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Wood JD, Detto M, Browne M, Kraft NJB, Konings AG, Fisher JB, Quetin GR, Trugman AT, Magney TS, Medeiros CD, Vinod N, Buckley TN, Sack L. The Ecosystem as Super-Organ/ism, Revisited: Scaling Hydraulics to Forests under Climate Change. Integr Comp Biol 2024; 64:424-440. [PMID: 38886119 DOI: 10.1093/icb/icae073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2024] [Revised: 05/27/2024] [Accepted: 06/03/2024] [Indexed: 06/20/2024] Open
Abstract
Classic debates in community ecology focused on the complexities of considering an ecosystem as a super-organ or organism. New consideration of such perspectives could clarify mechanisms underlying the dynamics of forest carbon dioxide (CO2) uptake and water vapor loss, important for predicting and managing the future of Earth's ecosystems and climate system. Here, we provide a rubric for considering ecosystem traits as aggregated, systemic, or emergent, i.e., representing the ecosystem as an aggregate of its individuals or as a metaphorical or literal super-organ or organism. We review recent approaches to scaling-up plant water relations (hydraulics) concepts developed for organs and organisms to enable and interpret measurements at ecosystem-level. We focus on three community-scale versions of water relations traits that have potential to provide mechanistic insight into climate change responses of forest CO2 and H2O gas exchange and productivity: leaf water potential (Ψcanopy), pressure volume curves (eco-PV), and hydraulic conductance (Keco). These analyses can reveal additional ecosystem-scale parameters analogous to those typically quantified for leaves or plants (e.g., wilting point and hydraulic vulnerability) that may act as thresholds in forest responses to drought, including growth cessation, mortality, and flammability. We unite these concepts in a novel framework to predict Ψcanopy and its approaching of critical thresholds during drought, using measurements of Keco and eco-PV curves. We thus delineate how the extension of water relations concepts from organ- and organism-scales can reveal the hydraulic constraints on the interaction of vegetation and climate and provide new mechanistic understanding and prediction of forest water use and productivity.
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Affiliation(s)
- Jeffrey D Wood
- School of Natural Resources, University of Missouri, Columbia, MO 65211, USA
| | - Matteo Detto
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA
| | - Marvin Browne
- Department of Earth System Science, Stanford University, 473 Via Ortega, Stanford, CA 94305, USA
| | - Nathan J B Kraft
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, 621 Charles E Young Drive South, Los Angeles, CA 90095, USA
| | - Alexandra G Konings
- Department of Earth System Science, Stanford University, 473 Via Ortega, Stanford, CA 94305, USA
| | - Joshua B Fisher
- Schmid College of Science and Technology, Chapman University, 1 University Drive, Orange, CA 92866, USA
| | - Gregory R Quetin
- Department of Geography, University of California, Santa Barbara, CA 93106, USA
| | - Anna T Trugman
- Department of Geography, University of California, Santa Barbara, CA 93106, USA
| | - Troy S Magney
- Department of Plant Sciences, University of California, Davis, CA 95616, USA
| | - Camila D Medeiros
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, 621 Charles E Young Drive South, Los Angeles, CA 90095, USA
| | - Nidhi Vinod
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, 621 Charles E Young Drive South, Los Angeles, CA 90095, USA
| | - Thomas N Buckley
- Department of Plant Sciences, University of California, Davis, CA 95616, USA
| | - Lawren Sack
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, 621 Charles E Young Drive South, Los Angeles, CA 90095, USA
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Li X, Black TA, Zha T, Jassal RS, Nesic Z, Lee SC, Bourque CPA, Hao S, Jin C, Liu P, Jia X, Tian Y. Long-term trend and interannual variation in evapotranspiration of a young temperate Douglas-fir stand over 2002-2022 reveals the impacts of climate change. PLANT, CELL & ENVIRONMENT 2024. [PMID: 38863246 DOI: 10.1111/pce.15000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Revised: 05/27/2024] [Accepted: 05/31/2024] [Indexed: 06/13/2024]
Abstract
The shortage of decades-long continuous measurements of ecosystem processes limits our understanding of how changing climate impacts forest ecosystems. We used continuous eddy-covariance and hydrometeorological data over 2002-2022 from a young Douglas-fir stand on Vancouver Island, Canada to assess the long-term trend and interannual variability in evapotranspiration (ET) and transpiration (T). Collectively, annual T displayed a decreasing trend over the 21 years with a rate of 1% yr-1, which is attributed to the stomatal downregulation induced by rising atmospheric CO2 concentration. Similarly, annual ET also showed a decreasing trend since evaporation stayed relatively constant. Variability in detrended annual ET was mostly controlled by the average soil water storage during the growing season (May-October). Though the duration and intensity of the drought did not increase, the drought-induced decreases in T and ET showed an increasing trend. This pattern may reflect the changes in forest structure, related to the decline in the deciduous understory cover during the stand development. These results suggest that the water-saving effect of stomatal regulation and water-related factors mostly determined the trend and variability in ET, respectively. This may also imply an increase in the limitation of water availability on ET in young forests, associated with the structural and compositional changes related to forest growth.
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Affiliation(s)
- Xinhao Li
- State Key Laboratory of Efficient Production of Forest Resources, Beijing Forestry University, Beijing, China
- School of Soil and Water Conservation, Beijing Forestry University, Beijing, China
- Beijing Engineering Research Center of Soil and Water Conservation, Beijing Forestry University, Beijing, China
| | - T Andrew Black
- Biometeorology and Soil Physics Group, University of British Columbia, Vancouver, British Columbia, Canada
| | - Tianshan Zha
- State Key Laboratory of Efficient Production of Forest Resources, Beijing Forestry University, Beijing, China
- School of Soil and Water Conservation, Beijing Forestry University, Beijing, China
- Beijing Engineering Research Center of Soil and Water Conservation, Beijing Forestry University, Beijing, China
| | - Rachhpal S Jassal
- Biometeorology and Soil Physics Group, University of British Columbia, Vancouver, British Columbia, Canada
| | - Zoran Nesic
- Biometeorology and Soil Physics Group, University of British Columbia, Vancouver, British Columbia, Canada
| | - Sung-Ching Lee
- Department of Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Jena, Germany
| | - Charles P-A Bourque
- Faculty of Forestry and Environmental Management, University of New Brunswick, Fredericton, New Brunswick, Canada
| | - Shaorong Hao
- State Key Laboratory of Efficient Production of Forest Resources, Beijing Forestry University, Beijing, China
- School of Soil and Water Conservation, Beijing Forestry University, Beijing, China
- Beijing Engineering Research Center of Soil and Water Conservation, Beijing Forestry University, Beijing, China
| | - Chuan Jin
- Key Laboratory of Agro-Forestry Environmental Processes and Ecological Regulation of Hainan Province, Hainan University, Haikou, China
| | - Peng Liu
- State Key Laboratory of Efficient Production of Forest Resources, Beijing Forestry University, Beijing, China
- School of Soil and Water Conservation, Beijing Forestry University, Beijing, China
- Beijing Engineering Research Center of Soil and Water Conservation, Beijing Forestry University, Beijing, China
| | - Xin Jia
- State Key Laboratory of Efficient Production of Forest Resources, Beijing Forestry University, Beijing, China
- School of Soil and Water Conservation, Beijing Forestry University, Beijing, China
- Beijing Engineering Research Center of Soil and Water Conservation, Beijing Forestry University, Beijing, China
| | - Yun Tian
- School of Soil and Water Conservation, Beijing Forestry University, Beijing, China
- Beijing Engineering Research Center of Soil and Water Conservation, Beijing Forestry University, Beijing, China
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3
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Different Vegetation Information Inputs Significantly Affect the Evapotranspiration Simulations of the PT-JPL Model. REMOTE SENSING 2022. [DOI: 10.3390/rs14112573] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Evapotranspiration (ET) is an essential part of the global water cycle, and accurate quantification of ET is of great significance for hydrological research and practice. The Priestley-Taylor Jet Propulsion Laboratory (PT-JPL) model is a commonly used remotely sensed (RS) ET model. The original PT-JPL model includes multiple vegetation variables but only requires the Normalized Difference Vegetation Index (NDVI) as the vegetation input. Other vegetation inputs (e.g., Leaf Area Index (LAI) and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)) are estimated by the NDVI-based empirical methods. Here we investigate whether introducing more RS vegetation variables beyond NDVI can improve the PT-JPL model’s performance. We combine the vegetation variables derived from RS and empirical methods into four vegetation input schemes for the PT-JPL model. The model performance under four schemes is evaluated at the site scale with the eddy covariance (EC)-based ET measurements and at the basin scale with the water balance-based ET estimates. The results show that the vegetation variables derived by RS and empirical methods are quite different. The ecophysiological constraints of the PT-JPL model constructed by the former are more reasonable in spatial distribution than those constructed by the latter. However, as vegetation input of the PT-JPL model, the scheme derived from empirical methods performs best among the four schemes. In other words, introducing more remotely sensed vegetation variables beyond NDVI into the PT-JPL model degrades the model performance to varying degrees. One possible reason for this is the unrealistic ET partitioning. It is necessary to re-parameterize the biophysical constraints of the PT-JPL model to ensure that the model obtains reasonable internal process simulations, that is, “getting the right results for right reasons.”
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Evapotranspiration Estimation with the S-SEBI Method from Landsat 8 Data against Lysimeter Measurements at the Barrax Site, Spain. REMOTE SENSING 2021. [DOI: 10.3390/rs13183686] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Evapotranspiration (ET) is a variable of the climatic system and hydrological cycle that plays an important role in biosphere–atmosphere–hydrosphere interactions. In this paper, remote sensing-based ET estimates with the simplified surface energy balance index (S-SEBI) model using Landsat 8 data were compared with in situ lysimeter measurements for different land covers (Grass, Wheat, Barley, and Vineyard) at the Barrax site, Spain, for the period 2014–2018. Daily estimates produced superior performance than hourly estimates in all the land covers, with an average difference of 12% and 15% for daily and hourly ET estimates, respectively. Grass and Vineyard showed the best performance, with an RMSE of 0.10 mm/h and 0.09 mm/h and 1.11 mm/day and 0.63 mm/day, respectively. Thus, the S-SEBI model is able to retrieve ET from Landsat 8 data with an average RMSE for daily ET of 0.86 mm/day. Some model uncertainties were also analyzed, and we concluded that the overpass of the Landsat missions represents neither the maximum daily ET nor the average daily ET, which contributes to an increase in errors in the estimated ET. However, the S-SEBI model can be used to operationally retrieve ET from agriculture sites with good accuracy and sufficient variation between pixels, thus being a suitable option to be adopted into operational ET remote sensing programs for irrigation scheduling or other purposes.
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5
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CubeSats deliver new insights into agricultural water use at daily and 3 m resolutions. Sci Rep 2021; 11:12131. [PMID: 34108564 PMCID: PMC8190154 DOI: 10.1038/s41598-021-91646-w] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Accepted: 05/18/2021] [Indexed: 11/27/2022] Open
Abstract
Earth observation has traditionally required a compromise in data collection. That is, one could sense the Earth with high spatial resolution occasionally; or with lower spatial fidelity regularly. For many applications, both frequency and detail are required. Precision agriculture is one such example, with sub-10 m spatial, and daily or sub-daily retrieval representing a key goal. Towards this objective, we produced the first cloud-free 3 m daily evaporation product ever retrieved from space, leveraging recently launched nano-satellite constellations to showcase this emerging potential. Focusing on three agricultural fields located in Nebraska, USA, high-resolution crop water use estimates are delivered via CubeSat-based evaporation modeling. Results indicate good model agreement (r2 of 0.86–0.89; mean absolute error between 0.06 and 0.08 mm/h) when evaluated against corrected flux tower data. CubeSat technologies are revolutionizing Earth observation, delivering novel insights and new agricultural informatics that will enhance food and water security efforts, and enable rapid and informed in-field decision making.
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Nassar A, Torres A, Merwade V, Dey S, Zhao L, Kim IL, Kustas WP, Nieto H, Hipps L, Gao R, Alfieri J, Prueger J, Alsina MM, McKee L, Coopmans C, Sanchez L, Dokoozlian N, Bambach Ortiz N, Mcelrone AJ. Development of High Performance Computing Tools for Estimation of High-Resolution Surface Energy Balance Products Using sUAS Information. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2021; 11747:117470K. [PMID: 35002013 PMCID: PMC8739179 DOI: 10.1117/12.2587763] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
sUAS (small-Unmanned Aircraft System) and advanced surface energy balance models allow detailed assessment and monitoring (at plant scale) of different (agricultural, urban, and natural) environments. Significant progress has been made in the understanding and modeling of atmosphere-plant-soil interactions and numerical quantification of the internal processes at plant scale. Similarly, progress has been made in ground truth information comparison and validation models. An example of this progress is the application of sUAS information using the Two-Source Surface Energy Balance (TSEB) model in commercial vineyards by the Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment - GRAPEX Project in California. With advances in frequent sUAS data collection for larger areas, sUAS information processing becomes computationally expensive on local computers. Additionally, fragmentation of different models and tools necessary to process the data and validate the results is a limiting factor. For example, in the referred GRAPEX project, commercial software (ArcGIS and MS Excel) and Python and Matlab code are needed to complete the analysis. There is a need to assess and integrate research conducted with sUAS and surface energy balance models in a sharing platform to be easily migrated to high performance computing (HPC) resources. This research, sponsored by the National Science Foundation FAIR Cyber Training Fellowships, is integrating disparate software and code under a unified language (Python). The Python code for estimating the surface energy fluxes using TSEB2T model as well as the EC footprint analysis code for ground truth information comparison were hosted in myGeoHub site https://mygeohub.org/ to be reproducible and replicable.
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Affiliation(s)
- Ayman Nassar
- Utah State University, Department of Civil and Environmental Engineering, Logan, UT, United States
- Utah Water Research Lab, Utah State University
| | - Alfonso Torres
- Utah State University, Department of Civil and Environmental Engineering, Logan, UT, United States
- Utah Water Research Lab, Utah State University
| | - Venkatesh Merwade
- Purdue University, Lyles School of Civil Engineering, Indiana, United States
| | - Sayan Dey
- Purdue University, Lyles School of Civil Engineering, Indiana, United States
| | - Lan Zhao
- Purdue University, Lyles School of Civil Engineering, Indiana, United States
| | - I Luk Kim
- Purdue University, Lyles School of Civil Engineering, Indiana, United States
| | - William P Kustas
- U. S. Department of Agriculture, Agricultural Research Service, Hydrology and Remote Sensing Laboratory, Beltsville, MD, United States
| | - Hector Nieto
- Complutum Tecnologías de la Información Geográfica, Madrid, Spain
| | - Lawrence Hipps
- Plants, Soils and Climate Department, Logan, Utah State University, UT 84322, USA
| | - Rui Gao
- Utah State University, Department of Civil and Environmental Engineering, Logan, UT, United States
- Utah Water Research Lab, Utah State University
| | - Joseph Alfieri
- U. S. Department of Agriculture, Agricultural Research Service, Hydrology and Remote Sensing Laboratory, Beltsville, MD, United States
| | - John Prueger
- U. S. Department of Agriculture, Agricultural Research Service, National Laboratory for Agriculture and the Environment, Ames, IA, United States
| | - Maria Mar Alsina
- E & J Gallo Winery Viticulture Research, Modesto, CA, United States
| | - Lynn McKee
- U. S. Department of Agriculture, Agricultural Research Service, Hydrology and Remote Sensing Laboratory, Beltsville, MD, United States
| | - Calvin Coopmans
- Utah State University, Electrical Engineering, Logan, UT, United States
| | - Luis Sanchez
- E & J Gallo Winery Viticulture Research, Modesto, CA, United States
| | - Nick Dokoozlian
- E & J Gallo Winery Viticulture Research, Modesto, CA, United States
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7
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Niu Z, He H, Zhu G, Ren X, Zhang L, Zhang K. A spatial-temporal continuous dataset of the transpiration to evapotranspiration ratio in China from 1981-2015. Sci Data 2020; 7:369. [PMID: 33110108 PMCID: PMC7591528 DOI: 10.1038/s41597-020-00693-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Accepted: 09/11/2020] [Indexed: 11/16/2022] Open
Abstract
The ratio of plant transpiration to total terrestrial evapotranspiration (T/ET) captures the role of vegetation in surface-atmosphere interactions. However, several studies have documented a large variability in T/ET. In this paper, we present a new T/ET dataset (also including transpiration, evapotranspiration data) for China from 1981 to 2015 with spatial and temporal resolutions of 0.05° and 8 days, respectively. The T/ET dataset is based on a model-data fusion method that integrates the Priestley-Taylor Jet Propulsion Laboratory (PT-JPL) model with multivariate observational datasets (transpiration and evapotranspiration). The dataset is driven by satellite-based leaf area index (LAI) data from GLASS and GLOBMAP, and climate data from the Chinese Ecosystem Research Network (CERN). Observational annual T/ET were used to validate the model, with R2 and RMSE values were 0.73 and 0.07 (12.41%), respectively. The dataset provides significant insight into T/ET and its changes over the Chinese terrestrial ecosystem and will be beneficial for understanding the hydrological cycle and energy budgets between the land and the atmosphere.
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Affiliation(s)
- Zhongen Niu
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
- National Ecosystem Science Data Center, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Honglin He
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China.
- National Ecosystem Science Data Center, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China.
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100049, China.
| | - Gaofeng Zhu
- Key Laboratory of Western China's Environmental Systems (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, 730000, China
| | - Xiaoli Ren
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
- National Ecosystem Science Data Center, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
| | - Li Zhang
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
- National Ecosystem Science Data Center, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Kun Zhang
- Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, 100101, China
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8
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McCabe MF, Miralles D, Holmes TR, Fisher JB. Advances in the Remote Sensing of Terrestrial Evaporation. REMOTE SENSING 2019; 11:1138. [PMID: 33505712 PMCID: PMC7837446 DOI: 10.3390/rs11091138] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
Abstract
Characterizing the terrestrial carbon, water and energy cycles depends strongly on a capacity to accurately reproduce the spatial and temporal dynamics of land surface evaporation. For this, and many other reasons, monitoring terrestrial evaporation across multiple space and time scales has been an area of focused research for many decades. Much of this activity has been supported by developments in satellite remote sensing, which have been leveraged to deliver new process insights, model development and methodological improvements. In this Special Issue, published contributions explored a range of research topics directed towards the enhanced estimation of terrestrial evaporation. Here we summarize these cutting-edge efforts and provide an overview of some of the state-of-the-art approaches for retrieving this key variable. Some perspectives on outstanding challenges, issues, and opportunities are also presented.
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Affiliation(s)
- Matthew F McCabe
- Water Desalination and Reuse Center, Division of Biological and Environmental Sciences and Engineering, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
- Correspondence:
| | - Diego Miralles
- Laboratory of Hydrology and Water Management, Ghent University, Coupure Links 653, 9000 Ghent, Belgium
| | - Thomas R.H. Holmes
- Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
| | - Joshua B Fisher
- Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA
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9
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An Improved Spatio-Temporal Adaptive Data Fusion Algorithm for Evapotranspiration Mapping. REMOTE SENSING 2019. [DOI: 10.3390/rs11070761] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Continuous high spatio-temporal resolution monitoring of evapotranspiration (ET) is critical for water resource management and the quantification of irrigation water efficiency at both global and local scales. However, available remote sensing satellites cannot generally provide ET data at both high spatial and temporal resolutions. Data fusion methods have been widely applied to estimate ET at a high spatio-temporal resolution. Nevertheless, most fusion methods applied to ET are initially used to integrate land surface reflectance, the spectral index and land surface temperature, and few studies completely consider the influencing factor of ET. To overcome this limitation, this paper presents an improved ET fusion method, namely, the spatio-temporal adaptive data fusion algorithm for evapotranspiration mapping (SADFAET), by introducing critical surface temperature (the corresponding temperature to decide soil moisture), importing the weights of surface ET-indicative similarity (the influencing factor of ET, which is estimated from remote sensing data) and modifying the spectral similarity (the differences in spectral characteristics of different spatial resolution images) for the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM). We fused daily Moderate Resolution Imaging Spectroradiometer (MODIS) and periodic Landsat 8 ET data in the SADFAET for the experimental area downstream of the Heihe River basin from April to October 2015. The validation results, based on ground-based ET measurements, indicated that the SADFAET could successfully fuse MODIS and Landsat 8 ET data (mean percent error: −5%), with a root mean square error of 45.7 W/m2, whereas the ESTARFM performed slightly worse, with a root mean square error of 50.6 W/m2. The more physically explainable SADFAET could be a better alternative to the ESTARFM for producing ET at a high spatio-temporal resolution.
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