Evaluating PlanetScope and UAV Multispectral Data for Monitoring Winter Wheat and Sustainable Fertilization Practices in Mediterranean Agroecosystems

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作者
Moletto-Lobos, Italo [1 ]
Cyran, Katarzyna [1 ]
Orden, Luciano [2 ,3 ]
Sánchez-Méndez, Silvia [2 ]
Franch, Belen [1 ,4 ]
Kalecinski, Natacha [4 ]
Andreu-Rodríguez, Francisco J. [2 ]
Mira-Urios, Miguel Á. [2 ]
Saéz-Tovar, José A. [2 ]
Guillevic, Pierre C. [5 ]
Moral, Raul [2 ]
机构
[1] Global Change Unit, Image Processing Laboratory, Universitat de Valencia, Paterna,46980, Spain
[2] Instituto de Investigación e Innovación Agrolimentaria y Agroambiental (CIAGRO-UMH), Universidad Miguel Hernández, Carretera de Carretera de Beniel Km 3.2, Orihuela,03312, Spain
[3] Departamento de Agronomía, Universidad Nacional del Sur (UNS), San Andrés 800, Bahía Blanca 8000, Buenos Aires, Argentina
[4] Department of Geographical Sciences, University of Maryland, College Park,MD,20742, United States
[5] Planet Labs Germany GmbH, Kurfürstendamm 22, Berlin,10719, Germany
关键词
Fertilizers;
D O I
10.3390/rs16234474
中图分类号
学科分类号
摘要
Cereal crops play a critical role in global food security, but their productivity is increasingly threatened by climate change. This study evaluates the feasibility of using PlanetScope satellite imagery and a UAV equipped with the MicaSense RedEdge multispectral imaging sensor in monitoring winter wheat under various fertilizer treatments in a Mediterranean climate. Eleven fertilizer treatments, including organic-mineral fertilizer (OMF) pellets, were tested. The results show that conventional inorganic fertilization provided the highest yield (8618 kg ha⁻1), while yields from OMF showed a comparable performance to traditional fertilizers, indicating their potential for sustainable agriculture. PlanetScope data demonstrated moderate accuracy in predicting canopy cover (R2 = 0.68), crop yield (R2 = 0.54), and grain quality parameters such as protein content (R2 = 0.49), starch (R2 = 0.56), and hectoliter weight (R2 = 0.51). However, its coarser resolution limited its ability to capture finer treatment-induced variability. MicaSense, despite its higher spatial resolution, performed poorly in predicting crop components, with R2 values below 0.35 for yield and protein content. This study highlights the complementary use of remote sensing technologies to optimize wheat management and support climate-resilient agriculture through the integration of sustainable fertilization strategies. © 2024 by the authors.
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