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TSEB implementation for the manuscript 'Evaluating the precise grapevine water stress detection using unmanned aerial vehicles and evapotranspiration-based metrics'

Digital.CSIC. Repositorio Institucional del CSIC
  • Burchard-Levine, Vicente
  • Borra-Serrano, Irene
  • Peña Barragán, José Manuel
  • Kustas, William P.
  • Guerra, José G.
  • Dorado, José
  • Mesías-Ruiz, Gustavo A.
  • Herrezuelo, Miguel
  • Mary, Benjamin
  • McKee, Lynn M.
  • Castro, Ana Isabel de
  • Sanchez-Élez, Sara
  • Nieto, Héctor
This dataset was used to run the remote sensing-based two-source energy balance (TSEB) model to estimate evapotranspiration and related crop stress indicators using high-resolution imagery from unmanned aerial vehicles (UAVs) over a a vineyard experimental site in Madrid, Spain ('El Socorro', Belmonte de Tajo, Madrid, Spain). The dataset includes an example script to run the python implementation of TSEB (pyTSEB, https://github.com/hectornieto/pyTSEB) forced with UAV imagery and meteorological data acquired over the study site. The model outputs are also provided for the different TSEB version (TSEB-PT and TSEB-2T). These results are associated with the manuscript titled 'Evaluating the precise grapevine water stress detection using unmanned aerial vehicles and evapotranspiration-based metrics' published in Irrigation Science., The experiment was undertaken under the Digital Agriculture Technologies for Irrigation efficiency (DATI) project (2020-2024) funded by the Spanish Ministry of Science and Innovation (AEI/10.13039/501100011033) and the PRIMA EU program. It additionally received support from the EO4WUE research project (TED2021-129814B-I00) funded by MCIN/AEI/10.13039/501100011033 and by the European Union NextGenerationEU/PRTR. We also would like to thank Dr. Bill Kustas and USDA for helping with research infrastructure and scientific support., 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., Peer reviewed




Evaluating the precise grapevine water stress detection using unmanned aerial vehicles and evapotranspiration-based metrics

Digital.CSIC. Repositorio Institucional del CSIC
  • Burchard-Levine, Vicente
  • Borra-Serrano, Irene
  • Peña Barragán, José Manuel
  • Kustas, William P.
  • Guerra, José G.
  • Dorado, José
  • Mesías-Ruiz, Gustavo A.
  • Herrezuelo, Miguel
  • Mary, Benjamin
  • McKee, Lynn M.
  • Castro, Ana Isabel de
  • Sanchez-Élez, Sara
  • Nieto, Héctor
© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/., Precise irrigation management requires accurate knowledge of crop water demand to adequately optimize water use efficiency, especially relevant in arid and semi-arid regions. While unoccupied aerial vehicles (UAV) have shown great promise to improve the water management for crops such as vineyards, there still remains large uncertainties to accurately quantify vegetation water requirements, especially through physically-based methods. Notably, thermal remote sensing has been shown to be a promising tool to evaluate water stress at different scales, most commonly through the Crop Water Stress Index (CWSI). This work aimed to evaluate the potential of a UAV payload to estimate evapotranspiration (ET) and alternative ET-based crop water stress indices to better monitor and detect irrigation requirements in vineyards. As a case study, three irrigation treatments within a vineyard were implemented to impose weekly crop coefficient (Kc) of 0.2 (extreme deficit irrigation), 0.4 (typical deficit irrigation) and 0.8 (over-irrigated) of reference ET. Both the original Priestley-Taylor initialized two-source energy balance model (TSEB-PT) and the dual temperature TSEB (TSEB-2T), which takes advantage of high-resolution imagery to discriminate canopy and soil temperatures, were implemented to estimate ET. In a first step, both ET models were evaluated at the footprint level using an eddy covariance (EC) tower, with modelled fluxes comparing well against the EC measurements. Secondly, in-situ physiological measurements at vine level, such as stomatal conductance (gst), leaf (Ψleaf) and stem (Ψstem) water potential, were collected simultaneously to UAV overpasses as plant proxies of water stress. Different variants of the CWSI and alternative metrics that take advantage of the partitioned ET from TSEB, such as Crop Transpiration Stress Index (CTSI) and the Crop Stomatal Stress Index (CSSI), were also evaluated to test their statistical relationship against these in-situ physiological indicators using the Spearman correlation coefficient (ρ). Both TSEB-PT and TSEB-2T CWSI related similarly to in-situ measurements (Ψleaf: ρ ~ 0.4; Ψstem: ρ ~ 0.55). On the other hand, stress indicators using canopy fluxes (i.e. CTSI and CSSI) were much more effective when using TSEB-2 T (Ψleaf: ρ = 0.45; Ψstem: ρ = 0.62) compared to TSEB-PT (Ψleaf: ρ = 0.18; Ψstem: ρ = 0.49), revealing important differences in the ET partitioning between model variants. These results demonstrate the utility of physically-based models to estimate ET and partitioned canopy fluxes, which can enhance the detection of vine water stress and quantitatively assess vine water demand to better manage irrigation practices., Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. Spanish Ministry of Science and Innovation and PRIMA EU, PCI2021-121932, PCI2021-121932, PCI2021-121932, PCI2021-121932, PCI2021-121932, PCI2021-121932, Spanish Ministry of Science and Innovation & European Union Next Generation EU/PRTR, FJC2021-047273-I ,FJC2021-047687-1 ,TED2021-129814B-I00, TED2021-129814B-I00, Spanish Ministry of Education and Professional Training, PRE2018-083227., Peer reviewed




Quantifying the Compatibility of Optical Reflectance Factors in a Field Intercomparison Experiment

Digital.CSIC. Repositorio Institucional del CSIC
  • Pacheco-Labrador, Javier
  • Peón, Juanjo
  • Jiménez, Marcos
  • Rodríguez-Pérez, José Ramón
  • Jiménez-Berni, José A.
  • Aragonés, David
  • Díaz-Delgado, Ricardo
  • Dorado, José
  • Castro, Ana Isabel de
  • Martín, M. Pilar
This work presents the results of an outdoor intercomparison experiment where the reflectance factors from five full-range field spectroradiometers (400–2500 nm) were compared using repeatability (RPT), reproducibility (RPR), and compatibility metrics. We confronted four analytical spectral devices (ASDs) and one Spectravista Corporation (SVC) sensor. Ancillary atmospheric characterization proved useful for filtering data acquired under suboptimal sky conditions. RPT uncertainties were low, while RPR uncertainty was significantly larger due to changes in the measured surface between measurements and directional effects induced by varying sun angles and illumination conditions. Compatibility proved to be a valuable metric for interpreting the intercomparison results and supports the importance and necessity for uncertainty quantification in field spectroradiometry. RPR uncertainty in dark heterogeneous surfaces, such as vegetation, might limit the identification of subtle differences associated with their biochemistry and require specific protocols that minimize these errors., This work was supported in part by Spanish National Research Council (CSIC) Interdisciplinary Thematic Platform (PTI) under Grant PTI-TELEDETECT; in part by Spanish Network of Optical Proximal Sensing (NetOPS) through MICIU/AEI/10.13039/501100011033 under Grant RED2022-134438-T; in part by Spanish Ministry of Science, Innovation and Universities through MCIN/AEI/10.13039/501100011033 under Project TED2021-129814B-I00; in part by European Union (EU) NextGenerationEU/PRTR; in part by the Sustainability for Mediterranean Hotspots in Andalusia, under Grant LIFEWATCH-2019-09-CSIC-4, POPE 2014–2020; in part by the SpaFLEXVal under Grant PID2022-137022OB-C33; in part by Fluorescence Explorer FLEX-S3 under Grant PCI2023-145988-2; in part by the eLTER Plus Project (INFRAIA- Integrating Activities for Starting Communities, Horizon 2020) under Agreement 871128; in part by European Union Recovery, Transformation and Resilience Funds, and NextGenerationEU/PRTR; and in part by the Partnership on Research and Innovation in the Mediterranean Area (PRIMA EU) through the Digital Agriculture Technologies for Irrigation efficiency (DATI)-Project “PCI2021-121932” and DECIMAL-Project “PID2020-113229RB-C41.” The work of Ricardo Díaz-Delgado was supported by “Salvador de Madariaga” through Spanish Ministry of Science, Innovation and Universities under Grant PRX22/00726, Peer reviewed