Method for Audible Representation of Meteorological Data Derived from Remote Sensing Satellites

Climate change has profoundly affected global ecological security. The most vulnerable region on Earth is the high-latitude Arctic. Identifying the changes in vegetation coverage and glaciers in high-latitude Arctic coastal regions is important for understanding the process and impact of global climate change. Ny-Ålesund, the northern-most human settlement, is typical of these coastal regions and was used as a study site. Vegetation and glacier changes over the past 35 years were studied using time series remote sensing data from Landsat 5/7/8 acquired in 1985, 1989, 2000, 2011, 2015 and 2019. Site survey data in 2019, a digital elevation model from 2009 and meteorological data observed from 1985 to 2019 were also used. The vegetation in the Ny-Ålesund coastal zone showed a trend of declining and then increasing, with a breaking point in 2000. However, the area of vegetation with coverage greater than 30% increased over the whole study period, and the wetland moss area also increased, which may be caused by the accelerated melting of glaciers. Human activities were responsible for the decline in vegetation cover around Ny-Ålesund owing to the construction of the town and airport. Even in areas with vegetation coverage of only 13%, there were at least five species of high-latitude plants. The melting rate of five major glaciers in the study area accelerated, and approximately 82% of the reduction in glacier area occurred after 2000. The elevation of the lowest boundary of the five glaciers increased by 50–70 m. The increase in precipitation and the average annual temperature after 2000 explains the changes in both vegetation coverage and glaciers in the study period.

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Evapotranspiration Estimation Using Remote Sensing Technology Based on a SEBAL Model in the Upper Reaches of the Huaihe River Basin

Atmosphere ◽

10.3390/atmos12121599 ◽

2021 ◽

Vol 12 (12) ◽

pp. 1599

Author(s):

Linshan Tan ◽

Kaiyuan Zheng ◽

Qiangqiang Zhao ◽

Yanjuan Wu

Keyword(s):

Remote Sensing ◽

Meteorological Data ◽

Regional Scale ◽

Landsat 8 ◽

Surface Reflectance ◽

Huaihe River ◽

Infiltration Capacity ◽

Mountain Area ◽

Study Region ◽

Sebal Model

Understanding the spatial and temporal variations of evapotranspiration (ET) is vital for water resources planning and management and drought monitoring. The development of a satellite remote sensing technique is described to provide insight into the estimation of ET at a regional scale. In this study, the Surface Energy Balance Algorithm for Land (SEBAL) was used to calculate the actual ET on a daily scale from Landsat-8 data and daily ground-based meteorological data in the upper reaches of Huaihe River on 20 November 2013, 16 April 2015 and 23 March 2018. In order to evaluate the performance of the SEBAL model, the daily SEBAL ET (ETSEBAL) was compared against the daily reference ET (ET0) from four theoretical methods: the Penman-Monteith (P-M), Irmak-Allen (I-A), the Turc, and Jensen-Haise (J-H) method, the ETMOD16 product from the MODerate Resolution Imaging Spectrometer (MOD16) and the ETVIC from Variable Infiltration Capacity Model (VIC). A linear regression equation and statistical indices were used to model performance evaluation. The results showed that the daily ETSEBAL correlated very well with the ET0, ETMOD16, and ETVIC, and bias between the ETSEBAL with them was less than 1.5%. In general, the SEBAL model could provide good estimations in daily ET over the study region. In addition, the spatial-temporal distribution of ETSEBAL was explored. The variation of ETSEBAL was significant in seasons with high values during the growth period of vegetation in March and April and low values in November. Spatially, the daily ETSEBAL values in the mountain area were much higher than those in the plain areas over the study region. The variability of ETSEBAL in this study area was positively correlated with elevation and negatively correlated with surface reflectance, which implies that elevation and surface reflectance are the important factors for predicting ET in this study area.

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Using machine learning and trapezoidal model to derive All-weather ET from Remote sensing Images and Meteorological Data

10.1109/agro-geoinformatics50104.2021.9530341 ◽

2021 ◽

Author(s):

Pengyu Hao ◽

Liping Di ◽

Eugene Yu ◽

Liying Guo ◽

Ziheng Sun ◽

...

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Meteorological Data ◽

Remote Sensing Images

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A Remote Sensing and Atmospheric Correction Method for Assessing Multispectral Radiative Transfer through Realistic Atmospheres and Clouds

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-18-0078.1 ◽

2019 ◽

Vol 36 (2) ◽

pp. 203-216 ◽

Cited By ~ 3

Author(s):

Jarred L. Burley ◽

Steven T. Fiorino ◽

Brannon J. Elmore ◽

Jaclyn E. Schmidt

Keyword(s):

Remote Sensing ◽

Radiative Transfer ◽

Performance Metrics ◽

Meteorological Data ◽

Weather Prediction ◽

Atmospheric Correction ◽

Atmospheric Conditions ◽

Diffuse Transmission ◽

Cloud Data ◽

Atmospheric Propagation

Abstract The ability to quickly and accurately model actual atmospheric conditions is essential to remote sensing analyses. Clouds present a particularly complex challenge, as they cover up to 70% of Earth’s surface, and their highly variable and diverse nature necessitates physics-based modeling. The Laser Environmental Effects Definition and Reference (LEEDR) is a verified and validated atmospheric propagation and radiative transfer code that creates physically realizable vertical and horizontal profiles of meteorological data. Coupled with numerical weather prediction (NWP) model output, LEEDR enables analysis, nowcasts, and forecasts for radiative effects expected for real-world scenarios. A recent development is the inclusion of the U.S. Air Force’s World-Wide Merged Cloud Analysis (WWMCA) cloud data in a new tool set that enables radiance calculations through clouds from UV to radio frequency (RF) wavelengths. This effort details the creation of near-real-time profiles of atmospheric and cloud conditions and the resulting radiative transfer analysis for virtually any wavelength(s) of interest. Calendar year 2015 data are analyzed to establish climatological limits for diffuse transmission in the 300–1300-nm band, and the impacts of various geometry, cloud microphysical, and atmospheric conditions are examined. The results show that 80% of diffuse band transmissions are estimated to fall between 0.248 and 0.889 under the assumptions of cloud homogeneity and maximum overlap and are sufficient for establishing diffuse transmission percentiles. The demonstrated capability provides an efficient way to extend optical wavelength cloud parameters across the spectrum for physics-based multiple-scattering effects modeling through cloudy and clear atmospheres, providing an improvement to atmospheric correction for remote sensing and cloud effects on system performance metrics.

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Spatial variability of mean daily estimates of actual evaporation from remotely sensed imagery and surface reference data

Hydrology and Earth System Sciences ◽

10.5194/hess-23-4891-2019 ◽

2019 ◽

Vol 23 (12) ◽

pp. 4891-4907 ◽

Cited By ~ 1

Author(s):

Robert N. Armstrong ◽

John W. Pomeroy ◽

Lawrence W. Martz

Keyword(s):

Remote Sensing ◽

Spatial Variability ◽

Land Surface ◽

Management Practices ◽

Reference Data ◽

Meteorological Data ◽

Net Radiation ◽

Study Region ◽

Evaporation Model ◽

Actual Evaporation

Abstract. Land surface evaporation has considerable spatial variability that is not captured by point-scale estimates calculated from meteorological data alone. Knowing how evaporation varies spatially remains an important issue for improving parameterisations of land surface schemes and hydrological models and various land management practices. Satellite-based and aerial remote sensing has been crucial for capturing moderate- to larger-scale surface variables to indirectly estimate evaporative fluxes. However, more recent advances for field research via unmanned aerial vehicles (UAVs) now allow for the acquisition of more highly detailed surface data. Integrating models that can estimate “actual” evaporation from higher-resolution imagery and surface reference data would be valuable to better examine potential impacts of local variations in evaporation on upscaled estimates. This study introduces a novel approach for computing a normalised ratiometric index from surface variables that can be used to obtain more-realistic distributed estimates of actual evaporation. For demonstration purposes the Granger–Gray evaporation model (Granger and Gray, 1989) was applied at a rolling prairie agricultural site in central Saskatchewan, Canada. Visible and thermal images and meteorological reference data required to parameterise the model were obtained at midday. Ratiometric indexes were computed for the key surface variables albedo and net radiation at midday. This allowed point observations of albedo and mean daily net radiation to be scaled across high-resolution images over a large study region. Albedo and net radiation estimates were within 5 %–10 % of measured values. A daily evaporation estimate for a grassed surface was 0.5 mm (23 %) larger than eddy covariance measurements. Spatial variations in key factors driving evaporation and their impacts on upscaled evaporation estimates are also discussed. The methods applied have two key advantages for estimating evaporation over previous remote-sensing approaches: (1) detailed daily estimates of actual evaporation can be directly obtained using a physically based evaporation model, and (2) analysis of more-detailed and more-reliable evaporation estimates may lead to improved methods for upscaling evaporative fluxes to larger areas.

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Harmonizing Multi-Source Remote Sensing Images for Summer Corn Growth Monitoring

Remote Sensing ◽

10.3390/rs11111266 ◽

2019 ◽

Vol 11 (11) ◽

pp. 1266 ◽

Cited By ~ 3

Author(s):

Mingzheng Zhang ◽

Dehai Zhu ◽

Wei Su ◽

Jianxi Huang ◽

Xiaodong Zhang ◽

...

Keyword(s):

Remote Sensing ◽

Time Series ◽

Kalman Filter ◽

Spatial Resolution ◽

Temporal Resolution ◽

Meteorological Data ◽

High Temporal Resolution ◽

Growth Monitoring ◽

Landsat 8 ◽

Data Set

Continuous monitoring of crop growth status using time-series remote sensing image is essential for crop management and yield prediction. The growing season of summer corn in the North China Plain with the period of rain and hot, which makes the acquisition of cloud-free satellite imagery very difficult. Therefore, we focused on developing image datasets with both a high temporal resolution and medium spatial resolution by harmonizing the time-series of MOD09GA Normalized Difference Vegetation Index (NDVI) images and 30-m-resolution GF-1 WFV images using the improved Kalman filter model. The harmonized images, GF-1 images, and Landsat 8 images were then combined and used to monitor the summer corn growth from 5th June to 6th October, 2014, in three counties of Hebei Province, China, in conjunction with meteorological data and MODIS Evapotranspiration Data Set. The prediction residuals ( Δ P R K ) in NDVI between the GF-1 observations and the harmonized images was in the range of −0.2 to 0.2 with Gauss distribution. Moreover, the obtained phenological curves manifested distinctive growth features for summer corn at field scales. Changes in NDVI over time were more effectively evaluated and represented corn growth trends, when considered in conjunction with meteorological data and MODIS Evapotranspiration Data Set. We observed that the NDVI of summer corn showed a process of first decreasing and then rising in the early growing stage and discuss how the temperature and moisture of the environment changed with the growth stage. The study demonstrated that the synthesized dataset constructed using this methodology was highly accurate, with high temporal resolution and medium spatial resolution and it was possible to harmonize multi-source remote sensing imagery by the improved Kalman filter for long-term field monitoring.

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Estimation and Validation of Land Surface Evaporation Using Remote Sensing and Meteorological Data in North China

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ◽

10.1109/jstars.2010.2040806 ◽

2010 ◽

Vol 3 (3) ◽

pp. 337-344 ◽

Cited By ~ 29

Author(s):

Jun Xiong ◽

Bingfang Wu ◽

Nana Yan ◽

Yuan Zeng ◽

Shufu Liu

Keyword(s):

Remote Sensing ◽

Land Surface ◽

North China ◽

Meteorological Data ◽

Surface Evaporation

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Prediction of Wheat Grain Protein by Coupling Multisource Remote Sensing Imagery and ECMWF Data

Remote Sensing ◽

10.3390/rs12081349 ◽

2020 ◽

Vol 12 (8) ◽

pp. 1349 ◽

Cited By ~ 3

Author(s):

Xiaobin Xu ◽

Cong Teng ◽

Yu Zhao ◽

Ying Du ◽

Chunqi Zhao ◽

...

Keyword(s):

Remote Sensing ◽

Linear Model ◽

Root Mean Square ◽

Vegetation Index ◽

Meteorological Data ◽

Hierarchical Linear Model ◽

Grain Protein ◽

Landsat 8 ◽

Mean Square ◽

Mean Square Errors

Industrialization production with high quality and effect on winter is an important measure for accelerating the shift from increasing agricultural production to improving quality in terms of grain protein content (GPC). Remote sensing technology achieved the GPC prediction. However, large deviations in interannual expansion and regional transfer still exist. The present experiment was carried out in wheat producing areas of Beijing (BJ), Renqiu (RQ), Quzhou, and Jinzhou in Hebei Province. First, the spectral consistency of Landsat 8 Operational Land Imager (LS8) and RapidEye (RE) was compared with Sentinel-2 (S2) satellites at the same ground point in the same period. The GPC prediction model was constructed by coupling the vegetation index with the meteorological data obtained by the European Center for Medium-range Weather Forecasts using hierarchical linear model (HLM) method. The prediction and spatial expansion of regional GPC were validated. Results were as follows: (1) Spectral information calculated from S2 imagery were highly consistent with LS8 (R2 = 1.00) and RE (R2 = 0.99) imagery, which could be jointly used for GPC modeling. (2) The predicted GPC by using the HLM method (R2 = 0.524) demonstrated higher accuracy than the empirical linear model (R2 = 0.286) and showed higher improvements across inter-annual and regional scales. (3) The GPC prediction results of the verification samples in RQ, BJ, Xiaotangshan (XTS) in 2018, and XTS in 2019 were ideal with root mean square errors of 0.61%, 1.13%, 0.91%, and 0.38%, and relative root mean square error of 4.11%, 6.83%, 6.41%, and 2.58%, respectively. This study has great application potential for regional and inter-annual quality prediction.

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