CN106525753A - Convenient and simple remote-sensing soil moisture monitoring method - Google Patents
Convenient and simple remote-sensing soil moisture monitoring method Download PDFInfo
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Abstract
The invention discloses a convenient and simple remote-sensing soil moisture monitoring method which comprises the following steps: firstly determining required data and an experiment area; extracting seventh band reflectivity data corresponding to each experiment point in a research area, choosing measured data, identical with the transit time of satellite data, from surface observation data, utilizing statistical software to perform regression analysis on the satellite seventh band reflectivity data and measured surface soil moisture data to obtain an optimal regression model, performing significance test on a regression equation, and determining a model equation for inverting soil moisture. By means of utilizing a single band to calculate the soil moisture content, the method has the characteristics of smaller calculated amount and fewer required early-stage processing procedures, guarantees the accuracy of monitoring results based on greatly simplifying data collection and preprocessing in the early stage, and is suitable for calculating the soil moisture content in a large scale.
Description
Technical field
The present invention relates to monitoring soil moisture method, is a kind of monitoring soil moisture side of the simplicity based on remotely-sensed data
Method.
Background technology
Soil moisture is the important characterization parameter for representing soil degradation or arid, and weather, the hydrology, ecology, agricultural etc.
The important indicator of degree of drought is weighed in field, is the key factor for affecting global climate and environmental change.Therefore, understand the soil water
Size and situation is divided to be of great practical significance in the reallocation of land and management, and agriculture-stock production.Remote sensing soil
Moisture inverting is a kind of method of monitoring soil moisture, and large-scale monitoring soil moisture is agricultural procedure research and envirment factor
The important component part of evaluation, and the soil-water environment inverting of regional scale even global range is land procedure schema research
In a requisite parameter, traditional monitoring soil moisture method is field field observation mostly, is wherein most accurately belonged to
Mass method, can draw the mass percent of accurate soil moisture, but consume substantial amounts of time and manpower.Soil is utilized also
The embedded type sensor measurement method that the characteristics of its corresponding electric conductivity of moisture difference is also different grow up, can save a large amount of
Time and labour force, but remain the monitoring on experimental point and can not be generalized on large-scale face.The above tradition
Monitoring meanss all there is identical shortcoming, it is both time-consuming, laborious, again can measuring point it is few, it is representative poor, so as to cause temporal resolution
It is low, it is impossible to realize the real-time dynamic monitoring of large area, soil moisture on a large scale.
With the continuous development of remote sensing technology, the eighties in 20th century Monitoring of Soil Moisture By Remote Sensing overcome traditional method
Defect, makes extensive area monitoring soil moisture become the target that can be realized.First, have abroad using visible ray and infrared
The research of Monitoring of Soil Moisture By Remote Sensing, such as using NDVI Monitoring of Drought indexes, vegetation state indices (VCI), thermal inertia and daily
NOAA-AVHRR data of evaporation capacity model and drafting soil moisture and geographical map etc..To the middle and late stage nineties in 20th century, with
NOAA/AVHRR and MODIS data it is commonly used, the remote-sensing inversion of optics and Thermal infrared bands is also gradually ripe.Also there is utilization
Ts (surface temperature), NDVI slopes under the satellite data (AVHRR, TM, SPOT etc.) of different spaces and time and spectral resolution
With the relation of soil moisture.Also part research points out the 6th wave bands of MODIS and the 7th wave band positioned at short infrared wave band in addition
More sensitive is changed to moisture, the estimation of soil moisture be can be used for this soil moisture index for building, also had only with the 7th ripple
The analysis that section is done, finds also there is preferable linear dependence between the wave band and soil moisture.
The content of the invention
In order to solve problems of the prior art, the present invention provides a kind of easy remote sensing monitoring soil moisture side
Method, overcomes.
The technical solution used in the present invention is：A kind of easy remote sensing monitoring soil moisture method, comprises the following steps：
Desired data and Experimental Area is determined first；Then MODIS Remote Sensing Reflectance data are utilized, extracts each in research area
The corresponding 7th wave band reflectivity data of experimental point, is chosen from ground observation data and is passed by period consistent reality with satellite data
Data are surveyed, and the regression analyses of the 7th wave band reflectivity data of satellite and actual measurement soil moisture data are carried out using statistical software,
After obtaining optimum regression model, significance test is carried out to regression equation, it is determined that for the model equation of Soil Moisture Retrieval.
The invention has the beneficial effects as follows：Compared with prior art, the present invention calculates soil water content tool using single band
There is amount of calculation less, the characteristics of required early stage processing procedure is less, simplify significantly the early stage process base of data collection and pretreatment
On plinth, it is ensured that the accuracy of monitoring result, it is adaptable to the calculating of larger range of soil water content.
Description of the drawings
Fig. 1 is the monitoring soil moisture model construction flow chart of the present invention；
Fig. 2 is the scatterplot between 8 experimental point actual measurement soil moistures and satellite data value；
Fig. 3 is to be embodied as surveying the comparison diagram between soil moisture and model assessment result；
Spring soil moisture model inversion figures of the Fig. 4 for certain region of specific embodiment；
Summer soil moisture model inversion charts of the Fig. 5 for certain region of specific embodiment.
Specific embodiment
Describe the present invention with specific embodiment below in conjunction with the accompanying drawings.
The present invention correlation technique linear regression analyses model, study two variables between dependency when using compared with
It is many.The relation discovery of analysis the 7th wave band Reflectivity for Growing Season data of MODIS and field inspection soil moisture data, 7 observation stations
Value is substantially all and fluctuates near fitting a straight line, such that it is able to think that the relation of the two is substantially linear negative correlation, and this
A little points are affected to cause by other all uncertain factors with the deviation of straight line, this with soil moisture content according to the 7th wave band
Linear relationship, it can be assumed that regression equation：
Y=β0+β1X+ε (1)
For linear model equations, wherein, β0+β1X represents that soil moisture Y changes with the change of the 7th wave band Reflectivity for Growing Season
Part；ε is random error, is the summation that other all uncertain factors affect, and its value is unobservable.It is assumed here that
ε～N (0, σ2),
Each parameter beta in equation of linear regression0、β1The computing formula of least-squares estimation value is as follows：
Finally do significance level inspection to find to equation, under given significance level (α=0.05), regression equation
Passed the inspection of regression parameter and regression equation, the standard deviation of residual error also very little, highly significant, but coefficient R2It is less
(<0.55) regression equation obtained by, illustrating is not optimal regression equation, needs to consider the improvement to regression equation again and enters
One-step optimization.
The relation between each observation station empirical value and satellite data is further being considered in detail, if considering, curve matching can
To find the key element coefficient R of all observation stations2Can be significantly increased, show that both relations not only use simple line
Property equation is determined, it is therefore desirable to which setting up regression equation new between variable and independent variable is analyzed, and in new equation
It is middle that the relation of curve matching is taken in, after analysis of experiments repeatedly, returning for both sides relation may finally will be reflected
Return equation to be assumed to be the form of curve matching regression equation, wherein ε is equally random error, be what all uncertain factors affected
Summation, its value are unobservable, β0、β1Least-squares estimation value computing formula is consistent with what (2) formula was given.It is assumed here that ε
～N (0, σ2), can use as inverse model, and the parameter and equation to regression equation is tested, and as a result shows, with original
Model compares (table 1), the coefficient R of the model2It is significantly improved, average each point improves 0.19, accordingly, it is considered to the
Curve matching relation between 7 wave band reflectance and soil moisture is feasible, and the residual error standard deviation of regression equation has also been obtained more
Good control, each parameter of regression equation, equation, residual error standard deviation, the equal highly significant of correlation coefficient, and in given significance
Under level, T and P inspections are all passed through respectively.Regression equation is set up to the data of all sample observation stations, obtains can be used to instead
The final mask regression equation of soil moisture is drilled, regression equation parameter can pass through (P<0.001) check, correlation coefficient is 0.62,
Significance level inspection is had also passed through, therefore, regression equation can make Soil Moisture Inversion model on a large scale.
1 each observation station Soil Moisture Inversion regression equation of table and correlation coefficient, significance test result
Note：Significance test:0‘***’0.001‘**’0.01‘*’0.05‘.’0.1‘’1
Technical scheme performs flow process as shown in Figure 1：The reflectance of the 7th wave band of MODIS satellites is determined first
Value and satellite pass by correspondence time actual measurement Soil moisture, then scatterplot carry out correlation analysiss to the two picture, it is determined that returning
Equation, and the coefficient to regression equation and equation is returned to test.Finally the Soil moisture of regression equation inverting is verified
Analysis, the regression model for obtaining optimum are Y=0.42395-2.37897x+3.96745x2, wherein Y represent model calculate soil
Earth moisture value, what x was represented is the reflectance value of the 7th wave band of MODIS satellites, and the model has passed through 0.001 significance test, shown
It is preferable that model is used for monitoring soil moisture research effect.
Now by taking the Soil Moisture Inversion of northern Tibet as an example, illustrate to build the detailed process of model.First, by download
MODIS satellite datas carry out reflectance extraction in ENVI remote sensing image processing softwares, and select and satellite transit time phase one
The actual measurement soil moisture data of cause.
Using statistical and analytical tool, the scatterplot made between measured value and satellite reflection rate is as shown in Figure 2.Obtained using final
To optimal models result and actual measurement soil moisture do relevance verification inspection as shown in figure 3, inversion result preferably, Ke Yiman
The monitoring soil moisture of the big regional scale of foot.
Inverting has been done such as to the soil moisture of Nagqu Diqu Nierong County and Naqu County spring and summer using the model finally
Fig. 4, shown in 5.
The above is only the preferred embodiment of the present invention, it is noted that for the ordinary skill people of the art
For member, under the premise without departing from the principles of the invention, some improvements and modifications can also be made, these improvements and modifications also should
It is considered as protection scope of the present invention.
Claims (1)
1. a kind of easy remote sensing monitoring soil moisture method, it is characterised in that comprise the following steps：
Desired data and Experimental Area is determined first；Then MODIS Remote Sensing Reflectance data are utilized, is extracted and is respectively tested in research area
The corresponding 7th wave band reflectivity data of point, is chosen from ground observation data and is passed by period consistent actual measurement number with satellite data
According to the regression analyses for carrying out the 7th wave band reflectivity data of satellite and actual measurement soil moisture data using statistical software are obtained
After optimum regression model, significance test is carried out to regression equation, it is determined that for the model equation of Soil Moisture Retrieval.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108414455A (en) * | 2018-01-30 | 2018-08-17 | 阳光农业相互保险公司 | Crops disaster caused by hail remote-sensing monitoring method for agricultural insurance Claims Resolution |
| CN108535338A (en) * | 2018-01-25 | 2018-09-14 | 中国科学院东北地理与农业生态研究所 | Thick spatial resolution satellite remote sensing soil moisture validity check method |
| CN108692969A (en) * | 2018-05-30 | 2018-10-23 | 中国林业科学研究院沙漠林业实验中心 | A kind of Sandy Soil Moisture monitoring method and system |
| CN116304524A (en) * | 2022-12-20 | 2023-06-23 | 宁夏回族自治区气象科学研究所 | Soil water content monitoring method, equipment, storage medium and device |
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108535338A (en) * | 2018-01-25 | 2018-09-14 | 中国科学院东北地理与农业生态研究所 | Thick spatial resolution satellite remote sensing soil moisture validity check method |
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| CN108414455B (en) * | 2018-01-30 | 2020-11-06 | 阳光农业相互保险公司 | Crop hail disaster remote sensing monitoring method for agricultural insurance claim settlement |
| CN108692969A (en) * | 2018-05-30 | 2018-10-23 | 中国林业科学研究院沙漠林业实验中心 | A kind of Sandy Soil Moisture monitoring method and system |
| CN116304524A (en) * | 2022-12-20 | 2023-06-23 | 宁夏回族自治区气象科学研究所 | Soil water content monitoring method, equipment, storage medium and device |
| CN116304524B (en) * | 2022-12-20 | 2024-04-09 | 宁夏回族自治区气象科学研究所 | Soil water content monitoring method, equipment, storage medium and device |
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Application publication date: 20170322 |