Surface temperature and radiation budget of snow-covered complex terrains

The temporal evolution of the snowpack is controlled by the surface temperature, which plays a key role in physical processes such as snowmelt. It shows large spatial variations in mountainous areas, where the illumination conditions are variable and depend on the topography. The surface energy budget is affected by the particular processes that occur in these areas, such as the modulation of the illumination by local slope and the re-illumination of the surface from surrounding slopes. These topography effects are often neglected in models, considering the surface as flat and smooth. Here we aim at estimating the surface temperature and the radiation budget of snow-covered complex terrains, in order to evaluate the role of the different processes that control their spatial variations. For this, a modelling chain is implemented to derive surface temperature from in-situ measurements. The main component is the Rough Surface Ray-Tracing (RSRT) model, based on a photon transport algorithm to quantify the impact of surface roughness in snow-covered areas. It is coupled to a surface scheme in order to estimate the radiation budget. To validate the model, we use in-situ measurements and satellite thermal observations (TIRS sensor aboard Landsat-8) in the Col du Lautaret area, in the French Alps. The satellite images are corrected from atmospheric effects with a single-channel algorithm. The results of the simulations show (i) an agreement between the simulated and observed surface temperature for a diurnal cycle in winter; (ii) the spatial variations of surface temperature are on the order of 5 to 10&#176;C between opposed slope orientations; (iii) the agreement with satellite observations is improved when considering topography effects. It is therefore necessary to account for these effects to estimate the spatial variations of the radiation budget and surface temperature over snow-covered complex terrain.&#160;

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Sensitivity Analysis and Validation of Daytime and Nighttime Land Surface Temperature Retrievals from Landsat 8 Using Different Algorithms and Emissivity Models

Remote Sensing ◽

10.3390/rs12172776 ◽

2020 ◽

Vol 12 (17) ◽

pp. 2776 ◽

Cited By ~ 2

Author(s):

Aliihsan Sekertekin ◽

Stefania Bonafoni

Keyword(s):

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

The United States ◽

In Situ Measurements ◽

Surface Radiation ◽

Radiation Budget ◽

Sensitivity Analyses ◽

Landsat 8

Land Surface Temperature (LST) is a substantial element indicating the relationship between the atmosphere and the land. This study aims to examine the efficiency of different LST algorithms, namely, Single Channel Algorithm (SCA), Mono Window Algorithm (MWA), and Radiative Transfer Equation (RTE), using both daytime and nighttime Landsat 8 data and in-situ measurements. Although many researchers conducted validation studies of daytime LST retrieved from Landsat 8 data, none of them considered nighttime LST retrieval and validation because of the lack of Land Surface Emissivity (LSE) data in the nighttime. Thus, in this paper, we propose using a daytime LSE image, whose acquisition is close to nighttime Thermal Infrared (TIR) data (the difference ranges from one day to four days), as an input in the algorithm for the nighttime LST retrieval. In addition to evaluating the three LST methods, we also investigated the effect of six Normalized Difference Vegetation Index (NDVI)-based LSE models in this study. Furthermore, sensitivity analyses were carried out for both in-situ measurements and LST methods for satellite data. Simultaneous ground-based LST measurements were collected from Atmospheric Radiation Measurement (ARM) and Surface Radiation Budget Network (SURFRAD) stations, located at different rural environments of the United States. Concerning the in-situ sensitivity results, the effect on LST of the uncertainty of the downwelling and upwelling radiance was almost identical in daytime and nighttime. Instead, the uncertainty effect of the broadband emissivity in the nighttime was half of the daytime. Concerning the satellite observations, the sensitivity of the LST methods to LSE proved that the variation of the LST error was smaller than daytime. The accuracy of the LST retrieval methods for daytime Landsat 8 data varied between 2.17 K Root Mean Square Error (RMSE) and 5.47 K RMSE considering all LST methods and LSE models. MWA with two different LSE models presented the best results for the daytime. Concerning the nighttime accuracy of the LST retrieval, the RMSE value ranged from 0.94 K to 3.34 K. SCA showed the best results, but MWA and RTE also provided very high accuracy. Compared to daytime, all LST retrieval methods applied to nighttime data provided highly accurate results with the different LSE models and a lower bias with respect to in-situ measurements.

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Assessing an Atmospheric Correction Algorithm for Time Series of Satellite-Based Water-Leaving Reflectance Using Match-Up Sites in Australian Coastal Waters

Remote Sensing ◽

10.3390/rs13101927 ◽

2021 ◽

Vol 13 (10) ◽

pp. 1927

Author(s):

Fuqin Li ◽

David Jupp ◽

Thomas Schroeder ◽

Stephen Sagar ◽

Joshua Sixsmith ◽

...

Keyword(s):

Coastal Waters ◽

Atmospheric Correction ◽

Correction Method ◽

In Situ Measurements ◽

Landsat 8 ◽

Correction Algorithm ◽

Simple Method ◽

North East ◽

The Impact

An atmospheric correction algorithm for medium-resolution satellite data over general water surfaces (open/coastal, estuarine and inland waters) has been assessed in Australian coastal waters. In situ measurements at four match-up sites were used with 21 Landsat 8 images acquired between 2014 and 2017. Three aerosol sources (AERONET, MODIS ocean aerosol and climatology) were used to test the impact of the selection of aerosol optical depth (AOD) and Ångström coefficient on the retrieved accuracy. The initial results showed that the satellite-derived water-leaving reflectance can have good agreement with the in situ measurements, provided that the sun glint is handled effectively. Although the AERONET aerosol data performed best, the contemporary satellite-derived aerosol information from MODIS or an aerosol climatology could also be as effective, and should be assessed with further in situ measurements. Two sun glint correction strategies were assessed for their ability to remove the glint bias. The most successful one used the average of two shortwave infrared (SWIR) bands to represent sun glint and subtracted it from each band. Using this sun glint correction method, the mean all-band error of the retrieved water-leaving reflectance at the Lucinda Jetty Coastal Observatory (LJCO) in north east Australia was close to 4% and unbiased over 14 acquisitions. A persistent bias in the other strategy was likely due to the sky radiance being non-uniform for the selected images. In regard to future options for an operational sun glint correction, the simple method may be sufficient for clear skies until a physically based method has been established.

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The importance of the turbulent ship wake regime for pollutant fate and transport

10.5194/egusphere-egu21-11011 ◽

2021 ◽

Author(s):

Amanda T. Nylund ◽

Rickard Bensow ◽

Mattias Liefvendahl ◽

Arash Eslamdoost ◽

Anders Tengberg ◽

...

Keyword(s):

Fate And Transport ◽

In Situ Measurements ◽

Ex Situ ◽

Landsat 8 ◽

Turbulent Regime ◽

Near Wake ◽

Ship Wake ◽

Spatiotemporal Scales ◽

The Impact

This interdisciplinary study with implications for fate and transport of pollutants from shipping, investigates the previously overlooked phenomenon of ship induced mixing. When a ship moves through water, the hull and propeller induce a long-lasting turbulent wake. Natural waters are usually stratified, and the stratification influences both the vertical and horizontal extent of the wake. The altered turbulent regime in shipping lanes governs the distribution of discharged pollutants, e.g. PAHs, metals, nutrients and non-indigenous species. The ship related pollutant load follows the trend in volumes of maritime trade, which has almost tripled since the 1980s. In heavily trafficked areas there may be one ship passage every ten minutes; today shipping constitutes a significant source of pollution.To understand the environmental impact of shipping related pollutants, it is essential to know their fate following regional scale transport. However, previous modelling efforts assuming discharge at the surface will not adequately reflect the input values in the regional models. Therefore, it is urgent to bridge the gaps between the spatiotemporal scales from high-resolution numerical modeling of the flow hydrodynamics around the ship, mixing processes and interaction of the ship and wake with stratification, and parameterization in regional oceanographic modeling. Here this knowledge gap is addressed by combining an array of methods; in situ measurements, remote sensing and numerical flow modeling.A bottom-mounted Acoustic Doppler Current Profiler was placed under a ship lane, for in-situ measurements of the vertical and temporal expansion of turbulent wakes. In addition, ex-situ measurements with Landsat 8 Thermal Infrared Sensor were used to estimate the longevity and spatial extent of the thermal signal from ship wakes. The computational modelling was conducted using well resolved 3D RANS modelling for the hull and the near wake (up to five ship lengths aft), a method typically used for the near wake behaviour in analysing the propulsion system. As this is not feasible to use for a far wake analysis, the predicted wake is then used as input for a 2D+time modelling for the sustained wake up to 30min after the ship passage. These results, both from measurements and numerical models, are then combined to analyse how ship-induced turbulence influence at what depth discharged pollutants will be found.This first step to cover the mesoscales of the turbulent ship wake is necessary to assess the impact of ship related pollution. In-situ measurements show median wake depth 13.5m (max 31.5m) and median longevity 10min (max 29min). Satellite data show median thermal wake signal 13.7km (max 62.5km). A detailed simulation model will only be possible to use for the first few 100m of the ship wake, but the coupling to a simplified 2D+time modelling shows a promising potential to bridge our understanding of the impact of the ship wake on the larger scales. Our model results indicate that the natural stratification affects the distribution and retention of pollutants in the wake region. The depth of discharge and the wake turbulence characteristics will in turn affect the fate and transport of pollutants on larger spatiotemporal scales.

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Evaluation of Seven Atmospheric Profiles from Reanalysis and Satellite-Derived Products: Implication for Single-Channel Land Surface Temperature Retrieval

Remote Sensing ◽

10.3390/rs12050791 ◽

2020 ◽

Vol 12 (5) ◽

pp. 791 ◽

Cited By ~ 6

Author(s):

Jingjing Yang ◽

Si-Bo Duan ◽

Xiaoyu Zhang ◽

Penghai Wu ◽

Cheng Huang ◽

...

Keyword(s):

Time Series ◽

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Single Channel ◽

Landsat 8 ◽

Atmospheric Profiles ◽

Atmospheric Profile

Land surface temperature (LST) is vital for studies of hydrology, ecology, climatology, and environmental monitoring. The radiative-transfer-equation-based single-channel algorithm, in conjunction with the atmospheric profile, is regarded as the most suitable one with which to produce long-term time series LST products from Landsat thermal infrared (TIR) data. In this study, the performances of seven atmospheric profiles from different sources (the MODerate-resolution Imaging Spectroradiomete atmospheric profile product (MYD07), the Atmospheric Infrared Sounder atmospheric profile product (AIRS), the European Centre for Medium-range Weather Forecasts (ECMWF), the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA2), the National Centers for Environmental Prediction (NCEP)/Global Forecasting System (GFS), NCEP/Final Operational Global Analysis (FNL), and NCEP/Department of Energy (DOE)) were comprehensively evaluated in the single-channel algorithm for LST retrieval from Landsat 8 TIR data. Results showed that when compared with the radio sounding profile downloaded from the University of Wyoming (UWYO), the worst accuracies of atmospheric parameters were obtained for the MYD07 profile. Furthermore, the root-mean-square error (RMSE) values (approximately 0.5 K) of the retrieved LST when using the ECMWF, MERRA2, NCEP/GFS, and NCEP/FNL profiles were smaller than those but greater than 0.8 K when the MYD07, AIRS, and NCEP/DOE profiles were used. Compared with the in situ LST measurements that were collected at the Hailar, Urad Front Banner, and Wuhai sites, the RMSE values of the LST that were retrieved by using the ECMWF, MERRA2, NCEP/GFS, and NCEP/FNL profiles were approximately 1.0 K. The largest discrepancy between the retrieved and in situ LST was obtained for the NCEP/DOE profile, with an RMSE value of approximately 1.5 K. The results reveal that the ECMWF, MERRA2, NCEP/GFS, and NCEP/FNL profiles have great potential to perform accurate atmospheric correction and generate long-term time series LST products from Landsat TIR data by using a single-channel algorithm.

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Estimation of Land Surface Temperature in an Agricultural Region of Bangladesh from Landsat 8: Intercomparison of Four Algorithms

Sensors ◽

10.3390/s20061778 ◽

2020 ◽

Vol 20 (6) ◽

pp. 1778 ◽

Cited By ~ 3

Author(s):

Md Qutub Uddin Sajib ◽

Tao Wang

Keyword(s):

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Cross Validation ◽

Single Channel ◽

Landsat 8 ◽

Atmospheric Parameters ◽

Moderate Resolution ◽

Split Window Algorithm

The presence of two thermal bands in Landsat 8 brings the opportunity to use either one or both of these bands to retrieve Land Surface Temperature (LST). In order to compare the performances of existing algorithms, we used four methods to retrieve LST from Landsat 8 and made an intercomparison among them. Apart from the direct use of the Radiative Transfer Equation (RTE), Single-Channel Algorithm and two Split-Window Algorithms were used taking an agricultural region in Bangladesh as the study area. The LSTs retrieved in the four methods were validated in two ways: first, an indirect validation against reference LST, which was obtained in the Atmospheric and Topographic CORection (ATCOR) software module; second, cross-validation with Terra MODerate Resolution Imaging Spectroradiometer (MODIS) daily LSTs that were obtained from the Application for Extracting and Exploring Analysis Ready Samples (A ρ ρ EEARS) online tool. Due to the absence of LST-monitoring radiosounding instruments surrounding the study area, in situ LSTs were not available; hence, validation of satellite retrieved LSTs against in situ LSTs was not performed. The atmospheric parameters necessary for the RTE-based method, as well as for other methods, were calculated from the National Centers for Environmental Prediction (NCEP) database using an online atmospheric correction calculator with MODerate resolution atmospheric TRANsmission (MODTRAN) codes. Root-mean-squared-error (RMSE) against reference LST, as well as mean bias error against both reference and MODIS daily LSTs, was used to interpret the relative accuracy of LST results. All four methods were found to result in acceptable LST products, leaving atmospheric water vapor content (w) as the important determinant for the precision result. Considering a set of several Landsat 8 images of different dates, Jiménez-Muñoz et al.’s (2014) Split-Window algorithm was found to result in the lowest mean RMSE of 1.19 ° C . Du et al.’s (2015) Split-Window algorithm resulted in mean RMSE of 1.50 ° C . The RTE-based direct method and the Single-Channel algorithm provided the mean RMSE of 2.47 ° C and 4.11 ° C , respectively. For Du et al.’s algorithm, the w range of 0.0 to 6.3 g cm−2 was considered, whereas for the other three methods, w values as retrieved from the NCEP database were considered for corresponding images. Land surface emissivity was retrieved through the Normalized Difference Vegetation Index (NDVI)-threshold method. This intercomparison study provides an LST retrieval methodology for Landsat 8 that involves four algorithms. It proves that (i) better LST results can be obtained using both thermal bands of Landsat 8; (ii) the NCEP database can be used to determine atmospheric parameters using the online calculator; (iii) MODIS daily LSTs from A ρ ρ EEARS can be used efficiently in cross-validation and intercomparison of Landsat 8 LST algorithms; and (iv) when in situ LST data are not available, the ATCOR-derived LSTs can be used for indirect verification and intercomparison of Landsat 8 LST algorithms.

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Comparison of Historical Water Temperature Measurements with Landsat Analysis Ready Data Provisional Surface Temperature Estimates for the Yukon River in Alaska

Remote Sensing ◽

10.3390/rs13122394 ◽

2021 ◽

Vol 13 (12) ◽

pp. 2394

Author(s):

Carson Baughman ◽

Jeffrey Conaway

Keyword(s):

Standard Deviation ◽

Surface Temperature ◽

Water Temperature ◽

In Situ Measurements ◽

Thematic Mapper ◽

Landsat 8 ◽

Yukon River ◽

Daily Water Temperature ◽

Daily Water

Water temperature is a key element of freshwater ecological systems and a critical element within natural resource monitoring programs. In the absence of in situ measurements, remote sensing platforms can indirectly measure water temperature over time and space. The Earth Resources Observation and Science (EROS) Center has processed archived Landsat imagery into analysis ready data (ARD), including Level-2 Provisional Surface Temperature (pST) estimates derived from the Landsat 4–5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Landsat 8 Thermal Infrared Sensor (TIRS). We compared in situ measurements of water temperature within the Yukon River in Alaska with 52 instances of pST estimates between June 2014 and September 2020. Agreement was good with an RMSE of 2.25 °C and only a slight negative bias in the estimated mean daily water temperature of −0.47 °C. For the 52 dates compared, the average daily water temperature measured by the USGS streamgage was 11.3 °C with a standard deviation of 5.7 °C. The average daily pST estimate was 10.8 °C with a standard deviation of 6.1 °C. At least in the case of large unstratified rivers in Alaska, ARD pST can be used to infer water temperature in the absence of or in tandem with ground-based water temperature monitoring campaigns.

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Land Surface Temperature Retrieval from Landsat 5, 7, and 8 over Rural Areas: Assessment of Different Retrieval Algorithms and Emissivity Models and Toolbox Implementation

Remote Sensing ◽

10.3390/rs12020294 ◽

2020 ◽

Vol 12 (2) ◽

pp. 294 ◽

Cited By ~ 24

Author(s):

Aliihsan Sekertekin ◽

Stefania Bonafoni

Keyword(s):

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Single Channel ◽

Thematic Mapper ◽

Surface Radiation ◽

Radiation Budget ◽

Bias Error ◽

Landsat 8 ◽

Retrieval Algorithms

Land Surface Temperature (LST) is an important parameter for many scientific disciplines since it affects the interaction between the land and the atmosphere. Many LST retrieval algorithms based on remotely sensed images have been introduced so far, where the Land Surface Emissivity (LSE) is one of the main factors affecting the accuracy of the LST estimation. The aim of this study is to evaluate the performance of LST retrieval methods using different LSE models and data of old and current Landsat missions. Mono Window Algorithm (MWA), Radiative Transfer Equation (RTE) method, Single Channel Algorithm (SCA) and Split Window Algorithm (SWA) were assessed as LST retrieval methods processing data of Landsat missions (Landsat 5, 7 and 8) over rural pixels. Considering the LSE models introduced in the literature, different Normalized Difference Vegetation Index (NDVI)-based LSE models were investigated in this study. Specifically, three LSE models were considered for the LST estimation from Landsat 5 Thematic Mapper (TM) and seven Enhanced Thematic Mapper Plus (ETM+), and six for Landsat 8. For the accurate evaluation of the estimated LST, in-situ LST data were obtained from the Surface Radiation Budget Network (SURFRAD) stations. In total, forty-five daytime Landsat images; fifteen images for each Landsat mission, acquired in the Spring-Summer-Autumn period in the mid-latitude region in the Northern Hemisphere were acquired over five SURFRAD rural sites. After determining the best LSE model for the study case, firstly, the LST retrieval accuracy was evaluated considering the sensor type: when using Landsat 5 TM, 7 ETM+, and 8 Operational Land Imager (OLI), and Thermal Infrared Sensor (TIRS) data separately, RTE, MWA, and MWA presented the best results, respectively. Then, the performance was evaluated independently of the sensor types. In this case, all LST methods provided satisfying results, with MWA having a slightly better accuracy with a Root Mean Square Error (RMSE) equals to 2.39 K and a lower bias error. In addition, the spatio-temporal and seasonal analyses indicated that RTE and SCA presented similar results regardless of the season, while MWA differed from RTE and SCA for all seasons, especially in summer. To efficiently perform this work, an ArcGIS toolbox, including all the methods and models analyzed here, was implemented and provided as a user facility for the LST retrieval from Landsat data.

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Atmospheric Correction of Thermal Infrared Landsat Images Using High-Resolution Vertical Profiles Simulated by WRF Model

Environmental Sciences Proceedings ◽

10.3390/ecas2021-10351 ◽

2021 ◽

Vol 8 (1) ◽

pp. 27

Author(s):

Lucas Ribeiro Diaz ◽

Daniel Caetano Santos ◽

Pâmela Suélen Käfer ◽

Nájila Souza da Rocha ◽

Savannah Tâmara Lemos da Costa ◽

...

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Single Channel ◽

Atmospheric Correction ◽

Wrf Model ◽

Thermal Infrared ◽

Atmospheric Effects ◽

Landsat 8 ◽

Vertical Profiles

Atmospheric profiles are key inputs in correcting the atmospheric effects of thermal infrared (TIR) remote sensing data for estimating land surface temperature (LST). This study is a first insight into the feasibility of using the weather research and forecasting (WRF) model to provide high-resolution vertical profiles for LST retrieval. WRF numerical simulations were performed to downscaling NCEP climate forecast system version 2 (CFSv2) reanalysis profiles, using two nested grids with horizontal resolutions of 12 km (G12) and 3 km (G03). We investigated the use of these profiles in the atmospheric correction of TIR data applying the radiative transfer equation (RTE) inversion single-channel approach. The MODerate resolution atmospheric TRANsmission (MODTRAN) model and Landsat 8 TIRS10 (10.6–11.2 µm) band were taken for the method application. The accuracy evaluation was performed using in situ radiosondes in Southern Brazil. We included in the comparative analysis the atmospheric correction parameter calculator (ACPC; NASA) web tool and profiles directly from the NCEP CFSv2 reanalysis. The atmospheric correction parameters from ACPC, followed by CFSv2, had better agreement with the ones calculated using in situ radiosondes. When applied into the RTE to retrieve LST, the best results (RMSE) were, in descending order: CSFv2 (0.55 K), ACPC (0.56 K), WRF G12 (0.79 K), and WRF G03 (0.82 K). The findings suggest that increasing the horizontal resolution of reanalysis profiles does not particularly improve the accuracy of RTE-based LST retrieval. However, the WRF results are yet satisfactory and promising, encouraging further assessments. We endorse the use of the well-known ACPC and also recommend the NCEP CFSv2 reanalysis profiles for TIR remote sensing atmospheric correction and LST single-channel retrieval.

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Modelling surface temperature and radiation budget of snow-covered complex terrain

10.5194/tc-2021-180 ◽

2021 ◽

Author(s):

Alvaro Robledano ◽

Ghislain Picard ◽

Laurent Arnaud ◽

Fanny Larue ◽

Inès Ollivier

Keyword(s):

Surface Temperature ◽

Energy Budget ◽

Complex Terrain ◽

Spatial Variations ◽

Monte Carlo Algorithm ◽

Absorption Enhancement ◽

Atmospheric Effects ◽

Landsat 8 ◽

Topographic Effects ◽

Radiative Processes

Abstract. The surface temperature controls the temporal evolution of the snowpack playing a key role in many physical processes such as metamorphism, snowmelt, etc. It shows large spatial variations in mountainous areas because the surface energy budget is affected by specific radiative processes that occur due to the topography, such as the modulation of the irradiance by the local slope, the shadows and the re-illumination of the surface from surrounding slopes. These topographic effects are often neglected in large scale models considering the surface as flat and smooth. Here we aim at estimating the surface temperature and the energy budget of snow-covered complex terrains, in order to evaluate the relative importance of the different processes that control the spatial variations. For this, a modelling chain is implemented to derive surface temperature in a kilometre-wide area from local radiometric and meteorological measurements at a single station. The main component is the Rough Surface Ray-Tracing (RSRT) model, based on a photon transport Monte Carlo algorithm to quantify the incident and reflected radiation on every facet of a mesh, describing the snow-covered surface. RSRT is coupled to a surface scheme in order to estimate the complete energy budget from which the surface temperature is solved. To assess the modelling chain performance, we use in situ measurements of surface temperature and satellite thermal observations (TIRS sensor aboard Landsat-8) in the Col du Lautaret area, in the French Alps. The satellite images are corrected from atmospheric effects with a single-channel algorithm. The results of the simulation show (i) an agreement between the simulated and observed surface temperature at the station for a diurnal cycle in winter within 0.3 °C; (ii) the spatial variations of surface temperature are on the order of 5 to 10 °C between opposed slope orientations and are well represented by the model; (iii) the importance of the considered topographic effects is up to 1 °C, the most important being the modulation of solar irradiance by the topography, followed by altitudinal variations in air temperature, long-wave thermal emission from surrounding terrain, spectral dependence of snow albedo, and absorption enhancement due to multiple bounces of photons in steep terrain. These results show the necessity of considering the topography to correctly assess the energy budget and the surface temperature of snow-covered complex terrain.

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Comparison of Leaf Area Index, Surface Temperature, and Actual Evapotranspiration Estimated Using the METRIC Model and In Situ Measurements

Sensors ◽

10.3390/s19081857 ◽

2019 ◽

Vol 19 (8) ◽

pp. 1857 ◽

Cited By ~ 2

Author(s):

Arturo Reyes-González ◽

Jeppe Kjaersgaard ◽

Todd Trooien ◽

David G. Reta-Sánchez ◽

Juan I. Sánchez-Duarte ◽

...

Keyword(s):

Remote Sensing ◽

Surface Temperature ◽

Leaf Area Index ◽

Leaf Area ◽

In Situ Measurements ◽

Actual Evapotranspiration ◽

Landsat 8 ◽

Area Index ◽

Metric Model

The verification of remotely sensed estimates of surface variables is essential for any remote sensing study. The objective of this study was to compare leaf area index (LAI), surface temperature (Ts), and actual evapotranspiration (ETa), estimated using the remote sensing-based METRIC model and in situ measurements collected at the satellite overpass time. The study was carried out at a commercial corn field in eastern South Dakota. Six clear-sky images from Landsat 7 and Landsat 8 (Path 29, Row 29) were processed and used for the assessment. LAI and Ts were measured in situ, and ETa was estimated using an atmometer and independent crop coefficients. The results revealed good agreement between the variables measured in situ and estimated by the METRIC model. LAI showed r2 = 0.76, and RMSE = 0.59 m2 m−2, the Ts comparison had an agreement of r2 = 0.87 and RMSE 1.24 °C, and ETa presented r2 = 0.89 and RMSE = 0.71 mm day−1.

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