Plastic mulching is an important technology in agricultural production both in China and the rest of the world. In spite of its benefit of increasing crop yields, the booming expansion of the plastic mulching area has been changing the landscape patterns and affecting the environment. Accurate and effective mapping of Plastic-Mulched Farmland (PMF) can provide useful information for leveraging its advantages and disadvantages. However, mapping the PMF with remote sensing is still challenging owing to its varying spectral characteristics with the crop growth and geographic spatial division. In this paper, we investigated the potential of Radarsat-2 data for mapping PMF. We obtained the backscattering intensity of different polarizations and multiple polarimetric decomposition descriptors. These remotely-sensed information was used as input features for Random Forest (RF) and Support Vector Machine (SVM) classifiers. The results indicated that the features from Radarsat-2 data have great potential for mapping PMF. The overall accuracies of PMF mapping with Radarsat-2 data were close to 75%. Although the classification accuracy with the back-scattering intensity information alone was relatively lower owing to the inherent speckle noise in SAR data, it has been improved significantly by introducing the polarimetric decomposition descriptors. The accuracy was nearly 75%. In addition, the features derived from the Entropy/Anisotropy/Alpha (H/A/Alpha) polarimetric decomposition, such as Alpha, entropy, and so on, made a greater contribution to PMF mapping than the Freeman decomposition, Krogager decomposition and the Yamaguchi4 decomposition. The performances of different classifiers were also compared. In this study, the RF classifier performed better than the SVM classifier. However, it is expected that the classification accuracy of PMF with SAR remote sensing data can be improved by combining SAR remote sensing data with optical remote sensing data.

Spectral indices based on unmanned aerial vehicle (UAV) multispectral images combined with machine learning algorithms can more effectively assess chlorophyll content in plants, which plays a crucial role in plant nutrition diagnosis, yield estimation and a better understanding of plant and environment interactions. Therefore, the aim of this study was to use UAV-based spectral indices deriving from UAV-based multispectral images as inputs in different machine learning models to predict canopy chlorophyll content of potato crops. The relative chlorophyll content was obtained using a SPAD chlorophyll meter. Random Forest (RF), support vector regression (SVR), partial least squares regression (PLSR) and ridge regression (RR) were employed to predict the chlorophyll content. The results showed that RF model was the best performing algorithm with an R² of 0.76, Root Mean Square Error (RMSE) of 1.97. Both RF and SVR models showed much better accuracy than PLSR and RR models. This study suggests that the best models, RF model, allow to map the spatial variation in chlorophyll content of plant canopy using the UAV multispectral images at different growth stages.