Remote sensing of forest gas exchange: Considerations derived from a tomographic perspective

Damm, Alexander; Paul‐Limoges, Eugénie; Kükenbrink, Daniel; Bachofen, Christoph; Morsdorf, Felix

doi:10.1111/gcb.15007

Cited by 19 publications

(10 citation statements)

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“…Additionally, a larger leaf area index will generally lead to a lower stability of under the same soil moisture (Farah et al, 2004). In general, due to the instability of , the above assumption will cause a negative bias of 10 %-20 % in the estimation of daily ET (Delogu et al, 2012;Ryu et al, 2012;Van Niel et al, 2012). This can explain why the validation in this paper showed that the ET estimated using SEBAL was underestimated.…”

Section: Temporal Scaling-up Methodsmentioning

confidence: 78%

Long time series of daily evapotranspiration in China based on the SEBAL model and multisource images and validation

Cheng

Jiao

et al. 2021

Earth Syst. Sci. Data

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Abstract. Satellite observations of evapotranspiration (ET) have been widely used for water resources management in China. An accurate ET product with a high spatiotemporal resolution is required for research on drought stress and water resources management. However, such a product is currently lacking. Moreover, the performances of different ET estimation algorithms for China have not been clearly studied, especially under different environmental conditions. Therefore, the aims of this study were as follows: (1) to use multisource images to generate a long-time-series (2001–2018) daily ET product with a spatial resolution of 1 km × 1 km based on the Surface Energy Balance Algorithm for Land (SEBAL); (2) to comprehensively evaluate the performance of the SEBAL ET in China using flux observational data and hydrological observational data; and (3) to compare the performance of the SEBAL ET with the MOD16 ET product at the point scale and basin scale under different environmental conditions in China. At the point scale, both the models performed best in the conditions of forest cover, subtropical zones, hilly terrain, or summer, respectively, and SEBAL performed better in most conditions. In general, the accuracy of the SEBAL ET (rRMSE = 44.91 %) was slightly higher than that of the MOD16 ET (rRMSE = 48.72 %). In the basin-scale validation, both the models performed better than in the point-scale validation, with SEBAL obtaining results superior (rRMSE = 13.57 %) to MOD16 (rRMSE = 32.84 %). Additionally, both the models showed a negative bias, with the bias of the MOD16 ET being higher than that of the SEBAL ET. In the daily-scale validation, the SEBAL ET product showed a root mean square error (RMSE) of 0.92 mm d−1 and an r value of 0.79. In general, the SEBAL ET product can be used for the qualitative analysis and most quantitative analyses of regional ET. The SEBAL ET product is freely available at https://doi.org/10.5281/zenodo.4243988 and https://doi.org/10.5281/zenodo.4896147 (Cheng, 2020a, b). The results of this study can provide a reference for the application of remotely sensed ET products and the improvement of satellite ET observation algorithms.

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Section: Temporal Scaling-up Methodsmentioning

confidence: 78%

Long time series of daily evapotranspiration in China based on the SEBAL model and multisource images and validation

Cheng

Jiao

et al. 2021

Earth Syst. Sci. Data

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“…Nevertheless, several limitations and assumptions must be considered when interpreting the results. Satellite‐based monitoring of drought only allows to evaluate state and changes of the upper tree crown (Damm et al, 2020), while possible damages happening lower in the canopy or even at stem level are not visible. Nevertheless, defoliation leads to translucency of the canopy crown and reflectance from the understory adds (unwanted) variation to the spectral signal (Barry et al, 2008).…”

Section: Discussionmentioning

confidence: 99%

Satellite data reveal differential responses of Swiss forests to unprecedented 2018 drought

Sturm

Santos

Schmid

et al. 2022

Global Change Biology

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Extreme events such as the summer drought of 2018 in Central Europe are projected to occur more frequently in the future and may cause major damages including increased tree mortality and negative impacts on forest ecosystem services. Here, we quantify the response of >1 million forest pixels of 10 × 10 m across Switzerland to the 2018 drought in terms of resistance, recovery, and resilience. We used the Normalized Difference Water Index (NDWI) derived from Sentinel‐2 satellite data as a proxy for canopy water content and analyzed its relative change. We calculated NDWI change between the 2017 pre‐drought and 2018 drought years (indicating resistance), 2018 and the 2019 post‐drought (indicating recovery), and between 2017–2019 (indicating resilience). Analyzing the data from this large natural experiment, we found that for 4.3% of the Swiss forest the NDWI declined between 2017 and 2018, indicating areas with low resistance of the forest canopy to drought effects. While roughly 50% of this area recovered, in 2.7% of the forested area NDWI continued to decline from 2018 to 2019, suggesting prolonged negative effects or delayed damage. We found differential forest responses to drought associated with site topographic characteristics and forest stand characteristics, and to a lesser extent with climatic conditions and interactions between these drivers. Low drought resistance and high recovery were most prominent at forest edges, but also on south‐facing slopes and lower elevations. Tree functional type was the most important driver of drought resilience, with most of the damage in stands with high conifer abundance. Our results demonstrate the suitability of satellite‐based quantification of drought‐induced forest damage at high spatial resolution across large areas. Such information is important to predict how local site characteristics may impact forest vulnerability to future extreme events and help in the search for appropriate adaptation strategies.

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“…Velpuri et al (2013) used ET estimated using MOD16 and the Operational Simplified Surface Energy Balance (SSEBop) with a spatial resolution of 1 km × 1 km to match the footprint of the flux tower data 440 in the USA. Several studies have indicated that the footprint should be determined by the height of the observation instrument and the intensity of the turbulence (Damm et al, 2020;Schmid, 1994). It should be noted that the footprint of the flux tower data used in this study varied with the meteorological conditions.…”

Section: Errors In Flux Tower Measurementsmentioning

confidence: 96%

Long time series of daily evapotranspiration in China based on the SEBAL model and multisource images and validation

Cheng¹,

Jiao²,

Li³

et al. 2020

Preprint

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Abstract. Satellite observations of evapotranspiration (ET) have been widely used for water resources management in China. An accurate ET product with a high spatiotemporal resolution is required for research on drought stress and water resources management. However, such a product is currently lacking. Moreover, the performances of different ET estimation algorithms for China have not been clearly studied, especially under different environmental conditions. Therefore, the aims of this study were as follows: (1) to use multisource images to generate a long time series (2001–2018) daily ET product with a spatial resolution of 1 km × 1 km based on the Surface Energy Balance Algorithm for Land (SEBAL); (2) to comprehensively evaluate the performance of the SEBAL ET in China using flux observational data and hydrological observational data; (3) to compare the performance of the SEBAL ET with the MOD16 ET product at the point-scale and basin-scale under different environmental conditions in China. At the point-scale, both the models performed best in the conditions of forest cover, subtropical zones, hilly terrain, or summer, respectively, and SEBAL performed better in most conditions. In general, the accuracy of the SEBAL ET (rRMSE = 44.91 %) was slightly higher than that of the MOD16 ET (rRMSE = 48.72 %). In the basin-scale validation, both the models performed better than in the point-scale validation, with SEBAL obtaining superior results (rRMSE = 19.15 %) to MOD16 (rRMSE = 33.62 %). Additionally, both the models showed a negative bias, with the bias of the MOD16 ET being higher than that of the SEBAL ET. In the daily-scale validation, the SEBAL ET product showed an RMSE of 0.92 mm/d and an r-value of 0.79. In general, the SEBAL ET product can be used for the qualitative analysis and most quantitative analysis of regional ET. SEBAL ET product is freely available at https://doi.org/10.5281/zenodo.4218413 (Cheng, 2020). The results of this study can provide a reference for the application of remotely sensed ET products and the improvement of satellite ET observation algorithms.

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Remote sensing of forest gas exchange: Considerations derived from a tomographic perspective

Cited by 19 publications

References 50 publications

Long time series of daily evapotranspiration in China based on the SEBAL model and multisource images and validation

Long time series of daily evapotranspiration in China based on the SEBAL model and multisource images and validation

Satellite data reveal differential responses of Swiss forests to unprecedented 2018 drought

Long time series of daily evapotranspiration in China based on the SEBAL model and multisource images and validation

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