CN106446444A - Soil moisture spatial predication research based on Bayes maximum entropy and priori knowledge - Google Patents
Soil moisture spatial predication research based on Bayes maximum entropy and priori knowledge Download PDFInfo
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Abstract
A Bayes maximum entropy (BME) method framework is introduced, soil moisture ground measured values and environment priori knowledge are fused in the framework, ground acquired data serves as hard data, soil moisture environmental data serves as soft data, a high-resolution soil moisture digital map is generated, and the estimated result contains the spatial correlation between ground sampling points and gives consideration to the relationship between soil moisture and the priori knowledge. According to the method, great significance is achieved for enriching remote sensing authenticity inspection subject theories and technologies, inversion errors of soil moisture products can be reduced, the practical value in relevant industry fields is improved, and meanwhile reference is provided for authenticity inspection of other low-resolution remote sensing products.
Description
Technical field
The invention belongs to satellite remote sensing date surface parameters inversion field, more particularly to it is directed to heterogeneous body low resolution distant
The validity check of sense product and accuracy improvements.
Background technology
Soil moisture is hydrological model, Climatic Forecast Models, draught monitor model, the Important Parameters of agricultural output assessment model,
It is also Global climate change and the significant data source of Land data assimilation research.Therefore, accurate measurements soil moisture have important
Academic significance and using value.Although traditional monitoring soil moisture method is capable of the soil moisture of accurate measurement single-point,
Large scale, the requirement of dynamic monitoring soil moisture can not be met.With the development and perfection of satellite remote sensing technology, have developed and be based on
Satellite visible-near-infrared and Thermal Infrared Data, active microwave, the monitoring soil moisture method of passive microwave are so that big chi
Degree, dynamic monitoring soil moisture are possibly realized.In view of the strong sensitivity to soil moisture and high time are repeated, passive microwave is distant
Sense data has become the key data source of land table soil moisture Remote Sensing Products, has wide answering in global soil moisture monitoring
Use prospect.Both at home and abroad each well-known research institution all issue the global soil moisture Remote Sensing Products of oneself, including U.S. AMSR-E,
AMSR-2, SMAP, and European Space Agency SMOS MIRAS, domestic No. FY-3 global soil moisture products all can be provided.
However, research shows that the Soil Moisture Inversion effect of microwave radiance transfer remote sensing is not reaching to expected precision（0.07
Cm3/cm3 or 0.04 cm3/cm3）, the functional need lance of the problem of inconsistency and " user " between the low precision of product and product
Shield projects, and strongly limit the practical value of Remote Sensing Products.This is because existing soil moisture business inversion algorithm is all
Grow up for average earth's surface, the Soil Moisture Inversion effect in heterogeneous earth's surface does not obtain sufficient authenticity inspection
Test.However, being limited by remote sensing device and image-forming principle, passive microwave satellite spatial resolution ratio is about tens kilometers（As AMSR-
2 data are 25Km, and SMOS MIRAS data is about 40Km, and FY-3 data is 25Km）, low spatial resolution characteristic determines microwave
Pixel is internal heterogeneous, there are multiple values of multiple atural objects or certain parameter in single microwave pixel, heterogeneous in pixel
Property brings difficulty to the validity check of soil moisture product.Therefore, how to obtain and can represent moonscope yardstick " very
Value " and can characterize special heterogeneity soil moisture numerical map become passive microwave soil moisture product authenticity inspection
Key issue.
Remote Sensing Products validity check is always hot issue and the challenge of remote sensing, and soil moisture remote sensing is produced
The validity check of product generally includes four kinds of methods：Actual measurement sample data inspection, image data inspection, land surface emissivity simplation examination
With associated arguments inspection.
However, the actual measurement sample data method of inspection is premised on the spatial representative of sampling point, but its method still suffers from itself
Some shortcomings, such as lack the effectively utilizes to priori, lead to check true value precision to reduce, and the ground monitoring requiring
Network data set is difficult satisfaction etc..The theoretical foundation of land-surface processes model inspection and contact associated arguments inspection is all soil moisture
With the relation of priori, but have ignored the importance of sampling point.So, there is complementarity in three kinds of methods of inspection, also occur in that comprehensive
The method closing thinking, such as Cokriging, Regression-kriging, layering Krieger, geographical weight recurrence etc., all can obtain satellite and see
, with reference to figure, when priori and soil moisture correlation are stronger, the testing accuracy of these methods is equal for the soil moisture surveying yardstick
Higher than ordinary Kriging.But these methods still show not in the priori message context of comprehensive utilization multi-source data type
Foot：As Cokriging can only take into account the envirment factor of single number type, layering Krieger simply using environmental information as one
Individual layering or classification foundation etc..Therefore, how preferably ground survey and two kinds of thoughts of priori to be merged, take into account sample
Space of points correlation and the relation of many Source Types priori, improve soil moisture product authenticity testing accuracy, are ten score values
Must study.Bayes's maximum entropy（Hereinafter referred to as BME）Method is to solve this problem to provide a thinking.
Christakos proposes BME method in nineteen ninety, and presenter is referred to as modern Geostatistical, to show and classical ground
The difference of statistics Kriging method.The method carries out the heterogeneous Journal of Sex Research of large-scale dimension and can merge many-sided different accuracy and quality
Data, and these data are divided into two aspects：（1）Exclusive data（KS）：According to data whether accurate be divided into hard data and
Soft data two class, the content of the studied attribute of the equal quantificational expression of two class data, difference is that hard data is deterministic value, and soft
The value of data has fuzzy quality, and form is codomain interval or probability distribution, such as the Field observation approximate data to certain point position,
Soil texture distribution obtaining from soil cartography etc..For hard data, soft data has ambiguity, obtains easily,
The features such as low cost；（2）Universal knowledege data（KG）：It is used for describing the data of global feature or the knowledge of space random field, such as
General nature rule, Heuristics and the statistic moments based on any rank of hard data（As mathematic expectaion, covariance, variance etc.）.
Based on these two aspects data, BME method is divided into two steps：（1）Using KG, based on principle of maximum entropy, calculate in survey region
The priori probability density function of non-measuring point variable distribution（Hereinafter referred to as pdf）, and when only considering hard data statistical error, institute
Obtain result as sample data assay on the spot；（2）Using KS, based on Bayes's conditional probability, update previous step and obtain
Priori pdf, obtain the posteriority pdf of non-measuring point in survey region.According to the posteriority pdf finally giving, can easily make
The soil moisture digital reference map of moonscope yardstick, and then remote sensing soil moisture product is tested.
Application study based on BME method has successfully applied to multiple fields.Gao etc. utilizes BME method to carry out
The space estimation research of the exposed soil moisture of Field Scale；History soil types distribution map is soft data by Brus, to adopt
The analysis result integrating sampling point, as hard data, has made 1:50000 Dutch soil type maps, result improves 15% than artwork precision；
Lee uses BME technique study urban heat land effect, and the city moon lowest temperature time-space distribution graph drawing obtains than with conventional method
Result precision improve 35.28%；Christakos has made the inhalable particles of North Carolina using BME method
Thing distribution map；Domestic Yang Yong etc. has also carried out the space estimation research of the soil organism using BME method.In addition, BME method is also
It is employed for epidemic disease space-time modeling, ecological study with resource investigation, meteorology and climate etc. is related to natural resources and phenomenal space divides
The multiple fields of cloth.
In a word, abroad research and application to BME method have been achieved for abundant achievement, but to passive microwave remote sensing soil
The ground check application aspect of earth moisture products is also rare.For this reason, soil moisture and priori are emphasized in this research expectation
Comprehensive utilization, takes into account the data analysis of sample prescription measurement, carries out the soil moisture digital reference map that can represent moonscope yardstick
Research, special heterogeneity in meticulous depiction pixel, this is the difference of this research and conventional BME method application example, also may be used
To regard the contribution that this research is studied to low resolution Remote Sensing Products validity check as.
In sum, Sample method stresses the spatial representative of sample prescription sampling point, land surface emissivity simplation examination side on the spot
Relation between method and contact associated arguments method of inspection concern soil moisture and priori, and the advantage of BME method is
Provide flexible data separate mode, make multiple sources, polytype data set have an opportunity to be simultaneously used to moonscope
The spatial analysis of yardstick, generates high-resolution soil moisture digital reference map, completes the validity check of soil moisture product,
It is intended to improve inversion algorithm, improve Product Precision.
Therefore, this research considers BME as method frame, merges ground survey and priori in this framework
Get up, using ground acquisition data or base station sight data as hard data, using priori environmental data as soft data, make to estimate
Calculate the spatial coherence that result had both contained between the sampling point of ground, take into account the relation of soil moisture and priori again.Permissible
It is envisioned that this method can improve the validity check precision of soil moisture product to a certain extent, testing accuracy will carry significantly
High.This achievement in research has great importance to abundant remote sensing validity check subjects theory and technology, and can reduce soil
The inversion error of earth moisture products, improves the practical value in relevant industries field, is also that other low resolution remote sensing are produced simultaneously
The validity check of product is offered reference.
Content of the invention
Acquisition can represent moonscope yardstick " true value " and can characterize the soil moisture numerical map of special heterogeneity,
Become the key issue of passive microwave remote sensing soil moisture product authenticity inspection.This research introduces BME method frame, integration
Using sampled data and priori, estimation result is made to embody melting of the spatial coherence of sampling point and soil moisture and priori
Close, generate high-resolution soil moisture numerical map, to improve the validity check precision of low resolution soil moisture product.Should
Method specifically includes following steps：
Step 1）According to soil moisture correlation and can acquiring principle, the priori data in collection research region, synchronous download
Collect passive microwave soil moisture product.
Step 2）Carry out screening and the conversion of auxiliary variable.Arrange and screen priori auxiliary variable, and analyze auxiliary
Variable and the correlation of soil water content, determine the crucial auxiliary variable participating in subsequent calculations and analysis.
Step 3）According to the thought of soil environment correlation method, soil water content may produce under the influence of varying environment
The different spatial distribution characteristic of life.Research and propose soil moisture and crucial auxiliary variable discretization, using the probability of discrete type
Distribution approaches soil moisture probability and is truly distributed, thus reaching structure forecast model.
Step 4）According to soil environment correlation method forecast model, carry out the soft data analysis and research in BME application.This research
Carry out the varying number Exploring Analysis that modeling point is with soft data point, select optimum modeling point and soft data point number combinations side
Formula.
Step 5）According to the soft data result of calculation under various combination, carry out covariance function matching and soil moisture space
Forecast analysis, and do spatial prediction evaluation.
Further, described passive microwave soil moisture product can be domestic FY-3 soil moisture product, external AMSR-2 soil
Earth moisture products or SMOS soil moisture product or SMAP soil moisture product etc..Described priori includes digital elevation, soil
Ground utilization, meteorologic parameter, vegetation index, surface temperature, surface albedo etc..
Brief description
Fig. 1 is BME method and step.
Fig. 2 is the covariance model of various combination mode：（a）Combination 1（b）Combination 2（c）Combination 3（d）Combination 4.
Fig. 3 is the soil moisture spatial prediction result under various combination mode.
Fig. 4 is the soil moisture error analysis under various combination mode.
Fig. 5 is the 1Km soil moisture NO emissions reduction method analogue value.
Specific embodiment
Pre- with the high precision soil moisture space of priori based on Bayes's maximum entropy to the present invention below in conjunction with the accompanying drawings
Survey research method to be described further.
This research has laid monitoring net in Hebei Shenzhou City trial zone, has laid 69 sampling points in the range of 25KM*25KM.By
Poor to the sensitiveness of microwave signal in icing soil moisture, the therefore FY-3 passive microwave soil moisture product analysis period is 4
On the moon 1 to October 31, the microwave soil moisture product in winter is not involved in researching and analysing.
Soil moisture variable and the correlation analysis of auxiliary variable
The influence factor of soil moisture includes the soil texture, landform, vegetation, meteorological element etc., dem data, present status of land utilization
Figure, satellite remote sensing exponent data all can affect the spatial distribution of soil moisture.This Preliminary Study chooses vegetation index, surface temperature
With surface albedo as auxiliary variable, collaborative Bayes's maximum entropy theory carries out the research of soil moisture spatial prediction.
Vegetation index, surface temperature and surface albedo auxiliary variable, available intermediate-resolution remote sensing MODIS product replaces
In generation, including LST product, synthesis NDVI products on the 16th and synthesis Albedo product on the 8th.Research was with regard to MODIS product and soil in the past
The analysis of earth moisture is all to have chosen many days generated datas, and it is limited in that time scale is longer and inconsistent, for further
Improve precision, this research carries out comprehensive analysis with the odd-numbered day for time scale, i.e. odd-numbered day SMC, odd-numbered day NDVI and odd-numbered day LST.First
With HANTS time series reconstructing method, data reconstruction is carried out to research area synthesis NDVI product on the 16th, interpolation obtains day by day
NDVI product line.
Multivariate statistical model ripe both at home and abroad is continued to use in this research, analyzes different bearing stage NDVI, LST, Albedo
Relation with soil moisture.Fitting formula form is that wherein SWC is soil moisture, and A variable is NDVI, and V variable is LST, and T becomes
Amount is Albedo.Through fitting formula optimized parameter as shown in table 1 it is seen then that soil moisture and NDVI, LST, Albedo have significantly
Correlation, this 3 variables carry out subsequent calculations and analysis as auxiliary variable.
Table 1 correlation analysis table
| Coefficient R | P-value(Significance F) | Standard error | a1 | a2 | a3 | a4 |
| 0.5384 | 0.0000** | 0.02 | 0.0468 | 0.0742 | 0.00025 | 0.0706 |
2. the soft data based on BME soil environment discrete correlation method generates
This partly mainly carries out modeling point and the exploratory analysis of soft data point under varying number combination, studies optimum modeling
Point and soft data point quantity combination.Premise is to ensure that data point sum is modeling 1 times of a number of point for 550 and soft data point
More than.4 kinds of various combination modes are as follows：
Combination 1：Modeling point 50, soft data point 500；
Combination 2：Modeling point 100, soft data point 450；
Combination 3：Modeling point 150, soft data point 400；
Combination 4：Modeling point 200, soft data point 350.
Basic framework
The all information that can collect during solve problem, data etc. are referred to as knowledge collection or knowledge base by BME（K）.According to
Its property is different, K can be divided into two big class, i.e. so-called generalized knowledge（G）And specific knowledge（S）.Generalized knowledge G includes often
Knowledge, physical laws, scientific theory etc., specific knowledge S then mainly includes hard data and soft data.
BME is a whole set of thinking framework and computational methods simple absolutely not, using process, BME is which consideration uses
Knowledge（G and S）And the reasoning from logic process of solve problem how is gone using knowledge, mainly include 3 stages：The priori stage,
Interstage and posteriority stage.As shown in figure 1, in the priori stage, using the principle of maximum entropy, finding and comprise the maximum amount of G's
Information, at utmost press close to the priori probability density function G of truthf；In the interstage, by specific knowledge S collected
Expressed with rational form；And in posteriority stage then G to be combinedfAnd S, under the framework of generalized Bayes condition formula
Update Gf, thus obtaining the posterior probability density function K based on K of estimationf.
When complete posteriority pdf has been obtained by BME method, generally require according to KfBe met application predicted value and
This stage is referred to as the stage of charting by uncertainty estimation herein.Therefore complete BME analysis process includes 4 stages, that is,
Priori stage, interstage, posteriority stage and drawing stage.
Soil environment discrete correlation method soft data calculates
Choose DOY=178, concrete calculating process taking combine 4 is as a example discussed in detail.
（1）Modeling point determines
Combination 4 is to choose 200 soil moisture data as modeling point data, soil moisture SMC of each sampling point extraction simultaneously,
Face temperature LST and vegetation index NDVI.Need before analysis data is normalized.
（2）Priori pdf calculates
Respectively soil moisture SMC, surface temperature LST and vegetation index NDVI are uniformly divided into 10 groups, then according to flow chart enters
Row calculates, and obtains the soil moisture probability distribution based on environmental data.
The soil moisture SM probability distribution based on surface temperature LST different grouping for the table 2
The soil moisture SM probability distribution based on NDVI different grouping for the table 3
（3）Soft data is chosen and is analyzed
Combination 4 uniformly chooses 350 soft data points around hard data, then generates the soil water under LST and NDVI respectively
Divide SM probability distribution.
（4）Soft data documenting
If multiple environment assistance datas, then soft data is multiplied by weighted value for probability, can contain various envirment factors and soil
The coefficient correlation of the water yield wants normalized as weight, weight.In combination 4, LST and NDVI weight factor is set to 0.7 and 0.3.
3. covariance function Fitting Analysis
It is built upon according to the upper soil water content space of modeling point certainly based on the spatial prediction of soil environment discrete correlation method
On the covariance model basis that correlation is drawn.The covariance model of four various combinations is as shown in Figure 2.From figure
Go out, when the soft data set up by the relation of soil moisture and auxiliary variable, wherein modeling point reaches some, its result energy
Preferably explain large-scale dimension internal relation, be that preferable condition is created in covariance simulation matching.Through analysis, this research 100
Modeling point can substantially meet covariance function fitting precision requirement.
Soil moisture spatial prediction is analyzed
Fig. 3 is the soil moisture spatial prediction result under four various combinations.The spatial framework substantially one obtaining under various combination
Cause, the spatial variations of especially combination 2,3,4 are closely similar.
For verifying various combination soil moisture spatial prediction result further, itself and Land Surface Temperatures are carried out correlation and divides
Analysis.As shown in figure 4, combination 3 predicted values are up to 0.85 with measured value coefficient correlation, combine 1 coefficient correlation by contrast relatively low
（0.73）.
This research is further by BME predicted value and 1km soil moisture NO emissions reduction result（Fig. 5）Contrasted, found space
Variation tendency is very much like, and the region of high level and low value distribution more meets.But NO emissions reduction method does not account for Land Surface Temperatures,
Only introduce other Remote Sensing Products, soil moisture estimated value is also generally relatively low.On the contrary, BME predicts the outcome can reflect
The Spatial Heterogeneous Environment feature of survey region, also can carry out accuracy correction using measured value to it to a certain extent.
The above is presently preferred embodiments of the present invention, and the present invention should not be limited to this embodiment and accompanying drawing institute is public
The content opened.Every without departing from complete equivalent or modification under spirit disclosed in this invention, both fall within the model of present invention protection
Enclose.
Claims (12)
1. obtain the soil moisture numerical map that can represent moonscope yardstick " true value " and special heterogeneity can be characterized,
Become the key issue of passive microwave remote sensing soil moisture product authenticity inspection.
2. this research introduces Bayes's maximum entropy method framework（BME）, integration use sampled data and priori, make estimation tie
Fruit embodies spatial coherence and soil moisture and the fusion of priori of sampling point, generates high-resolution soil moisture digitally
Figure, to improve the validity check precision of low resolution soil moisture product.
3. the method specifically includes following steps：
Step 1）According to soil moisture correlation and can acquiring principle, the priori data in collection research region, synchronous download
Collect passive microwave soil moisture product.
4. step 2）Carry out screening and the conversion of auxiliary variable.
5. arrange and screen priori auxiliary variable, and analyze the correlation of auxiliary variable and soil water content, determine ginseng
Crucial auxiliary variable with subsequent calculations and analysis.
6. step 3）According to the thought of soil environment correlation method, soil water content may produce under the influence of varying environment
Different spatial distribution characteristics.
7. research and propose soil moisture and crucial auxiliary variable discretization, the probability distribution using discrete type approaches soil moisture
Probability is truly distributed, thus reaching structure forecast model.
8. step 4）According to soil environment correlation method forecast model, carry out the soft data analysis and research in BME application.
9. the exploratory analysis of varying number compositional modeling point and soft data point is carried out in this research, selects optimum modeling point and soft
Number of data points combination.
10. step 5）According to soft data result of calculation, carry out covariance function matching and the analysis of soil moisture spatial prediction, and do
Spatial prediction is evaluated.
11. the method for claim 1 are it is characterised in that described passive microwave soil moisture product can be domestic FY-3
Soil moisture product, external AMSR-2 soil moisture product or SMOS soil moisture product or SMAP soil moisture product etc..
Priori described in 12. includes digital elevation, Land_use change, meteorologic parameter, vegetation index, surface temperature, the earth's surface reflection of light
Rate etc..
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| CN107944387A (en) * | 2017-11-22 | 2018-04-20 | 重庆邮电大学 | A kind of analysis method of the urban heat island special heterogeneity based on semivariation theory |
| CN107944387B (en) * | 2017-11-22 | 2021-12-17 | 重庆邮电大学 | Method for analyzing spatial heterogeneity of urban heat island based on semi-variation theory |
| CN109002604A (en) * | 2018-07-12 | 2018-12-14 | 山东省农业科学院科技信息研究所 | A kind of soil moisture content prediction technique based on Bayes's maximum entropy |
| CN109002604B (en) * | 2018-07-12 | 2023-04-07 | 山东省农业科学院科技信息研究所 | Soil water content prediction method based on Bayes maximum entropy |
| CN109460789B (en) * | 2018-11-07 | 2021-07-27 | 中国农业科学院农田灌溉研究所 | Soil moisture fusion method based on Bayes maximum entropy |
| CN109460789A (en) * | 2018-11-07 | 2019-03-12 | 中国农业科学院农田灌溉研究所 | A kind of soil moisture fusion method based on Bayes's maximum entropy |
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| CN110427995B (en) * | 2019-07-24 | 2023-11-17 | 中国科学院空天信息创新研究院 | Bayesian soil moisture estimation method based on multi-source remote sensing data |
| CN110852474A (en) * | 2019-09-24 | 2020-02-28 | 广州地理研究所 | Land water reserve prediction method, device and equipment based on decision tree algorithm |
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| CN112528555B (en) * | 2020-11-23 | 2023-02-21 | 中国科学院空天信息创新研究院 | Soil moisture map drawing method and device |
| CN112508758A (en) * | 2020-12-02 | 2021-03-16 | 中国科学院东北地理与农业生态研究所 | Ecological system internal type component and attribute component composition structure cooperative description method |
| CN112508758B (en) * | 2020-12-02 | 2022-12-06 | 中国科学院东北地理与农业生态研究所 | Ecological system internal type component and attribute component composition structure cooperative description method |
| CN113408019A (en) * | 2021-06-23 | 2021-09-17 | 河北地质大学 | Water system sediment geochemical anomaly mapping method based on BME-GWR |
| CN113408019B (en) * | 2021-06-23 | 2022-04-22 | 河北地质大学 | Water system sediment geochemical anomaly mapping method based on BME-GWR |
| CN117309506A (en) * | 2023-10-09 | 2023-12-29 | 中国矿业大学(北京) | Device for collecting water vapor of sunk cracks and method for identifying water vapor source |
| CN117309506B (en) * | 2023-10-09 | 2024-05-03 | 中国矿业大学(北京) | Device for collecting water vapor of sunk cracks and method for identifying water vapor source |
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