CN107782701A - A kind of agricultural arid monitoring method of multi- source Remote Sensing Data data - Google Patents

A kind of agricultural arid monitoring method of multi- source Remote Sensing Data data Download PDF

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CN107782701A
CN107782701A CN201710854261.3A CN201710854261A CN107782701A CN 107782701 A CN107782701 A CN 107782701A CN 201710854261 A CN201710854261 A CN 201710854261A CN 107782701 A CN107782701 A CN 107782701A
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张强
孙鹏
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Beijing Normal University
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Abstract

The present invention provides a kind of agricultural arid monitoring method of multi- source Remote Sensing Data data, and this method chooses the most frequently used four major classes (16) agricultural arid remote sensing monitoring index：Crop canopy temperature change (4), crop pattern and vegetation greenness change (5), soil moisture content transformation (3) and crop canopies water content (4), the comprehensive remote sensing Drought Model of structure is tested and corrected with reference to using research area's monitoring station standardization potential evapotranspiration mark SPEI and 20cm soil moisture contents data of bristling with anger, draught monitor and assessment can be carried out to data collecting region in real time on a large scale.This method can carry out continuous, dynamic, quantification, visual monitoring and analysis and research on fine space scale to agriculture risks, and decision support and information service are provided to prevent and reduce natural disasters.

Description

A kind of agricultural arid monitoring method of multi- source Remote Sensing Data data
Technical field
It is specifically a kind of to be supervised based on multi- source Remote Sensing Data data agricultural arid the present invention relates to agricultural arid monitoring technical field The method of survey.
Background technology
Have much for the research of agricultural arid Monitoring Index at present, in the environment of present climate warming, what arid occurred Frequency is in rising progressively, so the monitoring and the platform of early warning and draught monitor for arid have become many researchers A popular research field.
At present in similar techniques, than " being based on for the more typical application for having a Shenyang Inst. of Applied Ecology, Chinese Academy of Sciences The agricultural arid monitoring method of MODIS data " Patent No. CN200910248457.3:The patent is based on MODIS data Agricultural arid monitoring method.Agricultural drought is monitored, agricultural is determined using crop water supply index and rainfall anomaly index Drought index.MODIS data inversions vegetation index and surface temperature are utilized when carrying out Monitoring of drought, utilizes vegetation index and ground Table temperature computation crop water supply index.Rainfall Amount index is calculated using precipitation data.Finally rank is carried out to drought index to draw Divide to determine drought severity.
The above method although make use of remotely-sensed data carry out draught monitor, but due to its realize it is comparatively laborious, can only Arid situation is determined, and only only accounts for vegetation index and surface temperature, without the change of more consideration soil moisture, agriculture Field Forecast of Soil Moisture Content is both Water Balance in Cropland and the emphasis of soilplant atmosphere continuum water transform research, Ye Shibiao Levy the best indicator of agricultural arid.Therefore invention considers polymorphic type remote sensing drought index and soil moisture content number herein According to establishing the comprehensive remote sensing draught monitor model of moon yardstick.
The content of the invention
The purpose of the present invention is to overcome above-mentioned the deficiencies in the prior art, designs a kind of agricultural arid of multi- source Remote Sensing Data data Monitoring method, this method can carry out continuous, dynamic, quantification, visualization on fine space scale to agriculture risks Monitoring and analysis and research, provide decision support and information service to prevent and reduce natural disasters.
The technical scheme is that a kind of agricultural arid monitoring method method of multi- source Remote Sensing Data data is provided, this method tool Body comprises the following steps：
The original meteorological data and soil moisture content data of step 1) collection research area's monitoring station, and normalized precipitation Evapotranspire index, it is specific as follows：
Step 1.1, the difference between precipitation and evaporation capacity is calculated month by month, i.e.,：
Di=Pi-PETi
In formula：DiFor precipitation and the difference evapotranspired, PiFor monthly total precipitation, PETiFor moon evapotranspiration；
Step 1.2, DiNormal state is carried out to data sequence, the cumulative function of log-logistic probability distribution is：
α is scale parameter in formula, and β is form parameter, and γ is origin parameters, and f (x) is probability density function, and F (x) is Probability-distribution function, DiFor precipitation and the difference evapotranspired；
Step 1.3, normal state processing is standardized to sequence, obtains corresponding SPEI：
P probability distribution joins function, C in formula0、C2、C3、d1、d2、d3For empirical parameter.
As P≤0.5, P=F (x)；As P ＞ 0.5, P=1-F (x), other specification is respectively constant C0= 2.515517 C2=0.802853, C3=0.010328, d1=1.432788, d2=0.189269, d3=0.001308.
Step 2) collection research area's remotely-sensed data, calculates 16 kinds of agricultural arid remote sensing monitoring indexs, specific as follows：
16 kinds of agricultural arid remote sensing monitoring index calculation methods：
Wherein, ρred、ρnir、ρblue、ρswirInfrared band, near infrared band, blue wave band, short-wave infrared ripple are represented respectively The reflectivity of section；M, I is respectively the slope and intercept of soil baseline, fvFor vegetation coverage；ρV, red, ρV, nirRespectively vegetation exists The reflectivity of infrared band and near infrared band；NDVI,Respectively normalized differential vegetation index and its average value；NDVImin, NDVImaxThe minimum value and maximum of contemporaneity in the NDVI research phases are represented respectively；LST represents surface temperature, and unit is Kelvin Temperature；LSTmin, LSTmax, LSTmaxThe minimum value and maximum of contemporaneity in the LST research phases are represented respectively；A, b are respectively to plant By the intercept and slope on temperature profile space Zhong Gan sides；A ', b ' are respectively the intercept and slope on vegetation characteristics space Zhong Shi sides.
Step 3) selected section monitoring station soil moisture content data carry out skin with corresponding agricultural arid remote sensing monitoring index The inferior Research on correlation coefficient of that, a kind of optimal agricultural of fitting is filtered out from four major class agricultural arid remote sensing monitoring indexs respectively and is done Non-irrigated remote sensing monitoring index, is comprised the concrete steps that：
Wherein xiRepresent agricultural arid remote sensing monitoring index, yiRepresent soil moisture content data；
Step 4) considers different agricultural arid Indices in arid evolution and the shadow of crop different growth phases Sound is different, based on the comprehensive remote sensing draught monitor model of equation of linear regression structure moon yardstick, and is utilized as participating in modeling Website SPEI and soil moisture content test and correct, specific steps：
Y=β0+β1x1+β2x2+β3x3+β4x4+ε
Wherein, β0, β1, β2, β3And β4It is parameter；Y is soil moisture content data；xiIt is 4 kinds of remote sensing drought indexs.
The present invention compared with prior art, has following technique effect using above technical scheme：
Monitored herein from the agricultural arid based on website uncertain and remote sensing monitoring during space interpolation The characteristics of ageing strong, the comprehensive remote sensing drought index based on multi- source Remote Sensing Data data is constructed, the index space resolution ratio is 1km*1km, it can effectively avoid draught monitor precision caused by station data skewness (or without website and non-avaible) It is low；The moon Dimension Synthesis remote sensing drought index built in addition considers domestic and international the most frequently used remote sensing drought indexes at present, each remote sensing Draught monitor index has different space-time applicabilities.Therefore, remote sensing drought is carried out to different zones, Different Crop growth phase During monitoring, it should choose most suitable agricultural arid Monitoring Index, the present invention build the moon Dimension Synthesis remote sensing drought index more Beneficial to influence of the assessment area arid to crop growth.
Brief description of the drawings
Fig. 1 is the flow chart of drought monitoring method of the present invention；
Fig. 2 is scatter diagram of the Basin of Huaihe River comprehensive remote sensing drought index with modeling website soil moisture content；
Fig. 3 is the soil moisture content correlation of Basin of Huaihe River moon website of the Dimension Synthesis remote sensing drought indexes with having neither part nor lot in modeling Schematic diagram.
Embodiment
The present invention provides a kind of agricultural arid monitoring method of multi- source Remote Sensing Data data, to make the purpose of the present invention, technical side Case and effect are clearer, clear and definite, and referring to the drawings and give an actual example that the present invention is described in more detail.It should be understood that herein Described specific implementation only to explain the present invention, is not intended to limit the present invention.
A kind of agricultural arid monitoring method of multi- source Remote Sensing Data data provided by the invention is specifically, first selection agricultural arid is distant Feel monitoring index, bristle with anger in conjunction with research area's monitoring station standardization potential evapotranspiration and mark SPEI and soil moisture content data to structure Comprehensive remote sensing Drought Model is tested and corrected, and carries out draught monitor and assessment to data collecting region in real time.
Specifically, this method specifically includes following steps：
The original meteorological data and soil moisture content data of step 1) collection research area's monitoring station, and normalized precipitation Evapotranspire index；
Step 2) collection research area's remotely-sensed data, calculates agricultural arid remote sensing monitoring index；
Step 3) Selecting research area monitoring station soil moisture content data are carried out with corresponding agricultural arid remote sensing monitoring index Pearson correlation coefficient is studied, and it is distant that the optimal agricultural arid of fitting is filtered out from different classes of agricultural arid remote sensing monitoring index Feel monitoring index；
Step 4) considers different agricultural arid Indices in arid evolution and the shadow of crop different growth phases Sound is different, based on the comprehensive remote sensing draught monitor model of equation of linear regression structure moon yardstick, and is utilized as participating in modeling Website SPEI and soil moisture content test and correct.
The original meteorological data includes：The rainfall data of the year, month, day of the monitoring station, the soil moisture content number of the moon According to synthesis the moon yardstick MODIS remotely-sensed datas.
Research area's remotely-sensed data is measured by satellite remote sensing technology, and agricultural arid remote sensing monitoring index includes crop canopies Temperature change index, crop pattern and vegetation greenness change indicator, soil moisture content transformation index and crop canopies water content refer to Mark.
The crop canopy temperature change indicator includes temperature condition index TCI, Temperature vegetation drought index TVDI, planted By water supply index VSWI, preconditioned conjugate iteration VTCI；
The crop pattern and vegetation greenness change indicator include anomaly vegetation index AVI, enhancing vegetation index EVI, Ratio vegetation index RVI, normalized differential vegetation index NDVI and condition vegetation index VCI；
The soil moisture content transformation index includes improved vertical arid index M PDI, vertical arid indices P DI and can See light and short-wave infrared drought index VSDI；
The crop canopies water-content indicator whole world vegetation moisture index GVMI, surface water index LSWI, short-wave infrared Vertical dehydration index SPSI and water stress index MSI.
The computational methods of every index are as follows：
Wherein, ρred、ρnir、ρblue、ρswirInfrared band, near infrared band, blue wave band, short-wave infrared ripple are represented respectively The reflectivity of section；M, I is respectively the slope and intercept of soil baseline, fvFor vegetation coverage；ρV, red, ρV, nirRespectively vegetation exists The reflectivity of infrared band and near infrared band；NDVI,Respectively normalized differential vegetation index and its average value；NDVImin, NDVImaxThe minimum value and maximum of contemporaneity in the NDVI research phases are represented respectively；LST represents surface temperature, and unit is Kelvin Temperature；LSTmin, LSTmax, LSTmaxThe minimum value and maximum of contemporaneity in the LST research phases are represented respectively；A, b are respectively to plant By the intercept and slope on temperature profile space Zhong Gan sides；A ', b ' are respectively the intercept and slope on vegetation characteristics space Zhong Shi sides.
Bristled with anger using standardization potential evapotranspiration and mark SPEI, soil moisture content and the index related analysis of agricultural arid remote sensing monitoring Detailed process be：
Step 5.1, the difference between precipitation and evaporation capacity is calculated month by month, i.e.,：
Di=Pi-PETi
In formula：DiFor precipitation and the difference evapotranspired, PiFor monthly total precipitation, PETiFor moon evapotranspiration；
Step 5.2, DiNormal state is carried out to data sequence, the cumulative function of log-logistic probability distribution is：
α is scale parameter in formula, and β is form parameter, and γ is origin parameters, and f (x) is probability density function, and F (x) is Probability-distribution function, DiFor precipitation and the difference evapotranspired；
Step 5.3, normal state processing is standardized to sequence, obtains corresponding SPEI：
P probability distribution joins function, C in formula0、C2、C3、d1、d2、d3For empirical parameter；
As P≤0.5, P=F (x)；As P ＞ 0.5, P=1-F (x), other specification is respectively constant；
Step 5.4, the Pearson came dependency relation of SPEI, soil moisture content and agricultural arid remote sensing monitoring index is calculated respectively, And significance test is carried out, filter out the optimal agricultural arid of correlation from four major class agricultural arid remote sensing monitoring indexs respectively Remote sensing monitoring index, obtain four kinds of agricultural arid remote sensing monitoring results.
With four kinds of remote sensing drought indexs of the moon yardstick comprehensive remote sensing draught monitor mould is established using multiple linear regression equations Type, and be utilized as participate in modeling website SPEI and soil moisture content test and correct, detailed process is：
Y=β0+β1x1+β2x2+β3x3+β4x4+ε
Wherein, β0, β1, β2, β3And β4It is parameter；Y is soil moisture content data；xiIt is 4 kinds of remote sensing drought indexs.
Embodiment 1, as shown in figure 1, for the comprehensive remote sensing drought index building process of multi-source data, Fig. 2 is comprehensive remote sensing The coefficient correlation schematic diagram of drought index and modeling website soil moisture content, find the phase of comprehensive remote sensing drought index and soil moisture content Relation number has passed through 99% significance test, using soil moisture content as dependent variable multiple linear regression equations fitting precision very It is high；And in order to preferably reflect the applicability of the model of structure, as shown in figure 3, the soil moisture content website by having neither part nor lot in modeling Correlation analysis is carried out with the comprehensive remote sensing drought index of moon yardstick, as a result finds all to have passed through 99% significance test, Illustrate the change that can reflect 20cm soil moisture contents well from planar.
A kind of agricultural arid monitoring method of multi- source Remote Sensing Data data of the present invention, described above is only being preferable to carry out for the present invention Mode, it is noted that for those skilled in the art, under the premise without departing from the principles of the invention, also Some improvement can be made, these improvement also should be regarded as protection scope of the present invention.

Claims (8)

1. the agricultural arid monitoring method of a kind of multi- source Remote Sensing Data data, it is characterised in that methods described chooses agricultural arid remote sensing Monitoring index, the comprehensive remote sensing Drought Model of structure；Bristle with anger mark then in conjunction with research area's monitoring station standardization potential evapotranspiration SPEI and soil moisture content data are tested and corrected to the comprehensive remote sensing Drought Model of structure, and data collecting region is entered in real time Row draught monitor and assessment.
A kind of 2. agricultural arid monitoring method of multi- source Remote Sensing Data data according to claim 1, it is characterised in that this method Specifically include following steps：
The original meteorological data and soil moisture content data of step 1) collection research area's monitoring station, and normalized precipitation evapotranspires Index；
Step 2) collection research area's remotely-sensed data, calculates agricultural arid remote sensing monitoring index；
Step 3) Selecting research area monitoring station soil moisture content data carry out Pierre with corresponding agricultural arid remote sensing monitoring index Inferior Research on correlation coefficient, filtered out from different classes of agricultural arid remote sensing monitoring index and be fitted optimal agricultural arid remote sensing prison Survey index；
Step 4) considers that different agricultural arid Indices are in arid evolution and the influence of crop different growth phases Different, based on the comprehensive remote sensing draught monitor model of equation of linear regression structure moon yardstick, and it is utilized as participating in the station of modeling Point SPEI and soil moisture content are tested and corrected.
A kind of 3. agricultural arid monitoring method of multi- source Remote Sensing Data data according to claim 2, it is characterised in that the original Beginning meteorological data includes：The moon chi of the rainfall data of the year, month, day of the monitoring station, the soil moisture content data of the moon and synthesis The MODIS remotely-sensed datas of degree.
4. the agricultural arid monitoring method of a kind of multi- source Remote Sensing Data data according to claim 2, it is characterised in that described to grind Study carefully area's remotely-sensed data to be measured by satellite remote sensing technology, agricultural arid remote sensing monitoring index include crop canopy temperature change indicator, Crop pattern and vegetation greenness change indicator, soil moisture content transformation index and crop canopies water-content indicator.
A kind of 5. agricultural arid monitoring method of multi- source Remote Sensing Data data according to claim 4, it is characterised in that
The crop canopy temperature change indicator includes temperature condition index TCI, Temperature vegetation drought index TVDI, vegetation and supplied Aqua index VSWI, preconditioned conjugate iteration VTCI；
The crop pattern and vegetation greenness change indicator include anomaly vegetation index AVI, enhancing vegetation index EVI, ratio Vegetation index RVI, normalized differential vegetation index NDVI and condition vegetation index VCI；
The soil moisture content transformation index includes improved vertical arid index M PDI, vertical arid indices P DI and visible ray With short-wave infrared drought index VSDI；
The crop canopies water-content indicator whole world vegetation moisture index GVMI, surface water index LSWI, short-wave infrared are vertical Dehydration index SPSI and water stress index MSI.
6. the agricultural arid monitoring method of a kind of multi- source Remote Sensing Data data according to claim 5, it is characterised in that described each The computational methods of item index are as follows：
Wherein, ρred、ρnir、ρblue、ρswirRespectively represent infrared band, near infrared band, blue wave band, short infrared wave band Reflectivity；M, I is respectively the slope and intercept of soil baseline, fvFor vegetation coverage；ρv,red, ρv,nirRespectively vegetation is infrared The reflectivity of wave band and near infrared band；NDVI,Respectively normalized differential vegetation index and its average value；NDVImin, NDVImaxThe minimum value and maximum of contemporaneity in the NDVI research phases are represented respectively；LST represents surface temperature, and unit is Kelvin Temperature；LSTmin, LSTmax, LSTmaxThe minimum value and maximum of contemporaneity in the LST research phases are represented respectively；A, b are respectively to plant By the intercept and slope on temperature profile space Zhong Gan sides；A ', b ' are respectively the intercept and slope on vegetation characteristics space Zhong Shi sides.
7. the agricultural arid monitoring method of a kind of multi- source Remote Sensing Data data according to claim 4, it is characterised in that using mark Bristle with anger mark SPEI, the detailed process of soil moisture content and the index related analysis of agricultural arid remote sensing monitoring of standardization potential evapotranspiration be：
Step 5.1, the difference between precipitation and evaporation capacity is calculated month by month, i.e.,：
Di=Pi-PETi
In formula：DiFor precipitation and the difference evapotranspired, PiFor monthly total precipitation, PETiFor moon evapotranspiration；
Step 5.2, DiNormal state is carried out to data sequence, the cumulative function of log-logistic probability distribution is：
<mrow> <mi>f</mi> <mrow> <mo>(</mo> <mi>x</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <mi>&amp;beta;</mi> <mi>&amp;alpha;</mi> </mfrac> <msup> <mrow> <mo>(</mo> <mfrac> <mrow> <mi>x</mi> <mo>-</mo> <mi>&amp;gamma;</mi> </mrow> <mi>&amp;alpha;</mi> </mfrac> <mo>)</mo> </mrow> <mrow> <mi>&amp;beta;</mi> <mo>-</mo> <mn>1</mn> </mrow> </msup> <msup> <mrow> <mo>&amp;lsqb;</mo> <mn>1</mn> <mo>+</mo> <msup> <mrow> <mo>(</mo> <mfrac> <mrow> <mi>x</mi> <mo>-</mo> <mi>&amp;gamma;</mi> </mrow> <mi>&amp;alpha;</mi> </mfrac> <mo>)</mo> </mrow> <mi>&amp;beta;</mi> </msup> <mo>&amp;rsqb;</mo> </mrow> <mrow> <mo>-</mo> <mn>2</mn> </mrow> </msup> </mrow>
<mrow> <mi>F</mi> <mrow> <mo>(</mo> <mi>x</mi> <mo>)</mo> </mrow> <mo>=</mo> <msubsup> <mo>&amp;Integral;</mo> <mn>0</mn> <mi>x</mi> </msubsup> <mi>f</mi> <mrow> <mo>(</mo> <mi>x</mi> <mo>)</mo> </mrow> <mi>d</mi> <mi>t</mi> <mo>=</mo> <msup> <mrow> <mo>&amp;lsqb;</mo> <mn>1</mn> <mo>+</mo> <msup> <mrow> <mo>(</mo> <mfrac> <mi>&amp;alpha;</mi> <mrow> <mi>x</mi> <mo>-</mo> <mi>&amp;gamma;</mi> </mrow> </mfrac> <mo>)</mo> </mrow> <mi>&amp;beta;</mi> </msup> <mo>&amp;rsqb;</mo> </mrow> <mrow> <mo>-</mo> <mn>1</mn> </mrow> </msup> </mrow>
α is scale parameter in formula, and β is form parameter, and γ is origin parameters, and f (x) is probability density function, and F (x) is probability Distribution function, DiFor precipitation and the difference evapotranspired；
Step 5.3, normal state processing is standardized to sequence, obtains corresponding SPEI：
<mrow> <mi>S</mi> <mi>P</mi> <mi>E</mi> <mi>I</mi> <mo>=</mo> <mi>W</mi> <mo>-</mo> <mfrac> <mrow> <msub> <mi>C</mi> <mn>0</mn> </msub> <mo>+</mo> <msub> <mi>C</mi> <mn>2</mn> </msub> <mo>+</mo> <msub> <mi>C</mi> <mn>3</mn> </msub> <msup> <mi>W</mi> <mn>2</mn> </msup> </mrow> <mrow> <mn>1</mn> <mo>+</mo> <msub> <mi>d</mi> <mn>1</mn> </msub> <mi>w</mi> <mo>+</mo> <msub> <mi>d</mi> <mn>2</mn> </msub> <msup> <mi>w</mi> <mn>2</mn> </msup> <mo>+</mo> <msub> <mi>d</mi> <mn>3</mn> </msub> <msup> <mi>w</mi> <mn>3</mn> </msup> </mrow> </mfrac> </mrow>
<mrow> <mi>W</mi> <mo>=</mo> <msqrt> <mrow> <mo>-</mo> <mn>2</mn> <mi>ln</mi> <mi> </mi> <mi>P</mi> </mrow> </msqrt> </mrow>
P probability distribution joins function, C in formula0、C2、C3、d1、d2、d3For empirical parameter；
As P≤0.5, P=F (x)；As P ＞ 0.5, P=1-F (x), other specification is respectively constant；
Step 5.4, the Pearson came dependency relation of SPEI, soil moisture content and agricultural arid remote sensing monitoring index is calculated respectively, is gone forward side by side Row significance test, filter out the optimal agricultural arid remote sensing of correlation from four major class agricultural arid remote sensing monitoring indexs respectively Monitoring index, obtain four kinds of agricultural arid remote sensing monitoring results.
8. the agricultural arid monitoring method of a kind of multi- source Remote Sensing Data data according to claim 4, it is characterised in that with moon chi Four kinds of remote sensing drought indexs of degree establish comprehensive remote sensing draught monitor model using multiple linear regression equations, and are utilized as participating in The website SPEI and soil moisture content of modeling are tested and corrected, and detailed process is：
Y=β0+β1x1+β2x2+β3x3+β4x4+ε
Wherein, β0, β1, β2, β3And β4It is parameter；Y is soil moisture content data；xiIt is 4 kinds of remote sensing drought indexs.
CN201710854261.3A 2017-09-20 2017-09-20 A kind of agricultural arid monitoring method of multi- source Remote Sensing Data data Pending CN107782701A (en)

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