CN105929406A - Agricultural drought remote sensing monitoring method - Google Patents

Abstract

The invention discloses an agricultural drought remote sensing monitoring method, which comprises the following steps: 1) calculating enhanced vegetation index and land surface temperature according to surface reflectance and thermal infrared remote sensing images; 2) constructing an ETVDI model through the surface temperature and the enhanced vegetation index calculation result, and obtaining maximum value and minimum value of the surface temperature under the same enhanced vegetation index value and the number of pixel points at the points of same surface temperature values corresponding to different enhanced vegetation index values; and 3) carrying out polynomial fitting on the maximum value and minimum value corresponding to the same enhanced vegetation index value to obtain wet and dry boundary equations in the ETVDI model, and obtaining fitting parameters. The method overcomes the defect that a conventional TVDI model has vegetation index saturation easily in a dense vegetation area and the defect of uncertainty of wet and dry boundary equation fitting, thereby improving fitting precision; precision is higher in the expression aspect of temporal and spatial development of the drought; and the method can reflect the process of drought growing, development and elimination more truly, and provides reference for early warning and monitoring of the agricultural drought.

Description

A kind of Agriculture Drought remote-sensing monitoring method

Technical field

The present invention relates to remote sensing technology and vegetation study on monitoring field, particularly to a kind of Agriculture Drought remote sensing Monitoring method.

Background technology

Arid is difficult to discover due to its early period of origination, cycle length and involve the features such as scope is wide, has a strong impact on district Territory safety and social stability.Therefore, carrying out damage caused by a drought early warning and alert, research is suitable for the Monitoring of drought side in region Method is most important.

At present in monitoring soil moisture is studied, the inverting research of regional soil moisture based on remote sensing technology by In having that scope is wide, efficiency is high and the advantage such as Time Continuous, have become as an important research direction, existing Having established numerous soil moisture retrieval model, the most currently used most commonly used monitoring model is Sandholt Et al. propose TVDI (Temperature Vegetation Dryness Index, temperature vegetation drought index) mould Type.The model of described TVDI sees Fig. 1, and its vertical coordinate represents surface temperature LST, and abscissa represents vegetation Coverage, typically utilizes normalized differential vegetation index NDVI (Normalized Difference Vegetation Index) Represent.In the model, by the surface temperature value that the different vegetation index value of statistics is corresponding build trapezoidal or Triangle character space, for simplifying data processing amount, uses maximum and the method for minima, it is thus achieved that same Maximum surface temperature under vegetation index value and minimum surface temperature, and maximum surface temperature pixel composition is scattered Point approximation represents with straight line, is referred to as " dry limit " or " hot limit ", dissipating of minimum surface temperature pixel composition Point approximation represents with straight line, is referred to as on " wet limit " or " cold limit ".Computing formula is as follows:

${\mathrm{LST}}_{NDVI}=\frac{{\mathrm{LST}}_i-{\mathrm{LST}}_{min}}{{\mathrm{LST}}_{\max }-{\mathrm{LST}}_{min}}$

Wherein, LST_iRepresent surface temperature, LST_minRepresent and obtaining under a certain NDVI value surface temperature Little value, LST_maxRepresent the maximum of surface temperature under obtaining a certain NDVI value.

LST_max=a*NDVI+b

LST_min=c*NDVI+d

Wherein a, b, c, d be the parameter of linear fit straight line, and a, c are slope, and b, d are intercept, are constant.

But, there is the shortcoming high to the requirement of earth's surface cover type in TVDI, in theory in TVDI feature space Should include exposed soil, part vegetative coverage and complete 3 kinds of vegetative coverage conditions of vegetative coverage, this makes cover type Simple area is difficult to estimation, the scatterplot of these " not in full conformity with conditions " the dry and wet limit determined is originally It is the internal limit existed of feature space rather than theoretic dry and wet limit in matter, therefore easily causes the biggest Uncertainty.This model such as being advised, the NDVI vegetation index used easily is sent out in the area that vegetation is luxuriant Raw vegetation index is saturated, thus reduces the Monitoring of drought result precision in region luxuriant to vegetation, for vegetation The Monitoring of drought suitability in luxuriant area is poor.It is thus desirable to study a kind of vegetation luxuriant area of being suitable for Regional Drought Inspection by Remote Sensing System method.

Summary of the invention

Present invention is primarily targeted at the shortcoming overcoming prior art with not enough, it is provided that a kind of Agriculture Drought is distant Sense monitoring method, the method compensate for traditional TVDI model in high vegetative coverage region Monitoring of drought result not Defect accurately, and improve the fitting precision of dry and wet limit equation, the damage caused by a drought for high vegetated terrain is supervised Survey effective, and have that parameter is few, data easily obtain, the accurate advantage of result.

The purpose of the present invention is realized by following technical scheme: a kind of Agriculture Drought remote-sensing monitoring method, bag Include step:

(1) obtain containing near infrared band, red spectral band and the Reflectivity for Growing Season remote sensing image of blue wave band and Earth's surface Thermal Remote Sensing Image；

(2) remote sensing image obtained is carried out pretreatment；

(3) according to the Reflectivity for Growing Season meter of the near infrared band in remote sensing image, red spectral band and blue wave band Calculate and strengthen vegetation index EVI (Enhanced Vegetation Index)；Base area exterior heat infrared remote sensing eikonometer Calculate surface temperature LST (Land Surface Temperature)；

(4) utilize surface temperature LST and strengthen the result of calculation structure ETVDI model of vegetation index EVI, Abscissa represents and strengthens vegetation index EVI, and vertical coordinate represents surface temperature LST, and computing formula is:

$ETVDI=\frac{{\mathrm{LST}}_i-{\mathrm{LST}}_{min}}{{\mathrm{LST}}_{\max }-{\mathrm{LST}}_{min}};$

Wherein LST_iRepresent surface temperature, LST_minRepresent and obtaining under a certain EVI value the surface temperature of correspondence Little value, LST_maxRepresent surface temperature maximum corresponding under obtaining a certain EVI value；ETVDI is the biggest, Soil moisture is the lowest, and ETVDI is the least, and soil moisture is the highest；

(5) use fitting of a polynomial mode, dry limit equation and wet limit equation are fitted, concrete equation As follows:

Dry limit equation is:

LST_max=a₁*EVIⁿ+a₂*EVI^n-1+a₃*EVI^n-2+…+a_n*EVI+a₀；

Wet limit equation is:

LST_min=b₁*EVIⁿ+b₂*EVI^n-1+b₃*EVI^n-2+…+b_n*EVI+b₀；

Wherein a₀,a₁,a₂,…a_n；b₀,b₁,b₂,…b_nIt is fitting coefficient；

(6) definition is when 0 ＜ ETVDI≤0.3, is moistening；When 0.3 ＜ ETVDI≤0.6, for normally； When 0.6 ＜ ETVDI≤0.8, for light drought；When 0.8 ＜ ETVDI≤0.95, for middle drought；Work as ETVDI During ＞ 0.95, drought of attaching most importance to；The damage caused by a drought spatial distribution in study area is obtained according to ETVDI model calculation formula.

Concrete, in described step (2), the content of pretreatment includes atmospheric correction, radiation calibration, geometry Correction, image mosaic cutting.

Concrete, in described step (3), the computational methods strengthening vegetation index EVI are:

$EVI=2.5\×\frac{{\mathrm{\ρ}}_{NIR}-{\mathrm{\ρ}}_{RED}}{{\mathrm{\ρ}}_{NIR}+6.0{\mathrm{\ρ}}_{RED}-7.5{\mathrm{\ρ}}_{BLUE}+1};$

Wherein, ρ_NLR、ρ_REDAnd ρ_BLUERepresent near infrared band in remote sensing image, red spectral band and blue wave band respectively Reflectivity for Growing Season.

Concrete, in described 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 Air is in the transmitance of Thermal infrared bands；Temperature be T the black matrix radiance B (TS) at Thermal infrared bands= [L λ-L ↑-τ (1-ε) L ↓]/τ ε, TS=K2/ln (K1/B (TS)+1) simultaneously, K1 and K2 is normal Number.

Concrete, in described step (6), obtain the drought in study area according to ETVDI model calculation formula The step of feelings spatial distribution is:

(1) according to the ETVDI result of calculation obtained, raster file is produced；

(2) according to ETVDI damage caused by a drought divided rank, ETVDI raster file data are carried out interval division, Determine the pixel space of points distribution in different interval；

(3) the administrative division vector data in binding region, it is thus achieved that the concrete space of different grades of damage caused by a drought Position distribution.

The present invention compared with prior art, has the advantage that and beneficial effect:

The present invention proposes a kind of ETVDI model, relative to traditional TVDI model, overcomes it and is planting Easily be there is, by dense area, the defect that vegetation index is saturated, use fitting of a polynomial mode both to overcome simultaneously Uncertainty in former linear fit improves again dry limit and the fitting precision of wet limit equation, at the space-time of damage caused by a drought Development performance aspect precision is higher, can more truly reflect the process breeding, develop, withering away of damage caused by a drought, for agriculture Industry damage caused by a drought early warning and monitoring provides reference.

Accompanying drawing explanation

Fig. 1 is TVDI model schematic in prior art.

Fig. 2 is the present embodiment ETVDI model schematic.

Fig. 3 (a) is the present embodiment according to the surface temperature data on October 14th, 2004 study area and NDVI The TVDI illustraton of model of data construct.

Fig. 3 (b) is the present embodiment according to the surface temperature data on October 30th, 2004 study area and NDVI The TVDI illustraton of model of data construct.

Fig. 3 (c) is the present embodiment according to the surface temperature data on November 15th, 2004 study area and NDVI The TVDI illustraton of model of data construct.

Fig. 3 (d) is that the present embodiment is according to the surface temperature data in December in 2004 study area on the 1st and NDVI The TVDI illustraton of model of data construct.

Fig. 4 is damage caused by a drought spatial distribution map in the 2004 years study areas of TVDI model inversion according to Fig. 3 (a)-(d).

Fig. 5 (a) is the present embodiment according to the surface temperature data on October 14th, 2004 study area and EVI The ETVDI illustraton of model of data construct.

Fig. 5 (b) is the present embodiment according to the surface temperature data on October 30th, 2004 study area and EVI The ETVDI illustraton of model of data construct.

Fig. 5 (c) is the present embodiment according to the surface temperature data on November 15th, 2004 study area and EVI The ETVDI illustraton of model of data construct.

Fig. 5 (d) is that the present embodiment is according to the surface temperature data in December in 2004 study area on the 1st and EVI The ETVDI illustraton of model of data construct.

Fig. 6 is damage caused by a drought spatial distribution map in the 2004 years study areas of ETVDI model inversion according to Fig. 5 (a)-(d).

8, Tu7Shi Guangdong Province meteorological site 2004 rainfall distribution on days figure.

8, Tu8Shi Guangdong Province meteorological site JIUYUE is to December spatial distribution of precipitation figure.

Detailed description of the invention

Below in conjunction with embodiment and accompanying drawing, the present invention is described in further detail, but the embodiment party of the present invention Formula is not limited to this.

Embodiment 1

The present embodiment is improved for TVDI model, constructs ETVDI exponential model, sees Fig. 2, Concrete improvement is as follows:

(1) achievement in research of Chinese scholars shows, current NDVI is as the most general vegetation in the whole world Index, it is preferable for the regional using effect that vegetation is the most sparse, and the suitability is strong, but for planting The area that coating cover degree is higher, particularly with the southern area of China, is susceptible to saturated the showing of vegetation index As, and EVI just can make up this point, is therefore suitable for compared with NDVI for southern area of China EVI Property higher, the most in our current research by master mould NDVI use EVI be replaced.

(2) for " the dry limit " in TVDI model and the definition on " wet limit ", the most not yet there is correlational study. At the model construction initial stage, for convenience of calculation, linear relationship is used to be fitted, but in practice, the highest The discrete point of temperature or minimum temperature composition presents concavo-convex distribution substantially, and fitting of a polynomial relation is more sticked on Closing actual and precision is higher, therefore the present embodiment is attempted former linear fit equation is utilized polynomial-fitting function It is optimized, improves model parameter precision.

(3) setting up ETVDI model according to above-mentioned improving and optimizating, model sees Fig. 2, wherein abscissa generation Table strengthens vegetation index (EVI), and vertical coordinate represents surface temperature (LST), and A point represents and is dried exposed soil Earth, low EVI, high LST；B point represents moistening dense vegetation, and ground moistening is rising strong；C point represents wet Profit bare soil, low EVI, low LST；D point represents and is dried luxuriant vegetation, soil drought, transpiration Weak.Specific formula for calculation is:

$ETVDI=\frac{{\mathrm{LST}}_i-{\mathrm{LST}}_{min}}{{\mathrm{LST}}_{\max }-{\mathrm{LST}}_{min}}$

Wherein LST_iRepresent surface temperature, LST_minRepresent and obtaining under a certain EVI value the surface temperature of correspondence Little value, LST_maxRepresent surface temperature maximum corresponding under obtaining a certain EVI value；ETVDI is the biggest, Soil moisture is the lowest, and ETVDI is the least, and soil moisture is the highest；

Dry limit equation is:

LST_max=a₁*EVIⁿ+a₂*EVI^n-1+a₃*EVI^n-2+…+a_n*EVI+a₀；

Wet limit equation is:

LST_min=b₁*EVIⁿ+b₂*EVI^n-1+b₃*EVI^n-2+…+b_n*EVI+b₀；

Wherein a₀,a₁,a₂,…a_n；b₀,b₁,b₂,…b_nIt is fitting coefficient.

Advantage method described in the present embodiment being described below by an instantiation.

In the present embodiment, vegetation index data acquisition is with in MODIS vegetation index Product Data Set MOD13Q1 Strengthen vegetation index (EVI) and normalized differential vegetation index (NDVI) data, the time cycle be 16 days, Spatial resolution is 250 meters；Surface temperature data acquisition day data in MYD11A1 data set, empty Between resolution be 1000 meters.

By inlaying MODIS data, cutting, projection transform, image calculate and obtain in study area On October 14th, 2004, October 30, November 15 and the NDVI of 1 four time period of December, EVI and LST data.

One, prior art TVDI model

According to the surface temperature data in study area in 2004 and NDVI data, use TVDI model, obtain The maximum of the surface temperature being taken under identical NDVI value and the pixel point of minima and NDVI different value (shown in Fig. 3 (a)-(d), abscissa represents the value of NDVI to number, and left vertical coordinate represents surface temperature value, right Vertical coordinate represents the quantity of the pixel point of identical NDVI).By the maximum corresponding to identical NDVI value and Minima carries out dry limit equation and the wet limit equation (table 1) that linear fit obtains in TVDI model respectively, Rear combination TVDI model calculation formula obtains the damage caused by a drought spatial distribution (Fig. 4) in study area.

The damage caused by a drought result of TVDI model inversion study area October in 2004～December shows: early October Guangdong Save the outburst of damage caused by a drought large area, be mainly distributed on the east Guangdong Province in Guangdong Province, Leizhou Peninsula Area, China, middle part Area damage caused by a drought is relatively low；Arriving by the end of October subsequently, damage caused by a drought the most still ratio is more serious, spatial distribution relatively October The first tenday period of a month, the accumulative effect of damage caused by a drought was inconspicuous without significant change；Early November Leizhou Peninsula Area, China damage caused by a drought is further Increasing the weight of, East Guangdong, Northern Guangdong Province damage caused by a drought have been alleviated；Damage caused by a drought at the beginning of 12 months has strengthened, especially ground, East Guangdong District's damage caused by a drought increases the weight of substantially, and Leizhou Peninsula Area, China damage caused by a drought spatial distribution has slight change.The monitoring result of this model In trend can the spatial development change procedure of the preferable damage caused by a drought in image study district, but with actual feelings Condition compares, and the generation development degree of damage caused by a drought lays particular stress on, and analyzing its reason, to be primarily due to vegetation saturated, causes The precision of the part wet limit equation of matching is the highest.

Table 1 TVDI model does limit and wet limit equation

Two, ETVDI model of the present invention

ETVDI model (Fig. 5) is constructed according to enhancing vegetation index (EVI) and surface temperature (LST), Dry limit in model and wet limit be have employed polynomial method simultaneously and carried out matching (table 2).Contrast table 1 Understand, through the R of the equation of fitting of a polynomial with table 2²Being substantially at more than 0.9, fitting precision is higher, It is thus possible to show the spatial distribution of different brackets damage caused by a drought more accurately.

Owing to the vegetation of the NDVI saturated LST_NDVI of the causing model actual damage caused by a drought of Monitoring of drought result lays particular stress on, Therefore attempt NDVI uses EVI be replaced modeling, acquisition surface temperature under identical EVI value (shown in Fig. 5, abscissa represents EVI's to the pixel point quantity of maximum and minima and EVI different value Value, left vertical coordinate represents surface temperature value, and right vertical coordinate represents the quantity of the pixel point of identical NDVI).Logical Cross the maximum corresponding to identical EVI value and minima carries out fitting of a polynomial and obtains LST_EVI model respectively In dry limit equation and wet limit equation (table 2), obtain in study area finally according to ETVDI model calculation formula Damage caused by a drought spatial distribution (Fig. 6).

Table 2 ETVDI model does limit and wet limit equation

The damage caused by a drought result of ETVDI model inversion study area October in 2004～December shows: early October is wide There is fragmentary damage caused by a drought in Dong Sheng some areas, is distributed mainly on Delta of Pearl River；The damage caused by a drought relatively the first tenday period of a month by the end of October Having developed, the scope of arid spreads to periphery further, and subsequently to early November, damage caused by a drought accumulative effect is bright Manifesting, arid region is concentrated mainly on region, the Lezhou Peninsula, based on light drought, and fragmentary weight drought；At the beginning of 12 months, By regional area Rainfall Influence by the end of November, Lezhou Peninsula Regional Drought has been alleviated, and weight drought is basic to disappear, and East Guangdong Province damage caused by a drought has increased the weight of.The monitoring result of this model well indicate the breeding of damage caused by a drought, occur, Development and the process withered away, meet objective present situation.

Arid is main or causes owing to precipitation lacks, the minimizing of precipitation, causes moisture supply and demand uneven Weighing apparatus, and then it is evolved into damage caused by a drought.Therefore, the precipitation in area of space to a certain extent can be to damage caused by a drought There is the reference that development offer is certain.In consideration of it, the present invention (includes even according to 8, Guangdong Province meteorological site State, Shaoguan, Germany and Britain, Mei County, Guangzhou, Shantou, Luoding, Zhanjiang) 2004 days precipitation data, ginseng See Fig. 7, ETVDI the model calculation (Fig. 8) is carried out checking and analyzes.

According to Fig. 7: during Guangdong Province's precipitation in 2004 is concentrated mainly on April～JIUYUE, in October Part precipitation drastically reduces, and substantially occurs without precipitation, has fragmentary precipitation in November and December subsequently, Month accumulative rainfall amount is substantially at below 20mm.According to Fig. 8: JIUYUE precipitation is more, divides in space Being concentrated mainly on central and west regions, Guangdong on cloth, some areas precipitation is in more than 200mm, relatively small number of Area is concentrated mainly on east Guangdong Province.October the whole province in addition to Zhanjiang, other area substantially without effective precipitation, November and December have fragmentary precipitation, precipitation area to be concentrated mainly on some areas of East Guangdong and North Guangdong.

By above-mentioned comprehensive analysis: in early October, owing to JIUYUE precipitation is the most, short The condition of damage caused by a drought large-scale outbreak, the therefore actual serious drought of the monitoring result of TVDI is not the most possessed in time, And the monitoring result of ETVDI compares and meets objective present situation, embody pregnant at damage caused by a drought early period of origination of arid simultaneously Educate process, and along with November and the lasting minimizing of December precipitation, embody the development that damage caused by a drought builds up Journey, its monitoring result and objective news report are the most identical.

Above-described embodiment is the present invention preferably embodiment, but embodiments of the present invention are not by above-mentioned reality Execute the restriction of example, the change made under other any spirit without departing from the present invention and principle, modification, Substitute, combine, simplify, all should be the substitute mode of equivalence, within being included in protection scope of the present invention.

Claims (5)

1. an Agriculture Drought remote-sensing monitoring method, it is characterised in that include step:

(1) obtain containing near infrared band, red spectral band and the Reflectivity for Growing Season remote sensing image of blue wave band and Earth's surface Thermal Remote Sensing Image；

(2) remote sensing image obtained is carried out pretreatment；

(3) according to the Reflectivity for Growing Season meter of the near infrared band in remote sensing image, red spectral band and blue wave band Calculate and strengthen vegetation index EVI；Exterior heat infrared remote sensing image in base area calculates surface temperature LST；

(4) utilize surface temperature LST and strengthen the result of calculation structure ETVDI model of vegetation index EVI, Abscissa represents and strengthens vegetation index EVI, and vertical coordinate represents surface temperature LST, and computing formula is:

$ETVDI=\frac{{\mathrm{LST}}_i-{\mathrm{LST}}_{min}}{{\mathrm{LST}}_{\max }-{\mathrm{LST}}_{min}};$

Wherein LST_iRepresent surface temperature, LST_minRepresent and obtaining under a certain EVI value the surface temperature of correspondence Little value, LST_maxRepresent surface temperature maximum corresponding under obtaining a certain EVI value；ETVDI is the biggest, Soil moisture is the lowest, and ETVDI is the least, and soil moisture is the highest；

(5) use fitting of a polynomial mode, dry limit equation and wet limit equation are fitted, concrete equation As follows:

Dry limit equation is:

LST_max=a₁*EVIⁿ+a₂*EVI^n-1+a₃*EVI^n-2+…+a_n*EVI+a₀；

Wet limit equation is:

LST_min=b₁*EVIⁿ+b₂*EVI^n-1+b₃*EVI^n-2+…+b_n*EVI+b₀；

Wherein a₀,a₁,a₂,…a_n；b₀,b₁,b₂,…b_nIt is fitting coefficient；

(6) definition is when 0 ＜ ETVDI≤0.3, is moistening；When 0.3 ＜ ETVDI≤0.6, for normally； When 0.6 ＜ ETVDI≤0.8, for light drought；When 0.8 ＜ ETVDI≤0.95, for middle drought；Work as ETVDI During ＞ 0.95, drought of attaching most importance to；The damage caused by a drought spatial distribution in study area is obtained according to ETVDI model calculation formula.

Agriculture Drought remote-sensing monitoring method the most according to claim 1, it is characterised in that described step (2), in, the content of pretreatment includes atmospheric correction, radiation calibration, geometric correction, image mosaic cutting.

Agriculture Drought remote-sensing monitoring method the most according to claim 1, it is characterised in that described step (3), in, the computational methods strengthening vegetation index EVI are:

$EVI=2.5\×\frac{{\mathrm{\ρ}}_{NIR}-{\mathrm{\ρ}}_{RED}}{{\mathrm{\ρ}}_{NIR}+6.0{\mathrm{\ρ}}_{RED}-7.5{\mathrm{\ρ}}_{BLUE}+1};$

Wherein, ρ_NIR、ρ_REDAnd ρ_BLUERepresent near infrared band in remote sensing image, red spectral band and blue wave band respectively Reflectivity for Growing Season.

Agriculture Drought remote-sensing monitoring method the most according to claim 1, it is characterised in that described step (3), in, 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 Air is in the transmitance of Thermal infrared bands；Temperature be T the black matrix radiance B (TS) at Thermal infrared bands= [L λ-L ↑-τ (1-ε) L ↓]/τ ε, TS=K2/ln (K1/B (TS)+1) simultaneously, K1 and K2 is normal Number.

Agriculture Drought remote-sensing monitoring method the most according to claim 1, it is characterised in that described step (6), in, the step obtaining the damage caused by a drought spatial distribution in study area according to ETVDI model calculation formula is:

(1) according to the ETVDI result of calculation obtained, raster file is produced；

(2) according to ETVDI damage caused by a drought divided rank, ETVDI raster file data are carried out interval division, Determine the pixel space of points distribution in different interval；

(3) the administrative division vector data in binding region, it is thus achieved that the concrete space of different grades of damage caused by a drought Position distribution.