Abstract
Time series of vegetation indices can be utilized to capture crop phenology information, and have been widely used in land cover and crop classification, phenological feature extraction, and planting structure monitoring. This is of great significance for guiding agricultural production and formulating agricultural policies. According to the characteristics of the GF-6 satellite’s newly-added red edge bands, wide field view and high-frequency imaging, the time series of vegetation indices about multi-temporal GF-6 WFV data are used for the study of land cover and crop classification. In this study, eight time steps of GF-6 WFV data were selected from March to October 2019 in Hengshui City. The normalized difference vegetation index (NDVI) time series and 10 different red edge spectral indices time series were constructed. Then, based on principal component analysis (PCA), using two feature selection and evaluation methods, stepwise discriminant analysis (SDA) and random forest (RF), the red edge vegetation index of normalized difference red edge (NDRE) was selected. Seven different lengths of NDVI, NDRE and NDVI&NDRE time series were reconstructed by the Savizky-Golay (S-G) smoothing algorithm. Finally, an RF classification algorithm was used to analyze the influence of time series length and red edge indices features on land cover and crop classification, and the planting structure and distribution of crops in the study area were obtained. The results show that: (1) Compared with the NDRE red edge time series, the NDVI time series is more conducive to the improvement of the overall classification accuracy of crops, and NDRE can assist NDVI in improving the crop classification accuracy; (2) With the shortening of NDVI and NDRE time series, the accuracy of crop classification is gradually decreased, and the decline is gradually accelerated; and (3) Through the combination of the NDVI and NDRE time series, the accuracy of crop classification with different time series lengths can be improved compared with the single NDVI time series, which is conducive to improving the classification accuracy and timeliness of crops. This study has fully tapped the application potential of the new red edge bands of GF-6 WFV time series data, which can provide references for crop identification and classification of time series data such as NDVI and red edge vegetation index of different lengths. At the same time, it promotes the application of optical satellite data with red edge bands in the field of agricultural remote sensing.
Highlights
Land cover and crop classification have become vital aspects of remote sensing satellite data applications [1]
The objectives of this study are as follows: (1) using multi-temporal GF-6 WFV data to construct a variety of different red edge indices time series, select the red edge index time series with the highest classification importance, and design three different vegetation indices time series classification schemes combined with normalized difference vegetation index (NDVI) time series; and (2) random forest (RF) classification algorithm is used to analyze the influence of different time series length and optimal red edge index feature on the classification accuracy, which provides a reference for GF-6 WFV data to be better used in land cover and crop classification
G smoothing can effectively remove the noise and data errors caused by clouds, aerosols, and normalized difference red edge (NDRE) time series curves of orchards were easy to distinguish from spring maize, and other factors, so it is more consistent with the seasonal rhythm and phenological cotton, and minor crops, while the NDVI and NDRE time series curves of spring maize, changes of different crops
Summary
Land cover and crop classification have become vital aspects of remote sensing satellite data applications [1]. Time series remote sensing data can reflect the differences in the growth status of different crops, show different phenological characteristics, improve the separability and classification accuracy, and have been widely used in the field of agricultural remote sensing [5,6]. The use of multi-temporal remote sensing data to a construct normalized difference vegetation index (NDVI) and other vegetation indices time series, combined with the seasonal rhythms and phenological differences of different crops, has been widely carried out in crop classification, which has improved the accuracy of crop classification. Wardlow et al [7] used MODIS NDVI time series data to classify crops in Kansas, USA, and produced land use and land cover classification maps related to crops by using a hierarchical classification method. Hao et al [8] used Landsat-5 TM and HJ-1 CCD data to obtain NDVI time series data with high temporal resolution through data combination, combined with the method of optimal classification phase selection and support vector machine classification to classify crops in the Bole and Manas counties of Xinjiang, China
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