scholarly journals Evaluation of Sentinel-3A Wave Height Observations Near the Coast of Southwest England

2019 ◽  
Vol 11 (24) ◽  
pp. 2998 ◽  
Author(s):  
Francesco Nencioli ◽  
Graham D. Quartly
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Wave Height ◽  
Significant Wave Height ◽  
Mean Square ◽  
Significant Wave ◽  
In Situ Observations ◽  
Regression Slopes

Due to the smaller ground footprint and higher spatial resolution of the Synthetic Aperture Radar (SAR) mode, altimeter observations from the Sentinel-3 satellites are expected to be overall more accurate in coastal areas than conventional nadir altimetry. The performance of Sentinel-3A in the coastal region of southwest England was assessed by comparing SAR mode observations of significant wave height against those of Pseudo Low Resolution Mode (PLRM). Sentinel-3A observations were evaluated against in-situ observations from a network of 17 coastal wave buoys, which provided continuous time-series of hourly values of significant wave height, period and direction. As the buoys are evenly distributed along the coast of southwest England, they are representative of a broad range of morphological configurations and swell conditions against which to assess Sentinel-3 SAR observations. The analysis indicates that SAR observations outperform PLRM within 15 km from the coast. Within that region, regression slopes between SAR and buoy observations are close to the 1:1 relation, and the average root mean square error between the two is 0.46 ± 0.14 m. On the other hand, regression slopes for PLRM observations rapidly deviate from the 1:1 relation, while the average root mean square error increases to 0.84 ± 0.45 m. The analysis did not identify any dependence of the bias between SAR and in-situ observation on the swell period or direction. The validation is based on a synergistic approach which combines satellite and in-situ observations with innovative use of numerical wave model output to help inform the choice of comparison regions. Such an approach could be successfully applied in future studies to assess the performance of SAR observations over other combinations of coastal regions and altimeters.

scholarly journals ASSESSMENT OF SEASONAL VARIABILITY FOR WIND SPEED AND SIGNIFICANT WAVE HEIGHT USING SATELLITE ALTIMETER OVER MALAYSIAN SEAS

2018 ◽  
Vol XLII-4/W9 ◽  
pp. 153-158
Author(s):  
M. N. Uti ◽  
A. H. M. Din ◽  
A. H. Omar
Keyword(s):  
Wind Speed ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Wave Height ◽  
Seasonal Variability ◽  
Significant Wave Height ◽  
Northeast Monsoon ◽  
Mean Square ◽  
Satellite Altimeter ◽  
Significant Wave

<p><strong>Abstract.</strong> Malaysia is located in the equatorial region and experienced climate hot, humid and rainy throughout the year. These have brought four monsoon seasons to Malaysia which can be categorised as Northeast monsoon, Southwest monsoon, First-inter monsoon and Second-inter monsoon. Although Malaysia is surrounded by large scale marine resources, the lack of understanding in seasonal variability has affected the spatial and temporal analysis. Thus, this study will highlight the assessment of seasonal variability of wind speed and significant wave height over the Malaysian seas. For more than two decades satellite altimeter data were used to generate a prolonged trend of regional ocean wind speed and significant wave height in order to study the monsoons in Malaysia. A set of wind speed and significant wave height data are compared with the in-situ measurement to validate the accuracy of the wind speed and significant wave height observation using the satellite altimeter. Two selected buoys were using as benchmarks and assessed using the statistical analysis by conducting a root mean square error and a correlation calculation. Seasonal variations assessment is conducted with significance to analyse the monsoon effect towards the wind speed and significant wave height condition. As a result, both ocean parameters present a good value of root mean square error and positive correlation which were 0.7976 (wind speed) and 0.92 (significant wave height), which proves the measurement from satellite altimeter is reliable to use. In addition, the seasonal variation assessment illustrates during the Northeast monsoon, each part of the Malaysian seas experienced with great wind speed and significant wave height.</p>

scholarly journals Breaking Wave Height Estimation from Timex Images: Two Methods for Coastal Video Monitoring Systems

2020 ◽  
Vol 12 (2) ◽  
pp. 204 ◽  
Author(s):  
Umberto Andriolo ◽  
Diogo Mendes ◽  
Rui Taborda
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Wave Height ◽  
Video Data ◽  
Video Monitoring ◽  
Monitoring Systems ◽  
Breaking Wave ◽  
Mean Square ◽  
Beach Profile

The breaking wave height is a crucial parameter for coastal studies but direct measurements constitute a difficult task due to logistical and technical constraints. This paper presents two new practical methods for estimating the breaking wave height from digital images collected by shore-based video monitoring systems. Both methods use time-exposure (Timex) images and exploit the cross-shore length ( L H s ) of the typical time-averaged signature of breaking wave foam. The first method ( H s b , v ) combines L H s and a series of video-derived parameters with the beach profile elevation to obtain the breaking wave height through an empirical formulation. The second method ( H s b , v 24 ) is based on the empirical finding that L H s can be associated with the local water depth at breaking, thus it can be used to estimate the breaking wave height without the requirement of local bathymetry. Both methods were applied and verified against field data collected at the Portuguese Atlantic coast over two days using video acquired by an online-streaming surfcam. Furthermore, H s b , v 24 was applied on coastal images acquired at four additional field sites during distinct hydrodynamic conditions, and the results were compared to a series of different wave sources. Achievements suggest that H s b , v method represents a good alternative to numerical hydrodynamic modeling when local bathymetry is available. In fact, the differences against modeled breaking wave height, ranging from 1 to 3 m at the case study, returned a root-mean-square-error of 0.2 m. The H s b , v 24 method, when applied on video data collected at five sites, assessed a normalized root-mean-square-error of 18% on average, for dataset of about 900 records and breaking wave height ranging between 0.1 and 3.8 m. These differences demonstrate the potential of H s b , v 24 in estimating breaking wave height merely using Timex images, with the main advantage of not requiring the beach profile. Both methods can be easily implemented as cost-effective tools for hydrodynamic applications in the operational coastal video systems worldwide. In addition, the methods have the potential to be coupled to the numerous other Timex applications for morphodynamic studies.

scholarly journals Pola Distribusi Konsentrasi Klorofil-a di Laut Maluku berdasarkan Pengamatan In Situ INDESO Joint Expedition Program 2016 dan Data Penginderaan Jauh

Jurnal Segara ◽  
2017 ◽  
Vol 13 (2) ◽  
Author(s):  
Indra Hermawan ◽  
Agus Setiawan ◽  
Nikita Pusparini
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Mixed Layer ◽  
Mean Square

Tujuan dari penelitian ini adalah untuk mengetahui pola distribusi konsentrasi klorofil-a di perairan Laut Maluku yang termasuk dalam Wilayah Pengelolaan Perikanan RI 715 berdasarkan data pengamatan in situ dan penginderaan jauh. Penelitian ini dilakukan pada bulan September 2016 dan merupakan bagian dari Pelayaran Oseanografi INDESO Joint Expedition Program (IJEP) 2016 yang dilaksanakan oleh Badan Penelitian dan Pengembangan Kelautan dan Perikanan (Balitbang KP) menggunakan Kapal Riset Baruna Jaya VIII. Berdasarkan hasil pengamatan in situ pada 8 titik pengamatan di sepanjang Laut Maluku dari selatan ke utara, didapatkan bahwa konsentrasi klorofil-a di permukaan berkisar antara 0,1 hingga 0,6 mg/m3. Hasil perbandingan antara konsentrasi klorofil-a hasil pengamatan in-situ dengan model biogeokimia INDESO dan citra satelit SeaWiFS masing-masing memberikan root mean square error sebesar 0,1507 dan 0,1364 mg/m3. Sementara itu, secara vertikal konsentrasi klorofil-a maksimum (antara 0,4 hingga 1 mg/m3) ditemukan pada kedalaman antara 17 hingga 61 meter, yaitu pada lapisan mixed layer.

Prediction of Significant Wave Height Using Neural Network in the Java Sea (North of Surabaya)

2017 ◽  
Vol 862 ◽  
pp. 72-77
Author(s):  
Wimala L. Dhanistha ◽  
R.A. Atmoko ◽  
P. Juniarko ◽  
Ridho Akbar
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Wave Height ◽  
Mean Square ◽  
The North ◽  
Java Sea ◽  
The City

Indonesia is an archipelago, Surabaya is the second crowded city in Indonesia. So the shipping lane and the city is comparable. Neural network is models inspired by biological neural networks and used to estimate or approximate functions that can depend on a large number of inputs and are generally unknown. Neural network is used to predict the wave height in Java Sea (The North of Surabaya). The Root Mean Square Error average for the next one hour is 0.03 and the Root Mean Square Error average for the next six hours is 0.09. That’s mean the longest the prediction, the biggest Root Mean Square error.

scholarly journals UJI AKURASI PRODUK ESTIMASI CURAH HUJAN SATELIT GPM IMERG DI SURABAYA, INDONESIA

2018 ◽  
Vol 19 (2) ◽  
pp. 83
Author(s):  
Mukhamad Adib Azka ◽  
Prabu Aditya Sugianto ◽  
Andreas Kurniawan Silitonga ◽  
Imma Redha Nugraheni
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Mean Square

Curah hujan merupakan parameter meteorologi yang sangat berpengaruh dalam kehidupan. Saat ini, pengamatan secara in situ sangat kurang representatif untuk digunakan sebagai analisis karena jangkauannya yang sangat sempit sehingga memerlukan instrumen pendukung seperti satelit agar dapat memberikan gambaran yang lebih baik terkait distribusi hujan. Namun, data satelit juga belum tentu sepenuhnya benar karena resolusi dan kondisi dari setiap wilayah berbeda. Penelitian ini bertujuan untuk mendapatkan nilai akurasi, bias, korelasi, root mean square error (RMSE), dan mean absolute error (MAE) data estimasi curah hujan GPM IMERG dengan data curah hujan pengamatan langsung. Penelitian ini dilakukkan di Surabaya dengan menggunakan data estimasi curah hujan GPM IMERG dan data curah hujan pengamatan langsung dari Stasiun Meteorologi Kelas I Juanda Surabaya selama tahun 2017 mewakili musim hujan, musim kemarau, dan periode transisi. Hasil penelitian menunjukkan bahwa data curah hujan produk GPM IMERG memiliki korelasi yang sangat baik untuk memperkirakan akumulasi curah hujan bulanan. Sedangkan, untuk akumulasi harian, memiliki korelasi yang sangat rendah. Sementara itu untuk akumulasi sepuluh harian, data curah hujan produk satelit GPM IMERG memiliki korelasi yang baik terutama di periode musim hujan dan musim kemarau, akan tetapi memiliki korelasi yang rendah selama periode transisi dari musim hujan ke musim kemarau atau sebaliknya. Pada umumnya, produk ini sangat bagus dalam menentukan ada atau tidaknya hujan, tetapi performanya sangat rendah dalam menentukan besarnya intensitas curah hujan.

scholarly journals SARAL-AltiKa Wind and Significant Wave Height for Offshore Wind Energy Applications in the New England Region

2020 ◽  
Vol 13 (1) ◽  
pp. 57
Author(s):  
Xinba Li ◽  
Panagiotis Mitsopoulos ◽  
Yue Yin ◽  
Malaquias Peña
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
New England ◽  
Wave Height ◽  
Significant Wave Height ◽  
Surface Wind ◽  
Offshore Wind ◽  
Significant Wave ◽  
In Situ Observations

The SARAL-AltiKa dataset was evaluated for refined offshore wind energy resources assessment and potential metocean monitoring capability in the Southern New England region. Surface wind speed and Significant Wave Height (Hs) products were assessed with corresponding variables from buoy observations for 2014–2019. To increase the sample size, this study analyzed and applied an approach to collect data around the reference buoys beyond the satellite footprint at the expense of a bias increment. The study corroborated the accuracy of the SARAL-AltiKa measurements for the offshore area of interest and added details for stations closer to the coast compared with past studies. A proportional bias with underestimation of high values of Hs was found in coastal sites. Wind speed estimates on the other hand appear to be less sensitive to the closeness to the coast. The empirical relationship between wind strength and Hs in the buoy observations is reproduced to a large extent by the AltiKa measurements in locations where land contamination is minimal. The histograms of surface wind and Hs are well described by the Weibull distribution and the shape and scale parameters closely resemble those of the histograms of the collocated in situ observations. We use these results to extrapolate the winds to a target domain with no in situ observations for wind energy resource estimation.

scholarly journals Multi-decadal analysis of root-zone soil moisture applying the exponential filter across CONUS

2017 ◽  
Author(s):  
Kenneth J. Tobin ◽  
Roberto Torres ◽  
Wade T. Crow ◽  
Marvin E. Bennett
Keyword(s):  
Soil Moisture ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Root Zone ◽  
Reference Network ◽  
Mean Square ◽  
Moisture Variation ◽  
The U.S

Abstract. This study applied the exponential filter to produce an estimate of root-zone soil moisture (RZSM). Four types of microwave-based, surface satellite soil moisture were used. The core remotely sensed data for this study came from NASA’s long lasting AMSR-E mission. Additionally three other products were obtained from the European Space Agency Climate Change Initiative (CCI). These datasets were blended based on all available satellite observations (CCI-Active; CCI-Passive; CCI-Combined). All of these products were quarter degree and daily. We applied the filter to produce a soil moisture index (SWI) that others have successfully used to estimate RZSM. The only unknown in this approach was the characteristic time of soil moisture variation (T). We examined five different eras (1997–2002; 2002–2005; 2005–2008; 2008–2011; 2011–2014) that represented periods with different satellite data sensors. SWI values were compared with in situ soil moisture data from the International Soil Moisture Network at a depth ranging from 20 to 25 cm. Selected networks included the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) program (25 cm), Soil Climate Analysis Network (SCAN; 20.32 cm), SNOwpack TELemetry (SNOTEL; 20.32 cm), and the U.S. Climate Reference Network (USCRN; 20 cm). We selected in situ stations that had reasonable completeness. These datasets were used to filter out periods with freezing temperatures and rainfall using data from the Parameter elevation Regression on Independent Slopes Model (PRISM). Additionally, we only examined sites where surface and root zone soil moisture had a reasonable high lagged correlation coefficient (r > 0.5). The unknown T value was constrained based on two approaches: optimization of root mean square error (RSME) and calculation based on the NDVI value. Both approaches yielded comparable results; although, as to be expected, the optimization approach generally outperformed NDVI based estimates. Best results were noted at stations that had an absolute bias within 10 %. SWI estimates were more impacted by the in situ network than the surface satellite product used to drive the exponential filter. Average Nash-Sutcliffe coefficients (NS) for ARM ranged from −0.1 to 0.3 and were similar to the results obtained from the USCRN network (0.2 to 0.3). NS values from the SCAN and SNOTEL networks were slightly higher (0.1 to 0.5). These results indicated that this approach had some skill in providing an estimate of RZSM. In terms of root mean square error (RMSE; in volumetric soil moisture) ARM values actually outperformed those from other networks (0.02 to 0.04). SCAN and USCRN RMSE average values ranged from 0.04 to 0.06 and SNOTEL average RMSE values were higher ranging (0.05 to 0.07). These values were close to 0.04, which is the baseline value for accuracy designated for many satellite soil moisture missions.

Validation of SMAP and AMSR2 satellite soil moisture data over the Critical Zone Observatory in central Ganga plains, North India using ground-based observations

2021 ◽  
Author(s):  
Saroj Dash ◽  
Rajiv Sinha
Keyword(s):  
Soil Moisture ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Critical Zone ◽  
North India ◽  
Mean Square ◽  
Critical Zone Observatory ◽  
Level 3

&lt;p&gt;Soil moisture (SM) products derived from the passive satellite missions have been extensively used in various hydrological and environmental processes. However, validation of the satellite derived product is crucial for its reliability in several applications. In this study, we present a comprehensive validation of the descending SM product from Soil Moisture Active Passive (SMAP) Enhanced Level-3 (L3) radiometer (SMAP L3-Version 3) and the Advanced Microwave Scanning Radiometer 2 (AMSR2) Level-3 (Version 1), over the newly established Critical Zone Observatory (CZO) within the Ganga basin, North India. The AMSR2 soil moisture product used here, has been derived using the Land Parameter Retrieval Model (LPRM) algorithm. Four SM derived products from SMAP (L-band) and AMSR2 (C1- and C2- and X-band) are validated against the in-situ observations collected from 21 SM monitoring locations distributed over the CZO within a period from September 2017 to December 2019, for a total of 62 days. Since the remotely sensed SM product has a coarser spatial resolution (here 9 km for SMAP and 10 km for AMSR2), the assessment has been carried out for the temporal variation of the measured values. Four statistical metrics such as bias, root mean square error (RMSE), unbiased root-mean-square error (ubRMSE) and the correlation coefficient (R) have been used here for the evaluation. The SMAP Level-3 products are found to show a satisfactory correlation (R&gt;0.6) compared to the other three SM product. Both the SMAP L3 and the AMSR2 C2 SM shows a negative bias, -0.05 m&lt;sup&gt;3&lt;/sup&gt;/m&lt;sup&gt;3&lt;/sup&gt; and -0.04 m&lt;sup&gt;3&lt;/sup&gt;/m&lt;sup&gt;3 &lt;/sup&gt;respectively whereas these values are found to be 0.04 m&lt;sup&gt;3&lt;/sup&gt;/m&lt;sup&gt;3&lt;/sup&gt; and 0.06 m&lt;sup&gt;3&lt;/sup&gt;/m&lt;sup&gt;3&lt;/sup&gt; for C1 and X bands of AMSR2, respectively. Furthermore, the RMSE between the SMAP L3 and in-situ data is 0.07 m&lt;sup&gt;3&lt;/sup&gt;/m&lt;sup&gt;3&lt;/sup&gt;, which is slightly underperformed when considering the required accuracy of SMAP. This is possibly due to variation in the sampling depth along with the sampling day distribution over CZO. The AMSR2 SM products (C1-, C2- and X-bands) are found to have a higher RMSE than SMAP L3, ranging from 0.08-0.1 m&lt;sup&gt;3&lt;/sup&gt;/m&lt;sup&gt;3&lt;/sup&gt;. In addition, the ubRMSE for all remotely sensed soil moisture product range from 0.06-0.08 m&lt;sup&gt;3&lt;/sup&gt;/m&lt;sup&gt;3&lt;/sup&gt; with the lowest value for the SMAP L3 and AMSR2 C1. The results in this study can be used further for relevant hydrological modelling along with evaluating various downscaling strategies towards improving the coarser resolution satellite soil moisture.&lt;/p&gt;

scholarly journals Triple Collocation-Based Assessment of Satellite Soil Moisture Products with In Situ Measurements in China: Understanding the Error Sources

2020 ◽  
Vol 12 (14) ◽  
pp. 2275 ◽  
Author(s):  
Xiaotao Wu ◽  
Guihua Lu ◽  
Zhiyong Wu ◽  
Hai He ◽  
Tracy Scanlon ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Land Surface ◽  
Surface Model ◽  
Mean Square ◽  
Error Sources

With the increasing utilization of satellite-based soil moisture products, a primary challenge is knowing their accuracy and robustness. This study presents a comprehensive assessment over China of three widely used global satellite soil moisture products, i.e., Soil Moisture Active Passive (SMAP), European Space Agency (ESA) Climate Change Initiative (CCI) Soil Moisture, Soil Moisture and Ocean Salinity (SMOS). In situ soil moisture from 1682 stations and Variable Infiltration Capacity (VIC) model are used to evaluate the performance of SMAP_L3, ESA_CCI_SM_COMBINED, SMOS_CATDS_L3 from 31 March 2015 to 3 June 2018. The Triple Collocation (TC) approach is used to minimize the uncertainty (e.g., scale issue) during the validation process. The TC analysis is conducted using three triplets, i.e., [SMAP-Insitu-VIC], [CCI-Insitu-VIC], [SMOS-Insitu-VIC]. In general, SMAP is the most reliable product, reflecting the main spatiotemporal characteristics of soil moisture, while SMOS has the lowest accuracy. The results demonstrate that the overall root mean square error of SMAP, CCI, SMOS is 0.040, 0.028, 0.107 m3m−3, respectively. The overall temporal correlation coefficient of SMAP, CCI, SMOS is 0.68, 0.65, 0.38, respectively. The overall fractional root mean square error of SMAP, CCI, SMOS is 0.707, 0.750, 0.897, respectively. In irrigated areas, the accuracy of CCI is reduced due to the land surface model (which does not consider irrigation) used for the rescaling of the CCI_COMBINED soil moisture product during the merging process, while SMAP and SMOS preserve the irrigation signal. The quality of SMOS is most strongly impacted by land surface temperature, vegetation, and soil texture, while the quality of CCI is the least affected by these factors. With the increase of Radio Frequency Interference, the accuracy of SMOS decreases dramatically, followed by SMAP and CCI. Higher representativeness error of in situ stations is noted in regions with higher topographic complexity. This study helps to provide a guideline for the application of satellite soil moisture products in scientific research and gives some references (e.g., modify data algorithm according to the main error sources) for improving the data quality.

scholarly journals Estimation of Apple Flowering Frost Loss for Fruit Yield Based on Gridded Meteorological and Remote Sensing Data in Luochuan, Shaanxi Province, China

2021 ◽  
Vol 13 (9) ◽  
pp. 1630
Author(s):  
Yaohui Zhu ◽  
Guijun Yang ◽  
Hao Yang ◽  
Fa Zhao ◽  
Shaoyu Han ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Minimum Temperature ◽  
Price Regulation ◽  
Flowering Period ◽  
Mean Square ◽  
Prevention Measures ◽  
Agricultural Insurance

With the increase in the frequency of extreme weather events in recent years, apple growing areas in the Loess Plateau frequently encounter frost during flowering. Accurately assessing the frost loss in orchards during the flowering period is of great significance for optimizing disaster prevention measures, market apple price regulation, agricultural insurance, and government subsidy programs. The previous research on orchard frost disasters is mainly focused on early risk warning. Therefore, to effectively quantify orchard frost loss, this paper proposes a frost loss assessment model constructed using meteorological and remote sensing information and applies this model to the regional-scale assessment of orchard fruit loss after frost. As an example, this article examines a frost event that occurred during the apple flowering period in Luochuan County, Northwestern China, on 17 April 2020. A multivariable linear regression (MLR) model was constructed based on the orchard planting years, the number of flowering days, and the chill accumulation before frost, as well as the minimum temperature and daily temperature difference on the day of frost. Then, the model simulation accuracy was verified using the leave-one-out cross-validation (LOOCV) method, and the coefficient of determination (R2), the root mean square error (RMSE), and the normalized root mean square error (NRMSE) were 0.69, 18.76%, and 18.76%, respectively. Additionally, the extended Fourier amplitude sensitivity test (EFAST) method was used for the sensitivity analysis of the model parameters. The results show that the simulated apple orchard fruit number reduction ratio is highly sensitive to the minimum temperature on the day of frost, and the chill accumulation and planting years before the frost, with sensitivity values of ≥0.74, ≥0.25, and ≥0.15, respectively. This research can not only assist governments in optimizing traditional orchard frost prevention measures and market price regulation but can also provide a reference for agricultural insurance companies to formulate plans for compensation after frost.

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