Estimating alfalfa fiber components using machine learning algorithms based on in situ hyperspectral and Sentinel-2 data in the Hexi Corridor region

Abstract

Alfalfa, a high-quality forage, has good palatability and nutritional value. Neutral detergent fiber (NDF) and acid detergent fiber (ADF) are both key indicators of alfalfa quality. However, the uncertainties in existing studies regarding the sensitive bands and inversion mechanism for NDF and ADF contents estimations have limited the application of high-precision remote sensing-based inversion. In this study, using hyperspectral and Sentinel-2 (S2) multispectral data of cultivated alfalfa in the Hexi Corridor region from 2020 to 2022, we analyze the characteristic spectral band and vegetation indices (VIs) required to estimate the NDF and ADF contents of alfalfa. The key conclusions are as follows. (1) The sensitive bands selected using ASD hyperspectral data are mainly in the blue, green, red-edge, and short-wave infrared (SWIR) regions, while the sensitive bands based on S2 data cover a broader range between the blue and SWIR regions. (2) Among the 21 NDF and 21 ADF models based on ASD data in this study, the optimal models are both artificial neural network (ANN) models constructed by VIs (R² of 0.80 for both, RMSEs of 2.27% and 1.75% and mean absolute errors (MAEs) of 1.77% and 1.38% for NDF and ADF, respectively). For the S2 data, the optimal models are also ANN-based and constructed using VIs (with R² values of 0.66 and 0.72, RMSEs of 3.06% and 2.24%, and MAEs of 2.50% and 1.79% for NDF and ADF, respectively. (3) The inversion results using the optimal model indicate that the proportion of alfalfa area in the typical study area with NDF and ADF contents characterized by a supreme grade is greater than 60%. Overall, both the ASD hyperspectral and S2 multispectral data can accurately predict alfalfa NDF and ADF contents. This approach provides an effective technical means by which the management of local alfalfa production may be guided.