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Land Surface Temperature Estimation Using Remote Sensing II

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Biogeosciences Remote Sensing".

Deadline for manuscript submissions: closed (5 December 2023) | Viewed by 2636

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Dr. Anand Inamdar

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North Carolina Institute for Climate Studies, North Carolina State University, Raleigh, NC 27695, USA
Interests: earth radiation budgets; remote sensing of land surface parameters; surface energy balance from satellites
Special Issues, Collections and Topics in MDPI journals

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Dear Colleagues,

Land surface temperature (LST) is a basic determinant of the terrestrial thermal behavior which controls the effective radiating temperature of the Earth’s surface. It is an important aspect of climate and biology with a major influence on hydrology, meteorology, and climatology. It has been identified as one of the most important Earth system data records (EDR) by NASA, a legacy national weather service (NWS) requirement, and also an essential climate variable (ECV) required by the global climate observing system (GCOS) of the World Meteorological Organization (WMO). Over the years, applications of LST have expanded beyond its traditional use as a climate change indicator. It is an important indicator of the redistribution of energy at the land–atmosphere interface, plant water stress, monitoring of drought, land cover/land use change, urban heat island effects, heat stress, epidemiological studies, and so on. Additionally, the retrieval methods have expanded beyond the conventional thermal infrared and microwave with the launch of a new generation of hyperspectral sensors such as an infrared atmospheric sounding interferometer (IASI) and cross-track infrared sounder (CrIS).

The previous Special Issue on "Land Surface Temperature Estimation Using Remote Sensing" was a great success. The second volume solicits papers dealing with state-of-the-art techniques in remote sensing of LST, especially filling up gaps in LST measurements due to cloud contamination and extension of LST retrievals under all-weather conditions and applications to drought monitoring and crop health, novel climate change indicators derived from LST, etc.

Dr. Anand Inamdar
Guest Editor

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18 pages, 9716 KiB

Open AccessArticle

Primary Impact Evaluation of Surface Temperature Observations for Microwave Temperature Sounding Data Assimilation over Land

by Yibin Wu, Zhengkun Qin, Juan Li and Xuesong Bai

Remote Sens. 2024, 16(2), 395; https://doi.org/10.3390/rs16020395 - 19 Jan 2024

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Abstract

Observations from the Advanced Microwave Sounding Unit-A (AMSU-A) onboard polar-orbiting satellites are considered to be the most effective satellite data in terms of obviously reducing operational prediction errors. However, there are still significant difficulties in the application of AMSU-A low-level channel data assimilation over land. One of them is the inaccurate surface skin temperature (SKT) of the background on land areas, which leads to significant uncertainty in the accuracy of simulating brightness temperature (BT) in these channels. Therefore, improving the accuracy of SKT in the background field is a direct way to improve the assimilation effect of these low-level channel data over land. In this study, both high-spatio-temporal-resolution automatic weather station (AWS) observation data from China in September 2021 and the AMSU-A observation data from NOAA-15/18/19 and MetOp-A were used. Based on the Advanced Research version of the Weather Research and Forecast model (WRF-ARW) and Gridpoint Statistical Interpolation (GSI) assimilation system, we first analyzed the differences in SKT between AWS observations and model simulations and then attempted to directly replace the simulated SKT with the observation data. On this basis, the differences in BT simulation effects over the land area of Southwest China before and after replacement were meticulously analyzed and compared. In addition, the impacts of SKT replacement in areas with different terrain elevations and in cloudy areas were also evaluated. The results indicate that the SKTs of background fields were generally lower than the surface observations, whereas the diurnal variation in SKT was not well simulated. After replacing the SKT of the background field with station observations, the BT differences between the observation and background (O–B, observation minus background) were remarkably reduced, especially for channels 3–5 and 15 of the AMSU-A. The volume of data passing the GSI quality control significantly increased, and the standard deviation of O–B decreased. Further analysis showed that the improvement effect was better in areas at an elevation above 1600 m. Moreover, introducing SKT observations leads to a significant and stable improvement over BT simulations in cloudy areas over land. Full article

(This article belongs to the Special Issue Land Surface Temperature Estimation Using Remote Sensing II)

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19 pages, 6974 KiB

Open AccessArticle

Estimation of Land Surface Temperature from Chinese ZY1-02E IRS Data

by Xianhui Dou, Kun Li, Qi Zhang, Chenyang Ma, Hongzhao Tang, Xining Liu, Yonggang Qian, Jun Chen, Jinglun Li, Yichao Li, Tao Wang, Feng Wang and Juntao Yang

Remote Sens. 2024, 16(2), 383; https://doi.org/10.3390/rs16020383 - 18 Jan 2024

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Abstract

The role of land surface temperature (LST) is of the utmost importance in multiple academic disciplines, such as climatology, hydrology, ecology, and meteorology. To date, many methods have been proposed to estimate LST from satellite thermal infrared data. The single-channel (SC) algorithm can provide an accurate result in retrieving LST based on prior knowledge of known land surface emissivity (LSE). The SC algorithm is extensively employed for retrieving LST from Landsat series data due to its simplicity and its reliance on just one thermal infrared channel. The Thermal Infrared Sensor (IRS) on the Chinese ZY1-02E satellite is a pivotal instrument employed for gathering thermal infrared (TIR) data of land surfaces. The objective of this research is to evaluate the feasibility of a single-channel approach based on water vapor scaling (WVS) for deriving LST from ZY1-02E IRS data because of its wide spectrum range, i.e., 7~12 μm, which is affected strongly by both atmospheric water vapor and ozone. Three study areas, namely the Baotou, Heihe River Basin, and Yantai Sea sites, were selected as validation sites to evaluate the LST inversion accuracy. This evaluation was also conducted via cross-comparison between the retrieved LST and MODIS LST products. The results revealed that the WVS-based method exhibited an average bias of 0.63 K and an RMSE of 1.62 K compared to the in situ LSTs. The WVS-based method demonstrated reasonable accuracy through cross-validation with the MODIS LST product, with an average bias of 0.77 K and an RMSE of 2.0 K. These findings indicate that the WVS-based method is effective in estimating LST from ZY1-02E IRS data. Full article

(This article belongs to the Special Issue Land Surface Temperature Estimation Using Remote Sensing II)

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21 pages, 4044 KiB

Open AccessArticle

Reconstruction of Land Surface Temperature Derived from FY-4A AGRI Data Based on Two-Point Machine Learning Method

by Yueli Li, Shanyou Zhu, Yumei Luo, Guixin Zhang and Yongming Xu

Remote Sens. 2023, 15(21), 5179; https://doi.org/10.3390/rs15215179 - 30 Oct 2023

Cited by 1 | Viewed by 843

Abstract

Land surface temperature (LST) is one of the most important parameters of the interface between the earth surface and the atmosphere, and it plays a significant role in many research fields, such as agriculture, climate, hydrology, and the environment. However, the thermal infrared band of remote sensors is easily affected by clouds and aerosols, leading to many data gaps in LST products, which restricts the subsequent application of these products. In this paper, Beijing, China, is selected as the study area, and the LST data retrieved from Fengyun 4A (FY-4A) Advanced Geosynchronous Radiation Imager (AGRI) are reconstructed based on the two-point machine learning method. Firstly, the two-point machine learning model is built to reconstruct the theoretical clear-sky LST from simulated and actual images, and the accuracy of the reconstruction results is evaluated compared with the random forest algorithm and the inverse distance weighted method. Secondly, the actual LST under the influence of clouds is reconstructed by using the ERA5 reanalysis LST data as the auxiliary data, and the reconstruction accuracy is then evaluated by the field measurement LST data. The experimental results show that (1) the prediction accuracy of the two-point machine learning method is higher than that of the random forest method in both simulated data and actual data experiments; (2) the R² of reconstructed LST under theoretical clear-sky conditions is 0.6860 and the root mean square error (RMSE) is 2.9 K, while the R² of the reconstructed accuracy of actual LST under clouds is 0.7275 and the RMSE is 2.6 K, i.e., the RMSE decreases by 10.34%; (3) the two-point machine method combined with the auxiliary ERA5 LST data can well reconstruct LST under cloudy conditions and present a reasonable LST distribution. Full article

(This article belongs to the Special Issue Land Surface Temperature Estimation Using Remote Sensing II)

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