CN108268735A - Soil moisture NO emissions reduction method based on multi-source remote sensing satellite fused data - Google Patents
Soil moisture NO emissions reduction method based on multi-source remote sensing satellite fused data Download PDFInfo
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Abstract
The invention discloses a kind of soil moisture NO emissions reduction methods based on multi-source remote sensing satellite fused data, regional especially suitable for cloudy rain, including：It collects and arranges passive microwave soil moisture data set, LST and NDVI data sets and dem data；By the use of NDVI and dem data collection as auxiliary data, on being influenced that pixel is caused to lack serious LST images progress space interpolation by sexual intercourse, obtain approaching the LST data sets of surface temperature day by day of research area's all standing；The numerical relationship model between microwave soil moisture and optical remote sensing LST, NDVI is built, and utilize the model using Geographical Weighted Regression Model, obtain the soil moisture data set of high spatial resolution.The present invention effectively improves the universality of the reliability and NO emissions reduction of NO emissions reduction result in the area of research on a large scale, improves and carries out a wide range of space mapping and the precision and efficiency of monitoring to soil water content in cloudy rain area.
Description
Technical field
The present invention relates to the invertings of Remote Sensing soil water content and the technical field of NO emissions reduction, and in particular to Yi Zhongji
In the soil moisture NO emissions reduction method of multi-source remote sensing satellite fused data.
Background technology
The soil water is the unsaturation water body in soil horizon, is distributed widely in Terrestrial, is that surface and ground water is mutual
The tie of conversion, and be the necessary water source of plant growth, how many pairs of proportion of crop planting of soil water resources, growth play key
Effect.China is a unbalanced country of soil water resources distributed pole, in North of Huai River and vast central and west regions, precipitation
Measure less, water resource is poor, and soil water resources become one of key factor for restricting agricultural production；And it is enriched in water resource
The especially higher the south of the lower reaches of the Yangtze River of precipitation, higher soil water content tend to cause the waterlogged disaster of crops again,
It causes damages to agricultural production.Therefore, timely, correct monitoring is made to the state change of local soil water resources with commenting
Valency is that scientific utilization water resource carries out crops rational deployment, formulates suitable irrigation program or for specific drought and waterlogging calamity
Evil formulates rational scheme of disaster reduction, the final premise for ensureing crops and grain security production.Traditional monitoring soil moisture master
If real-time surface soil water content value is obtained by the means of artificial observation or automatic observatory in weather station.
However artificial observation is time-consuming and laborious and random error is uncontrollable, the observation data at automatic Observation station are also only that some is solid
Determine the Soil moisture of location point and assessment can not be made to the soil moisture content transformation in a certain range of continuous space, greatly
Amount lays a wide range of earth's surface monitoring soil moisture of automatic Observation station progress, and also there is the excessively high difficulties of observation cost.With section
The progress of skill, the acquisition of satellite remote sensing date and the propulsion of satellite remote sensing Soil Moisture Inversion technique study, effectively compensate for
Using disadvantage of the website observation soil hydrological data on spatial continuity and observation cost, make to obtain using satellite remote sensing date
The near real-time soil moisture content distribution image of high-spatial and temporal resolution is taken to be possibly realized.
The large space ruler based on satellite remote sensing is unfolded in some countries of USA and EU successively from nineteen seventies
Spend Soil Moisture Inversion research.Microwave remote sensing data have good penetrability to cloud layer and Vegetation canopy, are the monitoring soil water
Divide the ideal data source of change information.Common microwave remote sensing data include microwave radiometer data set and microwave scatterometer (thunder
Up to) data set.Microwave scatterometer receives the complicated mechanism of data inversion soil moisture, and data resolution is excessively high therefore accounts for
Larger and expensive with space, processing step is cumbersome, and refutation process has earth's surface auxiliary parameter strictly accurate want
It asks, the application cost for leading to the data is integrally higher.Microwave radiometer belongs to passive microwave technology, Data for Global coverage
Height, it is easy to obtain, and is suitble to the soil moisture monitoring of large area.Microwave radiometer includes rail lift and drops rail both of which
Data set, the data set of each pattern are 1-3 days (in China's most area for the revisiting period of mid low latitude region
Generally 1-2 days), higher temporal resolution, which ensure that, can accomplish soil moisture spatial variations close to daily
The near real-time monitoring of 1-2 times.However the spatial resolution of such data set is generally relatively low, generally in the range of 10-60km, into
To limit the key factor of soil moisture monitoring accuracy.
Space NO emissions reduction processing is done to passive microwave inverting soil moisture using optical remote sensing data, can be obtained
The soil moisture remote sensing image of more high spatial resolution.(Choi, M., ＆Hur, Y. (2012) .A such as Choi
microwave-optical/infrared disaggregation for improving spatial
representation of soil moisture using AMSR-E and MODIS products.Remote
Sensing Of Environment, 124,259-269) and (Chauhan, N.S., Miller, S., the ＆ such as Chauhan
Ardanuy,P.(2003).Spaceborne soil moisture estimation at high resolution: a
microwave-optical/IR synergistic approach.International Journal Of Remote
Sensing, 24,4599-4622) microwave inverting established according to " surface temperature --- vegetation " the triangle features relationship in region
Mathematical modulo of the soil moisture content about parameters such as the surface temperature of optical remote sensing inverting, vegetation index, surface albedos
Type, and carry out NO emissions reduction using this model and obtain high-resolution soil moisture remote sensing image；Piles etc. (Piles,
M.,Sanchez,N.,Vall-llossera,M.,Camps, A.,Martinez-Fernandez,J.,Martinez,J.,&
Gonzalez-Gambau,V.(2014).A Downscaling Approach for SMOS Land Observations:
Evaluation of High-Resolution Soil Moisture Maps Over the Iberian
Peninsula.Ieee Journal Of Selected Topics In Applied Earth Observations And
Remote Sensing, 7,3845-3857) SMOS passive microwave remote sensing soil moisture data are dropped using similar approach
Scale processing, and add L waves in the input parameter (in addition to the surface temperature of optics, vegetation index etc.) of mathematical model
The microwave bright temperature data of section so that the precision of NO emissions reduction data increases； Merlin(Merlin,O.,Al Bitar,A.,
Walker,J.P.,&Kerr,Y.(2010).An improved algorithm for disaggregating
microwave-derived soil moisture based on red, near-infrared and thermal-
Infrared data.Remote Sensing Of Environment, 114,2305-2316) utilize similar optical remote sensing
Data are established between microwave soil moisture and optical data based on " soil evaporation rate model (soil evaporative
Efficiency mode) " and then NO emissions reduction processing is carried out to microwave soil moisture, obtain the relatively satisfactory high score of overall effect
Resolution remote sensing soil moisture data set；(Kim, J., ＆Hogue, T.S. (2012) the .Improving Spatial Soil such as Kim
Moisture Representation Through Integration of AMSR-E and MODIS Products.Ieee
Transactions on Geoscience And Remote Sensing, 50,446-460) by establishing, " soil moisture refers to
Number (soilwetnessindex) " builds the relationship between microwave soil moisture and optical remote sensing data, completes similar
NO emissions reduction works.
Research of the above-mentioned a large amount of forefathers about passive microwave remote sensing soil moisture NO emissions reduction, has in common that, needs
The particular signal component of earth's surface soil moisture content transformation can be responded by being found from optical remote sensing data, build the component and microwave
The numerical relationship model of high correlation, high robust between inverting soil moisture is to complete the sky of microwave soil moisture
Between NO emissions reduction.However at least there are the deficiencies of two aspects for this process：(1) optical data is easily influenced by sexual intercourse weather, more
Pixel missing in cloud area of heavy rainfull is serious, the studies above be also concentrated mainly on mostly be fine day research area；(2) due to optical remote sensing
Data to the characterization of relation of soil moisture and unstable, often with geographical location, ground mulching component, landform because
Plain and other environment etc. the variation of factor and change, therefore this relationship is difficult with general in the larger research area of range
The stronger single mathematical model of adaptive is described (i.e. using same set of model coefficient).This can be reduced in extensive area
The correlation and robustness of mathematical model of the constructed microwave soil moisture about optical remote sensing parameter, finally influence high score
The precision of resolution soil moisture data.
Invention content
The present invention provides a kind of soil moisture NO emissions reductions based on multi-source remote sensing satellite fused data (to be promoted empty
Between resolution ratio) method, especially suitable for cloudy rain area.
A kind of soil moisture NO emissions reduction method based on multi-source remote sensing satellite fused data, includes the following steps：：
Step 1)：Collect and arrange passive microwave remote sensing soil moisture data set, surface temperature (LST) data set, vegetation
Cover index (NDVI) data set and digital elevation model (DEM) data set by pretreatment, obtain microwave remote sensing soil
Moisture data image, surface temperature (LST) image data, vegetation-cover index (NDVI) image data and digital elevation mould
Type (DEM) image data；
Step 2)：Vegetation-cover index (NDVI) image data and digital elevation model (DEM) obtained using step 1)
Image data is as auxiliary data, on being influenced that pixel is caused to lack by sexual intercourse in surface temperature in step 1) (LST) image data
Serious image carries out space interpolation, obtains the temperature data image of earth's surface day by day of research area's all standing；
Step 3)：Utilize Geographical Weighted Regression Model, construction step 1) obtained microwave remote sensing soil moisture image data
The vegetation-cover index that the temperature data of the earth's surface day by day image of research area's all standing that is obtained with step 2), step 1) obtain
(NDVI) numerical relationship model between image data, and at the space NO emissions reduction using model completion soil moisture
Reason obtains the soil moisture data set of high spatial resolution.
The method of the present invention on the basis of passive microwave remote sensing soil moisture data set space distributed intelligence is merged, utilizes
MODIS optical remote sensing data set invertings obtain the high-resolution soil moisture image of 1 km spatial resolution.This method is suitble to
It is very cheap in the soil moisture space distribution information drawing in cloudy rainy day gas area and monitoring, cost；Added simultaneously using geographical
The method that power returns improves forefathers and characterizes the microwave inverting soil water using single mathematical model coefficient in large scale research area
The deficiency of correlativity between divided data collection and optical remote sensing data, the reliability and this method for improving NO emissions reduction result exist
Universality in a wide range of research area.
In step 1), surface temperature (LST) data set, vegetation-cover index (NDVI) data set are optical remote sensing number
According to collection.
Passive microwave remote sensing soil moisture data set, surface temperature (LST) data set, vegetation-cover index (NDVI) number
It according to collection and digital elevation model (DEM) data set, pre-processes, obtains the tiff format remote sensing shadow under standard flat projection
Picture.
Specifically, passive microwave remote sensing soil moisture data set, surface temperature (LST) data set, vegetation-cover index
(NDVI) various data sets are done pre- place by data set and digital elevation model (DEM) data set using python language
Reason obtains tiff format (label image file format, using .GIF as extension name) remote sensing image under standard flat projection.
In step 2), on being influenced to cause pixel missing serious by sexual intercourse in surface temperature in step 1) (LST) image data
Image carry out space interpolation, specifically include：
Two intervals nearer time point t and t0, respectively it is corresponding with width surface temperature (LST) image TtAnd surface temperature
(LST) image Tt0, Tt0For surface effective pixel, not by the pixel of sexual intercourse image, coverage is more than 90% image, t0Moment
It is corresponding with vegetation-cover index (NDVI) image V0, the digital elevation model image in region directly represents with DEM, Suo Youying
As being corresponded for pixel, spatial resolution is identical, then TtExisting functional relation is used as offline between above-mentioned other images
Property mathematical model expression：
Tt=a0×Tt0+a1×V0+a2×DEM+b
a0,a1,a2And b is the coefficient of linear model；
Utilize t and t0Corresponding effective pixel value carries out regression coefficient fitting to above formula on the image at moment, later
Can using the formula in t moment surface temperature image lack pixel carry out interpolation, obtain research area's all standing by
Day surface temperature image data.
About t0The screening at time point：To complete the interpolation of surface temperature image in complete 1 year, every month is needed at least
There is a scape with reference to image, research area's clear sky coverage is more than 90% image, the surface temperature shadow in corresponding a certain some month in year
The interpolation of picture if current year does not meet the reference image of condition, can be extracted from other adjacent times to studying area's coverage
LST images more than 90% are used as with reference to image, it is necessary to be met at least scape every month and be referred to image.
For surface temperature (LST) image of random time t, meeting | t-t0|<It is selected in the threshold range of=30 days
Make | t-t0| minimum t0The image at moment is used as with reference to image, if current year does not meet the reference image of threshold range,
It is found near the phase same date in adjacent time according to similar approach, in the reference image set of time limit range and so on, i.e.,
It can be the reference image that image configuration of each coverage less than 90% meets condition.
Step 3) specifically includes：
Research area's all standing that microwave remote sensing soil moisture image data that step 1) obtains, step 2) are obtained by
Vegetation-cover index (NDVI) image data that day surface temperature image data, step 1) obtain is normalized at stretching
Reason normalizes the microwave remote sensing soil moisture image data SSM after stretch processing*It represents, normalizes grinding after stretch processing
Study carefully the temperature data image of the earth's surface day by day LST of area's all standing*It represents, normalizes the vegetation-cover index after stretch processing
(NDVI) image data NDVI*It represents, resampling later obtains SSM into 25km resolution ratio*25km i、LSTi *25km、NDVIi *25km, using following geographical weighting method formula：
Wherein, βk(ui,vi) coefficient function of the expression about geographical location, β0 25km(ui,vi)、β1 25km(ui,vi)、 β2 25km
(ui,vi)、β3 25km(ui,vi)、β4 25km(ui,vi)、β5 25km(ui,vi) for coefficient to be fitted, εi 25kmFor residual error, uiAnd viPoint
Transverse and longitudinal coordinates of the target location i in equatorial projection coordinate system is not represented；
Acquire coefficient [β of the 25km images about each pixelk 25km(ui,vi), k=0,1,2,3,4,5；εi 25km] after, profit
The spatial distribution collection of the coefficient is converted into 1km scales, i.e. [β with Technique of Cubic Spline Interpolationk 1km(ui,vi), k=0,1,2,
3,4,5；εi 1km], the space NO emissions reduction of completion passive microwave remote sensing soil moisture on 1km space scales, using the following formula
Obtain SSM*1km i：
SSM*1km iFor the passive microwave remote sensing soil moisture data set under 1km resolution ratio；NDVIi *1kmFor 1km resolution ratio
Under vegetation-cover index data set；LSTi *1kmFor the surface temperature data set under 1km resolution ratio.
General passive microwave remote sensing soil moisture image data spatial resolution is 25km, utilizes cubic spline interpolation
Technology is by the spatial distribution set of the coefficient from the scale of 25km (or the spatial discrimination of generic microwave soil moisture image
Rate scale) it is converted into 1km scales (or spatial resolution scale of generic optics NDVI and LST images), you can in 1km skies
Between the space NO emissions reduction of soil moisture SSM is completed on scale.
Compared with prior art, the invention has the advantages that：
The present invention is based on the space drops that multi-source remote sensing satellite fused data carries out satellite Retrieval soil moisture data set
Scale works, and obtains the high-resolution soil moisture space distribution informations of 1km.Research and existing skill compared to forefathers
Art the advantage is that：
Space interpolation is completed to optical remote sensing technology institute inverting surface temperature (LST) image by reasonable effective method,
Obtain the LST images day by day close to all standing.And then calculate the image for the space NO emissions reduction of soil moisture, greatly
The big spatial coverage for improving the soil moisture image after NO emissions reduction is obtained in cloudy rain area.
The structure that Geographical Weighted Regression technology completes NO emissions reduction model formation is introduced, for the different geography of different zones
It obtains being suitable for the regression coefficient of different pixels with weather feature fitting, greatly improves models fitting precision and surface soil water
Divide inversion accuracy.
The present invention is promoted in cloudy rainy day gas area covers the space of the high-resolution soil moisture content image after NO emissions reduction
Lid rate, at the same using Geographical Weighted Regression method improve forefathers large scale research area use single mathematical model system
The deficiency of this method of correlativity, effectively carries between number characterization microwave Soil Moisture Retrieval data set and optical remote sensing data
The universality of the reliability and NO emissions reduction of NO emissions reduction result in the area of research on a large scale has been risen, has been existed with cheap cost improvement
Cloudy rain area carries out a wide range of space mapping and the precision and efficiency of monitoring to soil water content.
Description of the drawings
Fig. 1 is the flow chart of the soil moisture NO emissions reduction method the present invention is based on multi-source remote sensing satellite fused data；
(a) is on June 22nd, 2013 in example used in the present invention, noon point respectively (represented earth's surface temperature respectively in Fig. 2
The high and extremely low situation of degree) before interpolation with the surface temperature LST after interpolation to the clear sky in experimental study area of the present invention space
Pixel coverage comparison diagram；
(b) respectively (represents earth's surface respectively for time point at midnight on December 22nd, 2012 in example used in the present invention in Fig. 2
The high and extremely low situation of temperature) before interpolation and interpolation after surface temperature LST to the fine of experimental study area of the present invention space
Empty pixel coverage comparison diagram；；
Fig. 3 is that 1 day to 2013 on August, 31, the daily LST fittings of September in 2012 are public in example used in the present invention
The coefficient of determination R of formula2The time series trend graph of (line above) and RMSE (following line)；
Fig. 4 is September 1 day to 2013 on August, 31, daily rail lifts in 2012 and drop board pattern in example used in the present invention
The formula fitting that AMSR2 passive microwave soil moistures data carry out NO emissions reduction using geographical weighted sum conventional method respectively determines
The probability distribution comparison diagram of coefficient；
For example used in the present invention, in four seasons of spring, summer, autumn and winter, respectively one date of selection uses geographical weighting to Fig. 5 respectively
Return the comparison of the soil moisture image obtained with conventional regression method and the former AMSR2 soil moistures image of 25km resolution ratio
Figure displaying：Spring, on April 24th, 2013, rail lift pattern；Summer, on July 24th, 2013, rail lift pattern；Autumn, 2012 9
The moon 15, board pattern dropped；Board pattern drops in winter, on December 15th, 2012.
Fig. 6 left hand views (i.e. a and c) for used in the present invention in example 1 day to 2013 August of September in 2012 31 days by
MODIS LST pixels (dark scatterplot) and interpolation obtain LST pixels (light scatterplot) and carry out the soil water that NO emissions reduction obtains respectively
The scatterplot comparison diagram (upper and lower two of the actual measurement Soil moisture for the correspondence time that score value and corresponding 65 soil moisture websites obtain
Figure represents ascending, descending board pattern respectively)；Fig. 6 right part of flg (i.e. b and d) is is evaluated this two classes Soil moisture by station data respectively
The probability of the difference (interpolation LST acquired results subtract MODIS LST acquired results) of obtained RMSE (root-mean-square error) point
Butut.
Specific embodiment
Below using China Jiangsu, Anhui and Hubei San Sheng as examples area (cloudy rainy day gas), with September 1 in 2012
Day is in August, the 2013 AMSR2 microwave soil moisture content data sets of 31 days as the microwave soil moisture content number for treating NO emissions reduction
According to being described in further detail with reference to specific attached drawing to the present invention.
As shown in Figure 1, for the present invention is based on the soil moisture NO emissions reduction methods of multi-source remote sensing satellite fused data
Flow chart.
Step 1 collects and arranges passive microwave remote sensing soil moisture data set and optical remote sensing data set (LST and NDVI
Data set) and other auxiliary data collection (DEM Law of DEM Data)；
The AMSR2 microwave soil that the present invention has collected 1 day in August, the 2013 25km resolution ratio of 31 days of September in 2012 contains
Water data (website：NASA ' s Earth Observing System Data and Information System), as
Passive microwave remote sensing soil moisture data set；
The present invention has collected 1km resolution ratio MODIS surface temperature (LST) day by day data collection (Aqua Satellite Observations),
As surface temperature (LST) data set；
The present invention has collected more days generated data collection (websites of 1km resolution ratio vegetation index (NDVI)： Land
Processes Distributed Active Archive Center), as vegetation-cover index (NDVI) data set；
The present invention has collected 1km resolution digitals elevation model (DEM) data set, as digital elevation model (DEM) number
According to collection；
Passive microwave remote sensing soil moisture data set, surface temperature (LST) data set, vegetation-cover index (NDVI) number
According to collection and digital elevation model (DEM) data set, various data sets are pre-processed using python language, are marked
Tiff format (label image file format, using .GIF as extension name) remote sensing image under directrix plane projection.1km NDVI data
Collection includes the synthesis NDVI products on the 16th that Terra satellites and Aqua satellites are observed respectively, is produced using the synthesis that two satellites provide
8 day phase differences of the product on the observation date obtain the NDVI products of synthesis in 8 days.
Step 2, by the use of NDVI and dem data collection as auxiliary data, with reference to closing on the LST images on date to by sexual intercourse
Influence causes pixel to lack serious LST images progress space interpolation, obtains approaching the surface temperature day by day of research area's all standing
LST data sets；
The LST data of Aqua MODIS sensors include number of days evidence and night data both of which, day data transit time
About at noon 1:30, night data transit time is about midnight 1:30, day data and night data transit time correspond to respectively it is micro-
The rail lift pattern of wave soil moisture data and the transit time of drop board pattern, will be respectively used to associative mode soil moisture data
NO emissions reduction processing.In example used in the present invention, interpolation below in relation to LST and during the NO emissions reduction of soil moisture,
Day (rail lift pattern) data and night (drop board pattern) data will be handled independently.
Assuming that time point t and t that two intervals are nearer0, respectively it is corresponding with a width LST images TtAnd Tt0, Tt0Image is ground
The effective pixel of table (not by the pixel of sexual intercourse image) coverage is more than 90% image, t0Shi Keyou NDVI images V0, in region
Elevation image directly represents that all images are corresponded for pixel, and spatial resolution is identical, then T with DEMtWith above-mentioned other shadows
There are the functional relations that certain is stablized as between.The relationship is expressed with following linear mathematical model：
Tt=a0×Tt0+a1×V0+a2×DEM+b (1)
a0,a1,a2And b is the coefficient of linear model.Utilize t and t0Corresponding effective pixel value on the image at moment
To above formula carry out regression coefficient fitting, later can utilize the formula in t moment LST images lack pixel into
Row interpolation obtains the LST images day by day close to earth's surface all standing.Daily corresponding data is independent by present example
Fitting unit is respectively fitted formula (1).Fig. 2 gives the reality after day mode data and night mode data difference interpolation
Example is a case each, it can be seen that LST data have large increase to the coverage of earth's surface after interpolation, while in LST after image interpolation
Good continuity is shown in terms of the spatial distribution of numerical value.The coefficient of determination (the R being fitted daily2) and RMSE values data
Sequence is as shown in Figure 3, it can be seen that R2It is continued for more than 0.4 and numerical value is higher than 0.5-0.6 mostly, and the big portions of RMSE
Point situation all in 3K hereinafter, demonstrate the formula can react well LST changing rules in a short time and and other
Remote sensing obtains the relationship between the parameter of land face, in the reliability for demonstrating the LST interpolation methods to a certain degree.
About t0The screening at time point：To complete the interpolation of LST in complete 1 year, at least scape ginseng every month is needed
Examine image (research area's coverage is more than 90% image).Present invention extraction interpolation time and adjacent altogether in 7 years
Being used as LST image of the research area's coverage more than 90% with reference to image of (2010 to 2016), can so meet every
An at least scape refers to image within a month.For the image of random time t, meeting | t-t0|<It is selected in=30 threshold range
Make | t-t0| minimum t0The image at moment is used as with reference to image.If current year does not meet the reference image of threshold range,
It is found near the phase same date in adjacent time according to similar approach, in the reference image set of 7 years and so on, you can with
It is the reference image that the condition that meets is configured in image of each coverage less than 90%.
Step 3, built using Geographical Weighted Regression Model microwave soil moisture and optical remote sensing inverting LST, NDVI it
Between numerical relationship model, and the space NO emissions reduction for being completed using the model soil moisture is handled, and obtains high-space resolution
The soil moisture data set of rate；
Present example is by soil moisture (SSM) data of LST, NDVI and AMSR2 of MODIS optical sensors
Collection does normalization stretch processing (tension values=(minimum value in original value-threshold value)/(in threshold value most in respective threshold range
Minimum value in big value-threshold value)), and band asterisk notation SSM is used respectively*,NDVI*,LST*It represents.By LST and NDVI data respectively
Resampling (is averaged) into 25km resolution ratio in 25km grids, and establishes following relational expression with AMSR2 soil moisture data：
SSM*=a × NDVI*2+b×NDVI*×LST*+c×LST*2+d×NDVI*+e×LST*+f (2)
A to f is fixed and coefficient to be fitted.Since universality of the same set of coefficient in larger research area is poor, below
Content will use geographical weighting method to be improved formula (2).Improved formula is expressed as follows：
β in above formulak(ui,vi) coefficient function of the expression about geographical location, β0To β5Prior art formula is corresponded to respectively
(4) a to e, i.e. β0 25km(ui,vi)、β1 25km(ui,vi)、β2 25km(ui,vi)、β3 25km(ui,vi)、 β4 25km(ui,vi)、β5 25km
(ui,vi) for coefficient to be fitted, εiFor residual error, uiAnd viThe transverse and longitudinal coordinate of equatorial projection coordinate system, β are represented respectivelyk
(ui,vi) unbiased esti-matorIt solves with reference to equation below：
X and Y represents independent variable matrix (LST and NDVI and constant-term variable) and dependent variable (SSM) vector respectively.W is represented
Weight matrix, each element w thereinijIt is acquired by the following formula (being known as adaptive double chi square functions)：
dijRepresent the Euclidean distance at i and j two positions pixel center, and b is fixed value, referred to as auto-adaptive function wave beam
Width, the determining of it need by a kind of cross-validation method, i.e., to determine satisfaction by way of computer Monte Carlo simulation
Make ∑ | yi-y* ≠i(x)|2(y here* ≠i(x) represent that the observation of regression point i is not involved in estimation process and is only used only i weeks
Enclose the y that a weighting obtainsiEstimated value) approximate minimum b values as parameter b position i final estimated value.
Acquire coefficient [β of the 25km images about each pixelk 25km(ui,vi), k=0,1,2,3,4,5；εi 25km] after, profit
The spatial distribution collection of the coefficient is converted into 1km scales, i.e. [β with Technique of Cubic Spline Interpolationk 1km(ui,vi), k=0,1,2,
3,4,5；εi 1km]。
The space NO emissions reduction of soil moisture SSM is completed on 1km space scales, i.e.,
The fitting unit independent as one of the corresponding data of every two days be respectively by present example to formula (3)
Number carries out the fitting based on Geographical Weighted Regression and solves；Pixels of the NDVI less than 0.05 is considered as pure water image surface first (lake, river
Deng) thus will not participate in calculating；Except cubic spline interpolation process uses the related work in ArcGIS desktop utilities tool box
Except tool is completed, remaining step is completed by python Programming with Pascal Language.
Full search time section (in September, 2012 in August, 2013) geographical weighting method and routine side is set forth in Fig. 4
The coefficient of determination probability distribution situation of method fitting formula.It can be seen that the annual fitting obtained with geographical weighting method determines system
Number is most of all more than 0.4, and whole value range will be much higher than with the result that conventional method obtains.It can be seen that geographical add
The fitting precision of power method, which will be significantly better than, uses conventional method.And Fig. 5 is then each from four seasons of search time Duan Liwei spring, summer, autumn and winter
From a secondary representational soil moisture NO emissions reduction imaging results figure is selected (with the NO emissions reduction of geographical weighting method and conventional method
As a result provide simultaneously) and compared with original AMSR2 microwave images.It can be seen that in all four dates, geography weighting
NO emissions reduction image is more nearly former AMSR2 microwaves shadow than the image of conventional NO emissions reduction on the spatial varying law of soil moisture
As the rule presented (in the region especially irised out in red circle).It is compared with geographical weighting method, conventional NO emissions reduction method
Although most of regions NO emissions reduction effect close to the former, the same period on date is used due to this method
Unique fitting regression coefficient, therefore it is difficult to ensure that the set coefficient can in a Large-scale areas accurate description its not
With the variation relation between the various Land Surface Parameters in subregion.
Step 4, precision test.Present example is observed using 65 soil moisture automatic Observation stations in experimental study area
The time point closest with satellite transit time soil moisture observation value to NO emissions reduction soil moisture data carry out precision
Verification and evaluation.
Precision test is related to two aspects, first, being obtained to the geographical NO emissions reduction data for weighting acquisition and with conventional method
The verification precision result of the NO emissions reduction data obtained is compared.Such as
Shown in table, the effect of precision test is by the RMSE and related coefficient (R) of NO emissions reduction data and station data come table
Sign.It can be seen that overall accuracy evaluation result or each website precision from 65 all data of soil moisture website
From the point of view of the average value of evaluation, the Soil moisture that Soil moisture that geographical weighting method obtains all is obtained than conventional method has more
Small RMSE and the R values of bigger, it was demonstrated that geographical weighting method can obtain more consistent more with website actual measurement Soil moisture
High-precision satellite remote sensing NO emissions reduction soil moisture data set.
Table 1 is using the soil moisture content value of soil automatic Weather Station observation to the NO emissions reduction with geographical weighted sum routine
The high spatial resolution soil water content data set that method obtains carries out the Comparative result of precision evaluation respectively.
Table 1
Second is that it is dropped using the Soil moisture by the use of original MODIS LST data as input data NO emissions reduction and with interpolation LST
The Soil moisture that scale obtains is verified respectively, compares difference of this two parts data in verification precision, such as
Shown in table 1 and Fig. 6.In summary, the original MODIS LST of Soil moisture ratio of precision obtained using interpolation LST
The Soil moisture accuracy error that data are obtained as input data NO emissions reduction, from
From the point of view of the RMSE differences for each website that the statistical data and Fig. 6 of table 1 provide, most of situation is (i.e. most of
Website) RMSE deviations in 0.02cm3/cm3Within.Generally it is considered that the soil moisture obtained with interpolation LST NO emissions reductions
Value is reliable, i.e., using LST interpolation methods provided by the invention to obtaining the higher NO emissions reduction of coverage in cloudy rain area
Soil moisture is effective.
Two in the soil moisture data set that table 1 obtains geographical weighting method NO emissions reduction with website soil moisture observation value
The LST NO emissions reductions that the Soil moisture and (2) that a digital data collection (1) is obtained using MODIS LST NO emissions reductions are gone out with interpolation obtain
Soil moisture carry out the Comparative result of precision evaluation respectively.
Table 2
Show that the method for the present invention effectively improves the reliability of NO emissions reduction result and drop ruler by Tables 1 and 2 data result
The universality in the area of research on a large scale is spent, soil water content is carried out in cloudy rain area with cheap cost improvement
A wide range of space mapping and the precision and efficiency of monitoring.
Claims (4)
- A kind of 1. soil moisture NO emissions reduction method based on multi-source remote sensing satellite fused data, which is characterized in that including with Lower step：：Step 1)：It collects and arranges passive microwave remote sensing soil moisture data set, surface temperature data set, vegetation-cover index number According to collection and Law of DEM Data collection, by pretreatment, microwave remote sensing soil moisture image data, surface temperature number are obtained According to image, vegetation-cover index image data and Law of DEM Data image；Step 2)：By the use of step 1) obtain vegetation-cover index image data and Law of DEM Data image as assist Data, on being influenced that the serious image progress space of pixel missing is caused to be inserted by sexual intercourse in surface temperature image data in step 1) Value obtains the temperature data image of earth's surface day by day of research area's all standing；Step 3)：Utilize Geographical Weighted Regression Model, construction step 1) obtained microwave remote sensing soil moisture image data and step The vegetation-cover index image data that the rapid temperature data of the earth's surface day by day image of research area's all standing 2) obtained, step 1) obtain Between numerical relationship model, and the space NO emissions reduction for being completed using the model soil moisture is handled, and obtains high spatial point The soil moisture data set of resolution.
- 2. the soil moisture NO emissions reduction method according to claim 1 based on multi-source remote sensing satellite fused data, It is characterized in that, in step 1), the image is label image file format.
- 3. the soil moisture NO emissions reduction method according to claim 1 based on multi-source remote sensing satellite fused data, It is characterized in that, in step 2), on being influenced that pixel is caused to lack serious shadow by sexual intercourse in surface temperature image data in step 1) As carrying out space interpolation, specifically include：Two intervals nearer time point t and t0, respectively it is corresponding with a width surface temperature image TtWith surface temperature image Tt0, Tt0 For surface effective pixel, not by the pixel of sexual intercourse image, coverage is more than 90% image, t0Moment is corresponding with vegetative coverage and refers to Number image V0, the digital elevation model image in region directly represents that all images are that pixel corresponds, space point with DEM Resolution is identical, then TtThe following linear mathematical model of existing functional relation is expressed between above-mentioned other images：Tt=a0×Tt0+a1×V0+a2×DEM+ba0,a1,a2And b is the coefficient of linear model；Utilize t and t0Corresponding effective pixel value carries out regression coefficient fitting to above formula on the image at moment, later can Using carry out interpolation of the formula to missing pixel in t moment surface temperature image, the earth's surface day by day of research area's all standing is obtained Temperature data image.
- 4. the soil moisture NO emissions reduction method according to claim 1 based on multi-source remote sensing satellite fused data, It is characterized in that, step 3) specifically includes：The earth's surface day by day of research area's all standing that microwave remote sensing soil moisture image data that step 1) obtains, step 2) are obtained Stretch processing is normalized in the vegetation-cover index image data that temperature data image, step 1) obtain, and normalization stretches Treated microwave remote sensing soil moisture image data SSM*Represent, normalize stretch processing after research area's all standing by Day surface temperature image data LST*It represents, normalizes the vegetation-cover index image data NDVI after stretch processing*Table Show, resampling later obtains SSM into 25km resolution ratio*25km i、It is public using following geographical weighting method Formula：Wherein, β0 25km(ui,vi)、β1 25km(ui,vi)、β2 25km(ui,vi)、β3 25km(ui,vi)、β4 25km(ui,vi)、β5 25km(ui, vi) for coefficient to be fitted, εi 25kmFor residual error, uiAnd viTransverse and longitudinals of the target location i in equatorial projection coordinate system is represented respectively Coordinate；Acquire coefficient [β of the 25km images about each pixelk 25km(ui,vi), k=0,1,2,3,4,5；εi 25km] after, utilize three The spatial distribution collection of the coefficient is converted into 1km scales, i.e. [β by secondary spline interpolation techniquesk 1km(ui,vi), k=0,1,2,3,4,5； εi 1km], the space NO emissions reduction of passive microwave remote sensing soil moisture is completed on 1km space scales, SSM is obtained using the following formula*1km i：SSM*1km iFor the passive microwave remote sensing soil moisture data set under 1km resolution ratio；NDVIi *1kmFor the plant under 1km resolution ratio Capped exponent data collection；LSTi *1kmFor the surface temperature data set under 1km resolution ratio.
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| CN113935956B (en) * | 2021-09-23 | 2022-03-25 | 中国矿业大学(北京) | Two-way mixed modeling mining area soil water content data missing repairing method |
| CN113935956A (en) * | 2021-09-23 | 2022-01-14 | 中国矿业大学(北京) | Two-way mixed modeling mining area soil water content data missing repairing method |
| CN113986905A (en) * | 2021-11-24 | 2022-01-28 | 中国科学院地理科学与资源研究所 | Method for reconstructing soil humidity missing data by remote sensing inversion |
| CN114461983A (en) * | 2022-02-09 | 2022-05-10 | 中国科学院、水利部成都山地灾害与环境研究所 | Satellite precipitation product space downscaling method based on water balance principle |
| CN115203625A (en) * | 2022-07-29 | 2022-10-18 | 应急管理部国家减灾中心 | Drought and waterlogging index data missing value interpolation method and device |
| CN115617935A (en) * | 2022-10-18 | 2023-01-17 | 中国水利水电科学研究院 | Underground water reserve deviation downscaling method based on fusion model |
| CN115661671B (en) * | 2022-12-22 | 2023-03-21 | 中国科学院地理科学与资源研究所 | Soil moisture microwave product downscaling method based on feature space |
| CN115661671A (en) * | 2022-12-22 | 2023-01-31 | 中国科学院地理科学与资源研究所 | Soil moisture microwave product downscaling method based on feature space |
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