CN115479960B - Method for monitoring soil humidity in crop growth process by combining SAR (synthetic aperture radar) with optical remote sensing data - Google Patents

Method for monitoring soil humidity in crop growth process by combining SAR (synthetic aperture radar) with optical remote sensing data Download PDF

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CN115479960B
CN115479960B CN202211111362.9A CN202211111362A CN115479960B CN 115479960 B CN115479960 B CN 115479960B CN 202211111362 A CN202211111362 A CN 202211111362A CN 115479960 B CN115479960 B CN 115479960B
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汪左
李虎
陈冬花
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Abstract

The invention discloses a method for monitoring soil humidity in a crop growth process by combining SAR and optical remote sensing data, which comprises the following steps: s1, based on sowing time T 1 SAR satellite data SAR (T) 1 ,θ 1 ) SAR (T) 1 ,θ 2 ) Calculating a sowing time T 1 Soil surface humidity M of (2) v (T 1 ) The method comprises the steps of carrying out a first treatment on the surface of the S2, based on period T i SAR satellite data SAR (T) i θ), period T i+1 Optical remote sensing data OPT (T) i+1 ) SAR satellite data SAR (T) i+1 θ) determining the period T i And period T i+1 The relative change of the soil humidity in the time phase, and then the time T is obtained i+1 Soil surface humidity M of (2) v (T i+1 ) Wherein i is sequentially from 1 to h; through forward model simulation analysis, an inversion model of the surface humidity of the bare soil is improved; based on a first-order discrete vegetation model, a soil humidity monitoring model based on combined SAR and optical data is constructed, and the problems of pathological inversion and mixed pixels of vegetation coverage soil humidity remote sensing inversion are solved; finally, the time series soil humidity monitoring of the crop growth process independent of ground observation data is realized.

Description

Method for monitoring soil humidity in crop growth process by combining SAR (synthetic aperture radar) with optical remote sensing data
Technical Field
The invention belongs to the technical field of soil environment monitoring, and particularly relates to a method for monitoring soil humidity in a crop growth process by combining SAR (synthetic aperture radar) and optical remote sensing data.
Background
Soil moisture (i.e., soil moisture content) is an important parameter in the study of the earth's system and is one of the core variables affecting the surface process, playing an important role in energy recycling, water recycling, ecosystems, and agricultural production. In the energy circulation of the earth surface and the atmosphere, the change of the soil humidity can influence earth surface parameters such as earth surface albedo, soil heat capacity, earth surface evaporation and the like, change sensible heat, latent heat and long wave radiation flux of earth surface to atmosphere, cause redistribution of earth surface energy, and play a very important role in regulating climate change. Soil moisture is a tie linking surface water and underground water in the process of forming, converting and consuming land water resources, plays a vital role in the hydrologic processes of precipitation, runoff, infiltration, evaporation and the like, and is an important input parameter of various hydrologic models, climate models, ecological models, atmosphere models and the like. Soil moisture is an important component of a Liu Miansheng-state system, is a basic condition for land plants and soil organisms to survive, and moderate soil humidity is beneficial to the dissolution and movement of various nutrients in soil, improves the nutrition condition of plants, and promotes the circulation of the nutrients, thereby influencing the photosynthesis and productivity of vegetation. Meanwhile, soil moisture is a key factor in agricultural production, soil humidity is an important index for researching plant water stress, carrying out drought monitoring, estimating crop yield and the like, is an important parameter of a crop growth state monitoring and estimating model, and can play a guiding role in agricultural production irrigation management. It can be seen that soil moisture is of great importance in the research of the earth science.
According to different sensor types, the current soil humidity remote sensing inversion method is divided into 3 types of optical remote sensing methods, passive microwave remote sensing methods and active microwave remote sensing methods:
1) Optical remote sensing method: soil moisture is estimated using spectral reflectance characteristics of soil surface, emissivity of soil surface, or surface temperature, and there are mainly an index-based method and a soil thermal inertia-based method. The method utilizes the principle that the reflectivity of dry soil is higher, and the reflectivity of similar wet soil in each wave band is correspondingly reduced, and inverts the soil humidity by constructing different spectrum indexes. The indices commonly used herein are mainly a distance-to-flat vegetation index, a temperature vegetation drought index TVDI, a soil humidity index SWI, a vertical drought index PDI, a soil moisture content temperature index SWCTI, a crop water deficiency index CWSI, a vegetation water supply index VSWI, and the like. The soil thermal inertia-based method utilizes good linear relation between the soil thermal inertia and the soil humidity and stronger sensitivity to invert the soil humidity, and has higher precision in the soil drought monitoring with smaller range and single type. The advantage of inverting the soil humidity by the optical remote sensing method is that the spatial resolution is high, and more satellite sensors can be selected; however, the optical wave band can only measure the reflection and emission of 1mm on the earth surface, belongs to a method for indirectly inverting the soil humidity, and cannot penetrate through vegetation canopy and is affected by atmospheric attenuation, so that the method has certain limitation in application.
2) The passive microwave remote sensing method comprises the following steps: soil humidity is inverted by using soil microwave emission or brightness temperature measured by a microwave radiometer. Researches show that for the bare earth surface, a simple linear relation exists between the soil humidity and the microwave emissivity (replaced by the common microwave bright temperature and ground temperature ratio), and the earth humidity of the bare earth surface can be inverted by establishing an empirical model through the linear relation; for vegetation covering the earth surface, the influence of vegetation is quantified by using a vegetation index (which can be a microwave remote sensing vegetation index or an optical remote sensing vegetation index) or a tau-omega model and the like, the soil roughness is described by using an H parameter, and then the soil humidity is inverted by combining the empirical model of bare soil. The multi-band multi-polarization multi-angle soil moisture inversion method is developed, becomes a standard algorithm of an AMSR-E global soil moisture data set and a standard algorithm of an SMOS global soil moisture data set, and is applied in a related way. The passive microwave remote sensing method can penetrate through the vegetation layer to detect soil with a depth of about 5cm below the ground surface, has a strong inversion physical foundation, has large data width and high time resolution, and is suitable for soil humidity inversion and application research on the global scale; in contrast, its low spatial resolution on the order of tens of kilometers greatly affects the effective use of data in small-area scale studies.
3) An active microwave remote sensing method comprises the following steps: soil moisture was inverted using the characteristic that the low frequency band (L, C band) radar backscatter coefficient was highly sensitive to soil moisture. Inversion algorithms can be categorized into 3 classes, empirical, theoretical, and semi-empirical. The empirical model is mostly to invert the soil humidity by establishing a regression relation between the multi-frequency multi-polarization multi-angle backscattering coefficient and the actual measured values such as soil humidity, soil roughness and the like of a certain depth, such as a classical Oh model and a Dubois model. The theoretical model is a forward model for modeling the surface backward radar scattering features. For bare earth surface, the theoretical model mainly comprises a Kirchhoff model and a small disturbance model (SPM), then an Integral Equation Model (IEM) for unifying the Kirchhoff model and the small disturbance model is developed, the real earth surface backscattering condition can be reproduced in a very wide earth surface roughness range, and the real earth surface backscattering condition is continuously improved and perfected to be developed into an Advanced Integral Equation Model (AIEM), so that the simulation result and the simulation precision of the model are continuously improved. The contribution of the vegetation layer to the microwave backscattering is an important factor influencing the inversion of soil humidity, and theoretical models for researching the microwave vegetation scattering characteristics, which are proposed by students, comprise a three-component vegetation scattering model, a first-order discrete vegetation model, a michigan microwave vegetation scattering model (MIMICS) and the like for vegetation covered earth surfaces. The theoretical model has more parameters, is difficult to directly use for inversion, and needs to construct a semi-empirical model on the basis. Aiming at the bare earth surface condition, the semi-empirical model generally utilizes IEM/AIEM to simulate earth surface backscattering characteristics under different earth surface roughness and soil humidity conditions, combines lookup table regression analysis, establishes correlation between backscattering coefficients and earth surface roughness and dielectric constant or soil humidity under different polarization combinations or incidence angle combinations, and further inverts the bare earth surface soil humidity, wherein the Shi model is particularly represented most. Aiming at vegetation covered earth surfaces, attema and the like, a semi-empirical model 'water-cloud' model for estimating soil moisture of the crop covered earth surfaces is provided, and is widely used; the MIMICS model is also commonly used to construct semi-empirical scattering models of vegetation cover surfaces, thereby inverting soil moisture. The active microwave remote sensing method can penetrate through the vegetation layer to detect soil with a depth of about 5cm below the ground surface, and has a strong inversion physical foundation; synthetic aperture radar (Synthetic Aperture Radar, SAR) can also provide high spatial resolution data on the order of meters to hundred meters; and a large number of comparison researches show that the accuracy of the active microwave algorithm for soil humidity inversion is higher than that of the optical algorithm and the passive microwave algorithm. The method for inverting the soil humidity by utilizing SAR data has the advantages of high spatial resolution, large detection depth and high inversion precision, has important application value and prospect in application of regional scale, and becomes one of important research directions in the field. However, in general, the soil moisture inversion method based on SAR has good inversion effect on bare or sparse vegetation ground surface, and the following two problems still exist on the high vegetation coverage ground surface:
firstly, the existing vegetation coverage inversion model depends on the coefficients of ground observation data regression experience and semi-experience inversion model, so that the practical application of the method in the vegetation coverage is limited;
secondly, in the existing vegetation coverage soil humidity inversion research, the vegetation is considered to cover the earth surface completely, and the change process of the earth surface from pure soil pixels to mixed pixels to pure vegetation pixels in the growth process from sowing to maturing of crops is omitted.
Disclosure of Invention
The invention provides a method for monitoring soil humidity in a crop growth process by combining SAR and optical remote sensing data, aiming at solving the problems.
The invention is realized in such a way that a method for monitoring soil humidity in a crop growth process by combining SAR and optical remote sensing data comprises the following steps:
s1, based on sowing time T 1 SAR satellite data SAR (T) 1 ,θ 1 ) SAR (T) 1 ,θ 2 ) Calculating a sowing time T 1 Soil surface humidity M of (2) v (T 1 )；
S2, based on period T i SAR satellite data SAR (T) i θ), period T i+1 Optical remote sensing data OPT (T) i+1 ) SAR satellite data SAR (T) i+1 θ) determining the period T i And period T i+1 The relative change of the soil humidity in the time phase, and then the time T is obtained i+1 Soil surface humidity M of (2) v (T i+1 ) Wherein i is sequentially from 1 to h;
SAR(T 1 ,θ 1 ) Represents the sowing time T 1 Radar incident angle theta 1 SAR satellite data, θ 1 For small angles, SAR (T 1 ,θ 2 ) Represents the sowing time T 1 Incidence angle theta of radar 2 SAR satellite data, θ 2 Is at a large angle.
Further, soil surface humidity M v (T 1 ) The acquisition method of (a) is specifically as follows:
s11, SAR (T) based on SAR satellite data 1 ,θ 1 ) SAR (T) 1 ,θ 2 ) Acquiring backscattering coefficient
Figure BDA0003843359960000051
A kind of electronic device with high-pressure air-conditioning system
Figure BDA0003843359960000052
Wherein θ 1 And theta 2 Respectively representing a small angle and a large angle;
s12, due to the sowing time T 1 Vegetation coverage F (T) 1 ) =0, then
Figure BDA0003843359960000053
Figure BDA0003843359960000054
S13, based on
Figure BDA0003843359960000055
Is->
Figure BDA0003843359960000056
Calculating the combined roughness Z of the bare soil s ；
S14, obtaining S2
Figure BDA0003843359960000057
Or->
Figure BDA0003843359960000058
Combined roughness Z of soil s Calculating the sowing time T 1 Soil surface humidity M of (2) v (T 1 )。
Further, the combined roughness Z of bare soil s The calculation formula of (2) is specifically as follows:
Figure BDA0003843359960000059
wherein, c and d are fitting coefficients,
Figure BDA00038433599600000510
represents the sowing time T 1 Incidence angle theta of radar 1 Soil meter under pq polarization modeA surface backscattering coefficient; />
Figure BDA00038433599600000511
Represents the sowing time T 1 Incidence angle theta of radar 2 Backscattering coefficient in pq polarization mode.
Further, sowing time T 1 Soil surface humidity M of (2) v (T 1 ) The calculation formula is specifically as follows:
Figure BDA00038433599600000512
A pp (θ)、B pp (θ)、C pp (θ) is a cubic polynomial of the angle of incidence θ of the radar, Z s Indicating the combined roughness of bare soil.
Further, period T i+1 Soil surface humidity M of (2) v (T i+1 ) The acquisition method of (a) is specifically as follows:
s21, based on period T i+1 Optical remote sensing data OPT (T) i+1 ) Obtaining normalized vegetation index NDVI (T i+1 )、NDVI veg (T i+1 ) And NDVI soil (T i+1 )；
S22, based on period T i+1 SAR satellite data SAR (Ti+1, theta) acquisition backscattering coefficient
Figure BDA00038433599600000513
And
Figure BDA00038433599600000514
s23, based on NDVI (T) i+1 )、NDVI veg (T i+1 ) And NDVI soil (T i+1 ) Calculating vegetation coverage F (T) i+1 ) Further calculate the time period T i By time period T i+1 Average vegetation coverage of (a)
Figure BDA00038433599600000515
S24, will
Figure BDA0003843359960000061
Z of combined roughness with bare soil s Calculate period T i And period T i+1 Relative change in phase soil humidity;
s25, period T i Soil surface humidity M of (2) v (T i ) Input period T i And period T i+1 Relative change of soil humidity in time phase, calculate time T i+1 Soil surface humidity M v (T i+1 )。
Further, period T i And period T i+1 The relative change formula of the soil humidity of the time phase is specifically as follows:
Figure BDA0003843359960000062
further, vegetation coverage F (T i ) The calculation formula is specifically as follows:
F(T i )＝(NDVI(T i )-NDVI soil (T i ))/(NDVI veg (T i )-NDVI soil (T i ))
further, the crops are grown in dry farmland, including wheat, corn, rape, peanut, etc.
Further, a large angle θ 2 With a small angle theta 1 The difference in (2) is greater than 10.
According to the invention, through forward model simulation analysis, an inversion model of the humidity of the exposed soil surface is improved; based on a first-order discrete vegetation model, a soil humidity monitoring model based on combined SAR and optical data is constructed, and the problems of pathological inversion and mixed pixels of vegetation coverage soil humidity remote sensing inversion are solved; finally, the time series soil humidity monitoring of the crop growth process independent of ground observation data is realized.
Drawings
Fig. 1 is a flowchart of a method for monitoring soil humidity in a crop growth process by combining SAR and optical remote sensing data according to an embodiment of the present invention.
Detailed Description
The following detailed description of the embodiments of the invention, given by way of example only, is presented in the accompanying drawings to aid in a more complete, accurate, and thorough understanding of the inventive concepts and aspects of the invention by those skilled in the art.
According to the invention, through forward model simulation analysis, an inversion model of the humidity of the exposed soil surface is improved; based on a first-order discrete vegetation model, a soil humidity monitoring model based on combined SAR and optical data is constructed, and the problems of pathological inversion and mixed pixels of vegetation coverage soil humidity remote sensing inversion are solved; finally, the time series soil humidity monitoring of the crop growth process independent of ground observation data is realized, and the crops comprise crops grown in dry farmland, such as wheat, corn, rape, peanut and the like, and can be also suitable for grasslands
(1) Backscattering analog lookup table construction
And simulating the surface backscattering coefficients under different polarization modes, incidence angles, soil humidity and soil surface roughness (related length and root mean square height) of the C wave band by using an AIEM model, constructing a backscattering simulation lookup table, and providing data support for the subsequent combined roughness inversion algorithm improvement and bare soil surface humidity semi-empirical inversion model comparison analysis.
(2) Bare soil surface humidity inversion model improvement
Based on the constructed backscattering simulation lookup table, the inversion algorithm of the combined roughness is improved by combining the analysis of the bare soil surface humidity semi-empirical inversion model expression and utilizing a nonlinear regression analysis method, and then the bare soil surface humidity inversion model is improved. In the prior researches, a plurality of functional forms of fitting the combined roughness by utilizing the difference between the multi-angle backscattering coefficients are adopted, wherein the functional forms comprise polynomial function fitting and logarithmic function fitting, and the functions are respectively shown as a formula (1), a formula (2) and a formula (3):
Figure BDA0003843359960000071
/>
Figure BDA0003843359960000072
Figure BDA0003843359960000073
wherein the subscript s represents soil (soil) surface scattering; z is Z s Representing the combined roughness of the soil surface, wherein the unit cm is the ratio of the square of the root mean square height to the relevant length; sigma (sigma) s The backscattering coefficient of the soil surface is expressed in dB; IS2 and IS7 respectively represent IS2 (19.2-26.7) and IS7 (41-. 5-45.2) incidence angle modes of ASAR; a, b are all coefficients obtained by fitting; the superscripts p, q denote the polarization, pq denotes one of hh, vv, hv or vh, pp denotes one of hh or vv, and therefore,
Figure BDA0003843359960000081
represents the radar incidence angle of 38 DEG, the backscattering coefficient of the soil surface in hh polarization mode,>
Figure BDA0003843359960000082
represents the angle of incidence of 22 DEG radar, the backscattering coefficient of the soil surface in hh polarization mode,>
Figure BDA0003843359960000083
represents the angle of incidence of 38 DEG radar, the backscattering coefficient of the soil surface in the vv polarization mode,>
Figure BDA0003843359960000084
represents the 22 DEG radar incidence angle, the backscattering coefficient of the soil surface in the vv polarization mode,>
Figure BDA0003843359960000085
represents the soil surface backscattering coefficient in the pq polarization mode in IS2 incidence angle mode, < >>
Figure BDA0003843359960000086
The soil surface backscattering coefficient in the pq polarization mode in the IS7 incidence angle mode IS shown.
The complex correlation coefficient of the three fitting expressions reaches more than 0.95, and the fitting effect is good. However, the polynomial models (1), (2) or the logarithmic model (3) have certain problems in practical application: for equations (1), (2), when the difference between the small angle and large angle backscattering coefficients is greater than 8.013dB and 6.272dB, the combined roughness will appear negative, and for equation (3) there will be a negative case. This negative occurrence is not uncommon in SAR images, and thus causes the occurrence of more pixels that fail inversion.
By observing the additive model expression (shown as formula (4)) of the soil surface humidity semi-empirical inversion model, the difference between the multi-angle backscattering coefficients and the combined roughness are found to be more in line with a form of an exponential function, as shown as formula (5):
Figure BDA0003843359960000087
Figure BDA0003843359960000088
wherein T is i Represents the growth period of wheat, when i=1, T i ＝T 1 The sowing time is represented as bare soil; m is M v (T i ) Indicating the time period T i The water content of the soil volume; θ is the angle of incidence of the radar, θ=θ 1 For small angles, θ=θ 2 Is a large angle, a large angle theta 2 With a small angle theta 1 The difference between (2) is more than 10 DEG;
Figure BDA0003843359960000089
indicating the time period T i The soil surface backscattering coefficient under the radar incidence angle theta and pp polarization modes; />
Figure BDA00038433599600000810
Represents the sowing time T 1 Incidence angle theta of radar 1 The soil surface backscattering coefficient under pq polarization mode; />
Figure BDA0003843359960000091
Represents the sowing time T 1 Incidence angle theta of radar 2 Backscattering coefficient in pq polarization mode; c. d is the fitting coefficient, A pp (θ)、B pp (θ)、C pp (θ) is a cubic polynomial of the radar incidence angle θ, and the expression is specifically as follows:
A pp (θ)＝a 1 sin 3 θ+b 1 sin 2 θ+c 1 sinθ+d 1 ；
B pp (θ)＝a 2 sin 3 θ+b 2 sin 2 θ+c 2 sinθ+d 2 ；
C pp (θ)＝a 3 sin 3 θ+b 3 sin 2 θ+c 3 sinθ+d 3 ；
in which a is 1 ，b 1 ，c 1 ，d 1 ，a 2 ，b 2 ，c 2 ，d 2 ，a 3 ，b 3 ，c 3 ，d 3 Is a fitting coefficient.
Fitting a formula (5) by using an AIEM simulation lookup table according to an exponential function relation, wherein under the condition of the incidence angle difference between IS2 and IS7, the fitted complex correlation coefficient reaches 0.996, which IS superior to the polynomial model and the logarithmic model; and the situation that the combined roughness has a negative value can not be generated, the actual situation is more met, and inversion failure is avoided. Therefore, the above method would be more advantageous in soil roughness inversion.
As crops sow and grow, the vegetation coverage of the soil surface gradually changes, and the problem of mixed pixels is brought. The first-order discrete vegetation model is a back scattering conceptual model of mixed pixels of different vegetation coverage earth surfaces, and can not directly realize soil humidity inversion of the vegetation coverage earth surfaces, but can realize relative change monitoring of soil humidity on the premise of acquiring vegetation coverage information and eliminating vegetation double-pass attenuation influence: sub-pel decomposition is performed by using the optical remote sensing data and a pel bipartite model, and pel vegetation coverage information of multiple phases is extracted; the first-order discrete vegetation model and the three-component scattering model are utilized to separate out the surface scattering components of the soil surface, and the influence of a vegetation double-pass attenuation factor is eliminated through the ratio of the multi-time phase surface scattering components; and finally, constructing a multi-time-phase soil humidity relative change monitoring model independent of ground observation data by combining the semi-empirical inversion model of the surface humidity of the bare soil and the multi-time-phase multi-polarization SAR data.
(3) Separating surface scattering components of first-order discrete vegetation model
The first-order discrete vegetation model is a mixed pixel back scattering conceptual model considering different vegetation coverage, and the model is expressed as follows:
Figure BDA0003843359960000101
wherein F (T) i ) For period T i And θ is the incidence angle of the radar, pp represents v or hh polarization mode, subscripts t, v, s, sv represent total scattering, vegetation body scattering, soil surface scattering, vegetation body and soil surface body-surface interaction scattering items respectively, therefore,
Figure BDA0003843359960000102
indicating the time period T i Back volume scattering coefficient component of vegetation in radar incidence angle theta, pp polarization mode, +.>
Figure BDA0003843359960000103
Indicating the time period T i Backscattering coefficient component of vegetation and soil body-surface interaction under radar incidence angle theta and pp polarization modes, ++>
Figure BDA0003843359960000104
Indicating the time period T i The backward scattering coefficient component of the soil under the radar incidence angle theta and pp polarization mode,
Figure BDA0003843359960000105
indicating the time period T i Total backscattering coefficient in radar incidence angle θ, pp polarization, +.>
Figure BDA0003843359960000106
Indicating the time period T i Is a vegetation double pass attenuation factor.
In the co-polarized pp (hh and vv polarization) state, when the radar is incident at an angle θ>At 20 °, the body-surface cross scatter in the first order discrete vegetation model
Figure BDA0003843359960000107
Negligible; at the same time, assuming that the crop cover layer is composed of randomly distributed fine cylinders, according to Freeman A three-component scattering model, the bulk scattering of the vegetation layer can be considered to be unaffected by polarization, about 3 times the total scattering of cross polarization, i.e.)>
Figure BDA0003843359960000108
Thus, the surface scattering component->
Figure BDA0003843359960000109
Can be separated into:
Figure BDA00038433599600001010
(4) Calculating pixel vegetation coverage by optical data pixel bipartite model
In the growth process of crops, the vegetation coverage is continuously changed, and the pixels are also changed from pure bare soil pixels to mixed pixels of soil and vegetation, and then to pure vegetation pixels. The pel bipartite model is the most commonly used mixed pel linear decomposition model, and pel vegetation coverage can be inverted by using the pel bipartite model. The pel bipartite model can be described as:
S t (T i )＝F(T i )·S veg (T i )+(1-F(T i ))·S soil (T i ) (8)
wherein F (T) i ) For period T i Is a pel vegetation coverage of (1); s is S t (T i ) Indicating the time period T i Is the total information of the pixels; s is S veg (T i ) Indicating the time period T i Remote sensing information of pure vegetation pixels; s is S soil (T i ) Indicating the time period T i Remote sensing information of pure soil pixels of (2), vegetation coverage of pixels F (T) i ) Can be expressed as formula (9):
F(T i )＝(S t (T i )-S soil (T i ))/(S veg (T i )-S soil (T i )) (9)
substituting NDVI as remote sensing information into formula (9) to obtain vegetation coverage F (T) i ) A calculation formula, such as formula (10):
F(T i )＝(NDVI(T i )-NDVI soil (T i ))/(NDVI veg (T i )-NDVI soil (T i )) (10)
in the formula, NDVI (T) i ) For period T i An actual NDVI value for the pel of (c); NDVI veg (T i ) For period T i NDVI value of the pure vegetation picture element, NDVI soil (T i ) For period T i NDVI values of bare soil pixels without vegetation.
(5) Construction of soil humidity relative change monitoring model in crop growth process
According to the formula (7), the vegetation coverage F alone cannot calculate the soil surface scattering component, and the vegetation double-pass attenuation factor L needs to be further eliminated 2 pp Is a function of (a) and (b). Considering that the vegetation cover layer changes have longer time scales than the soil humidity changes in the crop growth process, two time phases T with similar time are considered i And T i+1 On the vegetation coverage F and vegetation double-pass attenuation factor L 2 pp The variation is small, namely:
Figure BDA0003843359960000111
Figure BDA0003843359960000112
Figure BDA0003843359960000113
indicating the time period T i By time period T i+1 The average vegetation coverage of (2) can be calculated by comparing the scattering components of soil surface to eliminate L 2 pp A factor as shown in formula (13):
Figure BDA0003843359960000114
the right side of the equation can be calculated by remote sensing data; the left side of the equation is the ratio of the scattering components of the soil surface, and the substitution of the formula (4) into the equation can be obtained:
Figure BDA0003843359960000115
and then combining the formula (5) to obtain T i And T i+1 Relative change in phase soil humidity:
[M v (T i+1 )] m /[M v (T i )] n ＝k (15)
wherein m, n, k can be obtained by simplifying the formula (14).
Fig. 1 is a flowchart of a method for monitoring soil humidity in a crop growth process by combining SAR and optical remote sensing data, provided by an embodiment of the present invention, and the method specifically includes:
s1, SAR (T) based on SAR satellite data 1 ,θ 1 ) SAR (T) 1 ,θ 2 ) Acquiring backscattering coefficient
Figure BDA0003843359960000121
A kind of electronic device with high-pressure air-conditioning system
Figure BDA0003843359960000122
Wherein θ 1 And theta 2 Respectively represent a small angle and a large angleA degree;
SAR(T 1 ,θ 1 ) Represents the sowing time T 1 Radar incident angle theta 1 SAR satellite data, θ 1 For small angles, SAR (T 1 ,θ 2 ) Represents the sowing time T 1 Incidence angle theta of radar 2 SAR satellite data, θ 2 Is a large angle;
Figure BDA0003843359960000123
represents the sowing time T 1 Incidence angle theta of radar 1 Total backscattering coefficient in pq polarization,>
Figure BDA0003843359960000124
represents the sowing time T 1 Incidence angle theta of radar 2 Total backscattering coefficient in pq polarization mode.
S2, due to sowing time T 1 Vegetation coverage F (T) 1 ) =0, then get
Figure BDA0003843359960000125
Figure BDA0003843359960000126
/>
S3, will
Figure BDA0003843359960000127
Is->
Figure BDA0003843359960000128
Substituting the rough soil into the formula (5) to calculate the combined roughness Z of the bare soil s ；
S4, obtaining the product in the step S2
Figure BDA0003843359960000129
Or->
Figure BDA00038433599600001210
Combined roughness Z of soil s Substituting formula (4) to calculate seeding time T 1 Is wet on the soil surfaceDegree M v (T 1 )；
S5, based on T 2 Time-phased optical remote sensing data OPT (T 2 ) Obtaining normalized vegetation index NDVI (T) 2 )、NDVI veg (T 2 ) And NDVI soil (T 2 )，T 2 The period is a sowing period T 1 Is the next period of time;
s6, SAR (T) based on SAR satellite data 2 θ) acquisition of backscattering coefficient
Figure BDA00038433599600001211
And->
Figure BDA00038433599600001212
S7, NDVI (T) 2 )、NDVI veg (T 2 ) And NDVI soil (T 2 ) Substituting formula (10) to calculate vegetation coverage F (T) 2 ) F (T) 1 ) And F (T) 2 ) Substituting into formula (11) to obtain
Figure BDA00038433599600001213
S7, will
Figure BDA00038433599600001214
And Z obtained in the step S3 s Substituting formula (14) and simplifying to obtain T 1 And T 2 Phase soil humidity relative change formula (15);
s8, combining the formula (15) and M obtained in the step S4 v (T 1 ) Calculating to obtain M v (T 2 )。
And so on, any period T of the growth process i+1 Soil surface humidity inversion of (2):
s9, based on optical remote sensing data OPT (T) i+1 ) Obtaining normalized vegetation index NDVI (T) i+1 )、NDVI veg (T i+1 ) And NDVI soil (T i+1 ) The method comprises the steps of carrying out a first treatment on the surface of the SAR (T) based i+1 θ) acquisition of backscattering coefficient
Figure BDA0003843359960000131
And->
Figure BDA0003843359960000132
S10, NDVI (T) obtained in the step S9 i+1 )、NDVI veg (T i+1 ) And NDVI soil (T i+1 ) Substituting formula (10) to calculate vegetation coverage F (T) i+1 ) F (T) i ) And F (T) i+1 ) Substituting into formula (11) to obtain
Figure BDA0003843359960000133
S11, will
Figure BDA0003843359960000134
And Z obtained by S3 s Is substituted into formula (14) to be simplified to obtain T i And T i+1 Phase soil humidity relative change formula (15);
s12, combining the formula (15) and the last approaching period T i M of (2) v (T i ) Calculating to obtain M v (T i+1 )。
Winter wheat growth process time sequence soil humidity monitoring
Taking a typical planting area of winter wheat in Mongolian county of Anhui province as a research experiment area, taking a sowing period in the growth process of winter wheat as an initial time phase, and directly calculating by using a formula (4) and a formula (5) according to the prior knowledge of the soil exposure of the sowing period to obtain the soil surface combined roughness Z s Initial soil humidity M v (T 1 ) Then substituting formula (15) to obtain M v (T 2 ). Similarly, M can be calculated v (T 3 )，M v (T 4 ) … …, thereby obtaining time sequence soil humidity and realizing the monitoring of the soil humidity in the winter wheat growth process.
While the present invention has been described by way of example, it should be apparent that the practice of the invention is not limited by the foregoing, but rather is intended to cover various insubstantial modifications of the method concepts and teachings of the invention, either as applied to other applications without modification, or as applied directly to other applications, without departing from the scope of the invention.

Claims (3)

1. The method for monitoring the soil humidity in the crop growth process by combining SAR and optical remote sensing data is characterized by comprising the following steps of:
s1, based on sowing time T 1 SAR satellite data SAR (T) 1 ,θ 1 ) SAR (T) 1 ,θ 2 ) Calculating a sowing time T 1 Soil surface humidity M of (2) v (T 1 )；
S2, based on period T i SAR satellite data SAR (T) i θ), period T i+1 Optical remote sensing data OPT (T) i+1 ) SAR satellite data SAR (T) i+1 θ) determining the period T i And period T i+1 The relative change of the soil humidity in the time phase, and then the time T is obtained i+1 Soil surface humidity M of (2) v (T i+1 ) Wherein i is sequentially from 1 to h;
SAR(T 1 ,θ 1 ) Represents the sowing time T 1 Radar incident angle theta 1 SAR satellite data, θ 1 For small angles, SAR (T 1 ,θ 2 ) Represents the sowing time T 1 Incidence angle theta of radar 2 SAR satellite data, θ 2 Is a large angle;
soil surface humidity M v (T 1 ) The acquisition method of (a) is specifically as follows:
s11, SAR (T) based on SAR satellite data 1 ,θ 1 ) SAR (T) 1 ,θ 2 ) Acquiring backscattering coefficient
Figure FDA0004222873990000011
A kind of electronic device with high-pressure air-conditioning system
Figure FDA0004222873990000012
Wherein θ 1 And theta 2 Respectively representing a small angle and a large angle;
s12, due to the sowing time T 1 Vegetation coverage F (T) 1 )＝0，Then
Figure FDA0004222873990000013
Figure FDA0004222873990000014
S13, based on
Figure FDA0004222873990000015
Is->
Figure FDA0004222873990000016
Calculating the combined roughness Z of the bare soil s ；
S14, obtaining S2
Figure FDA0004222873990000017
Or->
Figure FDA0004222873990000018
Combined roughness Z of soil s Calculating the sowing time T 1 Soil surface humidity M of (2) v (T 1 )；
Combined roughness Z of bare soil s The calculation formula of (2) is specifically as follows:
Figure FDA0004222873990000019
wherein, c and d are fitting coefficients,
Figure FDA00042228739900000110
represents the sowing time T 1 Incidence angle theta of radar 1 The soil surface backscattering coefficient under pq polarization mode; />
Figure FDA00042228739900000111
Represents the sowing time T 1 Incidence angle theta of radar 2 Under pq polarization modeIs a backscatter coefficient of (2);
sowing time T 1 Soil surface humidity M of (2) v (T 1 ) The calculation formula is specifically as follows:
Figure FDA0004222873990000021
A pp (θ)、B pp (θ)、C pp (θ) is a cubic polynomial of the angle of incidence θ of the radar, Z s Representing the combined roughness of bare soil;
period T i+1 Soil surface humidity M of (2) v (T i+1 ) The acquisition method of (a) is specifically as follows:
s21, based on period T i+1 Optical remote sensing data OPT (T) i+1 ) Obtaining normalized vegetation index NDVI (T i+1 )、NDVI veg (T i+1 ) And NDVI soil (T i+1 )；
S22, based on period T i+1 SAR satellite data SAR (T) i+1 θ) acquisition of backscattering coefficient
Figure FDA0004222873990000022
And
Figure FDA0004222873990000023
s23, based on NDVI (T) i+1 )、NDVI veg (T i+1 ) And NDVI soil (T i+1 ) Calculating vegetation coverage F (T) i+1 ) Further calculate the time period T i By time period T i+1 Average vegetation coverage of (a)
Figure FDA0004222873990000024
S24, will
Figure FDA0004222873990000025
Z of combined roughness with bare soil s Calculate period T i And period T i+1 Relative change in phase soil humidity; />
S25, period T i Soil surface humidity M of (2) v (T i ) Input period T i And period T i+1 Relative change of soil humidity in time phase, calculate time T i+1 Soil surface humidity M v (T i+1 )；
Period T i And period T i+1 The relative change formula of the soil humidity of the time phase is specifically as follows:
Figure FDA0004222873990000026
vegetation coverage F (T) i ) The calculation formula is specifically as follows:
F(T i )＝(NDVI(T i )-NDVI soil (T i ))/(NDVI veg (T i )-NDVI soil (T i ))。
2. the method for monitoring soil moisture during crop growth combining SAR and optical remote sensing data as recited in claim 1, wherein said crop is grown in dry farmland including wheat, corn, canola, peanut.
3. The method for monitoring soil moisture during crop growth combining SAR and optical remote sensing data as recited in claim 1, wherein the large angle θ 2 With a small angle theta 1 The difference in (2) is greater than 10.
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