A global comparison between station air temperatures and MODIS land surface temperatures reveals the cooling role of forests

Mildrexler, David J.; Zhao, Maosheng; Running, Steven W.

doi:10.1029/2010jg001486

Cited by 242 publications

(232 citation statements)

References 72 publications

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“…The pixel-specific monthly differences between normalized LST and WB result in a forest stress index (FSI) computed at monthly intervals over the growing season. In spring during low water stress conditions, forests dissipate incoming shortwave solar radiation efficiently through partitioning to the latent heat flux (LE) associated with transpiration, and thereby maintain canopy temperatures close to that of the surrounding air temperatures (Mildrexler, Zhao, & Running, 2011a;Nemani & Running, 1997). The FSI value associated with a water surplus and low thermal stress is low.…”

Section: The Fvi and Its Conceptual Foundationmentioning

confidence: 99%

“…LST from the Moderate Resolution Imaging Spectroradiometer (MODIS) measures the canopy foliage temperature in vegetated areas, a unique and useful ecological parameter because critical temperature dependent physiological processes and associated energy fluxes occur in the vegetated canopy. A global analysis of the relationship between remotely sensed annual maximum LST from the Aqua MODIS sensor and the corresponding site-based maximum air temperature for every World Meteorological Organization station on Earth showed that LST is more tightly coupled to the radiative and thermodynamic characteristics of the Earth's surface (Mildrexler, Zhao, & Running, 2011a). LST is more sensitive to changes in vegetation density compared to T air and captures additional information on the biophysical controls on surface temperature, such as surface roughness and transpirational cooling (Mildrexler et al, 2011a).…”

Section: Land Surface Temperature and The Biophysical Link To Plant Cmentioning

confidence: 99%

“…A global analysis of the relationship between remotely sensed annual maximum LST from the Aqua MODIS sensor and the corresponding site-based maximum air temperature for every World Meteorological Organization station on Earth showed that LST is more tightly coupled to the radiative and thermodynamic characteristics of the Earth's surface (Mildrexler, Zhao, & Running, 2011a). LST is more sensitive to changes in vegetation density compared to T air and captures additional information on the biophysical controls on surface temperature, such as surface roughness and transpirational cooling (Mildrexler et al, 2011a). We use LST rather than T air to emphasize the direct thermal response of rising leaf temperatures and plant moisture stress associated with drought and increasing temperatures in the FVI.…”

Section: Land Surface Temperature and The Biophysical Link To Plant Cmentioning

confidence: 99%

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A forest vulnerability index based on drought and high temperatures

Mildrexler

Yang

Cohen

et al. 2016

Remote Sensing of Environment

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Increasing forest stress and tree mortality has been directly linked to combinations of drought and high temperatures. The climatic changes expected during the next decades -large increases in mean temperature, increased heat waves, and significant long-term regional drying in the western USA -will likely increase chronic forest stress and mortality. The aim of this research is to develop and apply a new forest vulnerability index (FVI) associated with drought and high temperatures across the Pacific Northwest region (PNW; Oregon and Washington) of the USA during the MODIS Aqua era (since 2003). Our technique incorporates the alterations to canopy water and energy exchange processes caused by drought and high temperatures with spatially continuous MODIS land surface temperature (LST) and evapotranspiration (ET), and with Parameter-elevation Relationships on Independent Slopes Model (PRISM) precipitation (P) data. With P and ET, we calculate a monthly water balance variable for each individual pixel normalized by forest type group (FTG), and then difference the water balance with the corresponding normalized monthly mean LST to calculate a monthly forest stress index (FSI). We then extract the pixel-specific (800-m resolution) statistically significant temporal trends of the FSI from 2003 to 2012 by month (April to October). The FVI is the slope of the monthly FSI across years, such that there is a FVI for each month. Statistically significant positive slopes indicate interannual increases in stress leading to expected forest vulnerability (positive FVI) for a given month. Positive FVI values were concentrated in the months of August and September, with peak vulnerability occurring at different times for different FTGs. Overall, increased vulnerability rates were the highest in drier FTGs such as Ponderosa Pine, Juniper, and Lodgepole Pine. Western Larch and Fir/Spruce/Mountain Hemlock groups occupy moister sites but also had relatively high proportion of positive FVI values. The Douglas-fir group had the second largest total area of increased vulnerability due to its large areal extent in the study area. Based on an analysis using imagery viewed in Google Earth, we confirm that areas with increased vulnerability are associated with greater amounts of stress and mortality. The FVI is a new way to conceptualize and monitor forest vulnerability based on first-order principles and has the potential to be generalized to other geographical areas.

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Section: The Fvi and Its Conceptual Foundationmentioning

confidence: 99%

Section: Land Surface Temperature and The Biophysical Link To Plant Cmentioning

confidence: 99%

Section: Land Surface Temperature and The Biophysical Link To Plant Cmentioning

confidence: 99%

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A forest vulnerability index based on drought and high temperatures

Mildrexler

Yang

Cohen

et al. 2016

Remote Sensing of Environment

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“…(4) is the EOM-derived respiration (R EOM ). While the study reported that the annual maximum LST (LST max ) from the Aqua/MODIS sensor increases faster than the site-observed maximum air temperature (T max ) as temperature increases (Mildrexler et al, 2011), the LST from the Terra/MODIS sensor was reported to correlate closely with the air temperature during daytime, nighttime and mean of the both (e.g., Sims et al, 2008;Ueyama et al, 2010). In our studied sites, the average of daytime LST and nighttime LST from the Terra/MODIS sensor also had a close relationship with the corresponding air temperature, with r 2 as high as to 0.91 and regression line approximating 1:1 line (Fig.…”

Section: Description Of the Rersmmentioning

confidence: 99%

A remote sensing model to estimate ecosystem respiration in Northern China and the Tibetan Plateau

Gao

et al. 2015

Ecological Modelling

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a b s t r a c tEcosystem respiration (R e ) is rarely quantified from remote sensing data because satellite technique is incapable of observing the key processes associated with soil respiration. In this study, we develop a Remote Sensing Model for R e (ReRSM) by assuming that one part of R e is derived from current photosynthate with the respiratory rate coupling closely with gross primary production (GPP), and the other part of R e is derived from reserved ecosystem organic matter (including plant biomass, plant residues and soil organic matter) with the respiratory rate responding strongly to temperature change. The ReRSM is solely driven by the Enhanced Vegetation Index (EVI), the Land Surface Water Index (LSWI) and the Land Surface Temperature (LST) from MODIS data. Multi-year eddy CO 2 flux data of five vegetation types in Northern China and the Tibetan Plateau (including temperate mixed forest, temperate steppe, alpine shrubland, alpine marsh and alpine meadow-steppe) were used for model parameterization and validation. In most cases, the simulated R e agreed well with the observed R e in terms of seasonal and interannual variation irrespective of vegetation types. The ReRSM could explain approximately 93% of the variation in the observed R e across five vegetation types, with the root mean square error (RMSE) of 0.04 mol C m −2 d −1 and the modeling efficiency (EF) of 0.93. Model comparison showed that the performance of the ReRSM was comparable with that of the RECO in the studied five vegetation types, while the former had much fewer parameters than the latter. The ReRSM parameters showed good linear relationships with the mean annual satellite indices. With these linear functions, the ReRSM could explain approximately 90% of the variation in the observed R e across five vegetation types, with the RMSE of 0.05 mol C m −2 d −1 and the EF of 0.89. These analyses indicated that the ReRSM is a simple and alternative approach in R e estimation and has the potential of estimating spatial R e . However, the performance of ReRSM in other vegetation types or regions still needs a further study.

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“…Water availability, surface type, soil humidity, local atmospheric and surface conditions affect the energy partitioning into latent (LE), sensible (H) and ground heat (G) fluxes (Mildrexler et al, 2011). Surface roughness affects the transferred sensible and latent heat by regulating vertical mixing of air in the surface layer (van Leeuwen et al, 2011), thereby regulating land surface temperature (LST).…”

Section: Introductionmentioning

confidence: 99%

Expansion of oil palm and other cash crops causes an increase of the land surface temperature in the Jambi province in Indonesia

et al. 2017

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Abstract. Indonesia is currently one of the regions with the highest transformation rate of land surface worldwide related to the expansion of oil palm plantations and other cash crops replacing forests on large scales. Land cover changes, which modify land surface properties, have a direct effect on the land surface temperature (LST), a key driver for many ecological functions. Despite the large historic land transformation in Indonesia toward oil palm and other cash crops and governmental plans for future expansion, this is the first study so far to quantify the impacts of land transformation on the LST in Indonesia. We analyze LST from the thermal band of a Landsat image and produce a highresolution surface temperature map (30 m) for the lowlands of the Jambi province in Sumatra (Indonesia), a region which suffered large land transformation towards oil palm and other cash crops over the past decades. The comparison of LST, albedo, normalized differenced vegetation index (NDVI) and evapotranspiration (ET) between seven different land cover types (forest, urban areas, clear-cut land, young and mature oil palm plantations, acacia and rubber plantations) shows that forests have lower surface temperatures than the other land cover types, indicating a local warming effect after forest conversion. LST differences were up to 10.1 ± 2.6 • C (mean ± SD) between forest and clear-cut land. The differences in surface temperatures are explained by an evaporative cooling effect, which offsets the albedo warming effect.Our analysis of the LST trend of the past 16 years based on MODIS data shows that the average daytime surface temperature in the Jambi province increased by 1.05 • C, which followed the trend of observed land cover changes and exceeded the effects of climate warming. This study provides evidence that the expansion of oil palm plantations and other cash crops leads to changes in biophysical variables, warming the land surface and thus enhancing the increase of the air temperature because of climate change.

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A global comparison between station air temperatures and MODIS land surface temperatures reveals the cooling role of forests

Cited by 242 publications

References 72 publications

A forest vulnerability index based on drought and high temperatures

A forest vulnerability index based on drought and high temperatures

A remote sensing model to estimate ecosystem respiration in Northern China and the Tibetan Plateau

Expansion of oil palm and other cash crops causes an increase of the land surface temperature in the Jambi province in Indonesia

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