Remote sensing rating method for growth vigor of crops on large scale

Abstract

The invention discloses a remote sensing rating method for growth vigor of crops on a large scale. The remote sensing rating method includes A, acquiring temporal MODIS (moderate-resolution imaging spectroradiometer) data of evaluation years, and preprocessing the temporal MODIS data to obtain temporal EVI (enhanced vegetation index) data; B, revising the temporal EVI data; C, acquiring temporal EVI data of comparison years, analyzing the similarity among the temporal EVI data of the comparison years and the revised temporal EVI data and finding out reference temporal EVI data for rating the growth vigor; D, grading and evaluating the temporal EVI data of specified numbers of periods of the evaluation years according to the reference temporal EVI data. The MODIS data contain quality evaluation information. The remote sensing rating method has the advantages that the remote sensing rating method is adaptive to the requirement on monitoring the growth vigor of the crops in complicated planting systems on a large scale, and influence of phenological change of the crops and change of crop planting structures can be effectively prevented as compared with the traditional simple historical same-period comparison method.

Description

technical field [0001] The invention relates to the technical field of remote sensing image processing, in particular to a remote sensing rating method for the growing condition of large-scale crops. Background technique [0002] Crops are related to the national economy and people's livelihood, and it is more important to grasp the crop growth situation as early as possible during the crop growth period than to accurately estimate the crop planting area and total output itself. [0003] Remote sensing technology is often used in large-scale crop growth monitoring because of its fast acquisition of surface information, wide coverage, short cycle, and strong real-time performance. But to carry out large-scale crop growth monitoring must solve three problems: (1) Accuracy problem. Some methods can achieve ideal high precision in small-scale experiments, but it is difficult to guarantee the accuracy in large-scale applications. (2) Speed ​​and efficiency issues. The cultivat...

AI Technical Summary

Problems solved by technology

However, to carry out large-scale monitoring of crop growth, three problems must be solved: (1) Accuracy

Some methods can achieve ideal high precision in small-scale experiments, but it is difficult to guarantee the accuracy in large-scale applications

(2) Speed ​​and efficiency issues

In order to achieve the purpose of real-time monitoring, the growth monitoring method must meet the requirements of fast speed and high efficiency. Therefore, some accurate processing methods suitable for small area applications are not suitable for large-scale operation.

(3) Cost issue

However, this method has the following defects: (1) It does not consider the impact of crop phenology changes

Due to the influence of different meteorological conditions in different years, the growth period of crops may have a large gap, so it is very unreasonable to compare the data of the same period over the years

(2) At present, there is no unified standard for crop growth grading and grading, and the lack of research on grading standards has led to different growth grading results obtained by different institutions

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