Evaluation of Spectroscopy and Methodological Pre-Treatments to Estimate Soil Nutrients in the Vineyard

Remote Sensing

Marta Rodríguez-Febereiro¹  Jorge Dafonte¹  María Fandiño¹  Javier J. Cancela¹  José Ramón Rodríguez-Pérez² 

[1] GI-1716, Proyectos y Planificación, Departamento Ingeniería Agroforestal, Escola Politécnica Superior de Enxeñaría, Universidade de Santiago de Compostela, Rúa Benigno Ledo s/n, 27002 Lugo, Spain;Grupo de Investigación en Geomática e Ingeniería Cartográfica (GEOINCA), Universidad de León, Avenida de Astorga s/n, 24401 León, Spain;

关键词: organic matter;    VIS;    NIR;    precision viticulture;    PLSR;    random forest;   

DOI  :  10.3390/rs14061326

来源: DOAJ

【 摘 要 】

The characterization of vineyard soil is a key issue for crop management, which directly affects the quality and yield of grapes. However, traditional laboratory analysis of soil properties is tedious and both time and cost consuming, which is not suitable for precision viticulture. For this reason, a fast and convenient soil characterization technique is needed for soil quality assessment and precision soil management. Here, spectroscopy appears as a suitable alternative to assist laboratory analysis. This work focuses on estimating soil properties by spectroscopy. Our study was carried out using 96 soil samples collected from three vineyards in Rias Baixas Designation of Origen (Galicia, Spain). The soils that were characterized include nitrogen (N), organic matter (OM) and clay content (Clay). The presented work compared two regression techniques (partial least squares (PLSR) and random forest (RF)) and four spectral ranges: visible—VIS (350–700 nm), near infrared—NIR (701–1000 nm), short wave infrared—SWIR (1001–2500 nm) and VIS-NIR-SWIR (350–2500 nm) in order to identify the more suitable prediction models. Moreover, the effect of pre-treatments in reflectance data (smoothing Svitzky–Golay, SG, baseline normalization, BN, first derivative, FD, standard normal variate, SNV, logarithm of 1/reflectance or spectroscopy (SP) and detrending, SNV-D) was evaluated. Finally, continuous maps of the soil properties were created based on estimated values of regression models. Our results identified PLSR as the best regression technique, with less computation time than RF. The data improved after applying transformation in reflectance data, with the best results from spectroscopy pre-treatment (logarithm of 1/Reflectance). PLSR performances have obtained determination coefficients (R²) of 0.69, 0.73 and 0.52 for nitrogen, organic matter, and clay, respectively, with acceptable accuracy (RMSE: 0.03, 1.06 and 2.90 %) in a short time. Furthermore, the mapping of soil vineyards generates information of high interest for the precision viticulture management, as well as a comparison between the methodologies used.

【 授权许可】

Unknown