Abstract
Remote sensing data with high spatial and temporal resolutions can help to improve the accuracy of the estimation of crop planting acreage, and contribute to the formulation and management of agricultural policies. Therefore, it is important to determine whether multisource sensors can obtain high spatial and temporal resolution remote sensing data for the target sensor with the help of the spatiotemporal fusion method. In this study, we employed three different sensor datasets to obtain one normalized difference vegetation index (NDVI) time series dataset with a 5.8-m spatial resolution using a spatial and temporal adaptive reflectance fusion model (STARFM). We studied the effectiveness of using multisource remote sensing data to extract crop classifications and analyzed whether the increase in the NDVI time series density could significantly improve the accuracy of the crop classification. The results indicated that multisource sensor data could be used for crop classification after spatiotemporal fusion and that the data source was not limited by the sensor platform. With the increase in the number of NDVI phases, the classification accuracy of the support vector machine (SVM) and the random forest (RF) classifier gradually improved. If the added NDVI phases were not in the optimal time period for wheat recognition, the classification accuracy was not greatly improved. Under the same conditions, the classification accuracy of the RF classifier was higher than that of the SVM. In addition, this study can serve as a good reference for the selection of the optimal time range for base image pairs in the spatiotemporal fusion method for high accuracy mapping of crops, and help avoid excessive data collection and processing.
Highlights
The spatial pattern of crops is the manifestation of agricultural production activities on land use and the efficient usage of natural resources and scientific field management [1,2]
The Landsat 8 and HJ-1A/B data with 30-m spatial resolution can better reflect the spatial heterogeneity in the crop distribution than can moderate resolution imaging spectroradiometer (MODIS)
The research in this study showed that multisource sensor data can be used for crop classification after spatiotemporal fusion and that the data source was not limited by the sensor platform, which provides a flexible choice for data sources
Summary
The spatial pattern of crops is the manifestation of agricultural production activities on land use and the efficient usage of natural resources and scientific field management [1,2]. With the growth of populations and the reduction of arable land, timely and accurate mapping of the spatial distribution of crops is an important foundation for growth monitoring, yield estimation, disaster assessment, grain production macro-control, and agricultural trade regulation [3,4]. The traditional approach for obtaining crop planting area was based on sampling surveys and step-by-step summaries [6]. This process is labor intensive and uses a large quantity of material resources, and the accuracy is greatly affected by a variety of subjective and objective factors [7,8]. Few sensors currently provide high spatial and temporal resolution remote sensing data for mapping the complexity and diversity of crop planting structures and farmland fragmentation
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