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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #328574

Research Project: Leveraging Remote Sensing, Land Surface Modeling and Ground-based Observations ... Variables within Heterogeneous Agricultural Landscapes

Location: Hydrology and Remote Sensing Laboratory

Title: Cloud tolerance of remote sensing technologies to measure land surface temperature

Author
item Holmes, T. - SCIENCE SYSTEMS, INC.
item Hain, C. - UNIVERSITY OF MARYLAND
item Anderson, Martha
item Crow, Wade

Submitted to: Hydrology and Earth System Sciences
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/20/2016
Publication Date: 8/1/2016
Citation: Holmes, T., Hain, C., Anderson, M.C., Crow, W.T. 2016. Cloud tolerance of remote sensing technologies to measure land surface temperature. Hydrology and Earth System Sciences. 20:3263-3275. doi:10.5194/hess-20-3263-2016.

Interpretive Summary: Accurate estimates of land surface temperature (LST) are valuable for a range of agricultural applications including: drought monitoring, numerical weather prediction, and irrigation scheduling. Conventional means of estimating LST from space relies on the thermal infrared (TIR) spectral window and is limited to cloud-free scenes. To also provide LST estimates during periods with clouds, a new method was developed to estimate LST based on passive microwave (MW) observations. This paper tests the cloud tolerance of the MW-LST product. In particular, we demonstrate its stable performance with respect to flux tower observation sites (4 in Europe and 9 in the United States), over a range of cloudiness conditions up to heavily overcast skies. The results show that TIR-based LST has slightly better performance than MW-LST for clear sky observations but suffers an increasing negative bias as cloud cover increases. This negative bias is caused by incomplete masking of cloud covered areas within the TIR scene that affects many applications of TIR-LST. In contrast, for MW-LST we find no direct impact of clouds on its accuracy and bias. MW-LST can therefore be used to improve TIR cloud screening. Moreover, the ability to provide LST estimates for cloud-covered surfaces can help expand current clear-sky-only satellite retrieval products to all-weather applications. This, in turn, will aid attempts to better monitor and management water resources within agricultural watersheds.

Technical Abstract: Conventional means to estimate land surface temperature (LST) from space relies on the thermal infrared (TIR) spectral window and is limited to cloud-free scenes. To also provide LST estimates during periods with clouds, a new method was developed to estimate LST based on passive microwave (MW) observations. The MW-LST product is informed by 6 polar orbiting satellites to create a global record with up to 8 observations per day for each 0.25° resolution grid box. For days with sufficient observations, a continuous diurnal temperature cycle (DTC) was fitted. The main characteristics of the DTC were scaled to match that of a geostationary based TIR-LST product. This paper tests the cloud tolerance of the MW-LST product. In particular, we demonstrate its stable performance with respect to flux tower observation sites (4 in Europe and 9 in the United States), over a range of cloudiness conditions up to heavily overcast skies. The results show that TIR-based LST has slightly better performance than MW-LST for clear sky observations but suffers an increasing negative bias as cloud cover increases. This negative bias is caused by incomplete masking of cloud covered areas within the TIR scene that affects many applications of TIR-LST. In contrast, for MW-LST we find no direct impact of clouds on its accuracy and bias. MW-LST can therefore be used to improve TIR cloud screening. Moreover, the ability to provide LST estimates for cloud-covered surfaces can help expand current clear-sky-only satellite retrieval products to all-weather applications.