Validation of Sentinel-3 SLSTR Land Surface Temperature Retrieved by the Operational Product and Comparison with Explicitly Emissivity-Dependent Algorithms

Abstract

Land surface temperature (LST) is an essential climate variable (ECV) for monitoring the Earth climate system. To ensure accurate retrieval from satellite data, it is important to validate satellite derived LSTs and ensure that they are within the required accuracy and precision thresholds. An emissivity-dependent split-window algorithm with viewing angle dependence and two dual-angle algorithms are proposed for the Sentinel-3 SLSTR sensor. Furthermore, these algorithms are validated together with the Sentinel-3 SLSTR operational LST product as well as several emissivity-dependent split-window algorithms with in-situ data from a rice paddy site. The LST retrieval algorithms were validated over three different land covers: flooded soil, bare soil, and full vegetation cover. Ground measurements were performed with a wide band thermal infrared radiometer at a permanent station. The coefficients of the proposed split-window algorithm were estimated using the Cloudless Land Atmosphere Radiosounding (CLAR) database: for the three surface types an overall systematic uncertainty (median) of –0.4 K and a precision (robust standard deviation) 1.1 K were obtained. For the Sentinel-3A SLSTR operational LST product, a systematic uncertainty of 1.3 K and a precision of 1.3 K were obtained. A first evaluation of the Sentinel-3B SLSTR operational LST product was also performed: systematic uncertainty was 1.5 K and precision 1.2 K. The results obtained over the three land covers found at the rice paddy site show that the emissivity-dependent split-window algorithms, i.e., the ones proposed here as well as previously proposed algorithms without angular dependence, provide more accurate and precise LSTs than the current version of the operational SLSTR product.

Determination of global land surface temperature using data from only five selected thermal infrared channels: Method extension and accuracy assessment

“…For example, because the biases of retrieved LST were found to vary with the land cover and climate types, and were different for daytime and nighttime observations, obtaining the coefficients of the SW-like equation separately according to the land cover type, climate type, and day/nighttime may improve the performance of the proposed method. Moreover, the coefficients of the SW-like equation can also be parameterized as a function of the atmospheric water vapor content to increase the ground brightness temperature retrieval accuracy under humid atmospheric conditions (Niclos et al, 2011;Pé rez-Planells et al, 2021). Detailed investigations are planned in one of our succeeding works.…”

“…Specifically, the coefficients in Eq. (1) were obtained as a function of the local viewing zenith angle, rather than constants (Niclos et al, 2011;Pé rez-Planells et al, 2021). Similar to the traditional SW method, these coefficients could be determined by regression analysis of the simulation data (Wan and Dozier, 1996).…”

Adequacy of the GK-2A AMI Land Surface Temperature Product According to Geographic Factors and Compared with Other Satellite Products (MODIS and S-VIRRS)

“…다른 방법으로는 열역학적 이론에 근거한 Stefan-Boltzmann 법칙 을 기반으로 열적외 카메라(thermal infrared camera)를 이용 하여 관측 대상의 표면에서의 특성(적외선 파장 에너지 및 복사열)을 통해 표면 온도를 산출할 수 있다 (Park et al, 2018). 이러한 열적외선을 이용한 온도 관측 방법은 지상에 서 관측은 물론 항공관측, 드론, 인공위성에 활용하여 넓은 영역을 주기적으로 관측할 수 있는 장점이 있다 (Jee et al, 2016) (Jee et al, 2016;Lee and Oh, 2019), MODIS (Shin et al, 2014;Chung et al, 2019), Sentinel 위성 (Yang et al, 2020;Pérez-Planells et al, 2021)으로 산정된 LST에 대한 검⋅보정연구가 주를 이루었다. 그에 비하여 정지궤도 위성(geostationary orbit satellite)은 대부분 기상위성으로 기상에 관한 인자가 주로 검증되었으며 지표면 온도 등 수문학적 주요 인자들에 대한 검증 및 분석에 대한 연구 (Baek and Choi, 2012;Lee et al, 2016;Jeong et al, 2017) 2에서 확인 할 수 있다.…”

Retrieval Consistency between LST CCI Satellite Data Products over Europe and Africa

“…(a) Temperature-based (T-based) validation, in which satellite data are compared with concurrent in-situ measurements at ground stations located in homogeneous sites [13][14][15]; (b) Satellite-satellite intercomparison, in which the satellite product being assessed is compared with a second well-characterized satellite product. Although this method cannot be considered as a validation source itself when validating a new satellite product, it provides useful information regarding spatial differences and consistency between the intercompared sensors [16,17]; (c) Radiance-based (R-based) validation, which validates the satellite derived LST at a well-characterized homogeneous site using reference LSTs estimated from satellite data corrected from atmospheric and emissivity effects with concurrent atmospheric profiles and known emissivities [18][19][20]; (d) Time-series intercomparisons, which are generally used to detect issues in the satellite sensor during its time life [21][22][23].…”