CN105929406B - A kind of Agriculture Drought remote-sensing monitoring method - Google Patents
A kind of Agriculture Drought remote-sensing monitoring method Download PDFInfo
- Publication number
- CN105929406B CN105929406B CN201610264159.3A CN201610264159A CN105929406B CN 105929406 B CN105929406 B CN 105929406B CN 201610264159 A CN201610264159 A CN 201610264159A CN 105929406 B CN105929406 B CN 105929406B
- Authority
- CN
- China
- Prior art keywords
- drought
- etvdi
- evi
- surface temperature
- lst
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000012544 monitoring process Methods 0.000 title claims abstract description 25
- 238000000034 method Methods 0.000 title claims abstract description 19
- 230000002708 enhancing effect Effects 0.000 claims abstract description 13
- 238000004364 calculation method Methods 0.000 claims abstract description 11
- 238000002310 reflectometry Methods 0.000 claims abstract description 6
- 238000011160 research Methods 0.000 claims description 16
- 239000002689 soil Substances 0.000 claims description 13
- 238000012821 model calculation Methods 0.000 claims description 6
- 230000003595 spectral effect Effects 0.000 claims description 6
- 238000012937 correction Methods 0.000 claims description 4
- 238000000205 computational method Methods 0.000 claims description 3
- 230000005855 radiation Effects 0.000 claims description 2
- 238000011161 development Methods 0.000 abstract description 5
- 230000007547 defect Effects 0.000 abstract description 3
- 230000008569 process Effects 0.000 abstract description 3
- 230000007812 deficiency Effects 0.000 abstract description 2
- 238000001556 precipitation Methods 0.000 description 15
- 230000008901 benefit Effects 0.000 description 4
- 238000013456 study Methods 0.000 description 4
- 230000008859 change Effects 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 2
- 230000001186 cumulative effect Effects 0.000 description 2
- 230000009467 reduction Effects 0.000 description 2
- 230000005457 Black-body radiation Effects 0.000 description 1
- 241001269238 Data Species 0.000 description 1
- PEDCQBHIVMGVHV-UHFFFAOYSA-N Glycerine Chemical compound OCC(O)CO PEDCQBHIVMGVHV-UHFFFAOYSA-N 0.000 description 1
- 238000009395 breeding Methods 0.000 description 1
- 230000001488 breeding effect Effects 0.000 description 1
- 239000003795 chemical substances by application Substances 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 239000004744 fabric Substances 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 230000002045 lasting effect Effects 0.000 description 1
- 239000011159 matrix material Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000002360 preparation method Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 230000000630 rising effect Effects 0.000 description 1
- 229920006395 saturated elastomer Polymers 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000005068 transpiration Effects 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
- G01S17/88—Lidar systems specially adapted for specific applications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
- G01J5/00—Radiation pyrometry, e.g. infrared or optical thermometry
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/55—Specular reflectivity
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Analytical Chemistry (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Health & Medical Sciences (AREA)
- Computer Networks & Wireless Communication (AREA)
- Chemical & Material Sciences (AREA)
- Electromagnetism (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Image Processing (AREA)
Abstract
The invention discloses a kind of Agriculture Drought remote-sensing monitoring methods, including step：(1) enhancing vegetation index and surface temperature are calculated separately according to Reflectivity for Growing Season and Thermal Remote Sensing Image；(2) it utilizes surface temperature and enhancing vegetation index result of calculation to build ETVDI models, and obtains the identical maximum value for enhancing the surface temperature under vegetation index value and minimum value and the pixel point quantity of the corresponding surface temperature value identical point of different enhancing vegetation index values；(3) by carrying out the dry and wet side equation in fitting of a polynomial acquisition ETVDI models to the corresponding maximum value of identical enhancing vegetation index value and minimum value, fitting parameter is obtained.The present invention overcomes traditional TVDI models, and in dense vegetation the area easily defect of generation vegetation index saturation and dry and wet side equation model, there are probabilistic deficiencies, improve fitting precision, the precision higher in terms of the space-time development performance of damage caused by a drought, it can more really reflect the process that damage caused by a drought breeds, develops, withering away, reference is provided for Agriculture Drought early warning and monitoring.
Description
Technical field
The present invention relates to remote sensing technology and vegetation study on monitoring field, more particularly to a kind of Agriculture Drought remote sensing monitoring side
Method.
Background technology
For arid since its early period of origination is not noticeable, the period is long and involves the features such as range is wide, seriously affects region security
And social stability.Therefore, damage caused by a drought early warning and alert is carried out, the Monitoring of drought method that research is suitable for region is most important.
At present in monitoring soil moisture research, the inverting of the regional soil moisture based on remote sensing technology is studied due to having
The advantages that range is wide, efficient and Time Continuous, has become an important research direction, it is wet to have established numerous soil
Inverse model is spent, wherein the most widely used monitoring model is the TVDI (Temperature that Sandholt et al. is proposed at present
Vegetation Dryness Index, temperature vegetation drought index) model.The model of the TVDI is referring to Fig. 1, ordinate
Surface temperature LST is represented, abscissa represents vegetation coverage, generally utilizes normalized differential vegetation index NDVI (Normalized
Difference Vegetation Index) it indicates.In the model, pass through the corresponding earth's surface of the different vegetation index values of statistics
Temperature value builds trapezoidal or triangle character space, is obtained using the method for maximum value and minimum value to simplify data processing amount
The maximum surface temperature under same vegetation index value and minimum surface temperature are obtained, and the scatterplot of maximum surface temperature pixel composition is close
It is indicated like with straight line, referred to as " dry while " or " while hot ", the scatterplot approximation of minimum surface temperature pixel composition is with one
Straight line indicates, referred to as " wet while " or " while cold ".Calculation formula is as follows：
Wherein, LSTiRepresent surface temperature, LSTminThe minimum value of the surface temperature in the case where obtaining a certain NDVI values is represented,
LSTmaxRepresent the maximum value of the surface temperature in the case where obtaining a certain NDVI values.
LSTmax=a*NDVI+b
LSTmin=c*NDVI+d
Wherein a, b, c, d are the parameter of linear fit straight line, and a, c are slope, and b, d are intercept, are constant.
But TVDI haves the shortcomings that require earth's surface cover type high, theoretically TVDI feature spaces planted agent includes naked
3 kinds of soil, part vegetative coverage and full vegetative coverage vegetative coverage conditions, this makes the simple area of cover type be difficult to be estimated
Calculate, the dry and wet determined by the scatterplot of these " not in full conformity with condition " when being substantially existing for feature space inside, and
It is not theoretic dry and wet side, therefore be easy to cause prodigious uncertainty.Such as suggest the NDVI vegetation used in the model
In vegetation luxuriant area vegetation index saturation easily occurs for index, to reduce the Monitoring of drought result to the luxuriant region of vegetation
Precision, it is poor for the Monitoring of drought applicability in the luxuriant area of vegetation.Therefore research one kind is needed to be suitable for vegetation luxuriantly
The Regional Drought Inspection by Remote Sensing System method in area.
Invention content
The shortcomings that it is a primary object of the present invention to overcome the prior art and deficiency, provide a kind of Agriculture Drought remote sensing monitoring
Method, this method compensate for traditional TVDI models in the defect of high vegetative coverage region Monitoring of drought result inaccuracy, and carry
The high fitting precision of dry and wet side equation, it is good for the Monitoring of drought effect of high vegetated terrain, and the few, data with parameter
It easily obtains, the advantage that result is accurate.
The purpose of the present invention is realized by the following technical solution：A kind of Agriculture Drought remote-sensing monitoring method, including step：
(1) Reflectivity for Growing Season remote sensing image and Ground Heat containing near infrared band, red spectral band and blue wave band are obtained
Infrared remote sensing image；
(2) remote sensing image of acquisition is pre-processed；
(3) according to the Reflectivity for Growing Season calculating enhancing of near infrared band, red spectral band and blue wave band in remote sensing image
Vegetation index EVI (Enhanced Vegetation Index)；Surface temperature LST is calculated according to Ground Heat infrared remote sensing image
(Land Surface Temperature)；
(4) it utilizes surface temperature LST and enhances the result of calculation structure ETVDI models of vegetation index EVI, abscissa represents
Enhance vegetation index EVI, ordinate represents surface temperature LST, and calculation formula is：
Wherein LSTiRepresent surface temperature, LSTminThe corresponding surface temperature minimum value in the case where obtaining a certain EVI values is represented,
LSTmaxRepresent the corresponding surface temperature maximum value in the case where obtaining a certain EVI values；ETVDI is bigger, and soil moisture is lower, and ETVDI is got over
Small, soil moisture is higher；
(5) use fitting of a polynomial mode, to it is dry in equation and it is wet while equation be fitted, specific equation is as follows：
Doing side equation is：
LSTmax=a1*EVIn+a2*EVIn-1+a3*EVIn-2+…+an*EVI+a0；
Wet side equation is：
LSTmin=b1*EVIn+b2*EVIn-1+b3*EVIn-2+…+bn*EVI+b0；
Wherein a0,a1,a2,…an；b0,b1,b2,…bnIt is fitting coefficient；
(6) definition is as 0 < ETVDI≤0.3, for moistening；It is normal as 0.3 < ETVDI≤0.6；As 0.6 <
When ETVDI≤0.8, for light drought；It is middle drought as 0.8 < ETVDI≤0.95；As ETVDI > 0.95, drought of attaching most importance to；According to
ETVDI model calculation formulas obtain the damage caused by a drought spatial distribution in research area.
Specifically, in the step (2), pretreated content includes atmospheric correction, radiation calibration, geometric correction, image
Inlay cutting.
Specifically, in the step (3), the computational methods of enhancing vegetation index EVI are：
Wherein, ρNLR、ρREDAnd ρBLUERespectively represent the ground of near infrared band in remote sensing image, red spectral band and blue wave band
Table reflectivity.
Specifically, in the step (3), the computational methods of surface temperature LST are：
L λ=[ε B (TS)+(1- ε) L ↓] τ+L ↑；
Wherein, ε is Land surface emissivity, and TS is earth's surface true temperature, and B (TS) is blackbody radiation brightness, and τ is that air exists
The transmitance of Thermal infrared bands；Radiance B (TS)=[L λ-L ↑-τ (1- ε) L of black matrix in Thermal infrared bands that temperature is T
↓]/τ ε, while TS=K2/ln (K1/B (TS)+1), K1 and K2 are constant.
Specifically, in the step (6), the damage caused by a drought spatial distribution in research area is obtained according to ETVDI model calculation formulas
The step of be：
(1) according to the ETVDI result of calculations of acquisition, raster file is produced；
(2) according to ETVDI damage caused by a drought divided ranks, interval division is carried out to ETVDI raster file data, determines different sections
Interior pixel space of points distribution；
(3) the administrative division vector data in binding region obtains the specific spatial position point of different grades of damage caused by a drought
Cloth.
Compared with prior art, the present invention having the following advantages that and advantageous effect：
The present invention proposes a kind of ETVDI models, relative to traditional TVDI models, overcomes it in dense vegetation area
The defect of vegetation index saturation easily occurs, while both having been overcome in former linear fit and not having be known using fitting of a polynomial mode
Property improve again it is dry while and it is wet while equation fitting precision, the precision higher in terms of the space-time development performance of damage caused by a drought can be truer
The process breed, develop, withered away for reflecting damage caused by a drought, reference is provided for Agriculture Drought early warning and monitoring.
Description of the drawings
Fig. 1 is TVDI model schematics in the prior art.
Fig. 2 is the present embodiment ETVDI model schematics.
Fig. 3 (a) is the present embodiment according to the surface temperature data and NDVI data structures studied on October 14th, 2004 in area
The TVDI illustratons of model built.
Fig. 3 (b) is the present embodiment according to the surface temperature data and NDVI data structures studied on October 30th, 2004 in area
The TVDI illustratons of model built.
Fig. 3 (c) is the present embodiment according to the surface temperature data and NDVI data structures studied on November 15th, 2004 in area
The TVDI illustratons of model built.
Fig. 3 (d) is that the present embodiment is built according to the surface temperature data and NDVI data studied on December 1st, 2004 in area
TVDI illustratons of model.
Fig. 4 is damage caused by a drought spatial distribution map in the TVDI model inversions research area in 2004 of (a)-(d) according to fig. 3.
Fig. 5 (a) is that the present embodiment is built according to the surface temperature data and EVI data studied on October 14th, 2004 in area
ETVDI illustratons of model.
Fig. 5 (b) is that the present embodiment is built according to the surface temperature data and EVI data studied on October 30th, 2004 in area
ETVDI illustratons of model.
Fig. 5 (c) is that the present embodiment is built according to the surface temperature data and EVI data studied on November 15th, 2004 in area
ETVDI illustratons of model.
Fig. 5 (d) is that the present embodiment is built according to the surface temperature data and EVI data studied on December 1st, 2004 in area
ETVDI illustratons of model.
Fig. 6 is damage caused by a drought spatial distribution map in the ETVDI model inversions research area in 2004 according to Fig. 5 (a)-(d).
Fig. 7 is 8, Guangdong Province meteorological site 2004 days rainfall distribution figure.
Fig. 8 is 8, Guangdong Province meteorological site September to spatial distribution of precipitation figure in December.
Specific implementation mode
Present invention will now be described in further detail with reference to the embodiments and the accompanying drawings, but embodiments of the present invention are unlimited
In this.
Embodiment 1
The present embodiment is improved for TVDI models, constructs ETVDI exponential models, specific to improve such as referring to Fig. 2
Under：
(1) achievement in research of domestic and foreign scholars shows that NDVI is right as the universal general vegetation index in the whole world at present
Using effect in the relatively sparse area of vegetation is preferable, strong applicability, but for the higher area of vegetation coverage, especially
It is easy to happen the phenomenon that vegetation index is saturated for the southern area of China, and EVI can just make up this point, therefore
It is stronger compared with the applicability of NDVI for southern area of China EVI, thus in our current research by the NDVI in master mould using EVI into
Row is replaced.
(2) for the definition of " dry while " and " while wet " in TVDI models, there has been no correlative studys at present.In model construction
Initial stage is convenience of calculation, is fitted using linear relationship, but in practice, maximum temperature or minimum temperature composition
Concave-convex distribution is presented in discrete point substantially, and fitting of a polynomial relationship is more bonded reality and precision higher, therefore the present embodiment is tasted
Examination optimizes former linear fit equation using polynomial-fitting function, improves model parameter precision.
(3) ETVDI models are established according to above-mentioned improving and optimizating, model represents enhancing vegetation referring to Fig. 2, wherein abscissa
Index (EVI), ordinate represent surface temperature (LST), and A points represent dry exposed soil, low EVI, high LST；B points represent wet
Moisten dense vegetation, ground moistening is rising strong；C points represent moistening bare soil, low EVI, low LST；D points represent dry luxuriant plant
Quilt, soil drought, transpiration are weak.Specific formula for calculation is：
Wherein LSTiRepresent surface temperature, LSTminThe corresponding surface temperature minimum value in the case where obtaining a certain EVI values is represented,
LSTmaxRepresent the corresponding surface temperature maximum value in the case where obtaining a certain EVI values；ETVDI is bigger, and soil moisture is lower, and ETVDI is got over
Small, soil moisture is higher；
Doing side equation is：
LSTmax=a1*EVIn+a2*EVIn-1+a3*EVIn-2+…+an*EVI+a0；
Wet side equation is：
LSTmin=b1*EVIn+b2*EVIn-1+b3*EVIn-2+…+bn*EVI+b0；
Wherein a0,a1,a2,…an；b0,b1,b2,…bnIt is fitting coefficient.
Below by a specific example come the advantages of illustrating the present embodiment the method.
Vegetation index data are using the enhancing vegetation in MODIS vegetation index Product Data Sets MOD13Q1 in the present embodiment
Index (EVI) and normalized differential vegetation index (NDVI) data, the time cycle is 16 days, spatial resolution is 250 meters；Earth's surface
It is 1000 meters that temperature data, which uses the day data in MYD11A1 data sets, spatial resolution,.
By being inlayed, being cut to MODIS data, projection transform, image calculate obtain research area in October, 2004
14 days, NDVI, EVI and LST data in October 30, four periods of November 15 and December 1.
One, prior art TVDI models
According to the surface temperature data and NDVI data in research area in 2004, using TVDI models, acquisition is identical
The maximum value and minimum value of surface temperature under NDVI values and NDVI different values pixel point number (shown in Fig. 3 (a)-(d),
Abscissa represents the value of NDVI, and left ordinate represents surface temperature value, and right ordinate represents the number of the pixel point of identical NDVI
Amount).The dry side in TVDI models is obtained respectively by carrying out linear fit to the corresponding maximum value of identical NDVI values and minimum value
Equation and wet side equation (table 1) finally combine TVDI model calculation formulas to obtain the damage caused by a drought spatial distribution (Fig. 4) in research area.
The damage caused by a drought in the research of TVDI model inversions 2004 area October~December the result shows that：Early October Guangdong Province's damage caused by a drought is
It is broken out through large area, is mainly distributed on east Guangdong Province, the Leizhou Peninsula Area, China in Guangdong Province, middle part damage caused by a drought is relatively low；With
It arrives afterwards by the end of October, damage caused by a drought is overall still than more serious, and for spatial distribution compared with early October without significant change, the cumulative effect of damage caused by a drought is unknown
It is aobvious；Early November Leizhou Peninsula Area, China damage caused by a drought is further aggravated, and East Guangdong, Northern Guangdong Province damage caused by a drought are alleviated；Damage caused by a drought at the beginning of 12 months is again
Enhanced, especially east Guangdong Province damage caused by a drought aggravates apparent, and Leizhou Peninsula Area, China damage caused by a drought spatial distribution has slight change.The model
Monitoring result can be in preferable image study area in trend damage caused by a drought spatial development change procedure, but with practical feelings
Condition compares, and the occurrence and development degree of damage caused by a drought is laid particular stress on, and analyzes its reason and is primarily due to vegetation saturation, causes the part of fitting wet
The precision of side equation is not high.
1 TVDI models of table it is dry while and it is wet while equation
Two, ETVDI models of the present invention
ETVDI models (Fig. 5) are constructed according to enhancing vegetation index (EVI) and surface temperature (LST), while in model
It is dry while and it is wet while use polynomial method and be fitted (table 2).Tables 1 and 2 is compared it is found that by fitting of a polynomial
Equation R2It is substantially at 0.9 or more, fitting precision is higher, so as to more accurately show the sky of different brackets damage caused by a drought
Between be distributed.
Since the vegetation saturation of NDVI causes the practical damage caused by a drought of LST_NDVI model Monitoring of drought results to lay particular stress on, attempt
NDVI is replaced modeling, the maximum value and minimum value and EVI of surface temperature of the acquisition under identical EVI values using EVI
(shown in Fig. 5, abscissa represents the value of EVI to the pixel point quantity of different value, and left ordinate represents surface temperature value, right ordinate
Represent the quantity of the pixel point of identical NDVI).By carrying out fitting of a polynomial to the corresponding maximum value of identical EVI values and minimum value
Respectively obtain LST_EVI models in it is dry in equation and it is wet while equation (table 2), finally according to ETVDI model calculation formulas obtain
Study the damage caused by a drought spatial distribution (Fig. 6) in area.
2 ETVDI models of table it is dry while and it is wet while equation
The damage caused by a drought in the research of ETVDI model inversions 2004 area October~December the result shows that：Early October Guangdong Province part
Fragmentary damage caused by a drought occurs for area, is distributed mainly on Delta of Pearl River；Damage caused by a drought is developed compared with the first tenday period of a month by the end of October, arid range
It is further spread to periphery, then to early November, damage caused by a drought cumulative effect obviously shows, and arid region is concentrated mainly on Leizhou half
Island region, based on light drought, fragmentary weight drought；At the beginning of 12 months, by regional area Rainfall Influence by the end of November, Lezhou Peninsula Regional Drought has
Alleviated, weight drought is basic to disappear, and east Guangdong Province damage caused by a drought is aggravated.The monitoring result of the model shows damage caused by a drought well
The process for breeding, occur, developing and withering away meets objective present situation.
Arid is main or caused by precipitation lacks, and the reduction of precipitation leads to moisture unbalanced supply-demand, in turn
It is evolved into damage caused by a drought.Therefore, the precipitation in area of space to a certain extent can provide centainly the occurrence and development of damage caused by a drought
With reference to.In consideration of it, the present invention is according to 8, Guangdong Province meteorological site (including Lianzhou City, Shaoguan, Germany and Britain, Mei County, Guangzhou, Shantou, sieve
Fixed, Zhanjiang) 2004 days precipitation datas verification analysis is carried out to ETVDI the model calculations (Fig. 8) referring to Fig. 7.
As can be seen from FIG. 7：During Guangdong Province's precipitation in 2004 is concentrated mainly on April~September, in precipitation urgency in October
Reduce sharply few, occur substantially without precipitation, then there is fragmentary precipitation in November and December, the moon accumulative rainfall amount be substantially at 20mm
Below.As can be seen from FIG. 8：September part precipitation is more, and Guangdong central and west regions, some areas drop are concentrated mainly in spatial distribution
Water is in 200mm or more, and relatively small number of area is concentrated mainly on east Guangdong Province.October the whole province in addition to Zhanjiang, other
Area has fragmentary precipitation, precipitation area to be concentrated mainly on the part of East Guangdong and North Guangdong without effective precipitation, November and December substantially
Area.
By above-mentioned comprehensive analysis：In early October, since September part precipitation is relatively more, in a short time still
Do not have the condition of damage caused by a drought large-scale outbreak, therefore the practical serious drought of monitoring result of TVDI, and the monitoring result of ETVDI
It is more conform with objective present situation, while embodying the arid preparation process in damage caused by a drought early period of origination, and with November and precipitation in December
Lasting reduction, embody the evolution that damage caused by a drought is built up, monitoring result and objective news report are coincide substantially.
The above embodiment is a preferred embodiment of the present invention, but embodiments of the present invention are not by above-described embodiment
Limitation, it is other it is any without departing from the spirit and principles of the present invention made by changes, modifications, substitutions, combinations, simplifications,
Equivalent substitute mode is should be, is included within the scope of the present invention.
Claims (3)
1. a kind of Agriculture Drought remote-sensing monitoring method, which is characterized in that including step：
(1) Reflectivity for Growing Season remote sensing image and earth's surface thermal infrared containing near infrared band, red spectral band and blue wave band are obtained
Remote sensing image；
(2) remote sensing image of acquisition is pre-processed；
(3) according to the Reflectivity for Growing Season calculating enhancing vegetation of near infrared band, red spectral band and blue wave band in remote sensing image
Index E VI；Surface temperature LST is calculated according to Ground Heat infrared remote sensing image；
(4) it utilizes surface temperature LST and enhances the result of calculation structure ETVDI models of vegetation index EVI, abscissa represents enhancing
Vegetation index EVI, ordinate represent surface temperature LST, and calculation formula is：
Wherein LSTiRepresent surface temperature, LSTminRepresent the corresponding surface temperature minimum value in the case where obtaining a certain EVI values, LSTmax
Represent the corresponding surface temperature maximum value in the case where obtaining a certain EVI values；ETVDI is bigger, and soil moisture is lower, and ETVDI is smaller, soil
Earth humidity is higher；
(5) use fitting of a polynomial mode, to it is dry in equation and it is wet while equation be fitted, specific equation is as follows：
Doing side equation is：
LSTmax=a1*EVIn+a2*EVIn-1+a3*EVIn-2+…+an*EVI+a0；
Wet side equation is：
LSTmin=b1*EVIn+b2*EVIn-1+b3*EVIn-2+…+bn*EVI+b0；
Wherein a0,a1,a2,…an；b0,b1,b2,…bnIt is fitting coefficient；
(6) definition is as 0 < ETVDI≤0.3, for moistening；It is normal as 0.3 < ETVDI≤0.6；When 0.6 < ETVDI≤
When 0.8, for light drought；It is middle drought as 0.8 < ETVDI≤0.95；As ETVDI > 0.95, drought of attaching most importance to；According to ETVDI models
Calculation formula obtains the damage caused by a drought spatial distribution in research area；
Obtaining the step of studying the damage caused by a drought spatial distribution in area according to ETVDI model calculation formulas is：
(1) according to the ETVDI result of calculations of acquisition, raster file is produced；
(2) according to ETVDI damage caused by a drought divided ranks, interval division is carried out to ETVDI raster file data, is determined in different sections
The pixel space of points is distributed；
(3) the administrative division vector data in binding region obtains the specific spatial position distribution of different grades of damage caused by a drought.
2. Agriculture Drought remote-sensing monitoring method according to claim 1, which is characterized in that in the step (2), pretreatment
Content include atmospheric correction, radiation calibration, geometric correction, image mosaic cut.
3. Agriculture Drought remote-sensing monitoring method according to claim 1, which is characterized in that in the step (3), enhancing is planted
Computational methods by index E VI are：
Wherein, ρNIR、ρREDAnd ρBLUEThe earth's surface for respectively representing near infrared band in remote sensing image, red spectral band and blue wave band is anti-
Penetrate rate.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610264159.3A CN105929406B (en) | 2016-04-25 | 2016-04-25 | A kind of Agriculture Drought remote-sensing monitoring method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610264159.3A CN105929406B (en) | 2016-04-25 | 2016-04-25 | A kind of Agriculture Drought remote-sensing monitoring method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN105929406A CN105929406A (en) | 2016-09-07 |
| CN105929406B true CN105929406B (en) | 2018-11-02 |
Family
ID=56836264
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610264159.3A Active CN105929406B (en) | 2016-04-25 | 2016-04-25 | A kind of Agriculture Drought remote-sensing monitoring method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105929406B (en) |
Families Citing this family (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107085712A (en) * | 2017-04-28 | 2017-08-22 | 山东省农业可持续发展研究所 | A kind of agricultural arid monitoring method based on MODIS data |
| CN108760643B (en) * | 2018-04-04 | 2021-03-16 | 西南石油大学 | Drought remote sensing monitoring method suitable for high-altitude area |
| CN108629460A (en) * | 2018-05-11 | 2018-10-09 | 中南林业科技大学 | Forest land Drought Model construction method based on space-time data |
| CN109115696A (en) * | 2018-08-30 | 2019-01-01 | 南京信息工程大学 | A kind of Monitoring of drought method based on MODIS data |
| CN109543654B (en) * | 2018-12-14 | 2023-04-18 | 常州大学 | Construction method of improved vegetation index reflecting crop growth conditions |
| CN110487793A (en) * | 2019-08-29 | 2019-11-22 | 北京麦飞科技有限公司 | Pest and disease damage time DYNAMIC DISTRIBUTION monitoring method and system |
| CN111289441B (en) * | 2020-02-21 | 2021-02-26 | 中国农业大学 | Multispectral field crop water content determination method, system and equipment |
| CN112858632B (en) * | 2021-01-14 | 2023-01-13 | 中国科学院空天信息创新研究院 | Grassland drought monitoring method comprehensively considering temperature and water stress |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2008051207A2 (en) * | 2005-10-21 | 2008-05-02 | Carnegie Institution Of Washington | Remote sensing analysis of forest disturbances |
| CN101908196A (en) * | 2009-06-03 | 2010-12-08 | 北京师范大学 | Method for estimating vegetation coverage based on vegetation-soil moisture response relation |
| CN102103077A (en) * | 2009-12-16 | 2011-06-22 | 中国科学院沈阳应用生态研究所 | MODIS data-based agricultural drought monitoring method |
| CN103994976A (en) * | 2013-11-28 | 2014-08-20 | 江苏省水利科学研究院 | MODIS data-based agricultural drought remote sensing monitoring method |
-
2016
- 2016-04-25 CN CN201610264159.3A patent/CN105929406B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2008051207A2 (en) * | 2005-10-21 | 2008-05-02 | Carnegie Institution Of Washington | Remote sensing analysis of forest disturbances |
| CN101908196A (en) * | 2009-06-03 | 2010-12-08 | 北京师范大学 | Method for estimating vegetation coverage based on vegetation-soil moisture response relation |
| CN102103077A (en) * | 2009-12-16 | 2011-06-22 | 中国科学院沈阳应用生态研究所 | MODIS data-based agricultural drought monitoring method |
| CN103994976A (en) * | 2013-11-28 | 2014-08-20 | 江苏省水利科学研究院 | MODIS data-based agricultural drought remote sensing monitoring method |
Non-Patent Citations (1)
| Title |
|---|
| 基于多时相遥感影像的盐渍化农田表层土壤水分反演研究;白燕英;《中国博士学位论文全文数据库 农业科技辑》;20150115(第1期);正文第6，9，32，96，97，103-106，116，117页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN105929406A (en) | 2016-09-07 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105929406B (en) | A kind of Agriculture Drought remote-sensing monitoring method | |
| CN106908415B (en) | A kind of big region crops time of infertility Soil Moisture Monitoring method based on amendment NDVI time series | |
| CN109829234B (en) | A kind of across scale Dynamic High-accuracy crop condition monitoring and yield estimation method based on high-definition remote sensing data and crop modeling | |
| CN108169161B (en) | Corn planting area soil humidity assessment method based on improved MODIS index | |
| CN108802728B (en) | Crop irrigation guiding method for assimilation of bipolar synthetic aperture radar and crop model | |
| CN106226260A (en) | A kind of combination microwave and the Soil Moisture Inversion method of infrared remote sensing image | |
| CN103424405B (en) | Drought monitoring method based on HJ-1A/1B CCD data | |
| Parkinson | Spatial patterns in the length of the sea ice season in the Southern Ocean, 1979–1986 | |
| KR20160057911A (en) | System for deducting agricultural drought index and method therefor | |
| CN113552320A (en) | Soil moisture remote sensing inversion method based on Chan method | |
| CN105466957A (en) | Method for estimating soil humidity based on FY-3C passive microwave data | |
| CN107941713A (en) | A kind of rice yield estimation method based on coupling crop modeling assimilation spectral reflectivity | |
| Genanu et al. | Remote sensing based estimation of Evapo-transpiration using selected algorithms: the case of Wonji Shoa sugar cane estate, Ethiopia | |
| CN110533287A (en) | Summer corn arid disaster loss quantifies the building and application of appraisal model | |
| CN115479960A (en) | Crop growth process soil humidity monitoring method combining SAR and optical remote sensing data | |
| Chen et al. | Spatiotemporal variation of vegetation net primary productivity and its responses to climate change in the Huainan Coal Mining Area | |
| Gao et al. | The effects of climate change and phenological variation on agricultural production and its risk pattern in the black soil area of northeast China | |
| CN116124709B (en) | Winter wheat drought unmanned aerial vehicle monitoring and distinguishing method based on chlorophyll relative content | |
| CN115326721A (en) | Method for estimating crop water stress | |
| CN112198814B (en) | High-precision thermal radiation directivity semi-empirical semi-physical simulation method | |
| de C. Teixeira et al. | Up scaling Table grape water requirements in the Low-Middle São Francisco river basin, Brazil | |
| CN110956322B (en) | Summer maize flowering phase high-temperature disaster risk prediction method under climate warming trend | |
| 原薗芳信 et al. | Applications of MODIS-visible bands index, greenery ratio to estimate CO2 budget of a rice paddy in Japan | |
| Mecikalski et al. | Application of high-resolution visible sharpening of partly cloudy pixels in Meteosat Second Generation infrared imagery | |
| Kępińska-Kasprzak et al. | Monitoring of Plant Cultivation Conditions Using Ground Measurements and Satellite Products. Water 2023, 15, 449 |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |