High spatial resolution monitoring land surface energy, water and CO2 fluxes from an Unmanned Aerial System

Abstract

High spatial resolution maps of land surface energy, water and CO2 fluxes, e.g. evapotranspiration (ET) and gross primary productivity (GPP), are important for agricultural monitoring, ecosystem and water resources management. However, it is not clear which is the optimal (e.g. coarsest possible) spatial resolution to capture those fluxes accurately. Unmanned Aerial Systems (UAS) can address this by collecting very high spatial resolution (<1 m, VHR) imagery. The objective of this study is to model ET and GPP dynamics using VHR optical and thermal imagery and quantify the influence of the spatial heterogeneity in the flux simulations and validations. The study was conducted at a deciduous willow bioenergy eddy covariance (EC) flux site in Denmark. Flight campaigns were conducted during the growing seasons of 2016 and 2017 with a hexacopter equipped with RGB, multispectral and thermal infrared cameras. A 'top-down' modeling approach consisting of the Priestley-Taylor Jet Propulsion Laboratory model and a light use efficiency model sharing the same canopy biophysical constraints was used to estimate ET and GPP. Model outputs were benchmarked by EC flux observations with the source weighted footprint. Our results indicate that our model can well estimate the instantaneous net radiation, ET, GPP, evaporative fraction, light use efficiency and water use efficiency with root-mean-square deviations (RMSD) of 31.6 W•m −2 , 41.2 W•m −2 , 3.12 μmol•C•m −2 •s −1 , 0.08, 0.16 g•C•MJ −1 and 0.35 g•C•kg −1 , respectively. Further, it is found that using a footprint model to sample different areas of VHR imagery can be a tool to provide better diurnal estimates to benchmark with EC data. Moreover, these VHR maps (0.3 m) allowed us to quantify metrics of spatial heterogeneity by using semivariogram analysis and by aggregating model inputs into different spatial resolutions. For instance, we find that in this site, the aggregation of simulated GPP using the source weighted mean of the EC footprint was about 30% lower in RMSD than using the arithmetic mean of the footprint. This demonstrates the accuracy of the modeled VHR spatial patterns.Nevertheless, we also find that imagery resolution consistent with the canopy size (around 1.5 m in our study) is sufficient to capture the spatial heterogeneity of the fluxes as transpiration and canopy assimilation of CO2 are processes regulated at the tree crown level. Our results highlight the importance of considering the land surface 3 heterogeneity for flux modeling and the source contribution within the EC footprint for model benchmarking at appropriate spatial resolutions.

Challenges in modeling and predicting floods and droughts: A review

“…However, the data will only be available at an uneven and longer than daily temporal resolution and therefore not necessarily allow for the detection of shorter events such as flash floods. Data at finer spatial and temporal resolution can be collected by drones, which have been previously used to map evapotranspiration (Wang et al, 2019), measure flow velocity (Eltner, Sardemann, & Grundmann, 2020), and estimate streamflow (Kang, Kim, Kim, & Kang, 2019; Tauro, Petroselli, & Arcangeletti, 2016).…”

Radiance-based NIRv as a proxy for GPP of corn and soybean

“…Particularly, GPP estimation at short time scales (e.g. sub-daily and daily) is still challenging (Bodesheim et al 2018, Wang et al 2019. Effective and parsimonious ways to estimate GPP with low dependence on climate forcing and model parameterization are highly required.…”

Intercomparison of In Situ Sensors for Ground-Based Land Surface Temperature Measurements

“…Surface heterogeneity or difference in FOV can contribute partially to the bias in temperatures, but correcting the significant systematic bias for each pixel is important in many applications that use airborne measurements of LST. For example, even a few degrees bias in LST can lead to significant error in the estimated energy fluxes using high resolution of the thermal images using aerial platforms like UAVs [ 78 , 79 , 80 ]. One way is to use high accuracy low cost IRTs along with IR cameras onboard to calibrate the BT measurements [ 81 ] as demonstrated here.…”