Impact of short infrared wave band on identification accuracy of corn and soybean area

Abstract: To study the impact of short infrared wave bands (1 100-2 500 nm) setting on crop classification accuracy, and to provide data and application support for designed new types of sensors, in the study, we took north part of Bei'an city, Heilongjiang province as the study area, and studied the impact of the short infrared wave bands of 1 560-1 660 nm (SWIR 1) and 2 100-2 300 nm (SWIR 1) on identification accuracy of two crops of corn (Zea may L) and soybean (Glycine max) by selecting OLI (Operational Land Imager) data carried by US LandSat-8. Based on the mono temporal and multi-temporal conditions, and on the basis of five wave bands of coastal blue (433-453 nm), blue (450-515 nm), green (525-600 nm), red (630-680 nm), and near-infrared (845-885 nm), with five wave band combinations without short infrared wave band were used. One additional short infrared wave band (SWIR 1) was involved, and two additional short infrared wave band (SWIR 1and SWIR 2) were also used, with a total of 6 alternative schemes. In the study, we analyzed the remote sensing identification ability of short infrared on two crops: corns and soybeans, based on the maximum likelihood supervised classification method. Mono temporal OLI image Data of August 7, 2014 were taken for the study and five multi temporal image data of June 13, June 29, July 15, August 7, and September 17 of 2014 were also taken. The 5 km × 5 km grids of the covered study area were taken as the study units and under the principle of equal probability of corn and soybean area ratio, and 21 ground samples were chosen as training samples. used A visual interpretation method was used for identification of crop within ground samples. Accuracy verification data were from the background investigation results of crop areas in the study area. These data were obtained by using visual correction based on Rapideye image automatic classification with a spatial resolution of 5 m. The result showed that under the condition of mono temporal image classification, introduction of short infrared wave band can greatly improve the classification capacity of corn and soybean. Under the condition of introducing one short infrared wave band, the overall classification accuracy has been improved from 87.0% to 90.8%, up by 3.8%, Kappa coefficient was improved from 0.74 to 0.82, with a significant decrease of "Pepper salt problem", The user's accuracy of corn classification has been improved from original 85.4% to 91.5%, up by 6.1%. Mapping accuracy was improved from 89.6% to 90.3%. The mapping accuracy of soybean has been improved from original 84.5% to 91.5%, up by 7.0%, with user's accuracy improved from 88.9% to 90.2%. The separation degree of corn and soybean has been improved from 1.53 to 1.93, indicating that the short wave infrared wave band can remarkably improve the separation capacity of corn and soybean. Under the condition of multi-temporal image classification, the improvement of identification capacity of corn and soybean by introducing of short infrared wave band was limited. Under the condition of introducing one short infrared wave band, the overall classification accuracy was improved from 92.4% to 92.9%, only up by 0.5%, indicating that multi-temporal data, to some extent, can replace short infrared wave band on improving crop identification effect. Regardless of mono temporal or multi-temporal conditions, introduction of two short infrared wave bands did not show any significant changes on overall identification capacity compared with one short infrared wave band. Both correlations of five images on two short infrared wave bands exceeded 0.96, indicating that introduction of redundant bands with strong correlation has limited effect in improving crop area identification accuracy. The results have further quantified the separating capacity of short infrared wave bands on two crops of corns and soybean, and provided basis for wave band setting of domestic satellite short-wave sensors.