Accurate estimates of land surface energy fluxes and irrigation requirements from UAV-based thermal and multispectral sensors

• DOI: 10.1016/j.isprsjprs.2023.03.009
• Publication Date: 2023-03-28T03:29:20Z
• Authors: Junxiang Peng; Hector Nieto; Mathias Neumann A...; Kirsten Kørup; Rene Larsen; Julien Morel; David Parsons; Zhenjiang Zhou; Kiril Manevski
• Keywords: 2. Zero hunger; Drought stress; 550; Evapotranspiration; Oceanography, Hydrology, Water Resources; Unmanned aerial vehicle; 15. Life on land; Oceanography; 6. Clean water; 631; 13. Climate action; Sap flow; Water Resources; Two system energy balance; Hydrology; Potato

Peer reviewed<br/>The two-source energy balance model estimates canopy transpiration (Tr) and soil evaporation (E) traditionally from satellite partitions of remotely sensed land surface temperature (LST) and the Priestley-Taylor equation (TSEB-PT) at seasonal time with limited accuracy. The high spatial–temporal resolution spectral data collected by unmanned aerial vehicles (UAVs) provide valuable opportunity to estimate Tr and E precisely, improve the understanding of the seasonal and the diurnal cycle of evapotranspiration (ET), and timely detect agricultural drought. The UAV data vary in spatial resolution and the uncertainty imposed on the TSEB-PT outcome has thus far not being considered. To address these challenges and prospects, a new energy flux modelling framework based on TSEB-PT for high spatial resolution thermal and multispectral UAV data is proposed in this paper. Diurnal variations of LST in agricultural fields were recorded with a thermal infrared camera installed on an UAV during drought in 2018 and 2019. Observing potato as a test crop, LST, plant biophysical parameters derived from corresponding UAV multispectral data, and meteorological forcing variables were employed as input variables to TSEB-PT. All analyses were conducted at different pixelation of the UAV data to quantify the effect of spatial resolution on the performance. The 1 m spatial resolution produced the highest correlation between Tr modelled by TSEB-PT and measured by sap flow sensors (R2 = 0.80), which was comparable to the 0.06, 0.1, 0.5 and 2 m pixel sizes (R2 = 0.76–0.78) and markedly higher than the lower resolutions of 2 to 24 m (R2 = 0.30–0.72). Modelled Tr was highly and significantly correlated with measured leaf water potential (R2 = 0.81) and stomatal conductance (R2 = 0.74). The computed irrigation requirements (IRs) reflected the field irrigation treatments, ET and conventional irrigation practices in the area with high accuracy. It was also found that using a net primary production model with explicit representation of temperature influences made it possible to distinguish effects of drought vis-a-vis heat stress on crop productivity and water use efficiency. The results showed excellent model performance for retrieving Tr and ET dynamics under drought stress and proved that the proposed remote sensing based TSEB-PT framework at UAV scale is a promising tool for the investigation of plant drought stress and water demand; this is particularly relevant for local and regional irrigations scheduling.<br/>Appreciation is expressed to the Innovation Fund Denmark which funded two relevant projects: ERA-NET Co-fund Waterworks 2015 project ‘POTENTIAL’ - Variable rate irrigation and nitrogen fertilization in potato; Engage the spatial variation - and project ’MOIST’ - Managing and optimizing irrigation by satellite tools - as well as the Graduate School of Technical Sciences at Aarhus University for financial support. The paper was also partly supported by the National Key Research and Development Program of China (2019YFE0125500-02) and Science and Technology Department of Guangdong Province (2019B020216001). Special thanks are also given to China Scholarship Council and S.C. Van Fonden for the financial support of the first author.<br/>