Abstract
Accurate estimation of crop area is essential to adjusting the regional crop planting structure and the rational planning of water resources. However, it is quite challenging to map crops accurately by high-resolution remote sensing images because of the ecological gradient and ecological convergence between crops and non-crops. The purpose of this study is to explore the combining application of high-resolution multi-temporal Sentinel-1 (S1) radar backscatter and Sentinel-2 (S2) optical reflectance images for maize mapping in highly complex and heterogeneous landscapes in the middle reaches of Heihe River, northwest China. We proposed a new two-step method of vegetation extraction and followed by maize extraction, that is, extract the vegetation-covered areas first to reduce the inter-class variance by using a Random Forest (RF) classifier based on S2 data, and then extract the maize distribution in the vegetation area by using another RF classifier based on S1 and/or S2 data. The results demonstrate that the vegetation extraction classifier successfully identified vegetation-covered regions with an overall accuracy above 96% in the study area, and the accuracy of the maize extraction classifier constructed by the combined multi-temporal S1 and S2 images is significantly improved compared with that S1 (alone) or S2 (alone), with an overall accuracy of 87.63%, F1_Score of 0.86, and Kappa coefficient of 0.75. In addition, with the introduction of multi-temporal S1 and/or S2 images in crop growing season, the constructed RF model is more beneficial to maize mapping.
Highlights
The pixel-by-pixel supervised learning algorithm, based on decision tree (DT), maximum likelihood classification (MLC), artificial neural network (ANN), support vector machine (SVM) and random forest (RF), were the most widely used methods for crop spatial distribution information extraction, e.g., Basukala et al, (2007) compared MLC, SVM and Random Forest (RF) methods to classify cotton, wheat and rice in the Khorezm Region of Uzbekistan and southern part of Autonomous Republic of Karakalpakstan, the results indicate that the RF performed much better than MLC and SVM, especially for the cases when the number of training samples is limited [34]
The results indicate that the Normalized difference vegetation index (NDVI), Normalized difference tillage index (NDTI) and soil tillage index (STI) are the three most important variables in the vegetation extraction process, especially NDVI from July to September (i.e., NDVI_Jul, NDVI_Aug, and NDVI_Sep), and their importance values reach 9.50%, 9.28% and 13.47%, respectively
We found that S1 images do not perform very well in the early maize planting area extraction, this is similar to the study of Demarez et al, (2019), which indicates that the classification accuracy of early crop is the lowest with Kappa ≈ 0.25, as their study did not conduct monthly synthesis processing for the multi-temporal images [30]
Summary
Satellite remote sensing (RS) has the unique ability of large-area observation, and great achievements have been made in crop mapping by RS. It has become a common way to obtain large-scale crop planting area information by using RS satellites, such as Landsat satellite, Terra and Aqua satellite, Gaofen-1 satellite, and Sentinel satellite [1,2], with the availability of a series of remote sensing cloud computing platforms, such as google earth engine and Amazon web services. Limited by the spatial and temporal resolution of high-resolution images and the processing ability of massive remote sensing images, low and moderate spatial resolution RS images [3], such as MODIS, AVHRR images, are dominant data sources in the previous study of crop classification and extraction, since these low and moderate remote sensing images hold considerable promise for large area crops mapping given their global coverage, daily temporal resolution, and free access
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
