气候变化领域集成服务门户(CCPortal): BESS-STAIR: A framework to estimate daily; 30m; and all-weather crop evapotranspiration using multi-source satellite data for the US Corn Belt

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DOI	10.5194/hess-24-1251-2020
	BESS-STAIR: A framework to estimate daily; 30m; and all-weather crop evapotranspiration using multi-source satellite data for the US Corn Belt
	Jiang C.; Guan K.; Pan M.; Ryu Y.; Peng B.; Wang S.
发表日期	2020
ISSN	1027-5606
起始页码	1251
结束页码	1273
卷号	24 期号:3
英文摘要	With increasing crop water demands and drought threats, mapping and monitoring of cropland evapotranspiration (ET) at high spatial and temporal resolutions become increasingly critical for water management and sustainability. However, estimating ET from satellites for precise water resource management is still challenging due to the limitations in both existing ET models and satellite input data. Specifically, the process of ET is complex and difficult to model, and existing satellite remote-sensing data could not fulfill high resolutions in both space and time. To address the above two issues, this study presents a new high spatiotemporal resolution ET mapping framework, i.e., BESS-STAIR, which integrates a satellite-driven water-carbon-energy coupled biophysical model, BESS (Breathing Earth System Simulator), with a generic and fully automated fusion algorithm, STAIR (SaTallite dAta IntegRation). In this framework, STAIR provides daily 30 m multispectral surface reflectance by fusing Landsat and MODIS satellite data to 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 Corn Belt and validated BESSSTAIR ET estimations using flux-tower measurements over 12 sites (85 site years). Results showed that BESS-STAIR daily ET achieved an overall R2 = 0:75, with root mean square error RMSE = 0:93 mm d-1 and relative error RE = 27:9 % when benchmarked with the flux measurements. In addition, BESS-STAIR ET estimations captured the spatial patterns, seasonal cycles, and interannual dynamics well in different sub-regions. The high performance of the BESSSTAIR framework primarily resulted from (1) the implementation of coupled constraints on water, carbon, and energy in BESS, (2) high-quality daily 30 m data from the STAIR fusion algorithm, and (3) BESS's applicability under all-sky conditions. BESS-STAIR is calibration-free and has great potentials to be a reliable tool for water resource management and precision agriculture applications for the US Corn Belt and even worldwide given the global coverage of its input data. © 2020 Author(s).
语种	英语
scopus关键词	Agricultural robots; Carbon; Crops; Data integration; Digital storage; Evapotranspiration; Input output programs; Mapping; Mean square error; Plants (botany); Remote sensing; Resource allocation; Satellites; Stairs; Water management; Agriculture applications; Crop evapotranspiration; Root mean square errors; Satellite remote sensing data; Spatial and temporal resolutions; Spatio-temporal resolution; Surface reflectance; Waterresource management; Information management; algorithm; carbon dioxide; drought stress; energy planning; evapotranspiration; leaf area; MODIS; remote sensing; satellite altimetry; water management; Corn Belt; United States; Zea mays
来源期刊	Hydrology and Earth System Sciences
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/159468
作者单位	Jiang, C., College of Agricultural Consumer and Environmental Sciences, University of Illinois at Urbana Champaign, Urbana, IL, United States, Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana Champaign, Urbana, IL, United States; Guan, K., College of Agricultural Consumer and Environmental Sciences, University of Illinois at Urbana Champaign, Urbana, IL, United States, Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana Champaign, Urbana, IL, United States, National Center of Supercomputing Applications, University of Illinois at Urbana Champaign, Urbana, IL, United States; Pan, M., Department of Civil and Environmental Engineering, Princeton UniversityNJ, United States; Ryu, Y., Department of Landscape Architecture and Rural Systems Engineering, Seoul National University, Seoul, South Korea; Peng, B., College of Agricultural Consumer and Environmental Sciences, University of Illinois at Urbana Champaign, Urbana, IL, United S...
推荐引用方式 GB/T 7714	Jiang C.,Guan K.,Pan M.,et al. BESS-STAIR: A framework to estimate daily; 30m; and all-weather crop evapotranspiration using multi-source satellite data for the US Corn Belt[J],2020,24(3).
APA	Jiang C.,Guan K.,Pan M.,Ryu Y.,Peng B.,&Wang S..(2020).BESS-STAIR: A framework to estimate daily; 30m; and all-weather crop evapotranspiration using multi-source satellite data for the US Corn Belt.Hydrology and Earth System Sciences,24(3).
MLA	Jiang C.,et al."BESS-STAIR: A framework to estimate daily; 30m; and all-weather crop evapotranspiration using multi-source satellite data for the US Corn Belt".Hydrology and Earth System Sciences 24.3(2020).