Vegetation indices and NIR-SWIR spectral bands as a phenotyping tool for water status determination in soybean

Autor: Marcos Rafael Nanni, José Renato Bouças Farias, Patricia Braga, A. L. H. Caranhato, Leandro Simões Azeredo Gonçalves, Liliane Marcia Mertz-Henning, M. B. Fuhrmann, Luís Guilherme Teixeira Crusiol, Alexandre Lima Nepomuceno, Alessandra Koltun, Norman Neumaier
Rok vydání: 2020
Předmět:
Zdroj: Precision Agriculture. 22:249-266
ISSN: 1573-1618
1385-2256
DOI: 10.1007/s11119-020-09740-4
Popis: Drought is one of the main limiting factors of soybean production. The great deal of time and effort that current available phenotyping methods demand hampers the selection of tolerant genotypes. Therefore, the development of techniques capable of determining the water status of plants in a fast and practical way may improve the ability to distinguish genotypes under water deficit conditions. The aim of this study was to correlate physiological variables such as relative water content and gas exchange measurements, with vegetation indices (VIs) and spectral bands in order to optimize tools for plant phenotyping. Two trials were carried out, one in a climatic chamber and one in the field. The soybean genotypes were submitted to water deficit and control (irrigated) conditions. The variables measured were relative water content, leaf temperature, photosynthesis, transpiration, stomatal conductance and internal CO2 content. The VIs NDWI(1000–1600), NDWI(1000–2300), NMDI, MSI and the spectral bands SWIR1600, SWIR2300, ρ1440, ρ1920, ρ1440+ρ1920, ρ1920−ρ1440 and SWIR−ρ1440 were obtained using a hyperspectral sensor. According to the results, the physiological measurements, the VIs and the spectral bands were able to differentiate the water conditions to which the genotypes were submitted and, in some cases, the indices and bands were more sensitive than the physiological measures to detect genotype effect. All indices and bands were efficient in determining the water status of soybean plants. However, the SWIR indices were the most sensitive, allowing the differentiation of a greater number of genotypes with high accuracy.
Databáze: OpenAIRE
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