estimated 25 h average surface SPIM concentrations (normalized root-mean-square error-NRMSE of less than 16%) is the first step in demonstrating the robustness of the method.

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European Remote Sensing: progress, challenges, and opportunities

Cited by 6 publications

References 17 publications

Seasonal-Spatial of Putrajaya Lake Water Quality Parameter (WQP) Concentration Using Geographic Information System (GIS)

Seasonal-Spatial of Putrajaya Lake Water Quality Parameter (WQP) Concentration Using Geographic Information System (GIS)

Satellite Soil Moisture Validation Using Hydrological SWAT Model: A Case Study of Puerto Rico, USA

Construction of Multi-Year Time-Series Profiles of Suspended Particulate Inorganic Matter Concentrations Using Machine Learning Approach

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