CN107085712A - A kind of agricultural arid monitoring method based on MODIS data - Google Patents
A kind of agricultural arid monitoring method based on MODIS data Download PDFInfo
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Abstract
The invention belongs to agricultural technology field, disclose a kind of agricultural arid monitoring method based on MODIS data, using MODIS remotely-sensed data temporal resolutions it is higher the characteristics of, realize agricultural drought monitoring；EVI and LST make use of to build temperature vegetation drought index TVDI, the relation between analysis TVDI and soil moisture builds soil moisture retrieval model；Finally, built soil moisture retrieval model is analyzed for draught monitor, and builds drought remote sensing monitoring platform.The present invention carries out agricultural drought dynamic monitoring using remote sensing technology, is examined through practical application, and this method is easy, efficient, easily operated, result is accurate, can be widely applied among agricultural drought monitors.
Description
Technical field
The invention belongs to agricultural technology field, more particularly to a kind of agricultural arid monitoring method based on MODIS data.
Background technology
The generating process of arid is potential, it is not easy to found；Agricultural drought is characterized in that coverage is big, is brought
Serious catastrophic effect and economic loss；Research, the process for evaluating arid occurrence and development, can take corresponding drought resisting to prevent
Calamity hazard mitigation measure, reduces agricultural disaster loss.With the development of remote sensing technology, remote sensing is dynamic, real-time, multispectral, cheap with its
Advantage, be that Monitoring of drought opens new approach.The vegetation index and surface temperature that remote sensing is obtained are that description earth surface is special
The two particularly significant parameters levied, therefore during arid generation, crop can be disclosed by the change of vegetation index or surface temperature
Physically different feature, reflects farmland hydro-thermal stress state indirectly.It can be divided into according to wave band used in remotely-sensed data：Visible ray,
Near-infrared, thermal infrared, microwave etc..Because used wave band is different, numerous models and method are generated.Such as water deficit index
Model, temperature vegetation drought index model etc..It is most of simply experimental although many for the model that agricultural drought disaster is monitored
Research.Therefore a kind of relatively accurate agricultural drought monitoring method is explored very necessary.
In summary, the problem of prior art is present be：Prior art, when carrying out Monitoring of drought, only refers to vegetation
Number and surface temperature calculate crop water supply index, and have ignored MODIS image processing techniques, data is obtained accuracy rate low；No
It can provide and be effectively ensured for the arid grade classification of foundation more specification.
The content of the invention
To solve the problem of prior art is present, it is an object of the invention to provide a kind of agricultural based on MODIS data
Drought monitoring method.
The present invention is achieved in that a kind of agricultural arid monitoring method based on MODIS data, described to be based on MODIS
The agricultural arid monitoring method of data includes：
It make use of EVI and LST to build temperature vegetation drought index TVDI, analyze the relation between TVDI and soil moisture,
Build soil moisture retrieval model；
Finally, built soil moisture retrieval model is analyzed for draught monitor, and builds drought remote sensing monitoring platform；
TVDI computation model is：
LSTEVIi.max=a+bEVIi
LSTEVIi.min=a'+b'EVIi
Farmland spectrum, temperature and the moisture data of a series of different soils moistures are gathered, basic data collection is built；With
This calculate EVI-LST spaces it is dry while with it is wet while, parameter a, b, a needed for acquisition TVDI models ', b '；
The calculating of soil moisture includes the calculating of crop water supply index：
VSWI is the crop water supply index of each pixel point corresponding to monitored farmland massif in formula；EVI is monitored agriculture
The enhancing vegetation index of each pixel point of MODIS images corresponding to the block of field；TsFor the remote sensing being monitored corresponding to farmland massif
The surface temperature of each pixel point of image；
The image processing method of each pixel point of MODIS images includes：
Farmland temperature parameter is obtained according to infrared spectral radiant, infrared spectrum emissivity is at selected wavelength and temperature
There is approximately uniform linear relationship, i.e.,：
εi2=εi1[1+k(T2-T1)]
In formula, εi1It is that wavelength is λi, spectral emissivity when temperature is T1；εi2It is that wavelength is λi, light when temperature is T2
Compose emissivity；T1, T2 are respectively two temperature not in the same time；K is coefficient；
Vi1For first temperature T1Under i-th of passage output signal, Vi2For first temperature T2Under i-th of passage
Output signal, T1At a temperature of emissivity εi1∈ (0,1), by randomly selecting one group of εi1, calculated by following formula in parameter εi1Under
The T actually obtainedi1：
If k ∈ (- η, η), by randomly selecting a k, in second temperature T2Under emissivity εi2Expression formula be：
Calculated by following formula in parameter εi1Under the T that actually obtainsi2：
The MODIS is disposed with multiple image wireless sensers for being used to monitor；The modulation of the image wireless senser
Signal x (t) fractional lower-order ambiguity function is expressed as：
Wherein, τ is delay skew, and f is Doppler frequency shift, 0<a,b<α/2, x*(t) x (t) conjugation is represented, when x (t) is
During real signal, x (t)<p>=| x (t) |<p>sgn(x(t))；When x (t) is time multiplexed signal, [x (t)]<p>=| x (t) |p-1x*(t)；
The MODIS imaging monitors method includes：
Step 1: deployment image wireless senser：In the detection zone that area is S=W × L, by image wireless sensing
Device is deployed in detection zone；
Step 2: selection cluster head：Whole detection zone is evenly dividing by grid, makes the size shape of each grid
Identical, the sensor node for selecting positional distance grid element center nearest in each grid is used as cluster head；
Step 3: sub-clustering：After the completion of cluster head selection, cluster head broadcast Cluster { ID, N, Hop } information, wherein, ID is section
The numbering of point, the hop count that N forwards for Cluster information, and N initial value are the hop count that 0, Hop is default；It is attached in cluster head
Near neighbor node receives N increases by 1 after Cluster information and forwards this information again, until N=Hop just no longer forwards Cluster
Information；Again to Cluster information to be transmitted to the neighbor node of oneself after the neighbor node forwarding Cluster information of cluster head, so
Feedback information Join { ID, N, an E are sent afterwardsir, dij, kiTo Cluster information to be transmitted to the node of oneself, most at last
Join information is transmitted to cluster head and represents oneself to add the cluster, wherein, EirRepresent the dump energy of the node now, dijRepresent two
Distance between node, kiRepresent that the node can monitor the size of obtained packet；If a node have received multiple
Cluster information, node just selects the N values small addition cluster, if the equal nodes of N just at will select a cluster and are added to this
Cluster；If node does not receive Cluster information, node sends Help information, adds a cluster nearest from oneself；
Step 4: cluster interior nodes constitute simple graph model：By step 3 obtain all nodes in cluster in cluster it is residing
Position, each node is regarded and is connected between a summit of figure, each two adjacent node with side；
Step 5: in cluster weights calculating：By the step 3, cluster head obtains the E of member node in clusterir、dijAnd ki,
The weights between two adjacent sections point i, j are calculated, the calculation formula of weights is：
Wij=a1(Eir+Ejr)+a2dij+a3(ki+kj)；
Wherein, Ejr、kjThe size for the data that node j dump energy and node j can be monitored, and a are represented respectively1+a2
+a3=1, such system just can be according to system to Eir、dijOr kiRequired proportion difference adjustment aiValue and be met
The weights that difference needs；
Crop water supply index is standardized：SDI=(VSWI-VSWId)/(VSWIw-VSWIdIn) × 100%, formula
SDI is the water supply index that each pixel point crop of remote sensing image corresponding to farmland massif is monitored after standardizing, and takes 0~100%,
Wherein SDI=0 represents severe drought, and SDI=100% represents to moisten very much；VSWIdAnd VSWIwIt is when respectively most non-irrigated and most humid
When crop water supply index；EVI classification step-length can be set to d, as d=0.05, and the temperature space of suitable for crop growth is 20
DEG C~45 DEG C when, VSWId=(n × d)/45, VSWIw=(n × d)/20, n is the number of enhancing vegetation index step-length, n >=1
Positive integer.
Further, need to carry out before the calculating of crop water supply index：The calculating of vegetation index：
EVI is enhancing vegetation index, ρNIR、ρRedAnd ρBlueThe MODIS's being respectively monitored corresponding to farmland massif is near
The spectral reflectivity of infrared band, red spectral band and blue wave band pixel；L is that background adjusts item；C1 and C2For fitting coefficient；G
For gain factor；When calculating MODIS-EVI, L=1, C1=6, C2=7.5, G=2.5；With calculating monitored farmland respectively
The EVI numbers of each pixel point of remote sensing image corresponding to block.
Further, after the calculating of vegetation index, also need to carry out：Surface Temperature Retrieval, its calculation formula is：
Ts=A0+A1T31-A2T32
A0=E1a31-E2a32
A1=1+A+E1b31
A2=A+E2b32
A=D31/E0
E1=D32(1-C31-D31)/E0
E2=D31(1-C32-D32)/E0
E0=D32C31-D31C32
Ci=εiτi
Di=[1+ (1- εi)τi]；
T in formulasIt is surface temperature (K), T31And T32It is MODIS the 31st and 32 wave bands brightness temperature, A respectively0, A1 and A2
It is the parameter of Split window algorithms, a31, b31, a31And b32It is constant, can use a respectively in the range of 0-50 DEG C of surface temperature31=-
64.60363, b31=0.440817, a32=-68.72575, b32=0.473453；Then calculated and supervised respectively using formula
Survey the T of each pixel point of remote sensing image corresponding to farmland massifsData；Wherein i refers to corresponding to monitored farmland massif
31st and 32 wave bands of MODIS images, respectively i=31 or 32；τiIt is that the remote sensing image being monitored corresponding to farmland massif is each
The wave band i of pixel point atmospheric transmittance, εiIt is the wave band i of each pixel point of remote sensing image corresponding to monitored farmland massif
Land surface emissivity.
Further, εi=εiw+PvRvεiv+(1-Pv)Rsεis,
εiw、εivAnd εisIt is the water body of each pixel point of remote sensing image corresponding to monitored farmland massif, vegetation and naked respectively
Soil takes ε respectively in the Land surface emissivity of the i-th wave band31w=0.99683, ε32w=0.99254, ε31v=0.98672, ε32v=
0.98990, ε31s=0.96767, ε31s=0.97790；RvAnd RsIt is the remote sensing image corresponding to monitored farmland massif respectively
Vegetation and the radiation ratio of exposed soil, the R of calculatingv=0.99240, Rs=1.00744；PvIt is corresponding to monitored farmland massif
The vegetation coverage of each pixel point.
Further, MODIS signal of video signal resolution ratio trust value computing method, including：
Gather the interaction times of n timeslice between network observations node i and node j：
Intervals t is chosen as an observation time piece, with observer nodes i and tested node j in 1 timeslice
Interior interaction times are as observation index, and true interaction times are denoted as yt, the y of n timeslice is recorded successivelyn, and preserved
In the communications records table of node i.
Further, the interaction times of (n+1)th timeslice are predicted, including：
According to the interaction times setup time sequence of the n timeslice collected, predicted down using third index flatness
Interaction times between one timeslice n+1 interior nodes i and j, predict interaction times, are denoted asCalculation formula is as follows：
Predictive coefficient an、bn、cnValue can by equation below calculate obtain：
Wherein：Be respectively once, secondary, Three-exponential Smoothing number, calculated by equation below
Arrive：
It is the initial value of third index flatness, its value is
α is smoothing factor (0<α<1) y of the time attenuation characteristic, the i.e. timeslice nearer from predicted value trusted, is embodiedt
Weight is bigger, the y of the timeslice more remote from predicted valuetWeight is smaller；Usually, if data fluctuations are larger, and long-term trend
Amplitude of variation is larger, and α when substantially rapidly rising or falling trend, which is presented, should take higher value (0.6~0.8), can increase in the recent period
Influence of the data to predicting the outcome；When data have a fluctuation, but long-term trend change it is little when, α can between 0.1~0.4 value；
If data fluctuations are steady, α should take smaller value (0.05~0.20).
Further, MODIS signal of video signal resolution ratio trust value computing method, in addition to：
Calculate direct trust value：
Node j direct trust value TDijFor prediction interaction timesWith true interaction times yn+1Relative error,
Further, MODIS signal of video signal resolution ratio trust value computing method, in addition to：
Collect direct trust value of the trusted node to node j：
Node i meets TD to allik≤ φ credible associated nodes inquire its direct trust value to node j, wherein φ
For the believability threshold of recommended node, according to the precision prescribed of confidence level, φ span is 0~0.4.
Further, MODIS signal of video signal resolution ratio trust value computing method, in addition to：
Calculate indirect trust values：
Trust value collected by COMPREHENSIVE CALCULATING, obtains node j indirect trust values TRij,Its
In, Set (i) is interacted and its direct trust value meets TD to have in observer nodes i associated nodes with j nodesik≤ φ section
Point set.
The present invention utilizes the characteristics of MODIS remotely-sensed data temporal resolutions are higher, realizes agricultural drought monitoring；TVD is adopted
Built with EVI, rather than NDVI.The characteristics of NDVI has to background information sensitivity and has saturability, is utilizing its progress
It is the presence of certain deficiency to build drought index.EVI overcomes NDVI and is carrying out vegetation as NDVI modified vegetation index
Deficiency during monitoring, therefore there is more preferable applicability compared with NDVI.
The agricultural arid monitoring method based on MODIS data that the present invention is provided, utilizes MODIS remotely-sensed data time resolutions
The characteristics of rate is higher, realizes agricultural drought monitoring.EVI and LST make use of to build temperature vegetation drought index TVDI, analysis
Relation between TVDI and soil moisture, builds soil moisture retrieval model.Finally, built soil moisture retrieval model is used for
Draught monitor is analyzed, and builds drought remote sensing monitoring platform.The present invention carries out agricultural drought dynamic monitoring, warp using remote sensing technology
Practical application is examined, and this method is easy, efficient, easily operated, result accurate, can be widely applied for agricultural drought and monitors it
In.
Parameter information and image that the present invention is obtained using network technology, make data obtain accuracy rate high；To set up more
The arid grade classification of specification is provided and is effectively ensured.
The MODIS signal of video signal resolution ratio trust value computing methods of the present invention, collection network observations node i and node j it
Between n timeslice interaction times：Intervals t is chosen as an observation time piece, with observer nodes i and tested
Interaction times of the node j in 1 timeslice are as observation index, and true interaction times are denoted as yt, n time is recorded successively
The y of piecen, and save it in the communications records table of node i.The higher information of resolution ratio has been obtained, has realized that agricultural drought is supervised
Survey.
Brief description of the drawings
Fig. 1 is the agricultural arid monitoring method flow chart provided in an embodiment of the present invention based on MODIS data.
Embodiment
In order to make the purpose , technical scheme and advantage of the present invention be clearer, with reference to embodiments, to the present invention
It is further elaborated.It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, it is not used to
Limit the present invention.
The application principle of the present invention is described in detail below in conjunction with the accompanying drawings.
As shown in figure 1, the agricultural arid monitoring method provided in an embodiment of the present invention based on MODIS data, including：
S101:Temperature vegetation drought index TVDI, the pass between analysis TVDI and soil moisture are built using EVI and LST
System, builds soil moisture retrieval model；
S102:Built soil moisture retrieval model is analyzed for draught monitor, and builds drought remote sensing monitoring platform；
TVDI computation model is：
LSTEVIi.max=a+bEVIi
LSTEVIi.min=a'+b'EVIi
Farmland spectrum, temperature and the moisture data of a series of different soils moistures are gathered, basic data collection is built；With
This calculate EVI-LST spaces it is dry while with it is wet while, parameter a, b, a needed for acquisition TVDI models ', b '；
The calculating of soil moisture includes the calculating of crop water supply index：
VSWI is the crop water supply index of each pixel point corresponding to monitored farmland massif in formula；EVI is monitored agriculture
The enhancing vegetation index of each pixel point of MODIS images corresponding to the block of field；TsFor the remote sensing being monitored corresponding to farmland massif
The surface temperature of each pixel point of image；
The image processing method of each pixel point of MODIS images includes：
Farmland temperature parameter is obtained according to infrared spectral radiant, infrared spectrum emissivity is at selected wavelength and temperature
There is approximately uniform linear relationship, i.e.,：
εi2=εi1[1+k(T2-T1)]
In formula, εi1It is that wavelength is λi, spectral emissivity when temperature is T1；εi2It is that wavelength is λi, light when temperature is T2
Compose emissivity；T1, T2 are respectively two temperature not in the same time；K is coefficient；
Vi1For first temperature T1Under i-th of passage output signal, Vi2For first temperature T2Under i-th of passage
Output signal, T1At a temperature of emissivity εi1∈ (0,1), by randomly selecting one group of εi1, calculated by following formula in parameter εi1Under
The T actually obtainedi1：
If k ∈ (- η, η), by randomly selecting a k, in second temperature T2Under emissivity εi2Expression formula be：
Calculated by following formula in parameter εi1Under the T that actually obtainsi2：
The MODIS is disposed with multiple image wireless sensers for being used to monitor；The modulation of the image wireless senser
Signal x (t) fractional lower-order ambiguity function is expressed as：
Wherein, τ is delay skew, and f is Doppler frequency shift, 0<a,b<α/2, x*(t) x (t) conjugation is represented, when x (t) is
During real signal, x (t)<p>=| x (t) |<p>sgn(x(t))；When x (t) is time multiplexed signal, [x (t)]<p>=| x (t) |p-1x*(t)；
The MODIS imaging monitors method includes：
Step 1: deployment image wireless senser：In the detection zone that area is S=W × L, by image wireless sensing
Device is deployed in detection zone；
Step 2: selection cluster head：Whole detection zone is evenly dividing by grid, makes the size shape of each grid
Identical, the sensor node for selecting positional distance grid element center nearest in each grid is used as cluster head；
Step 3: sub-clustering：After the completion of cluster head selection, cluster head broadcast Cluster { ID, N, Hop } information, wherein, ID is section
The numbering of point, the hop count that N forwards for Cluster information, and N initial value are the hop count that 0, Hop is default；It is attached in cluster head
Near neighbor node receives N increases by 1 after Cluster information and forwards this information again, until N=Hop just no longer forwards Cluster
Information；Again to Cluster information to be transmitted to the neighbor node of oneself after the neighbor node forwarding Cluster information of cluster head, so
Feedback information Join { ID, N, an E are sent afterwardsir, dij, kiTo Cluster information to be transmitted to the node of oneself, most at last
Join information is transmitted to cluster head and represents oneself to add the cluster, wherein, EirRepresent the dump energy of the node now, dijRepresent two
Distance between node, kiRepresent that the node can monitor the size of obtained packet；If a node have received multiple
Cluster information, node just selects the N values small addition cluster, if the equal nodes of N just at will select a cluster and are added to this
Cluster；If node does not receive Cluster information, node sends Help information, adds a cluster nearest from oneself；
Step 4: cluster interior nodes constitute simple graph model：By step 3 obtain all nodes in cluster in cluster it is residing
Position, each node is regarded and is connected between a summit of figure, each two adjacent node with side；
Step 5: in cluster weights calculating：By the step 3, cluster head obtains the E of member node in clusterir、dijAnd ki,
The weights between two adjacent sections point i, j are calculated, the calculation formula of weights is：
Wij=a1(Eir+Ejr)+a2dij+a3(ki+kj)；
Wherein, Ejr、kjThe size for the data that node j dump energy and node j can be monitored, and a are represented respectively1+a2
+a3=1, such system just can be according to system to Eir、dijOr kiRequired proportion difference adjustment aiValue and be met
The weights that difference needs；
Crop water supply index is standardized：SDI=(VSWI-VSWId)/(VSWIw-VSWIdIn) × 100%, formula
SDI is the water supply index that each pixel point crop of remote sensing image corresponding to farmland massif is monitored after standardizing, and takes 0~100%,
Wherein SDI=0 represents severe drought, and SDI=100% represents to moisten very much；VSWIdAnd VSWIwIt is when respectively most non-irrigated and most humid
When crop water supply index；EVI classification step-length can be set to d, as d=0.05, and the temperature space of suitable for crop growth is 20
DEG C~45 DEG C when, VSWId=(n × d)/45, VSWIw=(n × d)/20, n is the number of enhancing vegetation index step-length, n >=1
Positive integer.
Further, need to carry out before the calculating of crop water supply index：The calculating of vegetation index：
EVI is enhancing vegetation index, ρNIR、ρRedAnd ρBlueThe MODIS's being respectively monitored corresponding to farmland massif is near
The spectral reflectivity of infrared band, red spectral band and blue wave band pixel；L is that background adjusts item；C1 and C2For fitting coefficient；G
For gain factor；When calculating MODIS-EVI, L=1, C1=6, C2=7.5, G=2.5；With calculating monitored farmland respectively
The EVI numbers of each pixel point of remote sensing image corresponding to block.
Further, after the calculating of vegetation index, also need to carry out：Surface Temperature Retrieval, its calculation formula is：
Ts=A0+A1T31-A2T32
A0=E1a31-E2a32
A1=1+A+E1b31
A2=A+E2b32
A=D31/E0
E1=D32(1-C31-D31)/E0
E2=D31(1-C32-D32)/E0
E0=D32C31-D31C32
Ci=εiτi
Di=[1+ (1- εi)τi]；
T in formulasIt is surface temperature (K), T31And T32It is MODIS the 31st and 32 wave bands brightness temperature, A respectively0, A1 and A2
It is the parameter of Split window algorithms, a31, b31, a31And b32It is constant, can use a respectively in the range of 0-50 DEG C of surface temperature31=-
64.60363, b31=0.440817, a32=-68.72575, b32=0.473453；Then calculated and supervised respectively using formula
Survey the T of each pixel point of remote sensing image corresponding to farmland massifsData；Wherein i refers to corresponding to monitored farmland massif
31st and 32 wave bands of MODIS images, respectively i=31 or 32；τiIt is that the remote sensing image being monitored corresponding to farmland massif is each
The wave band i of pixel point atmospheric transmittance, εiIt is the wave band i of each pixel point of remote sensing image corresponding to monitored farmland massif
Land surface emissivity.
Further, εi=εiw+PvRvεiv+(1-Pv)Rsεis,
εiw、εivAnd εisIt is the water body of each pixel point of remote sensing image corresponding to monitored farmland massif, vegetation and naked respectively
Soil takes ε respectively in the Land surface emissivity of the i-th wave band31w=0.99683, ε32w=0.99254, ε31v=0.98672, ε32v=
0.98990, ε31s=0.96767, ε31s=0.97790；RvAnd RsIt is the remote sensing image corresponding to monitored farmland massif respectively
Vegetation and the radiation ratio of exposed soil, the R of calculatingv=0.99240, Rs=1.00744；PvIt is corresponding to monitored farmland massif
The vegetation coverage of each pixel point.
Further, MODIS signal of video signal resolution ratio trust value computing method, including：
Gather the interaction times of n timeslice between network observations node i and node j：
Intervals t is chosen as an observation time piece, with observer nodes i and tested node j in 1 timeslice
Interior interaction times are as observation index, and true interaction times are denoted as yt, the y of n timeslice is recorded successivelyn, and preserved
In the communications records table of node i.
Further, the interaction times of (n+1)th timeslice are predicted, including：
According to the interaction times setup time sequence of the n timeslice collected, predicted down using third index flatness
Interaction times between one timeslice n+1 interior nodes i and j, predict interaction times, are denoted asCalculation formula is as follows：
Predictive coefficient an、bn、cnValue can by equation below calculate obtain：
Wherein：Be respectively once, secondary, Three-exponential Smoothing number, calculated by equation below
Arrive：
It is the initial value of third index flatness, its value is
α is smoothing factor (0<α<1) y of the time attenuation characteristic, the i.e. timeslice nearer from predicted value trusted, is embodiedt
Weight is bigger, the y of the timeslice more remote from predicted valuetWeight is smaller；Usually, if data fluctuations are larger, and long-term trend
Amplitude of variation is larger, and α when substantially rapidly rising or falling trend, which is presented, should take higher value (0.6~0.8), can increase in the recent period
Influence of the data to predicting the outcome；When data have a fluctuation, but long-term trend change it is little when, α can between 0.1~0.4 value；
If data fluctuations are steady, α should take smaller value (0.05~0.20).
Further, MODIS signal of video signal resolution ratio trust value computing method, in addition to：
Calculate direct trust value：
Node j direct trust value TDijFor prediction interaction timesWith true interaction times yn+1Relative error,
Further, MODIS signal of video signal resolution ratio trust value computing method, in addition to：
Collect direct trust value of the trusted node to node j：
Node i meets TD to allik≤ φ credible associated nodes inquire its direct trust value to node j, wherein φ
For the believability threshold of recommended node, according to the precision prescribed of confidence level, φ span is 0~0.4.
Further, MODIS signal of video signal resolution ratio trust value computing method, in addition to：
Calculate indirect trust values：
Trust value collected by COMPREHENSIVE CALCULATING, obtains node j indirect trust values TRij,
Wherein, Set (i) is interacted and its direct trust value meets TD to have in observer nodes i associated nodes with j nodesik≤ φ's
Node set.
The agricultural arid monitoring method based on MODIS data that the present invention is provided, utilizes MODIS remotely-sensed data time resolutions
The characteristics of rate is higher, realizes agricultural drought monitoring.EVI and LST make use of to build temperature vegetation drought index TVDI, analysis
Relation between TVDI and soil moisture, builds soil moisture retrieval model.Finally, built soil moisture retrieval model is used for
Draught monitor is analyzed, and builds drought remote sensing monitoring platform.The present invention carries out agricultural drought dynamic monitoring, warp using remote sensing technology
Practical application is examined, and this method is easy, efficient, easily operated, result accurate, can be widely applied for agricultural drought and monitors it
In.
Parameter information and image that the present invention is obtained using network technology, make data obtain accuracy rate high；To set up more
The arid grade classification of specification is provided and is effectively ensured.
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the invention, all essences in the present invention
Any modifications, equivalent substitutions and improvements made within refreshing and principle etc., should be included in the scope of the protection.
Claims (9)
1. a kind of agricultural arid monitoring method based on MODIS data, it is characterised in that the agricultural based on MODIS data
Drought monitoring method includes：
EVI and LST make use of to build temperature vegetation drought index TVDI, the relation between analysis TVDI and soil moisture is built
Soil moisture retrieval model；
Finally, built soil moisture retrieval model is analyzed for draught monitor, and builds drought remote sensing monitoring platform；
TVDI computation model is：
<mrow>
<mi>T</mi>
<mi>V</mi>
<mi>D</mi>
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LSTEVIi.max=a+bEVIi
LSTEVIi.min=a'+b'EVIi
Farmland spectrum, temperature and the moisture data of a series of different soils moistures are gathered, basic data collection is built；In terms of this
Calculate EVI-LST spaces it is dry while with it is wet while, parameter a, b, a needed for acquisition TVDI models ', b '；
The calculating of soil moisture includes the calculating of crop water supply index：
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VSWI is the crop water supply index of each pixel point corresponding to monitored farmland massif in formula；EVI is with being monitored farmland
The enhancing vegetation index of each pixel point of MODIS images corresponding to block；TsFor the remote sensing image being monitored corresponding to farmland massif
The surface temperature of each pixel point；
The image processing method of each pixel point of MODIS images includes：
Farmland temperature parameter is obtained according to infrared spectral radiant, infrared spectrum emissivity has closely at selected wavelength with temperature
Like identical linear relationship, i.e.,：
εi2=εi1[1+k(T2-T1)]
In formula, εi1It is that wavelength is λi, spectral emissivity when temperature is T1；εi2It is that wavelength is λi, spectrum hair when temperature is T2
Penetrate rate；T1, T2 are respectively two temperature not in the same time；K is coefficient；
Vi1For first temperature T1Under i-th of passage output signal, Vi2For first temperature T2Under i-th of passage it is defeated
Go out signal, T1At a temperature of emissivity εi1∈ (0,1), by randomly selecting one group of εi1, calculated by following formula in parameter εi1Lower reality
Obtained Ti1：
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If k ∈ (- η, η), by randomly selecting a k, in second temperature T2Under emissivity εi2Expression formula be：
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Calculated by following formula in parameter εi1Under the T that actually obtainsi2：
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The MODIS is disposed with multiple image wireless sensers for being used to monitor；The modulated signal x of the image wireless senser
(t) fractional lower-order ambiguity function is expressed as：
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Wherein, τ is delay skew, and f is Doppler frequency shift, 0<a,b<α/2, x* (t) represents x (t) conjugation, when x (t) believes to be real
Number when, x (t)<p>=| x (t) |<p>sgn(x(t))；When x (t) is time multiplexed signal, [x (t)]<p>=| x (t) |p-1x*(t)；
The MODIS imaging monitors method includes：
Step 1: deployment image wireless senser：In the detection zone that area is S=W × L, by image wireless senser portion
Administration is in detection zone；
Step 2: selection cluster head：Whole detection zone is evenly dividing by grid, makes the size shape phase of each grid
Together, the sensor node for selecting positional distance grid element center nearest in each grid is used as cluster head；
Step 3: sub-clustering：After the completion of cluster head selection, cluster head broadcast Cluster { ID, N, Hop } information, wherein, ID is node
Numbering, the hop count that N forwards for Cluster information, and N initial value are the hop count that 0, Hop is default；Near cluster head
Neighbor node receives N increases by 1 after Cluster information and forwards this information again, and until N=Hop, just no longer forwarding Cluster believes
Breath；Again to Cluster information to be transmitted to the neighbor node of oneself after the neighbor node forwarding Cluster information of cluster head, then
Send feedback information Join { ID, N, an Eir, dij, kiCluster information is transmitted to the node of oneself, most at last Join
Information is transmitted to cluster head and represents oneself to add the cluster, wherein, EirRepresent the dump energy of the node now, dijRepresent two nodes
Between distance, kiRepresent that the node can monitor the size of obtained packet；If a node have received multiple Cluster
Information, node just selects the N values small addition cluster, if the equal nodes of N just at will select a cluster and are added to the cluster；If section
Point does not receive Cluster information, then node sends Help information, adds a cluster nearest from oneself；
Step 4: cluster interior nodes constitute simple graph model：All nodes location in cluster in cluster is obtained by step 3,
Each node is regarded and is connected between a summit of figure, each two adjacent node with side；
Step 5: in cluster weights calculating：By the step 3, cluster head obtains the E of member node in clusterir、dijAnd ki, calculate
Weights between two adjacent sections point i, j, the calculation formula of weights is：
Wij=a1(Eir+Ejr)+a2dij+a3(ki+kj)；
Wherein, Ejr、kjThe size for the data that node j dump energy and node j can be monitored, and a are represented respectively1+a2+a3
=1, such system just can be according to system to Eir、dijOr kiRequired proportion difference adjustment aiValue and be met difference
The weights needed；
Crop water supply index is standardized：SDI=(VSWI-VSWId)/(VSWIw-VSWIdSDI is in) × 100%, formula
The water supply index for each pixel point crop of remote sensing image being monitored after standardization corresponding to farmland massif, takes 0~100%, wherein
SDI=0 represents severe drought, and SDI=100% represents to moisten very much；VSWIdAnd VSWIwWhen respectively most non-irrigated and when most humid
Crop water supply index；EVI classification step-length can be set to d, as d=0.05, suitable for crop growth temperature space for 20 DEG C~
At 45 DEG C, VSWId=(n × d)/45, VSWIw=(n × d)/20, n for enhancing vegetation index step-length number, n >=1 it is just whole
Number.
2. the agricultural arid monitoring method as claimed in claim 1 based on MODIS data, it is characterised in that crop, which is supplied water, to be referred to
Need to carry out before several calculating：The calculating of vegetation index：
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EVI is enhancing vegetation index, ρNIR、ρRedAnd ρBlueThe near-infrared ripple for the MODIS being respectively monitored corresponding to farmland massif
The spectral reflectivity of section, red spectral band and blue wave band pixel；L is that background adjusts item；C1 and C2For fitting coefficient；G is gain
The factor；When calculating MODIS-EVI, L=1, C1=6, C2=7.5, G=2.5；It is right that monitored farmland massif institute is calculated respectively
The EVI numbers for each pixel point of remote sensing image answered.
3. the agricultural arid monitoring method as claimed in claim 2 based on MODIS data, it is characterised in that vegetation index
After calculating, also need to carry out：Surface Temperature Retrieval, its calculation formula is：
Ts=A0+A1T31-A2T32
A0=E1a31-E2a32
A1=1+A+E1b31
A2=A+E2b32
A=D31/E0
E1=D32(1-C31-D31)/E0
E2=D31(1-C32-D32)/E0
E0=D32C31-D31C32
Ci=εiτi
Di=[1+ (1- εi)τi]；
T in formulasIt is surface temperature (K), T31And T32It is MODIS the 31st and 32 wave bands brightness temperature, A respectively0, A1 and A2It is to split
The parameter of window algorithm, a31, b31, a31And b32It is constant, can use a respectively in the range of 0-50 DEG C of surface temperature31=-
64.60363, b31=0.440817, a32=-68.72575, b32=0.473453；Then calculated and supervised respectively using formula
Survey the T of each pixel point of remote sensing image corresponding to farmland massifsData；Wherein i refers to corresponding to monitored farmland massif
31st and 32 wave bands of MODIS images, respectively i=31 or 32；τiIt is that the remote sensing image being monitored corresponding to farmland massif is each
The wave band i of pixel point atmospheric transmittance, εiIt is the wave band i of each pixel point of remote sensing image corresponding to monitored farmland massif
Land surface emissivity.
4. the agricultural arid monitoring method as claimed in claim 3 based on MODIS data, it is characterised in that
εi=εiw+PvRvεiv+(1-Pv)Rsεis,
εiw、εivAnd εisIt is that water body, vegetation and the exposed soil for being monitored each pixel point of remote sensing image corresponding to farmland massif exist respectively
The Land surface emissivity of i-th wave band, takes ε respectively31w=0.99683, ε32w=0.99254, ε31v=0.98672, ε32v=
0.98990, ε31s=0.96767, ε31s=0.97790；RvAnd RsIt is the remote sensing image corresponding to monitored farmland massif respectively
Vegetation and the radiation ratio of exposed soil, the R of calculatingv=0.99240, Rs=1.00744；PvIt is corresponding to monitored farmland massif
The vegetation coverage of each pixel point.
5. the agricultural arid monitoring method as claimed in claim 1 based on MODIS data, it is characterised in that MODIS images are believed
Number resolution ratio trust value computing method, including：
Gather the interaction times of n timeslice between network observations node i and node j：
Intervals t is chosen as an observation time piece, with observer nodes i and tested node j in 1 timeslice
Interaction times are as observation index, and true interaction times are denoted as yt, the y of n timeslice is recorded successivelyn, and save it in section
In point i communications records table.
6. the agricultural arid monitoring method as claimed in claim 5 based on MODIS data, it is characterised in that prediction (n+1)th
The interaction times of timeslice, including：
According to the interaction times setup time sequence of the n timeslice collected, predicted using third index flatness next
Interaction times between timeslice n+1 interior nodes i and j, predict interaction times, are denoted asCalculation formula is as follows：
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Predictive coefficient an、bn、cnValue can by equation below calculate obtain：
Wherein：Be respectively once, secondary, Three-exponential Smoothing number, by equation below calculate obtain：
It is the initial value of third index flatness, its value is
α is smoothing factor (0<α<1) y of the time attenuation characteristic, the i.e. timeslice nearer from predicted value trusted, is embodiedtWeight is got over
Greatly, the y of the timeslice more remote from predicted valuetWeight is smaller；Usually, if data fluctuations are larger, and long-term trend change width
Degree is larger, and α when substantially rapidly rising or falling trend, which is presented, should take higher value (0.6~0.8), can increase Recent data pair
The influence predicted the outcome；When data have a fluctuation, but long-term trend change it is little when, α can between 0.1~0.4 value；If number
According to smooth fluctuations, α should take smaller value (0.05~0.20).
7. the agricultural arid monitoring method as claimed in claim 5 based on MODIS data, it is characterised in that MODIS images are believed
Number resolution ratio trust value computing method, in addition to：
Calculate direct trust value：
Node j direct trust value TDijFor prediction interaction timesWith true interaction times yn+1Relative error,
8. the agricultural arid monitoring method as claimed in claim 5 based on MODIS data, it is characterised in that MODIS images are believed
Number resolution ratio trust value computing method, in addition to：
Collect direct trust value of the trusted node to node j：
Node i meets TD to allik≤ φ credible associated nodes inquire its direct trust value to node j, and wherein φ is to push away
The believability threshold of node is recommended, according to the precision prescribed of confidence level, φ span is 0~0.4.
9. the agricultural arid monitoring method as claimed in claim 5 based on MODIS data, it is characterised in that MODIS images are believed
Number resolution ratio trust value computing method, in addition to：
Calculate indirect trust values：
Trust value collected by COMPREHENSIVE CALCULATING, obtains node j indirect trust values TRij,
Wherein, Set (i) is interacted and it to have in observer nodes i associated nodes with j nodes
Direct trust value meets TDik≤ φ node set.
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| CN107782701A (en) * | 2017-09-20 | 2018-03-09 | 北京师范大学 | A kind of agricultural arid monitoring method of multi- source Remote Sensing Data data |
| CN108548793A (en) * | 2018-03-26 | 2018-09-18 | 山东省农业可持续发展研究所 | A kind of wheat canopy water content inversion method of comprehensive Nir-Red-Swir spectral signatures |
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