2022 IEEE Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)最新文献

{"title":"Preliminary spray nozzle characterization activities through shadowgraphy at the AgroForestry Innovation Lab (AFI-Lab)","authors":"Lorenzo Becce, Saba Amin, Giovanni Carabin, F. Mazzetto","doi":"10.1109/MetroAgriFor55389.2022.9965106","DOIUrl":"https://doi.org/10.1109/MetroAgriFor55389.2022.9965106","url":null,"abstract":"The AgroForestry Innovations Lab (AFI-Lab) of the Free University of Bolzano-Bozen (unibz), in synergy with local farmers and machine builders, is launching a research thread investigating sprayer performance and drift mitigation strategies in the context of South Tyrol, Italy. Nozzle characterization via Particle/Droplet Image Analysis (PDIA) is a stepping stone in this direction; the present work involves the use of an Oxford Lasers N60 shadowgraph to (i) gather a database of nozzle characteristics for future reference, (ii) interface the shadowgraph itself to custom Matlab code through the use of both raw and post-processed particle data, (iii) develop a methodology to evaluate nozzle performance, with a flexible approach toward the validation of future nozzle behaviour models, (iv) gathering expertise for internationally-recognized certification activities of new nozzles, and finally to (v) support and provide input for in-development sprayer performance models, aimed at drift prediction in support of wind tunnel activities for real-scale machine performance evaluation. A set of three conventional hollow-cone nozzles and their anti-drift equivalents have been analysed at two operating pressures in different portions of the cone. The results of the data acquisitions have been parsed and stored by an automated algorithm in Matlab and used to develop the code for the visualization and comparison of the droplet distributions. The work is closed with some remarks and future perspectives enabled by the choice of this type of instrument, especially the characterization of cone geometry and droplet density across the cone aperture.","PeriodicalId":374452,"journal":{"name":"2022 IEEE Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)","volume":"300 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116262501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Use of satellite-based phenometrics, geophysical soil data and a semi-distributed agro-hydrological model for variable-rate irrigation management in a maize farm in northern Italy","authors":"A. Mayer, B. Ortuani, M. De Peppo, F. Nutini, A. Crema, M. Boschetti, G. Ragaglini, A. Facchi","doi":"10.1109/MetroAgriFor55389.2022.9964911","DOIUrl":"https://doi.org/10.1109/MetroAgriFor55389.2022.9964911","url":null,"abstract":"In the most recent agricultural seasons water scarcity problems in many areas of the Po plain had become more frequent; thus, the introduction of irrigation-saving methods and technologies is becoming essential, especially in areas that cannot benefit from a large availability of surface water resources. In the context of precision irrigation, aimed at increasing water and energy productivity at the farm scale by operating a spatial and temporal variable-rate irrigation if needed, agro-hydrological models play a crucial role. In this study, remote-sensed phenometrics and biophysical crop parameters, and delineation of homogeneous management zones through an electromagnetic induction (EMI) sensor, were used to parametrize a semi-distributed mechanistic agro-hydrological model in the case of a 300-ha maize farm located in Brescia (northern Italy). The modelling system was applied to simulate irrigation use in the current situation and in the case of an optimized and variable rate irrigation management. Average results for the period 2016–2020 showed a water and energy saving of about 20% in the case of the optimized irrigation management, confirmed by savings achieved in a pilot pivot in which the modelling procedure was used in 2021 to support the irrigation scheduling in combination with 7-days weather forecast data.","PeriodicalId":374452,"journal":{"name":"2022 IEEE Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123885426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of remotely-sensed evapotranspiration datasets at different spatial and temporal scales at forest and grassland sites in Italy","authors":"D. De Santis, Claudia d’Amato, Paulina Bartkowiak, S. Azimi, M. Castelli, R. Rigon, C. Massari","doi":"10.1109/MetroAgriFor55389.2022.9964755","DOIUrl":"https://doi.org/10.1109/MetroAgriFor55389.2022.9964755","url":null,"abstract":"Remote sensing measurements offer great potential to quantify evapotranspiration (ET) more accurately. This work assesses several remotely-sensed ET datasets at high, medium and coarse scale through a comparison with eddy covariance (EC) measurements. In particular, daily retrievals obtained from the Sen-ET model and the ECOSTRESS ET product, both having resolutions of tens of meters, were considered. In addition, 3-km daily ET estimates derived from SEVIRI were analyzed, together with 8-day MODIS ET composites at 500-m spatial resolution. The remotely-sensed actual ET retrievals at medium and coarse scale were compared with EC observations at five forest and grassland sites over different climate zones in Italy. Additionally, for one of these sites (Monte Bondone), the high-resolution ET estimates within the EC footprint were also evaluated. Sen-ET retrievals demonstrated a remarkable ability to describe EC latent heat fluxes (e.g., Pearson correlation coefficient, R, equal to 0.83, and unbiased root-mean-square-difference, ubRMSD, equal to 0.85 mm/day), and therefore great potential for producing high-resolution ET estimates, while ECOSTRESS data were less performant (e.g., R = 0.46, and $boldsymbol{ubRMSD}=1.65boldsymbol{mm}/text{day})$. Despite the spatial differences between EC footprint size and the medium along with coarse scale of remotely-sensed datasets, the latter both showed a good agreement with the measured EC fluxes. The SEVIRI ET, in particular, proved to be significantly suitable for the ET description also at a local scale (at daily time step, average R = 0.84, and average $boldsymbol{u b R M S D=mathbf0. 8 3}$ mm/day over the study sites), despite a tendency to underestimate in-situ measurements (negative biases ranging between 0.2 and 0.85 mm/day were observed). This study contributes to the topic of evaluating specific remotely-sensed ET datasets under different conditions. The obtained results generally confirm the value of satellite-derived retrievals for ET estimate and monitoring, as well as their potential to improve ET modeling.","PeriodicalId":374452,"journal":{"name":"2022 IEEE Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129905084","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}