Remote sensing monitoring method for multiple cropping mode of bulk grain and oil crops in one-year-two-cropping area

Abstract

The invention discloses a double cropping mode remote sensing monitoring method for bulk grain and oil crops in a double cropping area, and the method comprises the following steps: A, acquiring cropping information of a research area by combining with Chinese cropping zoning data, and carrying out geographical location zoning on the research area according to a longitude and latitude range; B, performing a series of processing on the high-resolution multispectral satellite image of the research area to obtain a preprocessed multiband image, an EVI gray image, an edgNDVI gray image, an LSWI gray image and a D-value gray image, of which the time phases can be distinguished in the northern region and the southern region of one year with two matures; C, classifying and identifying the two-season bulk grain and oil crops in the northern double-cropping area and the southern double-cropping area; D, optimizing a classification identification result by using a small plaque aggregation method; E, judging a multiple cropping mode. According to the invention, rapid and high-precision acquisition of the multiple cropping mode of the bulk grain and oil crops under a large spatial scale and a complex planting system can be realized.

Description

technical field [0001] The invention relates to a method for monitoring the multiple cropping mode of bulk grain and oil crops, in particular to a remote sensing monitoring method for the multiple cropping mode of bulk grain and oil crops in areas with two crops a year. Background technique [0002] Multiple cropping refers to the continuous planting of two or more crops on the same field within a year, which is an effective way to improve the utilization rate of resources such as land, light, and heat, and to increase the annual output per unit area. Monitoring the multiple cropping mode of bulk grain and oil crops is helpful to accurately grasp the spatial and temporal distribution pattern of bulk grain and oil crops in a year, which is of great significance for formulating agricultural policies, optimizing planting structure, and ensuring food security. [0003] With the rapid development of earth observation technology, satellite remote sensing has become one of the impo...

AI Technical Summary

Problems solved by technology

At present, domestic and foreign research mainly focuses on remote sensing classification, identification and distribution surveys of crops in a specific region (such as a certain administrative unit) and a certain season, and seldom involves multi-season crops in a year that comprehensively considers the division of agricultural ripening systems and the differences in crop types between the north and the south. Classification identification and distribution investigation are not conducive to a clear understanding of the agricultural planting structure under large spatial scales and multiple cropping methods

[0004] At the same time, most of the existing related technologies adopt the following implementation methods: 1. Supervised classification methods based on sample training (can be divided into two types: pixel-oriented and object-oriented), the accuracy of which depends largely on the number of samples and quality, and the sample training process is relatively time-consuming, which is not suitable for applications on large spatial scales

2. Vegetation index time series analysis method based on medium-resolution remote sensing data. This method needs the support of long-term and high-frequency optical images (such as MODIS), so its accuracy is often affected by cloudy and rainy weather, and there is a problem with medium spatial resolution. The problem of a large number of mixed cells caused by

3. The decision tree classification method based on multi-temporal high-resolution remote sensing data, which has fast operation speed and high classification accuracy; however, in the existing research, or due to the small number of satellite sensor bands selected and the low spectral recognition degree, crops Obviously mixed and misclassified; or because the selected spectral index is relatively single and cannot significantly reflect the differences in spectral characteristics of different crops, resulting in complex decision tree judgment conditions or poor classification results; or because the method design process does not involve post-processing of classification results , resulting in salt and pepper noise of pixel-scale classification in the classification results

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