Abstract
Reliable estimates of terrestrial latent heat flux (LE) at high spatial and temporal resolutions are of vital importance for energy balance and water resource management. However, currently available LE products derived from satellite data generally have high revisit frequency or fine spatial resolution. In this study, we explored the feasibility of the high spatiotemporal resolution LE fusion framework to take advantage of the Moderate Resolution Imaging Spectroradiometer (MODIS) and Chinese GaoFen-1 Wide Field View (GF-1 WFV) data. In particular, three-fold fusion schemes based on Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) were employed, including fusion of surface reflectance (Scheme 1), vegetation indices (Scheme 2) and high order LE products (Scheme 3). Our results showed that the fusion of vegetation indices and further computing LE (Scheme 2) achieved better accuracy and captured more detailed information of terrestrial LE, where the determination coefficient (R2) varies from 0.86 to 0.98, the root-mean-square error (RMSE) ranges from 1.25 to 9.77 W/m2 and the relative RSME (rRMSE) varies from 2% to 23%. The time series of merged LE in 2017 using the optimal Scheme 2 also showed a relatively good agreement with eddy covariance (EC) measurements and MODIS LE products. The fusion approach provides spatiotemporal continuous LE estimates and also reduces the uncertainties in LE estimation, with an increment in R2 by 0.06 and a decrease in RMSE by 23.4% on average. The proposed high spatiotemporal resolution LE estimation framework using multi-source data showed great promise in monitoring LE variation at field scale, and may have value in planning irrigation schemes and providing water management decisions over agroecosystems.
Highlights
Terrestrial latent heat flux (LE), which refers to the heat flux transferred from the land surface to the atmosphere through soil evaporation, vegetation transpiration and interception, is an essential component for characterizing the global and regional hydrological budget, energy redistribution and Sensors 2020, 20, 2811; doi:10.3390/s20102811 www.mdpi.com/journal/sensorsSensors 2020, 20, 2811 carbon cycles [1,2]
As the current LE products derived from satellite data generally have fine spatial resolution or high temporal resolution, the ESTARFM
This study showcased the comparison of three fusion schemes to integrate GF-1 WFV and Moderate Resolution Imaging Spectroradiometer (MODIS) data with the main purpose of generating high spatiotemporal resolution terrestrial LE products
Summary
Terrestrial latent heat flux (LE), which refers to the heat flux transferred from the land surface to the atmosphere through soil evaporation, vegetation transpiration and interception, is an essential component for characterizing the global and regional hydrological budget, energy redistribution and Sensors 2020, 20, 2811; doi:10.3390/s20102811 www.mdpi.com/journal/sensorsSensors 2020, 20, 2811 carbon cycles [1,2]. The accurate estimation of terrestrial LE at high spatial and temporal resolution is of great significance for a wide range of applications [3]. Robust and reliable acquisition of LE is an important prerequisite for water and soil conservation assessment, water resource management and terrestrial ecosystem monitoring at the field scale [4,5]. Ground-based measurements such as the eddy covariance (EC) method can provide continuous observations of LE [6,7]. Sparse point measurements prohibit the adequate depiction of LE variations, given the mismatch between small point and large spatial scales [8]. Remote sensing has long been regarded as the most feasible and efficient method to provide detailed and timely information on ecosystem dynamics, which can be used to estimate terrestrial
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