Multi-sensor approach for high space and time resolution land surface temperature

Desai, Ankur R.; Khan, Asim; Zheng, Ting; Paleri, Sreenath; Butterworth, Brian; Lee, Temple R.; Fisher, Joshua B.; Hulley, Glynn; Kleynhans, Tania; Gerace, Aaron; Townsend, Philip A.; Stoy, Paul C.; Metzger, Stefan

doi:10.1002/essoar.10507065.1

Cited by 4 publications

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“…For example, Desai et al. (2021) fit a simple linear regression between fine resolution ECOSTRESS and coarse resolution GOES to construct diurnal LST at the fine spatial resolution. The caveat of such an approach is that it assumes the regression coefficients are universal across the whole time period, regardless of the time of a day, seasons, and disturbances, which may not be the case in reality.…”

Section: Discussionmentioning

confidence: 99%

“…Interruption of data acquisition due to instrumental issues (e.g., the ECOSTRESS data acquisition gap from 29 September to 5 December in 2018 as a result of anomaly in the primary mass storage unit) may impede the LST diurnal cycle construction as well. Cross instrument calibration and data fusion of LST measurements from different platforms, for example, MODIS, Landsat 8, ECOSTRESS, and GOES could enrich the data availability and potentially alleviate the data gap issue (e.g., Anderson et al., 2021; Desai et al., 2021). In terms of ET, its calculation also depends on the availability of ancillary data sets, such as the Landsat 8 surface reflectance, which has a revisit cycle of 16 days and may lead to data bias during temporal interpolation.…”

Section: Discussionmentioning

confidence: 99%

“…In addition, such approaches may not be feasible when clouds affect a large area or a long period. The second type of approach utilizes LST data sets either derived from reanalysis (Desai et al., 2021; Shiff et al., 2021) or retrieved from microwave bands that can penetrate clouds (Duan et al., 2017; Dowling et al., 2021). However, reanalysis data sets can be biased by the model parameterization uncertainties (from both model structure and parameter deficiencies); microwave based LST can also contain bias compared to thermal infrared based estimates due to the discrepancies in their retrieval algorithms (Ermida et al., 2017).…”

Section: Discussionmentioning

confidence: 99%

“…More importantly, sampling bias of different times of day, different seasons (due to data gaps), and different land cover types needs to be carefully considered if using ML models. Pixelwise linear scaling could be a parsimonious and practical approach (Chang et al., 2022; Desai et al., 2021). For example, Desai et al.…”

Section: Discussionmentioning

confidence: 99%

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Resolve the Clear‐Sky Continuous Diurnal Cycle of High‐Resolution ECOSTRESS Evapotranspiration and Land Surface Temperature

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

Resolve the Clear‐Sky Continuous Diurnal Cycle of High‐Resolution ECOSTRESS Evapotranspiration and Land Surface Temperature

Wen

Fisher

Parazoo

et al. 2022

Water Resources Research

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“…(2022). For comparing to top‐down measurements, such as flux towers to satellites, source areas and (non‐)linearities in downscaling need to be taken into account, whether that is for carbon emissions in a salt marsh (Hill & Vargas, 2022), hotspots of methane in eddy covariance flux tower footprints (Rey‐Sanchez et al., 2022), or land surface temperature over heterogeneous landscapes (Desai, Khan, et al., 2021).…”

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confidence: 99%

Scaling Land‐Atmosphere Interactions: Special or Fundamental?

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Viewed from billions of kilometers away in space, Earth appears as a single "Pale Blue Dot," in the immortalized phrase of Carl Sagan bestowed upon the image taken by the Voyager 1 space probe. Coming closer, though, a sharper image emerges (Figure 1).One finds structure to that dot, shades of green and brown continents, a dark ocean, a bright cryosphere, and a hazy, thin blue atmosphere. Zooming further in, those components break into patterns of mountains and rivers, seas and bays, forests and grasslands, layers, and cloud decks. And getting closer, one finds each component has oscillations and variations of branches and rivulets, canyons and plateaus, currents and coastlines. These objects keep revealing more structure in finer, often self-similar form, like Mandelbrot's fractals, down to eddies and organisms, and further into leaves, cells, enzymes, molecules, and atoms.And then, if you wait seconds, days, decades, or eons, landscape patterns change. Bigger things typically take longer than smaller ones. As a result, the pattern changes-sometimes occurring slowly and subtly, ebbing and flowing in an oscillatory manner, or they can occur quickly and abruptly, morphing into a new state of order.Each element and its dynamics come with variations in space and time that can be encompassed by the concept of scale. Earth systems science is preoccupied with the interactions of these elements, which cannot be understood without a stipulation of the scales of interest (Ge et al., 2019). The most straightforward of these interactions are ones where common processes at all scales can be defined by a single relationship, often a power-law, leading to the concept of scale invariance (Paleri et al., 2022). The most interesting interactions are the ones that break those rules and lead to "upscale" and "downscale" behavior, whereby processes at one scale determine the shape and function of another scale. These are most common at the intersections of biology, hydrology, geology, and meteorology, often within what is termed the "critical zone." This interlocking also harkens to the origins of

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Space‐Scale Resolved Surface Fluxes Across a Heterogeneous, Mid‐Latitude Forested Landscape

et al. 2022

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The Earth's surface is heterogeneous at multiple scales owing to spatial variability in various properties. The atmospheric responses to these heterogeneities through fluxes of energy, water, carbon, and other scalars are scale‐dependent and nonlinear. Although these exchanges can be measured using the eddy covariance technique, widely used tower‐based measurement approaches suffer from spectral losses in lower frequencies when using typical averaging times. However, spatially resolved measurements such as airborne eddy covariance measurements can detect such larger scale (meso‐β, meso‐γ) transport. To evaluate the prevalence and magnitude of these flux contributions, we applied wavelet analysis to airborne flux measurements over a heterogeneous mid‐latitude forested landscape, interspersed with open water bodies and wetlands. The measurements were made during the Chequamegon Heterogeneous Ecosystem Energy‐balance Study Enabled by a High‐density Extensive Array of Detectors intensive field campaign. We ask, how do spatial scales of surface‐atmosphere fluxes vary over heterogeneous surfaces across the day and across seasons? Measured fluxes were separated into smaller‐scale turbulent and larger‐scale mesoscale contributions. We found significant mesoscale contributions to sensible and latent heat fluxes through summer to autumn which would not be resolved in single‐point tower measurements through traditional time‐domain half‐hourly Reynolds decomposition. We report scale‐resolved flux transitions associated with seasonal and diurnal changes of the heterogeneous study domain. This study adds to our understanding of surface‐atmospheric interactions over unstructured heterogeneities and can help inform multi‐scale model‐data integration of weather and climate models at a sub‐grid scale.

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Multi-sensor approach for high space and time resolution land surface temperature

Cited by 4 publications

References 0 publications

Resolve the Clear‐Sky Continuous Diurnal Cycle of High‐Resolution ECOSTRESS Evapotranspiration and Land Surface Temperature

Resolve the Clear‐Sky Continuous Diurnal Cycle of High‐Resolution ECOSTRESS Evapotranspiration and Land Surface Temperature

Scaling Land‐Atmosphere Interactions: Special or Fundamental?

Space‐Scale Resolved Surface Fluxes Across a Heterogeneous, Mid‐Latitude Forested Landscape

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