CN110954650A - Satellite remote sensing monitoring method for cotton canopy nitrogen - Google Patents
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Abstract
The invention discloses a satellite remote sensing monitoring method of cotton canopy nitrogen, which senses a plurality of electromagnetic radiation on the ground surface by using a remote sensing technology, then obtains a series of indexes, carries out inversion by various indexes, takes cotton as a research object, carries out real-time monitoring on the cotton, has accurate positioning without damaging crops, then establishes a characteristic spectrum parameter model of growth conditions and various vegetation indexes of the nitrogen, can conveniently and quickly obtain correct fertilizing amount by using the model, and solves the problem that the nitrogen content is difficult to determine, and the fertilizing amount is increased once to solve the high yield.
Description
Technical Field
The invention relates to a satellite remote sensing monitoring method for cotton canopy nitrogen.
Background
In the agricultural economic development, cotton accounts for a large part of proportion, the yield of cotton simultaneously restricts economic development and social progress, among elements influencing the growth of cotton, the influence of nitrogen on the cotton tends to be the greatest, the nitrogen not only influences the growth of the cotton, but also influences the synthesis of protein, nucleic acid and chlorophyll, the nitrogen in the cotton is mainly from soil, the demand of the cotton on the nitrogen is the greatest at the moment when the cotton grows, the nitrogen is one of the elements for keeping normal growth of crops and is also a key factor for increasing the yield of the cotton, in order to ensure that the cotton has more yield, the yield of the crops can be increased only by continuously increasing the fertilizing amount, the fertilizing amount is continuously increased, and the yield reaches 400 kg/hm2However, when the nitrogen content is excessive, the influence is that the cotton plants are higher than normal crops, the plants become dense, the illumination is further influenced, and meanwhile, the cotton is easy to drop bolls and bud off, so that the yield of the cotton is reduced; when the nitrogen content is lacking, the influence is that the cotton plant is short, the flowering quantity is reduced, the leaf quantity is reduced, the side branch is reduced, and the yield of the cotton is reduced. And how to realize the rapid monitoring of the nitrogen of the cotton, thereby having important significance for the growth diagnosis of the cotton and the accurate application of the nitrogen fertilizer.
Disclosure of Invention
The invention aims to provide a satellite remote sensing monitoring method of cotton canopy nitrogen, which is used for carrying out rapid and nondestructive monitoring on cotton canopy.
In order to solve the technical problems, the invention adopts the following technical scheme:
a satellite remote sensing monitoring method for cotton canopy nitrogen comprises the following steps:
s1 sampling design
Sampling by using a random sampling method, firstly selecting representative sample plants, dividing the sample plants into sample squares according to 30 meters by 30 meters in the field, sampling according to a five-division method, setting the distance between the two sample squares to be 2 kilometers, and collecting 48 samples in total; drying the sample, grinding, and storing in a sealing bag;
s2 laboratory measurement of canopy Nitrogen
Weighing dried and ground plant samples → pouring the plant samples into a digestion tube under the condition of not sticking to the tube wall → wetting the samples with distilled water → adding concentrated sulfuric acid, shaking uniformly, covering a bent neck funnel → placing the plant samples in a fume hood for water bath heating, taking down and cooling the plant samples → adding a few drops of hydrogen peroxide when the solution is completely brownish black, shaking → heating, then cooling and dropwise adding the plant samples for 2-3 times repeatedly → heating the solution for 5-10 minutes after the solution is colorless → cooling, transferring, fixing the volume and filtering, placing the plant samples in a Kjeldahl azotometer for distillation → distilling the plant samples in a triangular flask → adding three drops of an o-morpholine indicator → titrating with 1/2 sulfuric acid → recording the result;
s3 remote sensing image data processing
Acquiring a sampling area remote sensing image, then performing radiation correction processing and atmospheric correction processing, cutting the processed remote sensing image, and storing a data file;
opening ENVI software, selecting the cut file in a Toolbox toolbar to check and store the wave bands, and then extracting and storing the corresponding wave bands in the Toolbox toolbar; converting the extracted reflectivity and vegetation index into an excel file by utilizing arc GIS software;
s4, establishing a model
Sorting the vegetation indexes in a descending manner, screening out the vegetation indexes respectively, performing regression analysis to obtain a multiple linear regression equation, substituting the vegetation index data into the equation to obtain a group of nitrogen prediction data, and determining a final multiple linear model through the regression analysis;
and S5, verifying the validity of the model.
Further, in step S1, during sampling, the leaves cannot be too large or too small, and cannot be picked up either sick leaves or leaves with bugs, and leaves growing on the roadside or damaged by a vehicle cannot be picked up either, and the samples should be collected uniformly at ten to twelve points per day, and fresh samples just after being picked should be refrigerated to prevent and treat the hot weather from damaging the leaves, and should be dried immediately after reaching the laboratory, otherwise the leaves are subject to biological changes to cause mildewing, and experimental errors are increased.
Furthermore, if the sample may need to be stored for a long time, the sample should be sterilized and then placed in a polyvinyl chloride plastic bottle or bag for sealing and storage.
Compared with the prior art, the invention has the beneficial technical effects that:
the satellite remote sensing technology is utilized to carry out rapid and nondestructive monitoring on the cotton canopy, and the method has important significance for cotton growth diagnosis and accurate application of nitrogen fertilizers.
Drawings
The invention is further illustrated in the following description with reference to the drawings.
FIG. 1 is a schematic view of the Allan ten reclamation area;
FIG. 2 is a diagram showing the relationship between the predicted value and the actual measured value of the nitrogen content in the canopy;
fig. 3 is a spatial distribution diagram of nitrogen content at 7/14/2018.
Detailed Description
Example 1
In the research area, ten cotton fields in Alarville are selected, as shown in FIG. 1, a lot of rivers are arranged around the Alarville to provide water sources, so that the agriculture is mainly based on river water irrigation of the Tarim river or the multi-wave river. Because a large number of deserts exist near Alarler City, the highest temperature can reach 35 ℃ and the lowest temperature is 28 ℃ below zero in one day. The Arall reclamation area is known to have 6.57 kilo hectares of fields. The research area takes years as a unit, the number of raining times in summer is very small, snow falls very little in winter, and the sunshine is almost all bright and high every day, so that the annual precipitation is low, the evaporation capacity is large, the Alar precipitation in one year is 40.1-82.5 mm, the evaporation capacity is 1876.6-2558.9 mm, cotton is used as a plant which is pleased with light, and the light intensity are increased along with the increase of the light intensity in a certain intensity range, so that the cotton is mostly planted in the area. Because the content of each element in the new leaves at the bottom of the cotton is low, and the sun irradiation time at the upper part is long, the chlorophyll content is influenced, the third last leaf and the fourth last leaf are selected.
Landsat8 remote sensing image introduction
Landsat8 is a satellite synchronized to the sun and very close to the ground, with an orbital height of 705 km and an orbital inclination of 98.2 degrees. Accompanying Landsat8 are two sensors, one is a land imager (OLI) and the other is a push-broom imager (TIRS). We use 7 bands of terrestrial imager sensors. The satellite transits every 16 days and the sampling is done on the transit day. By comparison, compared with other satellites, Landsat8 has much higher flexibility than other satellites, so that the remote sensing image is more time-efficient. The Landsat8 can be used for accurately recording the geographical position of the research area, and a clear real map can be fed back, so that the data has higher credibility and higher use value. LI combines the spatial information of the high-resolution images with the spectral information of the mid-resolution images to improve the classification accuracy from 93.83% to 94.67%. In order to solve the problem of extraction of vegetation indexes of large-area crops in agriculture, the crops are taken as objects at present, and two methods are available, namely comprehensive utilization of spectral, texture and spatial information.
1. Sampling design
Sampling Using a random sampling method, representative plants were first selected and sampled by a five-point method on a field (30 m. about.30 m) cross. A distance of 2 km between two samples is specified. A total of 48 samples were collected. During picking, the leaves cannot be too large or too small, the leaves with diseases and insects cannot be picked, and the leaves growing on the roadside or damaged by a vehicle cannot be picked. The samples should be collected from ten to twelve points per day. Fresh samples just after picking should be refrigerated, prevent and treat that the weather is hot and destroys the blade, should dry immediately after having arrived the laboratory, otherwise take place biological change in the blade inside, lead to the blade to go mouldy, increased the error of experiment. If the sample may need to be preserved for a long time, the sample should be sterilized and then placed in a polyvinyl chloride plastic bottle or bag for sealing preservation. And (4) grinding after drying, storing the powder in a sealed bag, and performing an experiment as soon as possible to reduce experiment errors in order to avoid wetting.
2. Laboratory determination of canopy nitrogen
Weighing dried and ground plant samples → pouring the plant samples into a digestion tube under the condition that the plant samples do not adhere to the tube wall → wetting the samples with distilled water → adding concentrated sulfuric acid, shaking uniformly, covering a funnel with a bent neck → placing the plant samples in a fume hood for heating in a water bath, taking down and cooling the plant samples → adding a few drops of hydrogen peroxide when the solution is completely brownish black, shaking → heating, then cooling and dropwise adding the hydrogen peroxide for 2-3 times again → heating the solution for 5-10 minutes after the solution is colorless → cooling, transferring, fixing the volume and filtering. Placing into a Kjeldahl azotometer for distillation → distilling into a triangular flask → adding three drops of the o-phenanthroline indicator → titrating with 1/2 sulfuric acid → recording the result.
3 data processing
The Landsat8 remote sensing image, namely the image of day 14 and 7 months, is used in the test, and ENVI software is used for firstly carrying out two steps, wherein the first step is radiation correction; second is atmospheric correction. Secondly, cutting and extracting vegetation indexes by using arcGIS software; finally, correlation analysis and correlation analysis verification are carried out by using excel and other software.
3.1 radiation correction
Before correction, the image may cause numerical errors to the image due to the sensor itself or due to clouds, fog and the like in the air, and the most initial appearance of the image can be restored as much as possible through radiation correction, so that a solid foundation is made for later operation. Firstly, the DN value recorded by the sensor is converted into the radiance, the data type of FLAASH is automatically set by using the radiance correction tool Calibration in the ENVI software, and the Scale Factor is adjusted to be 0.1.
3.2 atmospheric correction
Due to the influence of various external conditions, the sensor is interfered when absorbing electromagnetic radiation, so that the influence is not accurate, because the sensor not only has various required vegetation index information, but also contains external interference information, so that the spectrum obtained by the sensor is different from the spectral information of the ground object, and therefore, the image is corrected by atmosphere to remove information except the ground object. The current atmospheric correction method can be roughly summarized into an atmospheric radiation transmission model method and a composite model method, a relative correction method based on image characteristics and a ground linear regression model method. Firstly, setting parameters by using a Calibration tool, then starting a FLAASH panel to set the parameters, then setting multispectral parameters, automatically selecting corresponding wave bands, and finally outputting.
3.3 cropping of regions
In 7 months in 2018, sampling is carried out every two plots in ten groups, after a plot (30 multiplied by 30) is selected, the plot is positioned to the center of a field by using GPRS, sampling is distributed by a quintuple method, geographic coordinates are recorded, XY coordinate values are inserted by using arc GIS, approximate positions are marked, the approximate positions are stored in a shipfile format, and then cutting is carried out by using ENVI software. Firstly, clicking an ROI tool to frame a region required by the user; secondly, double-clicking Subset Data from ROIs in a Subset box found in a Toolbox tool to select the file name to be cut by us; thirdly, selecting the region cropped in the first step in Spatial Subset via ROI Parameters; fourthly, selecting a path to store, and storing the path in a TIFF format.
3.4 extraction of the Vegetation index
Opening ENVI software, selecting Regions of Interest → Subset Data from the Toolbox toolbar, selecting the trimmed file to check bands, counting 7 bands, clicking OK, and selecting NO in Mask pixels output of ROI to select a path for saving. And then selecting Band Algebra → Spectral indexes in a Toolbox toolbar, selecting the files stored in the previous step in an Input scanner, sequentially clicking in a pop-up window Index according to the sequence, and extracting and storing the corresponding wave bands. Then, opening the arc GIS software, selecting a Catalog tool, selecting Toolboxes → system Toolboxes → Conversion Tools → Excel → Table To Excel, and converting the extracted reflectivity and vegetation index into an Excel file. The total number of the vegetation indexes is 16, and the treatment is completed according to descending order.
4 analysis of results
4.1 correlation analysis of vegetation index and canopy Nitrogen content
TABLE 1 correlation of vegetation index with canopy Nitrogen
It can be seen from table 1 that the red-green ratio index (RGRI) has the greatest correlation with the nitrogen of the canopy, which indicates that the red-green ratio index has the greatest influence on the nitrogen content of the canopy, and the higher the value is, the higher the nitrogen content is. The correlation between the Structure Insensitive Pigment Index (SIPI) and the nitrogen content of the canopy is the minimum, which shows that the influence of the structure insensitive pigment index on the nitrogen content of the canopy is the minimum, and the lower the value, the lower the nitrogen content is.
4.2 construction of multivariate Linear model
TABLE 2 regression statistics
Extracting vegetation indexes of July, counting 16 spectral indexes, sorting the vegetation indexes in a descending manner, respectively screening out the vegetation indexes, counting 48 sampling points, performing regression analysis, and making a scatter diagram to obtain R2= 0.5760. Dividing 70 samples into two data sets of a modeling set and a verification set, wherein the number of the modeling set is 50, the number of the verification set is 20, performing data regression analysis by using the 32 data sets to obtain R2=0.7122, at which point a multiple linear regression equation can be obtained, substituting 16 data into the equation to obtain a set of predicted data for nitrogen. By regression analysis, the following data can be obtained:
(1)K=771973.8382
(2)y=217.209x1+379.069x2+45.051x3-3863.71x4-4402.33x5-440442x8+290137x9+3611.438x10-512193x12-2.709x13+107.965x15-852.6x16+771973.8
(3)y=217.209*RGRI+379.069*MTVII+45.051*VARI+3863.71*WVWI+4402.33*TDVI-440442*OSAVI+2901378*SAVI+3611.438*AR-512193*MNLI-2.709*LAI+107.965*MNDW-852.6*SIPI+771973.8
the above (3) is the final multivariate linear model.
4.3 evaluation of modeling accuracy of multiple Linear regression
And (3) establishing the model, wherein a total data set is divided into two parts, one part is used as a test set, and the other part is used as a verification set. We first performed regression analysis using 32 test sets and then modeling, and then validated with 16 validation sets. The 16 verification sets are sequentially brought into the model, the nitrogen content of a group of predicted cotton canopy, the simultaneous prediction nitrogen content and the actually measured nitrogen content can be obtained, and by establishing a scatter diagram, R2=0.5603 can be seen from figure 3, so that the difference between the predicted value and the actually measured value is small, and the accuracy of the model is proved to be accurate.
Characteristic significance of 44 vegetation index inversion canopy nitrogen
The vegetation index can effectively combine all spectral information, and can reduce the input of non-vegetation information and increase the input of vegetation information, so that the characteristic is very favorable for the observation of the cotton canopy nitrogen. The model established by inversion of the canopy nitrogen through the vegetation index can accurately reflect the content distribution of the nitrogen in the research area, and the area displaying the blue part on the graph is an area with extremely low nitrogen content, the light blue part is an area with low nitrogen content, the gray part is an area with medium low nitrogen content, the yellow part is an area with medium nitrogen content, the orange part is an area with medium high nitrogen content, and the red part is an area with high nitrogen content. The result is more visual and convenient to observe.
As can be seen from FIG. 3, the nitrogen content distribution of the ten cotton clusters in Alarler is relatively uniform, the nitrogen content in a few parts of the regions in the northeast and southwest is low, the nitrogen content is between 0 and 9, and the nitrogen content accounts for about 10 percent of the research region; the nitrogen content is 9-18 and accounts for about 25% of the research area; in the northeast corner, the nitrogen content is in the range of 18-27, which accounts for about 20% of the research area; the place with the highest nitrogen content is concentrated at the north part of the middle part of the research area, the nitrogen content is between 27 and 30 percent and accounts for about 30 percent of the research area; the rest 15 percent of the total weight is river, road and other parts. As the proportion of the highest nitrogen content in the research area is the largest and the proportion of the lowest nitrogen content is the smallest, the nitrogen supplement of ten groups of cotton in Alarler is better, the nitrogen fertilizer can be properly reduced in later fertilization, otherwise, the yield of cotton is influenced by too high nitrogen content of the cotton.
The above-described embodiments are merely illustrative of the preferred embodiments of the present invention, and do not limit the scope of the present invention, and various modifications and improvements of the technical solutions of the present invention can be made by those skilled in the art without departing from the spirit of the present invention, and the technical solutions of the present invention are within the scope of the present invention defined by the claims.
Claims (3)
1. A satellite remote sensing monitoring method for cotton canopy nitrogen is characterized by comprising the following steps:
s1 sampling design
Sampling by using a random sampling method, firstly selecting representative sample plants, dividing the sample plants into sample squares according to 30 meters by 30 meters in the field, sampling according to a five-division method, setting the distance between the two sample squares to be 2 kilometers, and collecting 48 samples in total; drying the sample, grinding, and storing in a sealing bag;
s2 laboratory measurement of canopy Nitrogen
Weighing dried and ground plant samples → pouring the plant samples into a digestion tube under the condition of not sticking to the tube wall → wetting the samples with distilled water → adding concentrated sulfuric acid, shaking uniformly, covering a bent neck funnel → placing the plant samples in a fume hood for water bath heating, taking down and cooling the plant samples → adding a few drops of hydrogen peroxide when the solution is completely brownish black, shaking → heating, then cooling and dropwise adding the plant samples for 2-3 times repeatedly → heating the solution for 5-10 minutes after the solution is colorless → cooling, transferring, fixing the volume and filtering, placing the plant samples in a Kjeldahl azotometer for distillation → distilling the plant samples in a triangular flask → adding three drops of an o-morpholine indicator → titrating with 1/2 sulfuric acid → recording the result;
s3 remote sensing image data processing
Acquiring a sampling area remote sensing image, then performing radiation correction processing and atmospheric correction processing, cutting the processed remote sensing image, and storing a data file;
opening ENVI software, selecting the cut file in a Toolbox toolbar to check and store the wave bands, and then extracting and storing the corresponding wave bands in the Toolbox toolbar; converting the extracted reflectivity and vegetation index into an excel file by utilizing arc GIS software;
s4, establishing a model
Sorting the vegetation indexes in a descending manner, screening out the vegetation indexes respectively, performing regression analysis to obtain a multiple linear regression equation, substituting the vegetation index data into the equation to obtain a group of nitrogen prediction data, and determining a final multiple linear model through the regression analysis;
and S5, verifying the validity of the model.
2. The method for remotely sensing and monitoring the cotton canopy nitrogen with the satellite according to claim 1, wherein in the step S1, the leaves cannot be too large or too small during sampling, the diseased leaves cannot be picked, the leaves with insects cannot be picked, the leaves growing on the roadside or damaged by vehicles cannot be picked, the samples are collected at ten to twelve points every day, the fresh samples just after being picked are refrigerated, the leaves are prevented from being damaged by hot weather, the fresh samples are dried immediately after the laboratory, otherwise, the biological changes occur in the leaves, the leaves are mildewed, and the experimental error is increased.
3. The method for remotely sensing and monitoring the cotton canopy nitrogen with satellite according to claim 2, wherein if the sample may need to be preserved for a long time, the sample should be sterilized and then placed in a polyvinyl chloride plastic bottle or bag for sealing preservation.
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