A Remote Sensing Grading Method for Large-scale Crop Growth Condition

Abstract

The invention discloses a remote sensing rating method for the growth of large-scale crops, which includes: A. Obtain MODIS data of time-series resolution imaging spectrometer in the evaluation year, the MODIS data contains quality evaluation information, and perform preprocessing to obtain time-series enhanced vegetation Index EVI data; B, corrected time-series EVI data; C, obtain the time-series EVI data of the comparison year, and analyze the similarity between it and the corrected time-series EVI data, and find the benchmark time-series EVI data for growth evaluation; D, according to the benchmark time-series EVI data, graded evaluation time series EVI data of the specified number of periods in the evaluation year. The invention can adapt to the crop growth monitoring requirements of large-scale and complex planting systems, and can effectively avoid the influence of crop phenological changes and crop planting structure changes compared with the traditional simple historical 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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