BESS-STAIR: a framework to estimate daily, 30 m, 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 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 BESS-STAIR 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 BESS-STAIR 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.

A daily, 250 m and real-time gross primary productivity product (2000–present) covering the contiguous United States

“…Gross primary productivity (GPP) quantifies the amount of carbon dioxide (CO 2 ) fixed by plants through photosynthesis (Beer et al, 2010;Jung et al, 2017). Because GPP is the largest carbon flux and influences other ecosystem processes such as respiration and transpiration, monitoring GPP is crucial for understanding the global carbon budget and terrestrial-ecosystem dynamics (Bonan, 2019;Friedlingstein et al, 2019).…”

CubeSats deliver new insights into agricultural water use at daily and 3 m resolutions

“…In a high-resolution example, Guzinski and Nieto 21 employed a machine-learning fusion framework to retrieve E at 10 m spatial resolution every five days, using data from the Sentinel-2 and Sentinel-3 satellites. A number of approaches have also explored the fusion of higher spatial resolution LandSat data with the enhanced temporal resolution of MODIS to develop a 30 m daily product 22 , 23 . Although these and related studies provide E at varying resolutions and scales, none have achieved the long-term high-spatial and high-temporal (daily) resolution retrievals needed to drive precision agricultural applications and advances 16 , 24 .…”

“…In a high-resolution example, Guzinski and Nieto 21 employed a machine-learning fusion framework to retrieve E at 10 m spatial resolution every five days, using data from the Sentinel-2 and Sentinel-3 satellites. A number of approaches have also explored the fusion of higher spatial resolution LandSat data with the enhanced temporal resolution of MODIS to develop a 30 m daily product 22,23 .…”

Land Surface Modeling 2.0 for agricultural climate change impact assessments

“…to get more regression or process tuning targets [12]) and map land use areas more accurately [13]. Biophysical estimations, such as leaf area index, gross primary productivity, and ET, have recently become available over global cropland with improved resolutions and accuracy [14,15,16]. Early efforts of integrating these satellite measurements into regional or global process-based land surface models are promising, but still amount to parameter calibration in a manual set-up in most cases at the moment [17,18].…”