CN115479960A - Crop growth process soil humidity monitoring method combining SAR and optical remote sensing data - Google Patents
Crop growth process soil humidity monitoring method combining SAR and optical remote sensing data Download PDFInfo
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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 ) And SAR (T) 1 ,θ 2 ) Calculating the sowing time T 1 Surface humidity M of soil v (T 1 ) (ii) a S2, based on time T i SAR satellite data SAR (T) i θ), time period T i+1 Optical remote sensing data OPT (T) i+1 ) And SAR satellite data SAR (T) i+1 θ) determining the period T i And period T i+1 Relative change of soil humidity in time phase to obtain time T i+1 Soil surface humidity M v (T i+1 ) Wherein i takes values from 1 to h in sequence; improving an inversion model of the surface humidity of the bare soil through forward model simulation analysis; a soil humidity monitoring model based on a combined SAR and optical data is constructed based on a first-order discrete vegetation model, and the ill-conditioned inversion problem and the mixed pixel problem of the remote sensing inversion of the soil humidity of a vegetation coverage area are solved; finally, the soil humidity monitoring of the time sequence of the crop growth process independent of ground observation data is realized.
Description
Technical Field
The invention belongs to the technical field of soil environment monitoring, and particularly relates to a soil humidity monitoring method for crop growth process by combining SAR 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 one of the core variables affecting surface processes, playing an important role in energy cycling, water cycling, ecosystem, and agricultural production. In the energy circulation of the earth surface and the atmosphere, the change of the soil humidity can affect earth surface parameters such as earth surface albedo, soil heat capacity, earth surface evapotranspiration and the like, and the sensible heat, latent heat and long-wave radiation flux transmitted from the earth surface to the atmosphere are changed, so that the redistribution of the earth surface energy is caused, and the method plays a very important role in adjusting climate change. The soil moisture is a link for connecting surface water and underground water in the formation, conversion and consumption processes of land water resources, plays an important role in the hydrological processes of precipitation, runoff, infiltration, evapotranspiration and the like, and the soil humidity is an important input parameter of a plurality of hydrological models, climate models, ecological models, atmospheric models and the like. Soil moisture is an important component of a land ecological system, and is a basic condition for land plants and soil organisms to live, and proper soil humidity is favorable for dissolving and moving various nutrient substances in soil, so that the nutrient condition of the plants is improved, the circulation of nutrients is promoted, and the photosynthesis and the productivity of vegetation are influenced. Meanwhile, soil moisture is a key factor in agricultural production, soil humidity is an important index for researching plant water stress, monitoring drought, estimating yield of crops and the like, is an important parameter of a crop growth state monitoring and yield estimation model, and can play a guiding role in agricultural production irrigation management. It can be seen that soil moisture has a very important meaning in the research of geoscience.
According to different sensor types, the current soil humidity remote sensing inversion method is divided into 3 types, namely an optical remote sensing method, a passive microwave remote sensing method and an active microwave remote sensing method:
1) The optical remote sensing method comprises the following steps: soil humidity is estimated by utilizing the spectral reflection characteristic of the soil surface, the emissivity of the soil surface or the surface temperature, and an index-based method and a soil thermal inertia-based method are mainly adopted. The soil humidity inversion method utilizes the principle that the reflectivity of dry soil is higher, and the reflectivity of the same type of wet soil at each wave band is correspondingly reduced, and inverts the soil humidity by constructing different spectral indexes. The indexes commonly used in the method mainly comprise a 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 shortage index CWSI, a vegetation water supply index VSWI and the like. The soil thermal inertia-based method utilizes the good linear relation and the strong sensitivity between the soil thermal inertia and the soil humidity to invert the soil humidity, and has high precision in soil drought monitoring with a small range and a single type. The advantage of the optical remote sensing method for inverting the soil humidity is that the spatial resolution is high, and a plurality of 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 canopies and be influenced by atmospheric attenuation, so that the application of the method has certain limitation.
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 find that for the exposed earth surface, a simple linear relation exists between the soil humidity and the microwave emissivity (the common microwave brightness temperature is replaced by the ground temperature), and the exposed earth surface soil humidity can be inverted by establishing an empirical model through the linear relation; for vegetation covered ground surfaces, the influence of quantified vegetation such as vegetation indexes (microwave remote sensing vegetation indexes and optical remote sensing vegetation indexes) or tau-omega models is generally utilized, H parameters are used for describing the roughness of soil, and then the empirical model of bare soil is combined to invert the humidity of the soil. A multiband multi-polarization multi-angle soil humidity inversion method is also developed to become 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 relevant applications are obtained. The passive microwave remote sensing method can penetrate through the vegetation layer to detect soil with the depth of about 5cm below the earth surface, has strong inversion physical foundation, large data width and high time resolution, and is suitable for soil humidity inversion and application research of the global scale; in contrast, its low spatial resolution on the order of tens of kilometers greatly affects the efficient use of data in small area-scale studies.
3) The active microwave remote sensing method comprises the following steps: and (3) inverting the soil humidity by utilizing the characteristic that the backscattering coefficients of low-frequency band (L and C bands) radars are highly sensitive to the soil humidity. Inversion algorithms can be classified into 3 types of empirical models, theoretical models, and semi-empirical models. The empirical model is mostly used for inverting the soil humidity by establishing a regression relationship between the multi-frequency multi-polarization multi-angle backscattering coefficient and measured values such as soil humidity and soil roughness at a certain depth, such as a classical Oh model and a Dubois model. The theoretical model is a forward model used to simulate the scattering characteristics of surface back radar. For bare earth surfaces, theoretical models mainly comprise a Kirchhoff model and a Small Perturbation Model (SPM), then an Integral Equation Model (IEM) unifying the Kirchhoff model and the SPM is developed, the backscattering situation of the real earth surface can be reproduced in a wide earth surface roughness range, and an Advanced Integral Equation Model (AIEM) is developed through continuous improvement and perfection, so that the simulation result and the precision of the model are continuously improved. The contribution of the vegetation layer to microwave backscattering is an important factor influencing soil moisture inversion, and theoretical models for researching the microwave vegetation scattering property, which are proposed by students, for vegetation covered earth surfaces comprise a three-component vegetation scattering model, a first-order discrete vegetation model, a Michigan microwave vegetation scattering model (MIMICS) and the like. The theoretical model has more parameters, is difficult to be directly used for inversion, and needs to construct a semi-empirical model on the basis of the theoretical model. For the bare surface condition, the semi-empirical model usually utilizes IEM/AIEM to simulate the surface backscattering characteristics under different surface roughness and soil humidity conditions, and combines with look-up table regression analysis to establish the correlation between backscattering coefficient and surface roughness and dielectric constant or soil humidity under different polarization combinations or incident angle combinations, so as to invert the bare surface soil humidity, wherein the Shi model is most representative. Aiming at vegetation covered ground surfaces, attema and the like, a semi-empirical model 'water-cloud' model for estimating soil moisture of the crop covered ground surfaces is provided and widely used; MIMICS models are also commonly used to construct semi-empirical scattering models of vegetation-covered surfaces, and thus to invert soil moisture. The active microwave remote sensing method can also penetrate through the vegetation layer to detect the soil with the depth of about 5cm below the earth surface, and has stronger inversion physical foundation; synthetic Aperture Radars (SAR) can also provide high spatial resolution data on the meter to hectometer level; and a large number of comparison researches find that the precision of an active microwave algorithm for soil humidity inversion is higher than that of an optical algorithm and a passive microwave algorithm. The method for inverting the soil humidity by utilizing the SAR data has the advantages of high spatial resolution, large detection depth and high inversion accuracy, has important application value and prospect in the application of regional scale, and becomes one of important research directions in the field. However, in general, the soil humidity inversion method based on the SAR has a good inversion effect on bare or sparse vegetation ground surfaces, and still has the following two problems on the ground surface with high vegetation coverage:
the method has the advantages that firstly, the existing vegetation coverage area inversion model mostly depends on the ground observation data regression experience and the coefficient of the semi-empirical inversion model, so that the practical application of the method in the vegetation coverage area is limited;
and secondly, in the existing vegetation coverage area soil humidity inversion research, the vegetation covers the ground surface completely, and the change process of the ground surface from a pure soil pixel to a mixed pixel and then to a pure vegetation pixel is neglected in the growth process of crops from sowing to maturity.
Disclosure of Invention
The invention provides a soil humidity monitoring method for crop growth process combining SAR and optical remote sensing data, aiming at improving the problems.
The invention is realized in such a way that a soil humidity monitoring method combining SAR and optical remote sensing data in the crop growth process specifically comprises the following steps:
s1, based on sowing time T 1 SAR satellite data SAR (T) 1 ,θ 1 ) And SAR (T) 1 ,θ 2 ) Calculating the sowing time T 1 Soil surface humidity M v (T 1 )；
S2, based on the time T i SAR satellite data SAR (T) i θ), time period T i+1 Optical remote sensing data OPT (T) i+1 ) And SAR satellite data SAR (T) i+1 θ) determining the period T i And period T i+1 Relative change of soil humidity in time phase to obtain time T i+1 Soil surface humidity M v (T i+1 ) Wherein i takes values from 1 to h in sequence;
SAR(T 1 ,θ 1 ) Indicates a sowing time T 1 Radar incidence angle theta 1 SAR satellite data of theta 1 At a small angle, SAR (T) 1 ,θ 2 ) Indicates a sowing time T 1 Radar incident angle theta 2 SAR satellite data of theta 2 Is at a large angle.
Further, the soil surface humidity M v (T 1 ) The acquisition method specifically comprises the following steps:
s11, satellite data SAR (T) based on SAR 1 ,θ 1 ) And SAR (T) 1 ,θ 2 ) Obtaining the backscattering coefficient
And
wherein, theta 1 And theta 2 Respectively representing a small angle and a large angle;
s12, sowing time T 1 Vegetation coverage of F (T) 1 ) =0, then
S13, based on
And
calculating the combined roughness Z of the bare soil s ；
S14, the compound obtained in S2
Or
And combined roughness Z of soil s Calculating the sowing time T 1 Surface humidity M of soil v (T 1 )。
Further, the combined roughness Z of bare soil s The calculation formula of (a) is specifically as follows:
wherein c and d are fitting coefficients,
indicates a sowing time T 1 Angle of incidence theta of radar 1 The soil surface backscattering coefficient under the pq polarization mode;
indicates a sowing time T 1 Radar incident angle theta 2 And backscattering coefficient in pq polarization mode.
Further, a sowing period T 1 Soil surface humidity M v (T 1 ) The calculation formula is as follows:
A pp (θ)、B pp (θ)、C pp (theta) are each a cubic polynomial, Z, about the radar angle of incidence theta s Indicating the combined roughness of the bare soil.
Further, period T i+1 Soil surface humidity M v (T i+1 ) The acquisition method specifically comprises the following steps:
s21, baseAt a time 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 the time T i+1 SAR satellite data SAR (Ti +1, theta) acquisition post-scattering coefficient
And
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 ) And further calculates the time period T i To a period T i+1 Average vegetation coverage of
S24, mixing
And combined roughness of bare soil Z s Calculating the time period T i And period T i+1 Relative change of soil humidity in time phase;
s25, timing T i Surface humidity M of soil v (T i ) Input period T i And period T i+1 The relative change of soil humidity in time phase is used to calculate the time period T i+1 Humidity M of soil surface v (T i+1 )。
Further, a period T i And period T i+1 The formula of the relative change of the soil humidity in the time phase is as follows:
further, vegetation coverage F (T) i ) The calculation formula is 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 and comprise wheat, corn, rape, peanut and the like.
Further, a large angle θ 2 With small angle theta 1 The difference is greater than 10.
According to the method, a bare soil surface humidity inversion model is improved through forward model simulation analysis; a soil humidity monitoring model based on a combined SAR and optical data is constructed based on a first-order discrete vegetation model, and the ill-conditioned inversion problem and the mixed pixel problem of the remote sensing inversion of the soil humidity of a vegetation coverage area are solved; finally, the soil humidity monitoring of the time sequence of the crop growth process independent of ground observation data is realized.
Drawings
Fig. 1 is a flow chart of a soil humidity monitoring method for crop growth process 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 present invention will be given in order to provide those skilled in the art with a more complete, accurate and thorough understanding of the inventive concept and technical solutions of the present invention.
According to the method, a bare soil surface humidity inversion model is improved through forward model simulation analysis; a soil humidity monitoring model based on a combined SAR and optical data is constructed based on a first-order discrete vegetation model, and the ill-conditioned inversion problem and the mixed pixel problem of the remote sensing inversion of the soil humidity of a vegetation coverage area are solved; finally, the soil humidity monitoring of the time sequence of the crop growth process independent of the ground observation data is realized, wherein the crops comprise crops growing in dry farmlands, such as wheat, corn, rape, peanut and the like, and the soil humidity monitoring method can also be suitable for grasslands
(1) Backscatter simulation look-up table construction
And simulating the surface backscattering coefficients of the C wave band under different polarization modes, incidence angles, soil humidity and soil surface roughness (correlation length and root-mean-square height) by using the AIEM model, and constructing a backscattering simulation lookup table to provide data support for subsequent combination roughness inversion algorithm improvement and bare soil surface humidity semi-empirical inversion model comparative analysis.
(2) Bare soil surface humidity inversion model improvement
Based on the constructed backscattering simulation lookup table, combining with expression analysis of a semi-empirical inversion model of the bare soil surface humidity, and improving an inversion algorithm of combination roughness by using a nonlinear regression analysis method, thereby improving the inversion model of the bare soil surface humidity. In the existing research, there are various functional forms for fitting and combining the roughness by using the difference between the multi-angle backscattering coefficients, including polynomial function fitting and logarithmic function fitting, which are respectively shown as formula (1), formula (2) and formula (3):
in the formula, subscript s represents soil (soil) surface scattering; z s The unit cm represents the combined roughness of the soil surface, and is the ratio of the square of the root-mean-square height to the related length; sigma s Representing the backscattering coefficient of the soil surface in dB; IS2 and IS7 respectively represent IS2 (19.2-26.7 °) and IS7 (41-. 5-45.2 °) incident angle modes of ASAR; a and b are both coefficients obtained by fitting; the superscripts p, q denote the polarization mode, pq denotes one of hh, vv, hv or vh, pp denotes one of hh or vv, and therefore,
representing 38 deg. radarThe incidence angle, the soil surface backscattering coefficient in the hh polarization mode,
shows the soil surface backscattering coefficient under the 22-degree radar incidence angle and hh polarization mode,
the backscattering coefficient of the soil surface under the mode of the 38-degree radar incidence angle and the vv polarization is shown,
the backscattering coefficient of the soil surface under the 22-degree radar incidence angle and the vv polarization mode is shown,
represents the soil surface backscattering coefficient under the mode of IS2 incidence angle and pq polarization mode,
the soil surface backscattering coefficient in pq polarization mode in the IS7 incident angle mode IS shown.
The complex correlation coefficients of the three fitting expressions are all more than 0.95, and the fitting effect is good. However, the polynomial model (1), (2) or the logarithmic model (3) has certain problems in practical application: for equations (1) and (2), the combined roughness appears negative when the difference between the low angle and high angle backscattering coefficients is greater than 8.013dB and 6.272dB, respectively, and negative for equation (3). The negative value is not rare in the SAR image, so that most pixels causing inversion failure appear.
By observing an additive model expression (shown in formula (4)) of a soil surface humidity semi-empirical inversion model, it is found that the difference between the multi-angle backscattering coefficients and the combined roughness actually better conform to a certain exponential function relation form, as shown in formula (5):
in the formula, T i Represents the growth period of wheat, when i =1, T i ＝T 1 Indicates the sowing period, and is bare soil; m v (T i ) Represents a period T i The volume water content of the soil; theta is the incident angle of the radar, theta = theta 1 At a small angle, θ = θ 2 At a large angle, a large angle theta 2 At a small angle theta 1 Greater than 10 ° or less;
represents a period T i The soil surface backscattering coefficient under the radar incidence angle theta and the pp polarization mode;
indicates a sowing time T 1 Angle of incidence theta of radar 1 The soil surface backscattering coefficient under the pq polarization mode;
indicates a sowing time T 1 Angle of incidence theta of radar 2 Backscattering coefficient under pq polarization mode; c. d are fitting coefficients, A pp (θ)、B pp (θ)、C pp (θ) is a cubic polynomial about the radar incidence angle θ, and the expression is 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 the formula a 1 ，b 1 ，c 1 ，d 1 ，a 2 ，b 2 ，c 2 ，d 2 ，a 3 ，b 3 ，c 3 ，d 3 Are fitting coefficients.
Fitting formula (5) according to exponential function relationship by using AIEM simulation lookup table, wherein the fitted complex correlation coefficient reaches 0.996 under the condition of IS2 and IS7 incident angle difference, and IS superior to the polynomial model and logarithmic model; and the situation that the negative value of the combination roughness is generated can not be generated, the method is more in line with the actual situation, and the inversion failure is avoided. Therefore, the method can be more advantageous in soil roughness inversion.
With crop seeding and growth, the vegetation coverage on the soil surface gradually changes and brings mixed pixel problems. The first-order discrete vegetation model is a backscattering conceptual model of mixed pixels on the earth surface with different vegetation coverage degrees, cannot directly realize soil humidity inversion of the vegetation coverage earth surface, but can realize monitoring of relative change of soil humidity on the premise of acquiring vegetation coverage degree information and eliminating the influence of vegetation two-way attenuation: sub-pixel decomposition is performed by using optical remote sensing data and a pixel binary model, and multi-temporal pixel vegetation coverage information is extracted; separating out the surface scattering component of the soil surface by using a first-order discrete vegetation model and a three-component scattering model, and eliminating the influence of a vegetation two-way attenuation factor by using the ratio of multi-time phase surface scattering components; and finally, combining the semi-empirical inversion model of the surface humidity of the bare soil and multi-temporal multi-polarization SAR data to construct a multi-temporal soil humidity relative change monitoring model independent of ground observation data.
(3) First-order discrete vegetation model separation surface scattering component
The first-order discrete vegetation model is a mixed pixel backscattering conceptual model considering different vegetation coverage, and the model expression is as follows:
in the formula, F (T) i ) Is a period T i Coverage of pixel vegetationTheta is the incident angle of the radar, pp represents the vv or hh polarization mode, and the subscripts t, v, s, sv respectively represent the total scattering, the vegetation body scattering, the soil surface scattering, and the body-surface cross scattering term of the vegetation body and the soil surface, therefore,
represents a period T i The radar incidence angle theta, the backward volume scattering coefficient component of vegetation under the pp polarization mode,
represents a period T i The backward scattering coefficient component of the vegetation and the soil body-surface interaction under the radar incidence angle theta and the pp polarization mode,
represents a period T i The backward scattering coefficient component of the soil under the radar incidence angle theta and pp polarization mode,
represents a period T i Total backscattering coefficient under the radar incidence angle theta and pp polarization mode,
represents a period T i The vegetation double-pass attenuation factor of (1).
In the homopolarization pp (hh and vv polarization) state, when the radar incidence angle theta>Body-surface cross scattering in first-order discrete vegetation model at 20 DEG
Can be ignored; meanwhile, assuming that the crop covering layer is composed of small cylinders distributed randomly, according to the Freeman A three-component scattering model, the volume scattering of the vegetation layer can be considered to be not influenced by polarization and is about 3 times of the total cross-polarization scattering quantity, namely
Therefore, the surface scattering component
Can be separated into:
(4) Calculating pixel vegetation coverage by optical data pixel binary model
In the growing process of crops, the vegetation coverage degree is changed continuously, and the pixels are also transited from pure bare soil pixels to mixed pixels of soil and vegetation and then to pure vegetation pixels. The pixel binary model is the most common mixed pixel linear decomposition model, and pixel vegetation coverage can be inverted by using the pixel binary model. The pixel dichotomy 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)
in the formula, F (T) i ) Is a period T i The pixel vegetation coverage; s t (T i ) Represents a period T i The total information of the pixels; s veg (T i ) Represents a period T i Remote sensing information of the pure vegetation pixel; s soil (T i ) Represents a period T i The remote sensing information of the pure soil pixel can be obtained by the formula (8) to obtain the vegetation coverage F (T) of the pixel 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 ) Calculating formula, such as (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 ) Is a period T i The actual NDVI value of the pixel of (a); NDVI veg (T i ) Is a period T i NDVI value, NDVI of pure vegetation pixel soil (T i ) Is a period T i The NDVI value of the bare soil pixel without vegetation.
(5) Construction of monitoring model for relative change of soil humidity in crop growth process
According to the formula (7), only the vegetation coverage F can not calculate the soil surface scattering component, and the two-way attenuation factor L of the vegetation needs to be further eliminated 2 pp The influence of (c). Considering that vegetation cover changes have a longer time scale than soil moisture changes during crop growth, two time phases T that are considered to be close in time i And T i+1 Upper and lower vegetation coverage F and vegetation two-way attenuation factor L 2 pp The variation is small, i.e.:
the right side of the equation can be obtained by calculating remote sensing data; the left side of the equation is the ratio of the scattering components of the soil surface, and the equation (4) is substituted to obtain:
by combining with the formula (5), the method can obtainSolving to obtain T i And T i+1 Relative change in soil moisture in time phase:
[M v (T i+1 )] m /[M v (T i )] n ＝k (15)
wherein m, n, k can be simplified from the formula (14).
Fig. 1 is a flow chart of a soil humidity monitoring method for crop growth process combining SAR and optical remote sensing data according to an embodiment of the present invention, and the method specifically includes:
s1, satellite data SAR (T) based on SAR 1 ,θ 1 ) And SAR (T) 1 ,θ 2 ) Obtaining the backscattering coefficient
And
wherein, theta 1 And theta 2 Respectively representing a small angle and a large angle;
SAR(T 1 ,θ 1 ) Indicates a sowing time T 1 Radar incidence angle theta 1 SAR satellite data of theta 1 At a small angle, SAR (T) 1 ,θ 2 ) Indicates a sowing time T 1 Angle of incidence theta of radar 2 SAR satellite data of theta 2 Is a large angle;
indicates a sowing time T 1 Angle of incidence theta of radar 1 Total backscattering coefficient in pq polarization mode,
indicates a sowing time T 1 Radar incident angle theta 2 Total backscattering coefficient in pq polarization mode.
S2, due to the sowing time T 1 Vegetation coverage of F (T) 1 ) =0, then obtain
S3, mixing
And
substituting the formula (5) to calculate the combined roughness Z of the bare soil s ；
S4, the product obtained in the step S2
Or
And combined roughness Z of soil s Calculating the seeding time T by substituting the formula (4) 1 Soil surface humidity M v (T 1 )；
S5, based on T 2 Temporal optical remote sensing data OPT (T) 2 ) Obtaining a 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 The next epoch of (c);
s6, SAR satellite data SAR (T) based on SAR 2 θ) obtaining the back-scattering coefficient
And
s7, NDVI (T) obtained in the step S5 2 )、NDVI veg (T 2 ) And NDVI soil (T 2 ) Calculating vegetation coverage F (T) by substituting formula (10) 2 ) F (T) 1 ) And F (T) 2 ) Substituting into the formula (11) to obtain
S7, mixing
And Z obtained in step S3 s Substituting into formula (14) to obtain T 1 And T 2 A formula (15) of relative change of soil humidity in time phase;
s8, combining the formula (15) and the 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, OPT (T) based on optical remote sensing data i+1 ) Obtaining a normalized vegetation index NDVI (T) i+1 )、NDVI veg (T i+1 ) And NDVI soil (T i+1 ) (ii) a Based on SAR (T) i+1 θ) obtaining the post-scattering coefficient
And
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 the formula (11) to obtain
S11, mixing
And Z obtained in S3 s Into formula (14) to obtain T i And T i+1 A formula (15) of relative change of soil humidity in time phase;
s12, combining formula (15) and last adjacent period T i M of (A) v (T i ) Calculating to obtain M v (T i+1 )。
Winter wheat growth process time series soil humidity monitoring
Taking a typical winter wheat planting area 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 that soil is exposed in the sowing period to obtain the soil surface combination roughness Z s And initial soil moisture M v (T 1 ) Then substituting the formula (15) to obtain M v (T 2 ). Similarly, M can be calculated v (T 3 )，M v (T 4 ) \8230 \ 8230:, thereby obtaining time series soil humidity and realizing soil humidity monitoring in the growth process of winter wheat.
The invention has been described by way of example, and it is to be understood that its specific implementation is not limited to the details of construction and arrangement shown, but is within the scope of the invention.
Claims (9)
1. A soil humidity monitoring method combining SAR and optical remote sensing data in a crop growth process is characterized by comprising the following steps:
s1, based on sowing time T 1 SAR satellite data SAR (T) 1 ,θ 1 ) And SAR (T) 1 ,θ 2 ) Calculating the sowing time T 1 Soil surface humidity M v (T 1 )；
S2, based on time T i SAR satellite data SAR (T) i θ), time period T i+1 Optical remote sensing data OPT (T) i+1 ) And SAR satellite data SAR (T) i+1 θ) determining the period T i And period T i+1 Relative change of soil humidity in time phase to obtain time T i+1 Surface humidity M of soil v (T i+1 ) Wherein i is sequentially valued from 1 to h;
SAR(T 1 ,θ 1 ) Indicates a sowing time T 1 Radar incidence angle theta 1 Is as followsSAR satellite data, theta 1 At a small angle, SAR (T) 1 ,θ 2 ) Indicates a sowing time T 1 Radar incident angle theta 2 SAR satellite data of theta 2 Is a large angle.
2. The method for monitoring soil moisture during crop growth by combining SAR and optical remote sensing data as claimed in claim 1, wherein the soil surface moisture M v (T 1 ) The acquisition method specifically comprises the following steps:
s11, SAR satellite data SAR (T) based on SAR 1 ,θ 1 ) And SAR (T) 1 ,θ 2 ) Obtaining the backscattering coefficient
And
wherein, theta 1 And theta 2 Respectively representing a small angle and a large angle;
s12, sowing time T 1 Vegetation coverage of F (T) 1 ) =0, then
S13, based on
And
calculating the combined roughness Z of the bare soil s ；
S14, the compound obtained in S2
Or
And combined roughness Z of soil s Calculating the sowing time T 1 Soil surface humidity M v (T 1 )。
3. The method for monitoring soil moisture during crop growth by combining SAR with optical remote sensing data as claimed in claim 2, wherein the combined roughness Z of bare soil s The calculation formula of (a) is specifically as follows:
wherein c and d are both fitting coefficients,
indicates a sowing time T 1 Radar incident angle theta 1 The soil surface backscattering coefficient under the pq polarization mode;
indicates a sowing time T 1 Angle of incidence theta of radar 2 And backscattering coefficient in pq polarization mode.
4. The method for monitoring soil moisture during crop growth by combining SAR and optical remote sensing data as claimed in claim 2, wherein the sowing time T is 1 Soil surface humidity M v (T 1 ) The calculation formula is as follows:
A pp (θ)、B pp (θ)、C pp (theta) are each a cubic polynomial, Z, about the radar angle of incidence theta s Indicating the combined roughness of the bare soil.
5. The method for monitoring soil moisture during crop growth by combining SAR with optical remote sensing data as claimed in claim 2, wherein the time T is i+1 Soil surface humidity M v (T i+1 ) The acquisition method specifically comprises the following steps:
s21, based on time 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 the time T i+1 SAR satellite data SAR (T) i+1 θ) obtaining the back-scattering coefficient
And
s23, based on NDVI (T) i+1 )、NDVI veg (T i+1 ) And NDVI soil (T i+1 ) Calculating the vegetation coverage F (T) i+1 ) And further calculates the time period T i To the time period T i+1 Average vegetation coverage of
S24, mixing
And combined roughness of bare soil Z s Calculating the time period T i And a period T i+1 Relative change of soil humidity in time phase;
s25, timing T i Surface humidity M of soil v (T i ) Input period T i And period T i+1 The relative change of soil humidity in time phase is used to calculate the time period T i+1 Humidity M of soil surface v (T i+1 )。
6. As in claimThe method for monitoring the soil humidity in the crop growth process by combining SAR and optical remote sensing data, which is required by 5, is characterized in that the period T i And a period T i+1 The formula of the relative change of the soil humidity in the time phase is as follows:
7. the method for monitoring soil moisture during crop growth by combining SAR and optical remote sensing data as in claim 5, wherein vegetation coverage is F (T) i ) The calculation formula is as follows:
F(T i )＝(NDVI(T i )-NDVI soil (T i ))/(NDVI veg (T i )-NDVI soil (T i )) 。
8. the method for monitoring soil moisture during crop growth by combining SAR and optical remote sensing data according to claim 1, wherein said crop is grown in dry farmland, including wheat, corn, oilseed rape, peanut.
9. The method for monitoring soil moisture during crop growth by combining SAR and optical remote sensing data as claimed in claim 1, wherein the wide angle θ 2 At a small angle theta 1 The difference is greater than 10.
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| CN116049768A (en) * | 2023-04-03 | 2023-05-02 | 中国科学院空天信息创新研究院 | Active and passive microwave soil moisture fusion algorithm based on generalized linear regression model |
| CN117574161A (en) * | 2024-01-17 | 2024-02-20 | 航天宏图信息技术股份有限公司 | Surface parameter estimation method, device and equipment based on generation of countermeasure network |
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| CN117826112B (en) * | 2024-03-05 | 2024-05-31 | 天津智云水务科技有限公司 | Soil water content inversion method based on sar |
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