2026-09-15T17:41:07Zhttps://gredos.usal.es/oai/request

oai:gredos.usal.es:10366/1621442025-04-30T20:17:35Zcom_10366_4359com_10366_4349com_10366_3946com_10366_3823col_10366_4360

Sánchez Martín, Nilda Plaza Martín, Javier Criado, Marco Pérez-Sánchez, Rodrigo Gómez-Sánchez, M. Ángeles Morales Corts, María Remedios Palacios Riocerezo, Carlos 2025-01-21T10:59:01Z 2025-01-21T10:59:01Z 2023 Sánchez, N.; Plaza, J.; Marco Criado Nicolás; Pérez, R.; María Ángeles Gómez Sánchez; Morales, M. R.; Carlos Palacios Riocerezo. The second derivative of the NDVI time series as an estimator of fresh biomass: A case study of eight forage associations monitored via UAS. Drones. 7/6, pp. 347. 25/05/2023. ISSN 2504-446X. DOI: 10.3390/drones7060347 http://hdl.handle.net/10366/162144 10.3390/drones7060347 2504-446X The estimation of crop yield is a compelling and highly relevant task in the scenario of the challenging climate change we are facing. With this aim, a reinterpretation and a simplification of the Food and Agriculture Organization (FAO) fundamentals are presented to calculate the fresh biomass of forage crops. A normalized difference vegetation index (NDVI) series observed from a multispectral camera on board an unmanned aircraft system (UAS) was the basis for the estimation. Eight fields in Spain of different rainfed intercropping forages were flown over simultaneously, with eight field measurements from February to June 2020. The second derivative applied to the NDVI time series determined the key points of the growing cycle, whereas the NDVI values themselves were integrated and multiplied by a standardized value of the normalized water productivity (WP*). The scalability of the method was tested using two scales of the NDVI values: the point scale (at the precise field measurement location) and the plot scale (mean of 400 m2). The resulting fresh biomass and, therefore, the proposal were validated against a dataset of field-observed benchmarks during the field campaign. The agreement between the estimated and the observed fresh biomass afforded a very good prediction in terms of the determination coefficient (R2, that ranged from 0.17 to 0.85) and the agreement index (AI, that ranged from 0.55 to 0.90), with acceptable estimation errors between 10 and 30%. The best period to estimate fresh biomass was found to be between the second fortnight of April and the first fortnight of May DIPUTACIÓNDE SALAMANCA Junta de Castilla y León, Escalera de Excelencia eng producción forrajera modelización drones 2506.16 Teledetección (Geología) 3103.07 Cultivos Forrajeros 1209.14 Técnicas de Predicción Estadística The Second Derivative of the NDVI Time Series as an Estimator of Fresh Biomass: A Case Study of Eight Forage Associations Monitored via UAS info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion https://doi.org/10.3390/drones7060347 2018/00349/001 CLU-2018-04 info:eu-repo/semantics/openAccess Drones 7 6 347