BESS-STAIR: a framework to estimate daily, 30m, and all-weather crop evapotranspiration using multi-source satellite data for the US Corn Belt
Abstract
Abstract. With increasing crop water demands and drought threats, mapping andmonitoring of cropland evapotranspiration (ET) at high spatial and temporalresolutions become increasingly critical for water management andsustainability. However, estimating ET from satellites for precise waterresource management is still challenging due to the limitations in bothexisting ET models and satellite input data. Specifically, the process of ETis complex and difficult to model, and existing satellite remote-sensing datacould not fulfill high resolutions in both space and time. To address theabove two issues, this study presents a new high spatiotemporal resolution ETmapping framework, i.e., BESS-STAIR, which integrates a satellite-drivenwater–carbon–energy coupled biophysical model, BESS (Breathing Earth SystemSimulator), with a generic and fully automated fusion algorithm, STAIR(SaTallite dAta IntegRation). In this framework, STAIR provides daily 30'mmultispectral surface reflectance by fusing Landsat and MODIS satellite datato derive a fine-resolution leaf area index and visible/near-infrared albedo,all of which, along with coarse-resolution meteorological and CO2 data, are used to drive BESS to estimate gap-free 30 m resolution daily ET.We applied BESS-STAIR from 2000 through 2017 in six areas across the US CornBelt and validated BESS-STAIR ET estimations using flux-tower measurementsover 12 sites (85 site years). Results showed that BESS-STAIR daily ETachieved an overall R2=0.75, with root mean squaremore »
- Authors:
-
[1];
[3];
[1]
- Univ. of Illinois at Urbana-Champaign, IL (United States)
- Princeton Univ., NJ (United States)
- Seoul National Univ. (Korea, Republic of)
- Publication Date:
- Research Org.:
- Center for Advanced Bioenergy and Bioproducts Innovation (CABBI), Urbana, IL (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Biological and Environmental Research (BER)
- OSTI Identifier:
- 1616269
- Grant/Contract Number:
- SC0018420
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Hydrology and Earth System Sciences (Online)
- Additional Journal Information:
- Journal Name: Hydrology and Earth System Sciences (Online); Journal Volume: 24; Journal Issue: 3; Journal ID: ISSN 1607-7938
- Publisher:
- European Geosciences Union (EGU)
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 54 ENVIRONMENTAL SCIENCES
Citation Formats
Jiang, Chongya, Guan, Kaiyu, Pan, Ming, Ryu, Youngryel, Peng, Bin, and Wang, Sibo. BESS-STAIR: a framework to estimate daily, 30m, and all-weather crop evapotranspiration using multi-source satellite data for the US Corn Belt. United States: N. p., 2020.
Web. doi:10.5194/hess-24-1251-2020.
Jiang, Chongya, Guan, Kaiyu, Pan, Ming, Ryu, Youngryel, Peng, Bin, & Wang, Sibo. BESS-STAIR: a framework to estimate daily, 30m, and all-weather crop evapotranspiration using multi-source satellite data for the US Corn Belt. United States. https://doi.org/10.5194/hess-24-1251-2020
Jiang, Chongya, Guan, Kaiyu, Pan, Ming, Ryu, Youngryel, Peng, Bin, and Wang, Sibo. Fri .
"BESS-STAIR: a framework to estimate daily, 30m, and all-weather crop evapotranspiration using multi-source satellite data for the US Corn Belt". United States. https://doi.org/10.5194/hess-24-1251-2020. https://www.osti.gov/servlets/purl/1616269.
@article{osti_1616269,
title = {BESS-STAIR: a framework to estimate daily, 30m, and all-weather crop evapotranspiration using multi-source satellite data for the US Corn Belt},
author = {Jiang, Chongya and Guan, Kaiyu and Pan, Ming and Ryu, Youngryel and Peng, Bin and Wang, Sibo},
abstractNote = {Abstract. With increasing crop water demands and drought threats, mapping andmonitoring of cropland evapotranspiration (ET) at high spatial and temporalresolutions become increasingly critical for water management andsustainability. However, estimating ET from satellites for precise waterresource management is still challenging due to the limitations in bothexisting ET models and satellite input data. Specifically, the process of ETis complex and difficult to model, and existing satellite remote-sensing datacould not fulfill high resolutions in both space and time. To address theabove two issues, this study presents a new high spatiotemporal resolution ETmapping framework, i.e., BESS-STAIR, which integrates a satellite-drivenwater–carbon–energy coupled biophysical model, BESS (Breathing Earth SystemSimulator), with a generic and fully automated fusion algorithm, STAIR(SaTallite dAta IntegRation). In this framework, STAIR provides daily 30'mmultispectral surface reflectance by fusing Landsat and MODIS satellite datato derive a fine-resolution leaf area index and visible/near-infrared albedo,all of which, along with coarse-resolution meteorological and CO2 data, are used to drive BESS to estimate gap-free 30 m resolution daily ET.We applied BESS-STAIR from 2000 through 2017 in six areas across the US CornBelt and validated BESS-STAIR ET estimations using flux-tower measurementsover 12 sites (85 site years). Results showed that BESS-STAIR daily ETachieved an overall R2=0.75, with root mean square error RMSE=0.93 mm d-1 and relative error RE =27.9 % when benchmarkedwith the flux measurements. In addition, BESS-STAIR ET estimations capturedthe spatial patterns, seasonal cycles, and interannual dynamics well indifferent sub-regions. The high performance of the BESS-STAIR frameworkprimarily resulted from (1) the implementation of coupled constraints onwater, carbon, and energy in BESS, (2) high-quality daily 30 m data from theSTAIR fusion algorithm, and (3) BESS's applicability under all-skyconditions. BESS-STAIR is calibration-free and has great potentials to be areliable tool for water resource management and precision agricultureapplications for the US Corn Belt and even worldwide given the globalcoverage of its input data.},
doi = {10.5194/hess-24-1251-2020},
journal = {Hydrology and Earth System Sciences (Online)},
number = 3,
volume = 24,
place = {United States},
year = {Fri Mar 20 00:00:00 EDT 2020},
month = {Fri Mar 20 00:00:00 EDT 2020}
}
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