| 07 Jul 2026
The Cooling Efficiency Factor Index (CEFI): A New Satellite-Based Dataset for Research and Operational Monitoring of Land Surface Processes
Abstract. The cooling efficiency of the land surface, i.e., its ability to dissipate absorbed radiation and moderate temperature rise, is reflected in its apparent heat capacity, a property that varies throughout the day in response to the relative intensities of sensible and latent heat fluxes. Under clear-sky conditions, the daytime increase in apparent heat capacity can be reliably estimated using geostationary satellite data and used to derive a new dataset called Cooling Efficiency Factor Index (CEFI). This index quantifies land surface energy dissipation through turbulent and ecohydrological processes from 2005 to near real time at a spatial resolution of 5 km. The spatial distribution of the CEFI dataset is primarily determined by land cover, water availability, surface roughness, and wind speed. Its temporal variability can be exploited to derive proxies for variables and processes that are otherwise difficult to observe, especially in real time, such as evapotranspiration and wind speed anomalies. Accordingly, the CEFI dataset can serve as an indicator of vegetation drought stress, the condition in which plants close their stomata due to soil water limitation and high atmospheric water demand, as well as vegetation productivity. It can also detect flash droughts and support improved estimation of fire risk in natural ecosystems, crop production losses in agricultural areas, and dust formation in desert regions. In addition, the dataset can be used to quantify the cooling efficiency of urban areas. This paper provides access to a publicly available CEFI dataset updated in near real time. Given its broad range of applications, the dataset can be used for both research and operational monitoring, to constrain poorly observed processes in dynamical models, and as an additional predictor or predictand in machine learning applications.
This review concerns the manuscript entitled “The Cooling Efficiency Factor Index (CEFI): A New Satellite-Based Dataset for Research and Operational Monitoring of Land Surface Processes”. The authors produced a high-spatiotemporal-resolution cooling efficiency index with diurnal variation at a spatial resolution of 5 km for the period 2005–2025, based on geostationary satellite data. The authors find clear relationships between their index and the commonly used meteorological drought index, SPI, as well as with variations in vegetation and soil moisture. Several case studies are presented to demonstrate the integrative ability of the cooling efficiency index to represent land-surface properties at the large scale. I think the manuscript has a clear structure, with easily understandable text and figures. However, I have several comments to be addressed before I can recommend the manuscript for publication in Earth System Science Data.
Major points:
CEFI, the cooling efficiency index, is not purely a consequence of water availability; it also reflects the influence of energy availability that controls nonlinearly on the evaporation. I think this may explain why CEFI is decoupled from NDVI or evaporative fraction in some regions, such as the tropics and drylands (Fig. 5d,g). This decoupling may be particularly relevant when ecosystems experience stress that is not yet extreme and downregulates ET, or when land–atmosphere coupling strengths are changing. I suggest that the authors examine more deeply the joint roles of energy and water variability when interpreting the ecological and biophysical meaning of CEFI dynamics. One simple metric combining the energy and water controls on ET: correlation (ET, soil moisture) minus correlation (ET, net radiation), should be similarly valuable as evaporative fraction but more representing the land surface status, for the authors to have a better understanding of the derived cooling efficiency index.
I do not think the authors clearly explain the advantages of CEFI in the introduction or in the results sections, also when providing showcases regarding its applications. The authors may wish to clarify explicitly the advantages of CEFI relative to commonly used indices such as SPI in the introduction: does CEFI provide a more direct measure of land-surface cooling that goes beyond the atmospheric dryness conditions assessed by SPI? Does it provide a more accurate, near-real-time monitoring approach that could be applied in agricultural or urban studies? Does it lessen the need for ET observations from eddy-covariance measurements or ET and evaporative fraction derived from the satellite LST due to their model uncertainties (see ensemble ET models such as compiled in the OpenET project which also monitor ecosystem stress near-real time: https://etdata.org/methods/)?
For the applications presented in Figures 6 and 8, I would also expect to see comparisons between CEFI and other drought indices in relation to maize production and urban green space, respectively. In addition, the annual-scale maize-production case study may not adequately demonstrate the unique value of CEFI. An analysis at a finer spatial and temporal resolution would be more informative.
For the dust-variation case study, can the authors explain how much of the dust variability is influenced by wind and how much is uniquely associated with cooling efficiency? What processes might link cooling efficiency to dust and air pollution? The authors may wish to control for wind speed when examining the relationship between CEFI and dust. It would also be useful to investigate how local cooling effects, as represented by CEFI, and external wind systems (incl. upwind influences and wind direction) jointly regulate dust formation.
Minor points:
In the introduction, it would be helpful to clarify the advances of this study relative to the previous literature Zampieri et al., 2025.
Li et al., 2023 that is cited in the introduction is missing from the reference list.