Remote Sensing, Vol. 16, Pages 4250: Spatio-Temporal Generalization of VIS-NIR-SWIR Spectral Models for Nitrogen Prediction in Sugarcane Leaves

Remote Sensing, Vol. 16, Pages 4250: Spatio-Temporal Generalization of VIS-NIR-SWIR Spectral Models for Nitrogen Prediction in Sugarcane Leaves

Remote Sensing doi: 10.3390/rs16224250

Authors: Carlos Augusto Alves Cardoso Silva Rodnei Rizzo Marcelo Andrade da Silva Matheus Luís Caron Peterson Ricardo Fiorio

Nitrogen fertilization is a challenging task that usually requires intensive use of resources, such as fertilizers, management and water. This study explored the potential of VIS-NIR-SWIR remote sensing for quantifying leaf nitrogen content (LNC) in sugarcane from different regions and vegetative stages. Conducted in three regions of São Paulo, Brazil (Jaú, Piracicaba and Santa Maria), the research involved three experiments, one per location. The spectral data were obtained at 140, 170, 200, 230 and 260 days after cutting (DAC). From the hyperspectral data, clustering analysis was performed to identify the patterns between the spectral bands for each region where the spectral readings were made, using the Partitioning Around Medoids (PAM) algorithm. Then, the LNC values were used to generate spectral models using Partial Least Squares Regression (PLSR). Subsequently, the generalization of the models was tested with the leave-one-date-out cross-validation (LOOCV) technique. The results showed that although the variation in leaf N was small, the sensor demonstrated the ability to detect these variations. Furthermore, it was possible to determine the influence of N concentrations on the leaf spectra and how this impacted cluster formation. It was observed that the greater the average variation in N content in each cluster, the better defined and denser the groups formed were. The best time to quantify N concentrations was at 140 DAC (R2 = 0.90 and RMSE = 0.74 g kg−1). From LOOCV, the areas with sandier soil texture presented a lower model performance compared to areas with clayey soil, with R2 < 0.54. The spatial generalization of the models recorded the best performance at 140 DAC (R2 = 0.69, RMSE = 1.18 g kg−1 and dr = 0.61), decreasing in accuracy at the crop-maturation stage (260 DAC), R2 of 0.05, RMSE of 1.73 g kg−1 and dr of 0.38. Although the technique needs further studies to be improved, our results demonstrated potential, which tends to provide support and benefits for the quantification of nutrients in sugarcane in the long term.