Abstract:As one of the machine learning algorithms, random forest (RF) regression was proposed firstly to construct remote sensing monitoring model to inverse leaf SPAD value in different growth stages of wheat. The experiment was carried out during 2010—2013 in Jiangsu province. Based on the wheat leaves and synchronous China’s domestic HJ-CCD multi-spectral data in the jointing stage, the booting stage and the anthesis stage respectively, the relationships between SPAD and eight vegetation indices were analyzed at corresponding period. According to the selected vegetation indices which were significantly related to the leaf SPAD value in the 0.01 level, the model for estimating leaf SPAD value at each period was built by using RF algorithm, namely the RF-SPAD model. At the corresponding period, SVR-SPAD model which was based on the support vector regression (SVR) and BP-SPAD model which was based on the back propagation (BP) neural network were constructed as compared models. SVR and BP neural network were both machine learning algorithms. Based on R2 and RMSE, the learning abilities and generalization abilities of three models at each period were analyzed. The results showed that the RF-SPAD model at three stages presented the strongest learning ability, which its R2 was the highest as well as RMSE was the lowest, concretely, R2 and RMSE were 0.89 and 1.54 in jointing stage, 0.85 and 1.49 in booting stage and 0.80 and 1.71 in anthesis stage respectively. RF-SPAD model’s prediction ability was equal to or higher than the reference models which R2 and RMSE were 0.55 and 2.11 in jointing stage, 0.72 and 2.20 in booting stage, 0.60 and 3.16 in anthesis stage respectively.
