Evaluation of Moderate Resolution Imaging Spectroradiometer land surface visible and shortwave albedo products at FLUXNET sites

2010 ◽  
Vol 115 (D17) ◽  
Author(s):  
Kaicun Wang ◽  
Shunlin Liang ◽  
Crystal L. Schaaf ◽  
Alan H. Strahler
Keyword(s):  
Land Surface ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer

scholarly journals A Slight Temperature Warming Trend Occurred over Lake Ontario from 2001 to 2018

Land ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1315
Author(s):  
Xiaoying Ouyang ◽  
Dongmei Chen ◽  
Shugui Zhou ◽  
Rui Zhang ◽  
Jinxin Yang ◽  
...  
Keyword(s):  
Land Surface ◽  
Lake Ontario ◽  
Kendall Test ◽  
Warming Trend ◽  
Linear Regression Method ◽  
Ground Measurement ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Mean Square Difference

Satellite-derived lake surface water temperature (LSWT) measurements can be used for monitoring purposes. However, analyses based on the LSWT of Lake Ontario and the surrounding land surface temperature (LST) are scarce in the current literature. First, we provide an evaluation of the commonly used Moderate Resolution Imaging Spectroradiometer (MODIS)-derived LSWT/LST (MOD11A1 and MYD11A1) using in situ measurements near the area of where Lake Ontario, the St. Lawrence River and the Rideau Canal meet. The MODIS datasets agreed well with ground sites measurements from 2015–2017, with an R2 consistently over 0.90. Among the different ground measurement sites, the best results were achieved for Hill Island, with a correlation of 0.99 and centered root mean square difference (RMSD) of 0.73 K for Aqua/MYD nighttime. The validated MODIS datasets were used to analyze the temperature trend over the study area from 2001 to 2018, through a linear regression method with a Mann–Kendall test. A slight warming trend was found, with 95% confidence over the ground sites from 2003 to 2012 for the MYD11A1-Night datasets. The warming trend for the whole region, including both the lake and the land, was about 0.17 K year−1 for the MYD11A1 datasets during 2003–2012, whereas it was about 0.06 K year−1 during 2003–2018. There was also a spatial pattern of warming, but the trend for the lake region was not obviously different from that of the land region. For the monthly trends, the warming trends for September and October from 2013 to 2018 are much more apparent than those of other months.

scholarly journals Implementation of a soil albedo scheme in the CABLEv1.4b land surface model and evaluation against MODIS estimates over Australia

2014 ◽  
Vol 7 (2) ◽  
pp. 1671-1707
Author(s):  
J. Kala ◽  
J. P. Evans ◽  
A. J. Pitman ◽  
C. B. Schaaf ◽  
M. Decker ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Surface Albedo ◽  
Land Surface Model ◽  
Surface Model ◽  
Parameter Uncertainties ◽  
Australian Continent ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. Land surface albedo, the fraction of incoming solar radiation reflected by the land surface, is a key component of the earth system. This study evaluates snow-free surface albedo simulations by the Community Atmosphere Biosphere Land Exchange (CABLEv1.4b) model with the Moderate Resolution Imaging Spectroradiometer (MODIS) albedo. We compare results from two offline simulations over the Australian continent, one with prescribed background snow-free and vegetation-free soil albedo derived from MODIS (the control), and the other with a simple parameterisation based on soil moisture and colour. The control simulation shows that CABLE simulates albedo over Australia reasonably well, with differences with MODIS within an acceptable range. Inclusion of the parameterisation for soil albedo however introduced large errors for the near infra red albedo, especially for desert regions of central Australia. These large errors were not fully explained by errors in soil moisture or parameter uncertainties, but are similar to errors in albedo in other land surface models which use the same soil albedo scheme. Although this new parameterisation has introduced larger errors as compared to prescribing soil albedo, dynamic soil moisture-albedo feedbacks are now enabled in CABLE. Future directions for albedo parameterisations development in CABLE are discussed.

Spatiotemporal variability of land surface temperature in north-western Ethiopia

2021 ◽  
Author(s):  
Getachew Bayable ◽  
Getnet Alemu
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Spatiotemporal Variability ◽  
Environmental Challenges ◽  
Annual Variations ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
North Western

Abstract The aggravating deforestation, industrialization and urbanization are increasingly becoming the principal causes for environmental challenges worldwide. As a result, satellite-based remote sensing helps to explore the environmental challenges spatially and temporally. This investigation analyzed the spatiotemporal discrepancies in Land Surface Temperature (LST) and the link with elevation in Amhara region, Ethiopia. The Moderate Resolution Imaging Spectroradiometer (MODIS) LST data (2001–2020) was used. The pixel-based linear regression model was employed to explore the spatiotemporal discrepancies of LST changes pixel-wise. Furthermore, Sen's slope and Mann-Kendall were used for determining the extent of temporal shifts of the areal average LST and evaluating trends in areal average LST values, respectively. Coefficient of Variation (CV) was calculated to examine spatial and temporal discrepancies in seasonal and annual LST for each pixel. The distribution of average seasonal LST spatially ranged from 43.45–16.62℃, 39.89–14.59℃, 50.39-21.102℃ and 43.164–20.39℃ for autumn (September-November), summer (June-August), spring (March-May) and winter (December-February) seasons, respectively. The seasonal LST CV varied from1.096-10.72%, 0.7–11.06%, 1.29–14.76% and 2.19–10.35% for average autumn, summer, spring and winter seasons, respectively. The seasonal spatial LST trend varied from − 0.7 − 0.16, -0.4-0.224, 0.6 − 0.19 and − 0.6 − 0.32 for average autumn, summer, spring and winter seasons, respectively. Besides, the annual spatial LST slope varied from − 0.58 − 0.17. An insignificantly declining trend in LST shown at 0.036℃ yr− 1, 0.041℃ yr− 1, 0.074℃ yr− 1, 0.005℃ yr− 1 in autumn, summer, spring and winter seasons (P < 0.05), respectively. Moreover, the annual variations of mean LST decreased insignificantly at 0.046℃ yr− 1. Generally, the LST is tremendously variable in space and time and negatively correlated with an elevation.

Estimation of terrestrial latent heat flux over Qilian Mountains by the fusion of five satellite-derived products using Extremely Randomized Trees

2020 ◽  
Author(s):  
Ke Shang ◽  
Yunjun Yao ◽  
Junming Yang ◽  
Xiaowei Chen ◽  
Xiangyi Bei ◽  
...  
Keyword(s):  
Heat Flux ◽  
Latent Heat ◽  
Latent Heat Flux ◽  
Land Surface ◽  
Qilian Mountains ◽  
Fusion Method ◽  
Extremely Randomized Trees ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer

&lt;p&gt;The latent heat flux (LE) governs the associated heat flux from the interactions between the land surface and its atmosphere and plays an important role in the surface water and energy balance. The Qilian Mountains is the largest marginal mountain system in the northeast of the Qinghai-Tibet Plateau. An accurate representation of spatio-temporal patterns of LE over Qilian Mountains is essential in many terrestrial biosphere, hydrosphere, and atmosphere applications. Various satellite-derived LE products have been widely used to estimate terrestrial LE, yet each&amp;#160;individual LE&amp;#160;product exhibits large discrepancies. To reduce uncertainties from individual product and improve terrestrial LE estimation over Qilian Mountains, we produced five satellite-derived LE products using traditional algorithms based on Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI, LAI products and China Meteorological Forcing Dataset (CMFD), and implemented the fusion of these five LE products using Extremely Randomized Trees (Extra-Trees) combining information from ground-based measurements. A validation using ground-based measurements was applied at different plant function types and the validation results demonstrate that the fusion product using Extra-Trees outperformed all the LE products used in the fusion method. Comparing with three other machine learning fusion models such as Gradient Boosting Regression Tree (GBRT), Random Forest (RF) and Gaussian Process Regression (GPR), Extra-Trees exhibits the best performance in terms of both training and validation accuracy. This fusion LE product also outperformed when compared against two state-of-the-art global LE products such as Global Land Surface Satellite (GLASS) and Moderate Resolution Imaging Spectroradiometer (MODIS). The fusion LE product showed improvements in the linear correlation, bias and RMSE at different plant function types. Our results suggest that the fusion method using Extra-Trees could be successfully applied to other region and to enhance the estimation of other hydrometeorological variables.&lt;/p&gt;

Assessing the RST_VOLC algorithm implementation on infrared Sentinel 3 SLSTR data

2020 ◽  
Author(s):  
Alfredo Falconieri ◽  
Francesco Marchese ◽  
Giuseppe Mazzeo ◽  
Nicola Pergola ◽  
Valerio Tramutoli
Keyword(s):  
Land Surface ◽  
European Space Agency ◽  
Space Agency ◽  
Mt Etna ◽  
Multi Temporal ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
New Generation ◽  
Algorithm Implementation

&lt;p&gt;RSTVOLC is a multi-temporal algorithm developed for detecting volcanic hotspots that was successfully used to monitor active volcanoes located in different geographic areas exploiting both polar and geostationary satellite data. The algorithm runs operationally at the Institute of Methodologies for Environmental Analysis (IMAA) to monitor Italian volcanoes in near-real time by means of Advanced Very-High-Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) data. In this study, we assess the possible RSTVOLC implementation on data from the Sea and Land Surface Temperature Radiometer (SLSTR). The latter is a new generation sensor flying onboard the ESA (European Space Agency) Sentinel-3 mission, offering some spectral channels in the infrared bands particularly suited to identify high temperature surfaces such as lava flows. Here, we verify the RSTVOLC implementation on SLSTR data despite the absence of a multiannual time series of satellite records, by using synthetic spectral reference fields. Results achieved by investigating recent eruptions of Mt. Etna and Stromboli (Italy) volcanoes are presented and discussed.&lt;/p&gt;

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