Decreased latency in landsat-derived land surface temperature products: A case for near-real-time evapotranspiration estimation in California

October 7, 2025

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Publicated to: Agricultural Water Management. 283 108316- - 2023-06-01 283(), DOI: 10.1016/j.agwat.2023.108316

Authors:

Knipper, Kyle; Yang, Yun; Anderson, Martha; Bambach, Nicolas; Kustas, William; McElrone, Andrew; Gao, Feng; Alsina, Maria Mar

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Affiliations

ARS, USDA, Crops Pathol & Genet Res, 284 Hutchison Hall, Davis, CA 95616 USA - Author

ARS, USDA, Hydrol & Remote Sensing Lab, 10300 Baltimore Ave, Beltsville, MD 20705 USA - Author

ARS, USDA, Sustainable Agr Water Syst Unit, 239 Hopkins Rd, Davis, CA 95616 USA - Author

E&J Gallo Winery, Winegrowing Res, 600 Yosemite Blvd, Modesto, CA 95354 USA - Author

Mississippi State Univ, Dept Forestry, POB 9680, Mississippi State, MS 39762 USA - Author

Univ Calif Davis, Dept Land Air & Water Resources, Plant & Environm Sci Bldg, Davis, CA 95616 USA - Author

Univ Calif Davis, Dept Viticulture & Enol, 595 Hilgard Lane, Davis, CA 95616 USA - Author

Abstract

Acquiring accurate measurements of crop consumptive water use in the form of evapotranspiration (ET) is increasingly important in California cropping systems as demands for water resources shift under a changing climate. Growers use various methods for estimating ET to guide irrigation management, some of which are based on satellite remote sensing. Although this approach has proven reliable, operational applications remain hindered by latency in satellite product delivery, due in part to computationally expensive atmospheric correction steps. This is particularly true in approaches that utilize thermal infrared (TIR) imagery, which is sensitive to atmospheric corrections. The current study evaluates two approaches to derive a pseudoatmospherically corrected Land Surface Temperature (LST) in near-real-time (NRT) for ingestion into the ALEXI-DisALEXI ET model. Evaluation is done for selected Landsat scenes over California for the year 2022. Both approaches take advantage of the Landsat Collection 2 dataset, including availability of atmospheric correction parameters and atmospherically corrected LST. The first approach is based on the Radiative Transfer Equation (RTE) and atmospheric correction parameters from previous overpass available in the Collection 2 dataset. The second is based on a random forest (RF) regression model, using Landsat Collection 2 atmospheric correction parameters and LST as input for training. Results indicate the RF approach outperforms the RTE approach, with an average mean absolute error of 0.6 K, compared to 2.0 K for the RTE method. The RTE method produces more spatial and temporal variability in LST due to temporal differences in atmospheric transmissivity. When used to estimate ET, we find little difference between NRT LST-based ET estimates and ET derived using the Collection 2 LST product, albeit RF-based ET provides less day-to-day variation. Results suggest promise in using such an approach to derive LST and subsequently ET in NRT, and toward improving daily water management and irrigation efficiency in the vineyard and orchard systems of California.

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Keywords

AlgorithmEnergy-balanceFieldFluxesMapping daily evapotranspirationModelRetrievalScales

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Agricultural Water Management due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2023, it was in position 6/126, thus managing to position itself as a Q1 (Primer Cuartil), in the category Agronomy. Notably, the journal is positioned above the 90th percentile.

From a relative perspective, and based on the normalized impact indicator calculated from World Citations provided by WoS (ESI, Clarivate), it yields a value for the citation normalization relative to the expected citation rate of: 1.2. This indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: ESI Nov 13, 2025)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2026-04-07, the following number of citations:

WoS: 5

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Leadership analysis of institutional authors

This work has been carried out with international collaboration, specifically with researchers from: United States of America.

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: Last Author (ALSINA MARTI, MARIA DEL MAR).

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Awards linked to the item

This research was funded by the California Department of Food and Agriculture (Grant Number: 20-0001-031-SF) with funding also from NASA Applied Sciences-Water Resources Program (Grant Number: NNH17AE39I) and by the U.S. Department of Agriculture, Agricultural Research Service.

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