14 research outputs found
Approche experimentale des problemes lies a l'emissivite et a la temperature de surface, ainsi qu'a leur variabilite sptatio-temporelle, dans le cadre de la teledetection spatiale dans l'infrarouge thermique
No full textSIGLEINIST T 74359 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc
Surface soil moisture estimation from SEVIRI data onboard MSG satellite
No full textThe Institute of Electrical and Electronics Engineers; Geoscience and Remote Sensing Society<span class="MedBlackText">Land surface temperature (LST) and vegetation index or Fraction of Vegetation Cover (FVC) triangle space in regional scale has been demonstrated to be an effective way to monitor surface soil moisture condition. In this study, LST mid-morning rising rate from geostationary satellite data is applied instead of LST in the triangle space. A new soil water dryness index (Temperature Rate Vegetation Dryness Index, TRVDI) is presented from the LST mid-morning rising rate - FVC space to reflect surface soil moisture condition. Regional TRVDI is calculated over a region of the Iberian Peninsula using MSG SEVIRI data recorded on July 2006. The validation was performed with AMSR-E soil moisture product and Anticipant Precipitation Index (API) for two meteorological stations in the area. Results indicate that TRVDI reflects the variation in soil moisture to some extent and is suitable to monitor regional surface soil moisture and temporal variation. </span
Prediction of Root-Zone Soil Moisture and Evapotranspiration in Cropland Using HYDRUS-1D Model with Different Soil Hydrodynamic Parameter Schemes
No full textThis study provides a comprehensive assessment of the HYDRUS-1D model for predicting root-zone soil moisture (RZSM) and evapotranspiration (ET). It evaluates different soil hydrodynamic parameter (SHP) schemes—soil type-based, soil texture-based, and inverse solution—under varying cropping systems (Zea mays–Glycine max rotation and continuous Zea mays) and moisture conditions (irrigated and rainfed), aiming to understand water transport across different cultivation patterns. Using field measurements from 2002, the SHPs were optimized for each scheme and applied to predict RZSM and ET from 2003 to 2007. The inverse solution scheme produced nearly unbiased RZSM predictions with a root mean square error (RMSE) of 0.011 m3m⁻3, compared to RMSEs of 0.036 m3m⁻3 and 0.042 m3m⁻3 for the soil type-based and soil texture-based schemes, respectively. For ET predictions, comparable accuracy was achieved, with RMSEs of 66.4 Wm⁻2, 69.5 Wm⁻2, and 68.2 Wm⁻2 across the three schemes. RZSM prediction accuracy declined over time in the continuous Zea mays field for all schemes, while systematic errors predominated in the Zea mays–Glycine max rotation field. ET accuracy trends mirrored RZSM in irrigated systems but diverged in rainfed croplands due to the decoupling of ET and RZSM under arid conditions
Algorithm Development for Land Surface Temperature Retrieval: Application to Chinese Gaofen-5 Data
No full textLand surface temperature (LST) is a key variable in the study of the energy exchange between the land surface and the atmosphere. Among the different methods proposed to estimate LST, the quadratic split-window (SW) method has achieved considerable popularity. This method works well when the emissivities are high in both channels. Unfortunately, it performs poorly for low land surface emissivities (LSEs). To solve this problem, assuming that the LSE is known, the constant in the quadratic SW method was calculated by maintaining the other coefficients the same as those obtained for the black body condition. This procedure permits transfer of the emissivity effect to the constant. The result demonstrated that the constant was influenced by both atmospheric water vapour content (W) and atmospheric temperature (T0) in the bottom layer. To parameterize the constant, an exponential approximation between W and T0 was used. A LST retrieval algorithm was proposed. The error for the proposed algorithm was RMSE = 0.70 K. Sensitivity analysis results showed that under the consideration of NEΔT = 0.2 K, 20% uncertainty in W and 1% uncertainties in the channel mean emissivity and the channel emissivity difference, the RMSE was 1.29 K. Compared with AST 08 product, the proposed algorithm underestimated LST by about 0.8 K for both study areas when ASTER L1B data was used as a proxy of Gaofen-5 (GF-5) satellite data. The GF-5 satellite is scheduled to be launched in 2017
A Multi-Channel Method for Retrieving Surface Temperature for High-Emissivity Surfaces from Hyperspectral Thermal Infrared Images
No full textThe surface temperature (ST) of high-emissivity surfaces is an important parameter in climate systems. The empirical methods for retrieving ST for high-emissivity surfaces from hyperspectral thermal infrared (HypTIR) images require spectrally continuous channel data. This paper aims to develop a multi-channel method for retrieving ST for high-emissivity surfaces from space-borne HypTIR data. With an assumption of land surface emissivity (LSE) of 1, ST is proposed as a function of 10 brightness temperatures measured at the top of atmosphere by a radiometer having a spectral interval of 800–1200 cm−1 and a spectral sampling frequency of 0.25 cm−1. We have analyzed the sensitivity of the proposed method to spectral sampling frequency and instrumental noise, and evaluated the proposed method using satellite data. The results indicated that the parameters in the developed function are dependent on the spectral sampling frequency and that ST of high-emissivity surfaces can be accurately retrieved by the proposed method if appropriate values are used for each spectral sampling frequency. The results also showed that the accuracy of the retrieved ST is of the order of magnitude of the instrumental noise and that the root mean square error (RMSE) of the ST retrieved from satellite data is 0.43 K in comparison with the AVHRR SST product
Deep Mixture Model-Based Land Surface Temperature Retrieval for Hyperspectral Thermal IASI Sensor
No full textInternational audienceA deep mixture model was developed to retrieve land surface temperatures (LSTs) from infrared atmospheric sounding interferometer (IASI) observations. The IASI brightness temperature (Tb) data and the Advanced Very High Resolution Radiometer onboard MetOp (AVHRR/MetOp) LST data were randomly divided into training and test datasets, and a deep mixture model was constructed to simulate radiation transmission in order to invert the LST. The constructed model could evaluate dataset characteristics that included global features, local features, and time-domain predictions, covering most of the features of the satellite dataset. For the test datasets, the root mean square error (RMSE) indicated that the LST in Algeria and South Africa could be retrieved with an error of less than 2 K and 2.5 K, respectively. Compared with the AVHRR/MetOp LST product in March and December 2019 for Algeria and South Africa, the LST could be retrieved with the maximum RMSE of 2.5 K. The LST retrievals at nighttime had an RMSE of less than 2.0 K, which was superior to those retrieved during daytime for Algeria. This deep mixture model can be applied to time-series temperature prediction. INDEX TERMS Hyperspectral thermal infrared, land surface temperature retrieval, IASI, deep learning
A simple parameterization for sensible and latent heat fluxes during unstable daytime
No full texthis study firstly develops <span class="hit">a simple parameterization for sensible</span> and <span class="hit">latent</span> <span class="hit">heat</span> <span class="hit">fluxes</span> under unstable conditions. The <span class="hit">parameterization</span> consists of some unknown variables considered as constants during the daytime and some known functions related to surface temperature and air temperature. The <span class="hit">sensible</span> <span class="hit">heat</span> <span class="hit">flux</span> (H) is expressed as a quadratic function of the difference between surface temperature and air temperature, and the <span class="hit">latent</span> <span class="hit">heat</span> <span class="hit">flux</span> (LE) is <span class="hit">parameterized</span> as a function of the saturated water vapor pressure at surface temperature as well as the difference between surface temperature and air temperature. The accuracy of the <span class="hit">parameterization</span> <span class="hit">for</span> H and LE is evaluated by the measurements from Yucheng station, north of China. <span class="hit">For</span> H, the coefficient of determination (R<sup><font size="2">2</font></sup>) is 0.925, the root mean square error (RMSE) is 27.8W/m <sup><font size="2">2</font></sup>, and the bias (BIAS) is -14.2W/m<sup><font size="2">2</font></sup>, and <span class="hit">for</span> LE, the R<sup><font size="2">2</font></sup>, RMSE, and BIAS are 0.946, 24.7W/m<sup><font size="2">2</font></sup>, and 0.5W/m <sup><font size="2">2</font></sup>, respectively. With the assumption that surface available energy is known, the minimization technique is used to inverse <span class="hit">heat</span> <span class="hit">fluxes</span>. The H is underestimated by 30.7W/m<sup><font size="2">2</font></sup>, and the corresponding LE is overestimated by 30.4W/m<sup><font size="2">2</font></sup>. The RMSEs of H and LE are 54.1 and 56.6W/m<sup><font size="2">2</font></sup>, and R<sup><font size="2">2</font></sup> are 0.775 and 0.806, respectively. The method can estimate H and LE at any time during unstable daytime without the need to calculate the resistance. The remotely sensed data from the geostationary meteorological satellite can be utilized adequately by the method in the future
Estimation of evaporative fraction from temporal changes of temperature and net radiation
No full textTo resolve uncertainties in evapotranspiration (ET) estimates caused by the retrieval error of remotely sensed data, this study develops an evaporative fraction (EF) parameterization based on surface energy balance and the assumption of generally invariant EF during the daytime. EF is deduced as a function of temporal change of surface temperatures, temporal change of air temperature, temporal change of net radiation, and fractional vegetation cover. The EF parameterization is evaluated by the simulated data from a soil-vegetation-atmosphere transfer model with a coefficient of determination (R2) of 0.786 and a root mean square error (RMSE) of 0.117. When the EF parameterization is used to estimate the daily ET of the Yucheng station in North China by in situ measurements, the estimated results are acceptable with an RMSE of 0.7 mm (relative RMSE of 25%) and an R2 of 0.837. 2013 IEEE
Estimation of evaporative fraction from temporal changes of temperature and net radiation
No full textDaily Evaporative Fraction Parameterization Scheme Driven by Day–Night Differences in Surface Parameters: Improvement and Validation
No full textIn a previous study, a daily evaporative fraction (EF) parameterization scheme was derived based on day–night differences in surface temperature, air temperature, and net radiation. Considering the advantage that incoming solar radiation can be readily retrieved from remotely sensed data in comparison with surface net radiation, this study simplified the daily EF parameterization scheme using incoming solar radiation as an input. Daily EF estimates from the simplified scheme were nearly equivalent to the results from the original scheme. In situ measurements from six Ameriflux sites with different land covers were used to validate the new simplified EF parameterization scheme. Results showed that daily EF estimates for clear skies were consistent with the in situ EF corrected by the residual energy method, showing a coefficient of determination of 0.586 and a root mean square error of 0.152. Similar results were also obtained for partly clear sky conditions. The non-closure of the measured energy and heat fluxes and the uncertainty in determining fractional vegetation cover were likely to cause discrepancies in estimated daily EF and measured counterparts. The daily EF estimates of different land covers indicate that the constant coefficients in the simplified EF parameterization scheme are not strongly site-specific
