The effect of pixel heterogeneity for remote sensing based retrievals of evapotranspiration in a semi-arid tree-grass ecosystem

Burchard-Levine, Vicente; Nieto, Héctor; Riaño, David; Migliavacca, Mirco; El‐Madany, Tarek S.; Guzinski, Radoslaw; Carrara, Arnaud; Martín, M. Pilar

doi:10.1016/j.rse.2021.112440

Cited by 34 publications

(18 citation statements)

References 82 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are many scaling issues in investigating surface-atmosphere exchanges, and challenges from the regional to global scale in dealing with pixel heterogeneity [11]. Landscapes with complex surface cover, although they are usually classified under a singular land cover type in low resolutions, may hinder the applicability of remote sensing techniques and cause surface heterogeneity at finer scales, making surface observation difficult [12]. In site heterogeneity, a study [13] showed an increased spatiotemporal variability of surface temperatures at a high resolution due to boundary-layer turbulence, which induces errors in LST and heat flux estimates.…”

Section: Introductionmentioning

confidence: 99%

Comparison on Land-Use/Land-Cover Indices in Explaining Land Surface Temperature Variations in the City of Beijing, China

Khan

Ullah

Chen

2021

Land

View full text Add to dashboard Cite

The urban thermal environment is closely related to landscape patterns and land surface characteristics. Several studies have investigated the relationship between land surface characteristics and land surface temperature (LST). To explore the effects of the urban landscape on urban thermal environments, multiple land-use/land-cover (LULC) remote sensing-based indices have emerged. However, the function of the indices in better explaining LST in the heterogeneous urban landscape has not been fully addressed. This study aims to investigate the effect of remote-sensing-based LULC indices on LST, and to quantify the impact magnitude of green spaces on LST in the city built-up blocks. We used a random forest classifier algorithm to map LULC from the Gaofen 2 (GF-2) satellite and retrieved LST from Landsat-8 ETM data through the split-window algorithm. The pixel values of the LULC types and indices were extracted using the line transect approach. The multicollinearity effect was excluded before regression analysis. The vegetation index was found to have a strong negative relationship with LST, but a positive relationship with built-up indices was found in univariate analysis. The preferred indices, such as normalized difference impervious index (NDISI), dry built-up index (DBI), and bare soil index (BSI), predicted the LST (R2 = 0.41) in the multivariate analysis. The stepwise regression analysis adequately explained the LST (R2 = 0.44) due to the combined effect of the indices. The study results indicated that the LULC indices can be used to explain the LST of LULC types and provides useful information for urban managers and planners for the design of smart green cities.

show abstract

Section: Introductionmentioning

confidence: 99%

Comparison on Land-Use/Land-Cover Indices in Explaining Land Surface Temperature Variations in the City of Beijing, China

Khan

Ullah

Chen

2021

Land

View full text Add to dashboard Cite

show abstract

“…However, land surface heterogeneity is an inevitable problem in calculating ET with coarse resolution remote sensing data [28][29][30], and mainly includes different surface landscapes, surface variables, and surface topography [31,32]. Many studies have shown that land surface heterogeneity can cause errors in latent heat flux (LE) estimations [33].…”

Section: Introductionmentioning

confidence: 99%

Estimating Evapotranspiration over Heterogeneous Surface with Sentinel-2 and Sentinel-3 Data: A Case Study in Heihe River Basin

et al. 2022

View full text Add to dashboard Cite

Evapotranspiration (ET) is an important part of surface–atmosphere interactions, connecting the transfer of matter and energy. Land surface heterogeneity is a natural attribute of the Earth’s surface and is an inevitable problem in calculating ET with coarse resolution remote sensing data, which results in significant error in the ET estimation. This study aims to explore the effect and applicability of the evaporative fraction and area fraction (EFAF) method for correcting 1 km coarse resolution ET. In this study we use the input parameter upscaling (IPUS) algorithm to estimate energy fluxes and the EFAF method to correct ET estimates. Five ground stations in the midstream and downstream regions of the Heihe River Basin (HRB) were used to validate the latent heat flux (LE) calculated by the IPUS algorithm and EFAF method. The evaluation results show that the performance of the EFAF method is superior to that of the IPUS algorithm, with the coefficient of determination (R2) increasing, the root mean square error (RMSE) decreasing, and the mean bias error (MBE) decreasing by 17 W/m2 on average. In general, the EFAF method is suitable for correcting the deviation in LE estimated based on Sentinel data caused by land surface heterogeneity and can be applied to obtain accurate estimates of ET.

show abstract

“…Their scattered (or open) tree overstory superimposing a continuous herbaceous understory have very different structural and phenological characteristics. These features, combined with the complex non‐linear relationship between model parameters and flux output, cause for greater model uncertainty (Burchard‐Levine et al, 2020, 2021). To improve ET simulations through a two‐source perspective, Burchard‐Levine et al (2020) proposed seasonally changing TSEB (TSEB‐2S) to accommodate the contrasting phenology of trees and grasses.…”

Section: Introductionmentioning

confidence: 99%

A remote sensing‐based three‐source energy balance model to improve global estimations of evapotranspiration in semi‐arid tree‐grass ecosystems

Burchard-Levine

Nieto

Riaño

et al. 2021

Global Change Biology

Self Cite

View full text Add to dashboard Cite

It is well documented that energy balance and other remote sensing‐based evapotranspiration (ET) models face greater uncertainty over water‐limited tree‐grass ecosystems (TGEs), representing nearly 1/6th of the global land surface. Their dual vegetation strata, the grass‐dominated understory and tree‐dominated overstory, make for distinct structural, physiological and phenological characteristics, which challenge models compared to more homogeneous and energy‐limited ecosystems. Along with this, the contribution of grasses and trees to total transpiration (T), along with their different climatic drivers, is still largely unknown nor quantified in TGEs. This study proposes a thermal‐based three‐source energy balance (3SEB) model, accommodating an additional vegetation source within the well‐known two‐source energy balance (TSEB) model. The model was implemented at both tower and continental scales using eddy‐covariance (EC) TGE sites, with variable tree canopy cover and rainfall (P) regimes and Meteosat Second Generation (MSG) images. 3SEB robustly simulated latent heat (LE) and related energy fluxes in all sites (Tower: LE RMSD ~60 W/m2; MSG: LE RMSD ~90 W/m2), improving over both TSEB and seasonally changing TSEB (TSEB‐2S) models. In addition, 3SEB inherently partitions water fluxes between the tree, grass and soil sources. The modelled T correlated well with EC T estimates (r > .76), derived from a machine learning ET partitioning method. The T/ET was found positively related to both P and leaf area index, especially compared to the decomposed grass understory T/ET. However, trees and grasses had contrasting relations with respect to monthly P. These results demonstrate the importance in decomposing total ET into the different vegetation sources, as they have distinct climatic drivers, and hence, different relations to seasonal water availability. These promising results improved ET and energy flux estimations over complex TGEs, which may contribute to enhance global drought monitoring and understanding, and their responses to climate change feedbacks.

show abstract

The effect of pixel heterogeneity for remote sensing based retrievals of evapotranspiration in a semi-arid tree-grass ecosystem

Cited by 34 publications

References 82 publications

Comparison on Land-Use/Land-Cover Indices in Explaining Land Surface Temperature Variations in the City of Beijing, China

Comparison on Land-Use/Land-Cover Indices in Explaining Land Surface Temperature Variations in the City of Beijing, China

Estimating Evapotranspiration over Heterogeneous Surface with Sentinel-2 and Sentinel-3 Data: A Case Study in Heihe River Basin

A remote sensing‐based three‐source energy balance model to improve global estimations of evapotranspiration in semi‐arid tree‐grass ecosystems

Contact Info

Product

Resources

About