Mapping Daily Evapotranspiration at Field Scale Using the Harmonized Landsat and Sentinel-2 Dataset, with Sharpened VIIRS as a Sentinel-2 Thermal Proxy

Abstract

Accurate and frequent monitoring of evapotranspiration (ET) at sub-field scales can provide valuable information for agricultural water management, quantifying crop water use and stress toward the goal of increasing crop water use efficiency and production. Using land-surface temperature (LST) data retrieved from Landsat thermal infrared (TIR) imagery, along with surface reflectance data describing albedo and vegetation cover fraction, surface energy balance models can generate ET maps down to a 30 m spatial resolution. However, the temporal sampling by such maps can be limited by the relatively infrequent revisit period of Landsat data (8 days for combined Landsats 7 and 8), especially in cloudy areas experiencing rapid changes in moisture status. The Sentinel-2 (S2) satellites, as a good complement to the Landsat system, provide surface reflectance data at 10–20 m spatial resolution and 5 day revisit period but do not have a thermal sensor. On the other hand, the Visible Infrared Imaging Radiometer Suite (VIIRS) provides TIR data on a near-daily basis with 375 m resolution, which can be refined through thermal sharpening using S2 reflectances. This study assesses the utility of augmenting the Harmonized Landsat and Sentinel-2 (HLS) dataset with S2-sharpened VIIRS as a thermal proxy source on S2 overpass days, enabling 30 m ET mapping at a potential combined frequency of 2–3 days (including Landsat). The value added by including VIIRS-S2 is assessed both retrospectively and operationally in comparison with flux tower observations collected from several U.S. agricultural sites covering a range of crop types. In particular, we evaluate the performance of VIIRS-S2 ET estimates as a function of VIIRS view angle and cloud masking approach. VIIRS-S2 ET retrievals (MAE of 0.49 mm d−1 against observations) generally show comparable accuracy to Landsat ET (0.45 mm d−1) on days of commensurate overpass, but with decreasing performance at large VIIRS view angles. Low-quality VIIRS-S2 ET retrievals linked to imperfect VIIRS/S2 cloud masking are also discussed, and caution is required when applying such data for generating ET timeseries. Fused daily ET time series benefited during the peak growing season from the improved multi-source temporal sampling afforded by VIIRS-S2, particularly in cloudy regions and over surfaces with rapidly changing vegetation conditions, and value added for real-time monitoring applications is discussed. This work demonstrates the utility and feasibility of augmenting the HLS dataset with sharpened VIIRS TIR imagery on S2 overpass dates for generating high spatiotemporal resolution ET products.

Improving the spatiotemporal resolution of remotely sensed ET information for water management through Landsat, Sentinel-2, ECOSTRESS and VIIRS data fusion

“…Previous studies (Knipper et al 2019b ; Semmens et al 2016 ) focusing on these regions have demonstrated that the 16-day revisit of Landsat 8 was not enough to capture the vineyard response to stress. Other studies showed Landsat sampling was even more insufficient for crops like alfalfa which have higher ET temporal dynamics due to monthly cuttings, and pose a significant challenge to ET modeling (Anderson et al 2021 ; Xue et al 2021 ).…”

“…Model results are compared to flux measurements from EC systems as observed, and with a correction for energy balance closure error, which is reported on the order of 10–30% (Allen et al 2011 ). Following previous studies (Anderson et al 2021 ; Xue et al 2021 ), a residual correction approach was used at most sites, assigning the energy budget residual to the daily latent heat flux. At SLM001 and SLM002, a modified residual correction was used to minimize impacts of nighttime shifts in wind patterns from off-field (Anderson et al, 2018 ).…”

“…For example, Anderson et al ( 2021 ) incorporated ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) as an additional Landsat-like TIR data source. Xue et al ( 2021 ) tested using TIR data from the Visible Infrared Imaging Radiometer Suite (VIIRS) I–5 band together with VSWIR data from Sentinel-2 (S2; Table 1 ) for augmenting Landsat ET timeseries. Both studies have demonstrated the value of additional temporal sampling complementing Landsat in improved ET monitoring.…”

Spatial-Temporal Variation in Paddy Evapotranspiration in Subtropical Climate Regions Based on the SEBAL Model: A Case Study of the Ganfu Plain Irrigation System, Southern China

“…With the continuous development of remote sensing (RS) technology in recent decades [7][8][9], satellite sensors with multiple spatial, temporal, and spectral resolutions (e.g., Land Remote Sensing Satellite (Landsat), Moderate Resolution Imaging Spectroradiometer (MODIS), Advanced Very-High-Resolution Radiometer (AVHRR), and Sentinel) have become ideal tools for estimating ET at a regional scale [10][11][12][13]. Various models have been reported for estimating ET with RS datasets [14][15][16].…”

Estimating Evapotranspiration over Heterogeneous Surface with Sentinel-2 and Sentinel-3 Data: A Case Study in Heihe River Basin

“…In recent years, algorithms for correcting biased ET estimations caused by land surface heterogeneity have mainly focused on the temperature downscaling method [2,46,47]. By obtaining high-resolution ET, the mixed pixels in low-resolution data are reduced, and the influence of the heterogeneous surface on ET is reduced [44].…”