High-Resolution Monitoring and Assessment of Evapotranspiration and Gross Primary Production Using Remote Sensing in a Typical Arid Region

Abstract

Land surface evapotranspiration (ET) and gross primary productivity (GPP) are critical components in terrestrial ecosystems with water and carbon cycles. Large-scale, high-resolution, and accurately quantified ET and GPP values are important fundamental data for freshwater resource management and help in understanding terrestrial carbon and water cycles in an arid region. In this study, the revised surface energy balance system (SEBS) model and MOD17 GPP algorithm were used to estimate daily ET and GPP at 100 m resolution based on multi-source satellite remote sensing data to obtain surface biophysical parameters and meteorological forcing data as input variables for the model in the midstream oasis area of the Heihe River Basin (HRB) from 2010 to 2016. Then, we further calculated the ecosystem water-use efficiency (WUE). We validated the daily ET, GPP, and WUE from ground observations at a crop oasis station and conducted spatial intercomparisons of monthly and annual ET, GPP, and WUE at the irrigation district and cropland oasis scales. The site-level evaluation results show that ET and GPP had better performance than WUE at the daily time scale. Specifically, the deviations in the daily ET, GPP, and WUE data compared with ground observations were small, with a root mean square error (RMSE) and mean absolute percent error (MAPE) of 0.75 mm/day and 26.59%, 1.13 gC/m2 and 36.26%, and 0.50 gC/kgH2O and 39.83%, respectively. The regional annual ET, GPP, and WUE varied from 300 to 700 mm, 200 to 650 gC/m2, and 0.5 to 1.0 gC/kgH2O, respectively, over the entire irrigation oasis area. It was found that annual ET and GPP were greater than 550 mm and 500 gC/m2, and annual oasis cropland WUE had strong invariability and was maintained at approximately 0.85 gC/kgH2O. The spatial intercomparisons from 2010 to 2016 revealed that ET had similar spatial patterns to GPP due to tightly coupled carbon and water fluxes. However, the WUE spatiotemporal patterns were slightly different from both ET and GPP, particularly in the early and late growing seasons for the oasis area. Our results demonstrate that spatial full coverage and reasonably fine spatiotemporal variation and variability could significantly improve our understanding of water-saving irrigation strategies and oasis agricultural water management practices in the face of water shortage issues.

Optical Remote Sensing in Provisioning of Ecosystem-Functions Analysis—Review

“…One study reported that currently, remote-sensing data (optical and radar) are not sufficient to determine biomass above 100 to 150 t × ha −1 in forest stands [ 104 ]. For primary production, one study reported that ground measurements offer far more accurate results than satellite data [ 109 ]. However, it should be mentioned that this study was based on satellite data at a resolution of 100 m. The usage of scale greatly affects the final accuracy of the model, regardless of the data used.…”

“…GPP[gC × m −2 × day −1 ] = (1.17 × (EVI MOD09−0.08 ) + 0.03) × PARTOA TERRA-MODIS; Landsat 5,7 [109] Gross primary production of maize in China (temperate climate). TERRA-MODIS, POLDER [110] Determination of global CI from MODIS and POLDER data using NDHD.…”

Contributions of climatic and social factors to two water use efficiency indicators in the Yellow River

“…Although the difference between WUEe and WUEc can be effectively understood at the observation level (Wang, Li, & Tan, 2020), it is difficult to spatiotemporally distinguish the water consumption of ecosystems and canopies when they are quantitatively stripped at a large scale (Bai et al, 2020). Therefore, studies on WUE have usually considered only WUEe (Yan et al, 2021). Early WUE studies mainly focused on crop leaves or individual levels (Donovan et al, 2007) and gradually extended to farmlands (Wang et al, 2018), grasslands (Wang, Ma, Li, & Yuan, 2020), forests (Jiang et al, 2019) and other ecosystems (Driscoll et al, 2020).…”