CN113836490A - STARFM surface temperature fusion prediction method based on data linear regression - Google Patents
STARFM surface temperature fusion prediction method based on data linear regression Download PDFInfo
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Abstract
The invention discloses a STARFM (start frequency modulation) surface temperature fusion prediction method based on data linear regression, which comprises the following steps of: step 1, obtaining remote sensing data; step 2, processing remote sensing data; step 3, inverting the Landsat-8 ground surface temperature; step 4, performing unsupervised classification based on Landsat-8; step 5, performing linear regression on the temperature data category by category; and 6, performing space-time fusion based on the temperature data. According to the method disclosed by the invention, a category-by-category temperature data linear regression fusion scheme is provided based on MOD11A1 and Landsat-8 surface temperature data at the basic time, the difference is corrected, and the precision of a fusion prediction result is effectively improved. By adopting the thermal infrared remote sensing technology, a new way is provided for obtaining and applying the regional temperature.
Description
Technical Field
The invention belongs to the field of surface temperature prediction, and particularly relates to a STARFM surface temperature fusion prediction method based on data linear regression in the field.
Background
The high space/high time ground surface temperature (LST) is an important parameter in the process of energy exchange between the ground surface and the atmosphere, is an important component and a composition of energy balance and water balance, plays an important role in various ecological processes, has profound significance in the research of fine scales such as agricultural cultivation, farmland evapotranspiration and the like, the accurate determination and estimation of climate evolution, the evaluation of ecological environment water resources, the guidance of agricultural irrigation, the monitoring of agricultural drought, the improvement of agricultural water resource utilization and the like, and needs to be mastered in various fields and departments. The traditional point measurement mode has the problems of long repeated observation period and high cost, and the high spatial heterogeneity of the earth surface causes more complex change of the earth surface temperature in space and time.
Currently, there are two main categories of thermal infrared sensors for obtaining surface temperature: one is high spatial/low temporal resolution, e.g. TM, ETM +, TIRS, ASTER, revisit cycle is 16 days; the other is a low spatial/high temporal resolution, e.g. AVHRR, MODIS, with good timeliness, but a spatial resolution of 1 km. How to combine the thermal band data of the two types of sensors has important significance in generating LST data with high spatial/temporal resolution.
Most of high-resolution data sets generated by the existing space-time fusion algorithm are used for remote sensing reflectivity data, temperature data are rarely researched, different sensors and different temperature inversion algorithms can cause temperature data difference based on the particularity of the temperature data and time variation (different sensor transit time differences), but no researcher is found to consider in the method for predicting Landsat-8 surface temperature based on the space-time fusion algorithm disclosed at present.
In addition, most of the current temperature-based space-time fusion researches do not perform data linear regression on high-resolution and low-resolution temperature data, and due to the difference of sensors and temperature inversion algorithms, the consistency of the high-resolution and low-resolution temperature input data may have larger difference, so that a fusion result generates larger error.
Disclosure of Invention
The invention aims to provide a STARFM surface temperature fusion prediction method based on data linear regression.
The invention adopts the following technical scheme:
in a STARFM surface temperature fusion prediction method based on linear regression of data, the improvement comprising the steps of:
step 1, obtaining remote sensing data;
step 2, processing the remote sensing data: reprojection and registration of Landsat-8 data with MOD11A1 data based on ENVI, cropping based on the region of interest, transforming them to the same coordinate system;
step 3, Landsat-8 surface temperature inversion: performing surface temperature inversion on the 10 th wave band of Landsat-8 based on a single-channel inversion algorithm to obtain Landsat-8 surface temperature data;
step 4, performing unsupervised classification based on Landsat-8;
and 5, performing linear regression on the temperature data by category: based on the classification result, a least square method is adopted to construct a linear relation between the earth surface temperature of each category of Landsat-8 and the corresponding MOD11A1 earth surface temperature, and linear regression is carried out on Landsat-8 earth surface temperature data on the basis of the MOD11A1 earth surface temperature;
and 6, performing space-time fusion based on temperature data: based on t0、t1MOD11A1 data at time t0Using STARFM time-space fusion to the Landsat-8 earth surface temperature data after time linear regression, and performing t1And predicting the Landsat-8 surface temperature data at the moment to obtain the Landsat-8 surface temperature at the predicted moment.
The invention has the beneficial effects that:
according to the method disclosed by the invention, a category-by-category temperature data linear regression fusion scheme is provided based on MOD11A1 and Landsat-8 surface temperature data at the basic time, the difference is corrected, and the precision of a fusion prediction result is effectively improved. By adopting the thermal infrared remote sensing technology, a new way is provided for obtaining and applying the regional temperature.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The method comprises the steps of classifying the data based on Landsat-8, carrying out data linear regression processing on high-resolution and low-resolution temperature data category by category, and generating high-resolution Landsat-8LST data by combining a single-channel inversion algorithm and a STARFM space-time fusion algorithm. The data regression mainly comprises two important processes: (1) the remote sensing image classification (2) based on the maximum likelihood method is based on a classification chart, linear relations between MOD11A1 and each category in Landsat-8 earth surface temperature data are obtained by adopting a least square idea, and finally linear regression is carried out on the Landsat-8 earth surface temperature data by taking MOD11A1 data as a reference. The method specifically comprises the following steps:
step 1, obtaining remote sensing data; landsat-8 data from USGS website, MOD11A1 data from NASA website;
step 2, processing the remote sensing data: the Landsat-8 data and the MOD11A1 data are reprojected and registered based on ENVI5.3, and are cut based on the research area and transformed to the same coordinate system;
step 3, Landsat-8 surface temperature inversion: performing surface temperature inversion on the 10 th wave band of Landsat-8 based on a single-channel inversion algorithm to obtain Landsat-8 surface temperature data;
step 4, performing unsupervised classification based on Landsat-8;
and 5, performing linear regression on the temperature data by category: based on classification results (classification graphs), a least square method is adopted to construct a linear relation between the earth surface temperature of each class of Landsat-8 and the corresponding MOD11A1 earth surface temperature, and linear regression is carried out on Landsat-8 earth surface temperature data on the basis of the MOD11A1 earth surface temperature;
and 6, performing space-time fusion based on temperature data: based on t0、t1MOD11A1 data at time t0Using STARFM time-space fusion to the Landsat-8 earth surface temperature data after time linear regression, and performing t1And predicting the Landsat-8 surface temperature data at the moment to obtain the Landsat-8 surface temperature at the predicted moment.
Introduction of single-channel inversion algorithm:
universal Single-channel algorithm (SC) based on
And Sobrino proposes that temperature inversion is carried out under the condition that only one thermal band exists, and the specific formula is as follows:
Ts＝γ[ε-1(ψ1Lsensor+ψ2)+ψ3]+δ
δ＝-γLsensor+Tsensor
C1，C2constant of Planck function, C1＝1.19104×108W/(m2·ster·μm)，C2＝1.43877×104μm·K，λ＝10.904μm，bγ1324K, ε is the ground emissivity, LsensorThe radiance value received by the satellite height sensor is in W/(m 2. ster. mu.m), and can be obtained by radiometric calibration of thermal waveband data. T issensorThe brightness temperature is corresponding to the brightness of the radiation on the satellite.
Ψ1＝0.04019ω2+0.02916ω+1.01523
Ψ2＝-0.38333ω2-1.50294ω+0.20324
Ψ3＝0.00918ω2+1.36072ω-0.27514
In the formula, ω is the atmospheric moisture content,. psi1，ψ2，ψ3Is an atmospheric functional parameter.
Introduction of STARFM spatiotemporal fusion algorithm:
a Spatial Temporal Adaptive Reflection Fusion Model (STARFM) is a most typical Fusion algorithm in data reconstruction-based methods, and the algorithm is proposed by Gao and the like, and the specific algorithm is as follows:
the reflectivity C of a heterogeneous coarse resolution pixel at t if errors in geometric matching and atmospheric correction are ignoredtFrom the reflectivity of the homogeneous picture elements of fine resolution in the corresponding area
And corresponding abundance
Expressing:
a hypothetical premise for the STARFM algorithm is
It can be calculated using the surrounding neighboring homogeneous coarse resolution image elements (referred to herein as pure image elements), and for one of the homogeneous image elements of coarse resolution (pure image element), the surface reflectivity thereof can be expressed in terms of the surface reflectivity of the high spatial resolution data, that is:
L(xi，yi，tk)＝M(xi，yi，tk)+εk (1-2)
wherein, L (x)i，yi，tk) And M (x)i，yi，tk) High and low spatial resolution data, respectively, at tkValue of time of reflection，εkIs the difference in reflectivity (caused by bandwidth and geometric distortion) between the two. T of desired prediction0L (x) of (A)i，yi，t0) Can be expressed as follows:
L(xi，yi，t0)＝M(xi，yi，t0)+ε0 (1-3)
this algorithm further assumes that the pel (x)i，yi) The type of surface coverage and the system error (epsilon) at t0Time sum tkThe time remains unchanged, and further: epsilon0＝εk
L(xi，yi，t0)＝M(xi，yi，t0)+L(xi，yi，tk)-M(xi，yi，tk) (1-4)
In practice, however, the ideal situation is not satisfied, because the scanning field of the geospatial resolution data is large, and the observed quantity is likely not homogeneous pixels; at two different times, the type of surface coverage of the same area may change; both the earth surface coverage state and the Bidirectional Reflectance Distribution Function (BRDF) caused by illumination geometry changes are likely to change.
Thus, the STARFM algorithm utilizes a weight function WijkTo solve the prediction error of the reflectivity caused by the above 3 problems, a weighting function W is usedijkTo calculate t0And finally obtaining the reflectivity of the high-resolution data during prediction according to the reflectivity of the central pixel. Can be expressed as follows:
where w is the size of the search box, (x)w/2，yw/2And) is the center pel of the moving search box.
The STARFM algorithm calculates the final weight function W based on the three factors of spectrum, phase and spaceijk. Mainly comprising spectral differences SijkTime phase difference TijkCenter pixel (x)w/2，yw/2) And candidate picture element (x)i，yi) At tkSpatial distance d of timeijk. The expression is as follows:
Sijk＝|L(xi，yi，tk)-M(xi，yi，tk)| (1-6)
Tijk＝|M(xi，yi，tk)-M(xi，yi，t0)| (1-7)
wherein SijkThe smaller the value of (A), the smaller the difference between the spectral characteristics of the fine resolution pixel and the surrounding pixels, and the larger weight should be assigned to the candidate pixel. T isijkSmaller is said to be at tkTime sum t0The smaller the vegetation change between times, the larger weight should be allocated to the candidate pixel. A is a specific constant, dijkRefers to the actual Euclidean distance (meters), dijkA small value corresponds to a larger weight. Combining spectral, temporal and spatial distances yields CijkThe expression is as follows:
Cijk＝Dijk×Sijk×Tijk (1-9)
the final weight can be expressed as:
the following details should be considered in the specific application of the STARFM algorithm:
(1) determination of the spectrum similar pixel:
generally, a threshold analysis method is adopted, whether the pixels belong to the same ground object or not is judged through the spectrum difference of other pixels and a central pixel in a window, and if all the wave band pixels in the window meet the formula (1-11), the pixel is considered to be similar to the pixel to be processed.
|f(i，j)-f(xw/2，yw/2)|＜Lstdv·2/m (1-11)
In the formula: f (i, j) is a candidate pixel for intra-window similarity detection, f (x)w/2，yw/2) Is a central pixel, i.e. a pixel to be processed, LstdvM is the pre-estimated total number of total classes of surface features for which the standard deviation of the high resolution image is known.
(2) And (3) filtering the similar pixels of the spectrum, wherein the algorithm considers that a qualified similar pixel should meet (1-12), (1-13):
Sijk＝max(|L(xω/2，yω/2，tk)-M(xω/2，yω/2，tk)|)+σlm (1-12)
Tijk＝max(|M(xω/2，yω/2，tk)-M(xω/2，yω/2，t0)|)+σmm (1-13)
max (·) indicates that if the input fusion image logarithm is more than one pair, the maximum value of the two formulas of a certain central pixel to be processed in the images is taken as the threshold value of the qualified condition. Sigmal，σmRepresenting the uncertainty, σ, of the reflectivity of high and low resolution data, respectivelylmRepresenting uncertainty, σ, between high and low resolution datammRepresenting the uncertainty of the two input low spatial resolution data due to the temporal difference.
Claims (1)
1. A STARFM surface temperature fusion prediction method based on data linear regression is characterized by comprising the following steps:
step 1, obtaining remote sensing data;
step 2, processing the remote sensing data: reprojection and registration of Landsat-8 data with MOD11A1 data based on ENVI, cropping based on the region of interest, transforming them to the same coordinate system;
step 3, Landsat-8 surface temperature inversion: performing surface temperature inversion on the 10 th wave band of Landsat-8 based on a single-channel inversion algorithm to obtain Landsat-8 surface temperature data;
step 4, performing unsupervised classification based on Landsat-8;
and 5, performing linear regression on the temperature data by category: based on the classification result, a least square method is adopted to construct a linear relation between the earth surface temperature of each category of Landsat-8 and the corresponding MOD11A1 earth surface temperature, and linear regression is carried out on Landsat-8 earth surface temperature data on the basis of the MOD11A1 earth surface temperature;
and 6, performing space-time fusion based on temperature data: based on t0、t1MOD11A1 data at time t0Using STARFM time-space fusion to the Landsat-8 earth surface temperature data after time linear regression, and performing t1And predicting the Landsat-8 surface temperature data at the moment to obtain the Landsat-8 surface temperature at the predicted moment.
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| CN108985959A (en) * | 2018-08-09 | 2018-12-11 | 安徽大学 | A kind of wheat powdery mildew remote-sensing monitoring method based on Surface Temperature Retrieval technology |
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