DOI: 10.20350/digitalcsic/16235
title: TSEB implementation for the manuscript 'Evaluating the precise grapevine water stress detection using unmanned aerial vehicles and evapotranspiration-based metrics'

description: [Description of methods used for collection/generation of data] For the data located in 'meteo' folder, an Eddy-Covariance (EC) tower was used to sample all variables described above. The tower was instrumented with an integrated open-path infrared gas analyzer and 3D Sonic anemometer Campbell Scientific1 (IRGASON, Campbell Scientific, Logan, UT, USA) to measure ecosystem-level carbon and water gas exchanges alongwith meterological forcings. The raw data were sampled at a frequency of 20 Hz and recorded using a CR6 datalogger (Campbell Scientific, Logan, UT, USA). Regarding the images from the UAV system in the 'inputs' folder, a DJI Matrice-300 UAV (DJI Technology Co., Ltd, Shenzhen, China) was used to acquire visible near infrared (VNIR), thermal (TIR) and RGB imagery using the sensors Parrot Sequoia+ (Parrot S.A., Paris, France), DJI’s Zenmuse H20T and DJI’s Zenmuse P1, respectively. Regarding the images in the 'outputs' folder, these are the images resulting from applying the different versions of TSEB as shown in the python script ('run_tseb_main.py')
description: [Methods for processing the data] The Easyflux datalogger program (Easyflux-DL, Campbell Scientific, 2020) corrected the raw high-frequency data from the EC tower using the full suite of standard corrections and adjustments, including spike filtering, measurement quality control flags and applying correction for high/low frequency losses, to generate corrected half-hourly turbulent fluxes. More details of EC data post-processing are available in the EasyFlux-DL product manual (Campbell Scientific, 2020). UAV images were processed using OpenDroneMap (https://www.opendronemap.org/), an open-source drone processing software. Raw TIR H20T image tiles (i.e. in R-JPEG format) were first converted to single band radiometric temperatures using the open-source DJI Thermal SDK software (https://www.dji.com/downloads/softwares/dji-thermal-sdk). These individual temperature image tiles were then mosaicked together with OpenDroneMap. Congruently, multispectral images from Sequoia+ were radiometrically calibrated using camera corrections, such as vignetting, black level and gain/exposure compensations, using the available routines developed for OpenDroneMap (https://github.com/OpenDroneMap/ODM/blob/master/opendm/multispectral.py).
description: All the inputs and outputs of the two-source energy balance (TSEB) model are in the 'inputs' and 'outputs folder. Another readme file explicitely describes this data. Also described below: ## inputs ### meteo A csv file with meterological and EC measurements during UAV overpass time. ### UAV UAV imagery are stored in seperate folders for each date (in YYYYMMDD). Each input is available over the study site at 2m spatial resolution. ## outputs The model outputs are available for both TSEB-PT and TSEB-2T versions using pyTSEB (https://github.com/hectornieto/pyTSEB). In each folder, both Main ('Main') and ancillary ('Anc') output data is made available.