CN110427995A - A kind of Bayes's soil moisture evaluation method based on multi- source Remote Sensing Data data - Google Patents

A kind of Bayes's soil moisture evaluation method based on multi- source Remote Sensing Data data Download PDF

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CN110427995A
CN110427995A CN201910671852.6A CN201910671852A CN110427995A CN 110427995 A CN110427995 A CN 110427995A CN 201910671852 A CN201910671852 A CN 201910671852A CN 110427995 A CN110427995 A CN 110427995A
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王春梅
顾行发
谢秋霞
韩乐然
余涛
孟庆岩
占玉林
杨健
李娟�
魏香琴
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Aerospace Information Research Institute of CAS
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Abstract

The present invention relates to a kind of Bayes's soil moisture evaluation method based on multi- source Remote Sensing Data data.This method is directed to how to obtain high-precision soil moisture probability-weighted soft data, the calculating that 12 kinds of multi-source datas are weighted probability soft data has been merged for the first time, albedo (A) is obtained including NO emissions reduction FY3-B soil moisture product, using MODIS product, vegetation index NDVI(V) and surface temperature LST(T)；Altitude data is obtained using ASTER product；And the gradient, slope aspect, planar curvature, profile curvature, surface roughness, humidity index and relief intensity data are calculated by the altitude data that ASTER product obtains；And probability-weighted soft data is obtained using two kinds of Weight Determinations of multivariate correlation analysis and principal component analysis.The present invention also analyzes the precision analysis of different soft data quantity, and give one's hand sufficient soft data quantity, and the soil moisture spatial distribution for obtaining higher precision plays a significant role.

Description

A kind of Bayes's soil moisture evaluation method based on multi- source Remote Sensing Data data
Technical field
The present invention relates to a kind of soil moisture evaluation method based on multi-source data, especially fusion NO emissions reduction soil moisture Bayes's soil moisture evaluation method of the multi- source Remote Sensing Data datas such as product data and terrain.
Background technique
Lacking, there are soil moisture (SM) data of high spatial resolution, which to have become, improves Watershed Scale Eco-hydrological Model One of main bottleneck of accuracy.Bayes's maximum entropy (BME) algorithm is calculated for modeling the estimation of extensive special heterogeneity Method, and can integrate a plurality of types of data with different accuracy and quality.It theoretically, will be with SM sky using BME algorithm Between relevant a plurality of types of data integrations SM precision can be improved into SM Spatial outlier.
Soil moisture (SM) is not only hydrological model, Climatic Forecast Models, and mould is estimated in draught monitor model and crop yield An important factor for key parameter and Global climate change and surface data assimilation of type.Traditional SM in situ measurement can be Single-point precise measurement SM is not able to satisfy the requirement of extensive dynamic SM monitoring.With the development of satellite remote sensing technology and perfect, open Many is sent out based on visible/near infrared, the SM monitoring method of thermal infrared and active/passive microwave satellite data, this is but also prison SM is surveyed to be possibly realized.In particular, passive and active microwave satellite data is only influenced minimum by cloudy day and rainy weather, and it is passive Microwave data particularly provides the high-sensitivity method of extensive observation SM.Therefore, it is distant to have become land face SM for remote sensing microwave data The main input for feeling product is with a wide range of applications in global SM monitoring.However, most of quilts in most of regions Dynamic microwave remote sensing product as was expected accurate (RMSE > 0.06cm3 / cm 3).Due to the essence of passive microwave SM product Spend lower, real value is very limited.In addition, most of passive microwave SM searching algorithms are all directed to uniform outer surface progress Optimization.Due to the limitation of sensoring and image-forming principle, the spatial resolution of passive microwave satellite is very low, at tens kilometers In range.This causes the inside of microwave pixel heterogeneous, this complicates the verifying of SM product.Therefore, there is an urgent need to develop tools There is the SM product of high spatial resolution.
Summary of the invention
BME algorithm considers the uncertainty of data, and can effectively be merged according to the principle of maximum informational entropy various Data.The data definition accurately measured is hard data (HD) by BME, and will have probabilistic data definition is soft data (SD).SD is converted to by probability SD by using probability-distribution function (such as histogram and gauss of distribution function), and is used HD calibrates the probability SD of SM to obtain distribution function after probability.Finally, estimating SM by using distribution function after probability.This Invention particular content is as follows:
A kind of Bayes's soil moisture evaluation method based on multi- source Remote Sensing Data data, comprising the following steps:
Step 1 obtains soil moisture soft data and carries out data processing
Specified region, the soil moisture data of scheduled date are obtained using FY3-B soil Reel moisture monitoring product；And to FY3-B soil Earth moisture data carries out NO emissions reduction processing, is divided 25km resolution ratio FY3-B soil moisture data space by using regression model Resolution rises to 1km resolution ratio soil moisture data；
(1)
WhereinA 25For 25 km average values of albedo (A),V 25For the 25 km average values of vegetation index NDVI(V),T 25For earth's surface Temperature LST(T) 25 km average values,mWithnIt is in FY3-B pixel respectivelyiRow and thejThe quantity of 1km pixel in column；A ijIt is In FY3-BiRow and thejThe albedo value of 1Km pixel in column,V ijIt is in FY3-BiRow and thej1Km pixel in column NDVI value,T ijIt is in FY3-B iRow and thejThe surface temperature LST value of 1Km pixel in column；
Linear regression model (LRM) are as follows: SM=a1+a2 A+a3 V+a4 T+a5 AV+a6 AT+a7 VT (2)
Wherein SM is soil moisture；a1、a2、a3、a4、a5、a6And a7It is regression coefficient；A is albedo, V is vegetation index, T is Surface temperature；
Firstly, the A that formula (1) is calculated25、V25And T25Formula (2) are brought into, according to corresponding grid cell size 25km resolution ratio FY3-B soil moisture data SM25, pass through the regression coefficient in regression analysis computation model；
Secondly, by the albedo (A) of MODIS product 1km resolution ratio, vegetation index NDVI(V) and surface temperature LST(T) bring into The Soil moisture SM of 1km resolution ratio has accordingly been calculated in model formation (2)1；
Step 2 obtains environmental factor soft data
Albedo (A) is obtained using MODIS product, vegetation index NDVI(V) and surface temperature LST(T)；Use ASTER product Obtain altitude data；And the gradient, slope aspect, planar curvature, section song are calculated by the altitude data that ASTER product obtains Rate, surface roughness, humidity index and relief intensity data；
Step 3 obtains NO emissions reduction FY3-B soil moisture data SM using step 11Environmental factor soft data is obtained with step 2, is adopted With histogram form of probability, probability soft data is obtained；
Step 4 determines environmental factor soft data in step 2 using multivariate correlation analysis method and principal component analytical method Weighted value, and obtain probability-weighted soft data；
Firstly, calculating thejA environmental factor and soil moisture data between quantitative probabilities, be denoted as PSD(EF j ):
(3)
Wherein Ef represents the environmental factor data obtained in step 2；nRepresent SM'snA section；kIndicate the of SMkIt is a Section,kValue from 1 ton；mRepresent Ef'smA section；It is the of SM in first of Ef intervalkIn a interval Sampling number；It is the of SMkTotal number of sample points in a section；EfmIt ismThe environmental factor in a section；Each In section, probability distribution data are constant values, and the size in all sections is identical；
Secondly, calculating wherein thejThe weighted value w of a environmental factor data j Are as follows:
(4)
Wherein,iFor the number of environmental factor in step 2,iFor value from 1 to 11, Rj isjA environmental factor and the FY3B soil water Related coefficient between divided data； EfjIt isjA environmental factor；NCC(Efj) it isjThe normalization phase relation of a environmental factor Number, the sum of NCC of all environmental factors are equal to 1, wherein NCC(Efj) value more than or equal to 0.18 environmental factor be leading environment The factor retains its environmental factor respective weights value, NCC(Efj) environmental factor weighted value of the value less than 0.18 be 0；
Again, the probability-weighted for calculating SM, is denoted as WPSD,
(5)
Step 5, actual measurement obtain soil moisture hard data
In step 1 the same area, phase same date, the pedotheque of 0-5cm depth is collected, and measures soil using aluminium case baking method Earth moisture content；All parametric cubics are measured, and average value is used for subsequent analysis, are denoted as；
Step 6, using Bayes's maximum entropy method to the soil moisture probability-weighted soft data in step 4And in step 5 Measured dataIt is merged as hard data, estimates soil moisture data, be as a result denoted as；
Step 7 assesses Prediction of Soil Water Content using root-mean-square error (RMSE) and related coefficient (R)As a result standard Exactness, wherein
(6)
(7)
Wherein,nIt is the total degree that soil moisture is surveyed in step 5,It is in step 5iSecondary actual measurement Soil moisture,It is in step 5nThe average value of secondary actual measurement soil moisture,It is in step 6iSecondary Bayes's maximum entropy Soil moisture,In step 6nThe average value of secondary Bayes's maximum entropy soil moisture.
Further, probability-weighted soft data number is set as 4 classes, respectively 500,450,400 and 350, obtains Soil moisture estimation result under four type different weights probability soft data numbers.
This method estimates soil moisture using hard data and soil moisture probability-weighted soft data.This method will be real Soil moisture data are surveyed as hard data, soft data then has chosen the data such as terrain, it is contemplated that terrain is to the soil water The influence divided.Higher resolution and higher is estimated the advantage of the invention is that multi- source Remote Sensing Data data is integrated into BME algorithm The SM of precision.
Detailed description of the invention
Bayes soil moisture evaluation method schematic diagram of the Fig. 1 based on multi- source Remote Sensing Data data；
Fig. 2 survey region is summarized；
The spatial prediction of the SM content in three corn growth stages of the Fig. 3 based on MCA and PCA method；
Fig. 4 is using the estimation SM of BME algorithm and NO emissions reduction FY3-B SM product compared with in-site measurement SM.
Specific embodiment
Object of this investigation is to estimate higher resolution and higher by the way that multi- source Remote Sensing Data data to be integrated into BME algorithm The SM of precision, as shown in Fig. 1, specific embodiment is as follows for process approach signal:
One, hard data prepares
Survey region (Fig. 2) is located at Hebei province's Hengshui City, is located at northwest China (38 ° 3'00 " N, 115 ° of 27'54 " E).This is One typical farming region, only a few land cover pattern (exposed soil, corn, orchard and meadow), the soil texture is uniform.The ground Area belongs to typical continental monsoon climate, and summer temperature is high, abundant rainfall, and winter cold is dry.Therefore, SM content is raw in agricultural It plays an important role in production, especially in summer.In order to measure SM content, field trial has been carried out in summer.Sample point such as Fig. 2 It is shown.Corn, cotton and peanut are the chief crops in these places.In The Soils are loams, including about 44.6% silt Mud, 17.5% clay and 37.9% sand.
In summer, corn accounts for nearly the 50% of the research gross area.In our current research, corn is divided into spring maize and summer corn.Spring Corn seeding is in late April or the first tenday period of a month in May, and last 10 days in August harvest.Summer corn is usually at the beginning of late May or 6 months Sowing, October harvest.Therefore, experiment is carried out June to September.This period includes the sowing of corn growth, heading stage, flower Phase and maturity period.
It tests in June 27 (sowing stage, i.e., the 178th day), August 14th (heading stage of corn, i.e., the 226th day) and 9 7 (stage at florescence, i.e., the 249th day) of the moon carries out for 2014.We have selected about 25 kilometers of area × 25 kilometer as key Sample areas.Sample areas includes SM at, the parameter of each website by 23 samples, soil dielectric constant, and vegetation is aqueous Amount and leaf area index (LAI).Pedotheque is collected in the depth of 0-5cm to use aluminium case to measure SM content.Measure all ginsengs Average value three times, and is used for subsequent analysis by number.During this investigation it turned out, actual measurement SM data in situ are divided into two parts.One The HD for dividing actual measurement SM data in situ to be used as BME analysis.Another is for verifying the estimation SM from BME algorithm.It uses E5071C Vector Network Analyzer(Keysight Technologies Inc., Santa Rosa, CA, USA) Soil dielectric constant is measured in the lab.Using comprising 101 needles and total length is more than that the needle plate of 1m measures thick equal Fang Gaodu With the correlation length of roughness parameter.Use sampling and drying means measurement vegetation water content (VWC).It is planted using LAI-2200C Object Canopy Analyzer (LI-COR Biosciences, Lincoln, NE, USA) measures LAI five times.Ground data, including SM contain Amount, soil dielectric constant and vegetation water content, as shown in table 1.)
1. ground data of table abstract
DO N SMC (g/cm3) VWC (kg/m 2 ) H (cm) LAI
178th day Exposed soil/meadow/orchard 64 5.62–48.1 0 2.3–20.1 1.578 0.4–3.4 /
226th day Maize Region/orchard 60 9.1–40.9 0.48–5.13 3.7–25.0 1.33 0.2–3.0 2.89
249th day Maize Region/orchard 60 8.6–39.3 1.53–3.90 3.4–17.8 1.33 0.4–2.2 3.35
(N: sampling number;R: soil dielectric constant;: the average soil weight;VWC: vegetation water content;H: average corn is high Degree;DO: main object)
Two, multi-source remote sensing soft data obtains
There are many factor for influencing SM retrieval, such as surface temperature, Vegetation canopy water content and roughness.It is traditional to utilize 2-3 kind mostly Auxiliary data estimates SM, or does not account for terrain influence, or do not account for the soil moisture after NO emissions reduction.Therefore, only It is difficult using a kind of data to assess SM.Therefore, we are by the FY3-B SM, NDVI-LST-Albedo of 25km resolution ratio NDVI, Albedo and the LST product of MODIS(MODIS) and ASTER DEM Satellite Product data combine estimation SM space Distribution.During this investigation it turned out, we used three kinds of MODIS products, i.e. NDVI, Albedo and LST product, resolution ratio 1 km.Advanced spaceborne heat radiation and reflected radiation meter global digital elevation model (ASTER DEM), spatial resolution is up to 30 m.The FY3-B(third wind and cloud B satellite of 25km resolution ratio) SM product, these auxiliary datas are for using BME algorithm development more High-resolution SM data.
FY3-B SM product is free (download link http://satellite.nsmc.org.cn/PortalSite/ Default.aspx).The product is on the Coriolis satellite of the horizontal and vertical polarization data based on 10.65 GHz data AMSR-E(Advanced Microwave scanning radiometer-earth observing system) sensor SM searching algorithm exploitation.It is calculated in AMSR-E In method, Q/H model selected as roughness model estimate the influence of surface roughness.
These three MODIS products are MOD11A1(surface temperature product, LST), MCD43B3(Albedo product) and MOD13A2(vegetation index product: normalized differential vegetation index product and enhancing vegetation index, NDVI and EVI), they all have sky Between resolution ratio 1km.MOD11A1 LST product is using the Split-window algorithm of MODIS the 31st and the 32nd wave band with the resolution ratio of 1km It retrieves (Yang et al.1199).MCD43B3 Albedo product was produced by 16 days anisotropic models, represented MCD43A The average value of the basic 500m value of Albedo product.MOD13A2 NDVI product by using MODIS specific synthetic method, Based on production assurance index, from daily, retrieved in the Bidirectional surface reflection of atmospheric correction, removal low quality pixel is simultaneously kept The NDVI value of high quality.ASTER DEM is based on the detailed observation to NASA a new generation earth observation satellite TERRA.This is current One of most complete global digital elevation data (DEM), with 30 meters of resolution ratio.During this investigation it turned out, 30 meters of ASTER DEM Data are re-sampled to 1 kilometer of resolution ratio.
Using ASTER dem data, it is extracted the gradient, slope aspect, planar curvature, profile curvature, surface roughness, humidity and refers to 7 kinds of data (table 2) such as several and relief intensity.This research is generated by LST, Albedo, NDVI, ASTER DEM and by ASTEM DEM 7 class data as environmental factor data, directly affect SM estimation.Utilize the NDVI-LST-Albedo/ in nearly 3 periods MODIS, FY3-B SM and ASTER dem data: on June 27th, 2014,2014 on August 14, and on September 7th, 2014, i.e., it is beautiful Three breeding times (the 178th day, the 226th day and the 249th day) of rice.
Seven kinds of terrain datas (gradient, slope aspect, planar curvature, profile curvature, surfaces that 2 ASTER dem data of table generates Roughness, humidity index and relief intensity)
Title Algorithm description
Slope aspect From each pixel to its maximum direction of fall of adjacent pixel change rate.
The gradient The gradient is the second dervative of digital elevation model variation
Planar curvature It is perpendicular to the second dervative on the surface in greatest gradient direction.
Profile curvature It is second dervative of the curved surface along greatest gradient direction.
Surface roughness (sr) ,It is slope, unit: radian.SR is the long-pending ratio with its projected area of earth surface in given area.
Humidity index (WI) , As indicates to flow through on surface the catchment area for the per unit contour length that certain is put.It is the gradient.
Relief intensity ,(DEMmax)nIt isn×nMaximum value in region；(DEMmin)nIt isn×nMinimum value in region.In our current research,nSetting It is 3.
Three, the estimation of Bayes's soil moisture and verifying
Some researchers show to depend on SM, NDVI and LST using the development of Microwave Optics/infrared product Synergistic method Between schematic relationships.Many researchs use MODIS satellite image data with finer than FY3-B original resolution (25 km) Resolution ratio (1 km) obtain SM information.
In our current research, MODIS albedo (A), NDVI(V) and LST(T) with 25 km FY3-B SM spatial resolution phases Matching.SM is obtained with the spatial resolution of 1km using linear regression model (LRM).FY3-B SM, MODIS albedo, NDVI and LST For linear regression analysis, microwave and optics/IR parameter intensity are connected to improve the FY3-B of coarse resolution sky Between resolution ratio.The resolution ratio of MODIS albedo, NDVI and LST are that 25 km calculating is as follows:
(1)
Wherein A25, V25And T25It is albedo respectively, the 25 km average values of NDVI and LST,mWithnIt is in FY3-B pixel respectivelyiRow and thejThe quantity of 1 km pixel in column.
Linear regression model (LRM) uses as follows:
(2)
Wherein SM is the SM of 25km;A1, a2, a3, a4, a5, a6 and a7 are regression coefficients；Use the time series in April to October Clear sky data carry out the regression equation in Estimation Study region.Then the regression model of application estimation, including regression coefficient, estimate 25km The 1 km SM of FY3-B, 1km albedo, NDVI and LST.Although each regression coefficient (i.e. a1, a2, a3, a4, a5, a6 and a7) Each FY3-B pixel is remained unchanged to fixing the date, but each value changes between not same date, because all variables are at any time Between change and regression relation change based on these variables.But this method has ignored the importance of SM ground data. Therefore, it is necessary to merge ground data to obtain the numerical map of the higher precision of SM.
In order to carry out probability statistics to each environmental factor data, using identical discretization method, by each environment because The range of subdata is also divided intomA section.In conjunction with discrete SM and environmental factor data, by calculate each Interval Discrete environment because Quantitative probabilities between son and SM data, derive the probability SD of SM.Between obtained SM and environmental factorm×nIt is quantitative Probabilistic relation is probability SD (i.e. PSD(Ef))。
(3)
Wherein Ef represents one of environmental factor, i.e. NDVI in this research, LST, Albedo, DEM, the gradient, slope aspect, Plane Curved Rate, profile curvature, surface roughness, humidity index and relief intensity.nRepresent SM'snA section；kIndicate the of SMkIt is a Section, from 1 ton；mRepresent Ef'smA section；L represents first of section of SM, from 1 tom；It is between first of Ef Every the of middle SMkSampling number in a interval.It is the of SMkTotal number of sample points in a section；EfmIt ismIt is a The environmental factor in section.In each section, probability distribution data are constant values, and the size in all sections is identical.This is general Rate distribution form is known as histogram form of probability.
Since different environmental factors has different influences to SM content, the weighting of the varying environment factor should be specified Value.Weighted value is normalized using equation 10.Assuming that the quantity of environmental factor is i, then adding for SM is obtained using lower equation Weigh probability SD(, that is, WPSD).
(5)
Wherein W1 is the weighted value of the first environment factor, and Wj isjThe weighted value of a environmental factor.In this study, using two Kind of method MCA(multivariate correlation analysis) and PCA(principal component analysis) the method calculating varying environment factor weighted value.Different rings The normalizated correlation coefficient of the border factor is considered as weighted value.
MCA method is a kind of multiple linear regression model:
The related coefficient normalizing equation of the border factor (R):
(4)
SM is SM content, it represents NO emissions reduction FY3-B SM in this research；R1 is the related coefficient of first environmental factor； Ef1 is first environmental factor；NCC(Efj) it isjThe normalizated correlation coefficient of a environmental factor.In our current research, environment because Subdata is auxiliary data, i.e. NDVI-LST-Albedo/MODIS product data, ASTER dem data, the gradient, slope aspect, plane Curvature, profile curvature, surface roughness, humidity index and relief intensity, totally 11 kinds of auxiliary datas (i.e. i=11).All environment The sum of NCC of the factor is equal to 1.NCC value is considered the weighted value of environmental factor, to generate probability-weighted SD.
Reduce the number of environmental factor using principal component analysis (PCA) method.In this approach, multivariable factor quilt Multiple principal components are reduced to, to simplify modeling, and have obtained more steady result.Firstly, being handled by PCA influences soil 11 environmental factor data of moisture recycling, obtain 11 main components. EfiData be not standardized environment in Eq.11 because Subdata, but the main component after PCA.The NCC value of 11 main components calculates.Then, selection and SM are highly relevant Dominant factor of several principal components as the different corn growth stages.It is used to obtain finally, the NCC value of these dominant factors is considered as Obtain the weighted value of probability-weighted SD.
According to the condition probability formula of Bayes's maximum entropy, the prior probability of HD modification SM content can be used.Parameter z quilt The posterior probability being defined as in the position of prediction x0.z0Posterior probability be expressed as
Wherein h is HD points；M-h is SD points；SM estimation is using special-temporal slices knowledge integration graphic user interface (SEKS- GUI) software development.This is the software package provided free, for establishing space-time model, and by combining scientific software Library, Bayes's maximum entropy library and correlation GUI file are predicted and mapping parameters.In conjunction with FY3-BSM retrieval products, MODIS and The environmental factor data of ASTERDEM product predict SM content using Bayes's maximum entropy method (MEM).Probability SD is using straight Square figure form of probability, by SM content and environmental factor discretization.The rear PDF of SM is developed in SEKS-GUI software, i.e., Utilize the probability-weighted SD of HD data (i.e. scene SM measurement) calibration SM.
In this study, the accuracy of SM prediction result: RMSE(root-mean-square error is assessed using two statistical indicators), That is formula 6 and R(related coefficient), i.e. formula 7.
(6)
(7)
Wherein,It is actual measurement SM in situ；It is the SM predicted using BME algorithm；nIt is practical survey in situ Measure the sum of SM.
In the present embodiment, with the related coefficient for calculating FY3-B SM NO emissions reduction and 11 kinds of auxiliary datas, including NDVI- LST-Albedo/MODIS, ASTER dem data generate the gradient, slope aspect, planar curvature, profile curvature, surface roughness, Humidity index and relief intensity product.(table 2).
Related coefficient and NCC(normalizated correlation coefficient are calculated in three growth phases of corn), as shown in table 3.In addition to Except 226th day (heading stage of corn), the strongest correlation between NDVI and SM content.NCC is in heading stage highest (0.68), minimum (0.427) in sowing time.At the 226th day, the correlation between LST and SM content was most strong (NCC:-0.718). Therefore, leading factor is different according to growth stage.In sowing stage (the 178th day), LST, NDVI, planar curvature and section are bent Rate (absolute value >=0.372 of NCC) is the principal element of SM retrieval.In heading stage (the 226th day), LST, NDVI, albedo and Profile curvature (absolute value >=0.395 of NCC) is leading factor.In florescence (the 249th day), NDVI, profile curvature, surface area It is principal element with humidity index (absolute value >=0.198 of NCC).
Related coefficient between table 3. NO emissions reduction FY3-B SM and Ef
Ef R/178th NCC/178th R/226th NCC/226th R/249th NCC/249th
Albedo / / 0.454 -1.767 0.010 0.010
LST -0.355 2.241 -0.718 2.795 -0.070 -0.073
NDVI 0.427 -2.697 0.680 -2.647 0.580 0.604
DEM -0.003 0.020 0.259 -1.008 -0.180 -0.188
Surface roughness 0.006 -0.035 0.023 -0.090 -0.010 -0.010
Relief intensity -0.116 0.733 0.024 -0.093 -0.050 -0.052
Humidity index 0.092 -0.582 -0.269 1.046 0.190 0.198
Planar curvature -0.383 2.418 0.210 -0.817 0.030 0.031
The gradient -0.019 0.117 0.014 -0.055 0.010 0.010
Slope aspect -0.180 1.134 -0.085 0.332 0.190 0.198
Profile curvature 0.372 -2.349 -0.395 1.536 0.260 0.271
(Ef: environmental factor;R: related coefficient;NCC: normalizated correlation coefficient;178th day: the corn seeding phase;226th day: Corn heading stage;249th day: corn florescence)
Then, dominant factor of the several main components highly relevant from SM as the different corn growth stages is selected.These masters The NCC of inducement is considered as the weighted value of these dominant factors, as shown in table 4.From table 4, on the day of the 178th day, PC2, PC4, PC8 and PC9 are chosen as principal element (NCC >=0.181).The 226th day, PC2, PC6, PC8 and PC9 be chosen as it is leading because Son, NCC >=0.243.At the 249th day, select PC2, PC4, PC8 and PC10 as dominant factor, and NCC >=0.593(table 4).
Table 4. reduced after PCA FY3-B SM andEfBetween related coefficient
Ef R/178th NCC/178th R/226th NCC/226th R/249th NCC249th
PC1 -0.146 0.114 -0.137 0.327 0.019 0.059
PC2 -0.507 0.394 -0.637 1.522 -0.193 -0.593
PC3 -0.003 0.003 -0.044 0.104 0.053 0.162
PC4 -0.257 0.200 0.072 -0.171 0.281 0.862
PC5 -0.076 0.059 -0.034 0.082 -0.044 -0.136
PC6 0.056 -0.044 0.204 -0.488 0.069 0.211
PC7 -0.058 0.045 -0.029 0.070 -0.050 -0.153
PC8 -0.392 0.306 0.102 -0.243 0.199 0.609
PC9 0.233 -0.181 0.139 -0.332 0.156 0.480
PC10 -0.134 0.104 -0.087 0.209 -0.260 -0.797
PC11 / / 0.034 -0.080 0.096 0.294
(Ef: environmental factor;R: related coefficient;NCC: normalizated correlation coefficient;178th day: the corn seeding phase;226th day: Corn heading stage;249th day: corn florescence)
In general, using two kinds of data (HD and SD) in BME analysis.HD refers to thering is small error and high-precision certain numbers According to or true, such as data in situ, there is high-precision historical data.Herein, HD is measured in three field experiments SM content.HD, i.e. original position SM data are used to estimate the posterior probability density in SEKS-GUI software in the rear class of BME algorithm Distribution.From the perspective of the theory of knowledge, we not fully depend on certain data or certain facts to the understanding of certain things, But uncertain information is additionally depended on, such as with the remotely-sensed data of some mistakes, expert opinion and Heuristics.These data It is known as SD in BME analysis.NO emissions reduction FY3-B SM data and auxiliary data with some errors are considered SD.It is auxiliary Helping data includes NDVI-LST-Albedo/MODIS, seven seed types that ASTER dem data and ASTER dem data obtain Data, the i.e. gradient, slope aspect, planar curvature, profile curvature, surface roughness, humidity index and relief intensity.Due to probability SD's Quantity may influence probability SD, therefore different probability SD points (table 5) are arranged at 4 kinds, and case 1 is 500 soft numbers Strong point;Case 2 is 450 soft data points；Case 3 is 400 soft data points；Case 4 is 350 soft data points (table 5).
5 HD of table points and probability SD points
(SD: soft data；HD: hard data；178th day: the corn seeding phase；226th day: corn heading stage；249th day: corn Florescence)
In our current research, MCA and PCA method is respectively used to obtain the weighted value (table 3 and table 4) of the varying environment factor.Therefore, 2 Three growth phases of (two methods calculate weighing value, MCA and PCA) × 3(corn) × 4 cases (table 5), in this study Have estimated totally 24 probability SD data sets.
SEKS-GUI software has user-friendly interface, and is the library BMElib(BME write with Matlab) building 's.In three corn growth stages (sowing stage, i.e., the 178th day, heading-stage, i.e., the 226th day, florescence, i.e., the 249th day) SM content distribution figure is as shown in Figure 3.In Fig. 3, preceding four column represent four cases, 5) case 1 to case 4(is shown in Table.Last column It represents in the 178th day (corn seeding phase), the drop ruler at the 226th day (heading stage of corn) and the 249th day (florescence of corn) Spend FY3-B SM.In each case, it is shown that use MCA and PCA in three growth phases of corn (sowing, heading and florescence) 6 distribution maps of the SM estimation that method generates.
During this investigation it turned out, being based on two methods (MCA and PCA), the spatial distribution of the SM estimated using BME algorithm is 1 The resolution ratio of kilometer, for calculating the weighted value of environmental factor.MCA and PCA method uses IDL(interactive data language) journey Sequence is realized.At each pixel region (1 kilometer × 1 kilometer), it will be assumed that the estimated value of SM is uniform.It can from Fig. 3 Out, the BME algorithm at three, this research area corn growth stage, sowing time, heading stage and florescence has estimated the spatial distribution map of SM.In The bottom of each SM distribution map has some pieces, and for especially case 4. in these blocks, the SM of estimation is not fine.For example, In The heading stage of corn, i.e., the 249th day, the prediction SM using MCA method are the big yellow block of SM distribution map bottom, especially case Example 4. is at MCA the 249th day, from 500 to 350 from case 1 to the quantity of case 4(soft data point), the quantity of soft data point compared with Small, the area of yellow block is larger.On other dates (178 or 226 days), the quantity of soft data is smaller, and the area of block is bigger.Block is raw At the reason of may be the missing values of existing probability soft data or to lack the value of auxiliary data in the block region.When each In phase (each column of Fig. 3), the space structure of the estimation SM of 4 cases is similar, and there is similar SM to change for they Range.At the 178th day, the variation range of the estimation SM value of 4 cases was 0.08 to 0.3 m3/m3, when SM value is less than 0.012 When m3/m3, the negligible amounts of SM pixel.At the 226th day, the variation range of the estimation SM value of case 4 was 0.08 to 0.36 m3 / m3.At the 249th day, it was 0.12 to 0.26 m3/m3 that the variation range of the estimation SM value of 4 cases is very narrow.Obviously, estimate SM value larger space variation occur at the 226th day.The 178th day and the 249th day, SM seemed more evenly.178th day and The reason of 249th day homogeneous SM may be vegetation more evenly.Estimation SM based on MCA method is compared with PCA method, is estimated The space structure of the PCA based on SMA of meter is more consistent in 4 cases.Between estimation SM from MCA and PCA method Spatial distribution characteristic shows preliminary realization.Therefore, the quantitative verification work of estimation SM has been carried out in our current research.
Practical SM measurement in situ is divided into two parts.A part of original position actual measurement SM data (88 points in total) are used as hard number According to.Another (54 points in total) is for verifying the estimation SM from BME algorithm.Three lifes in corn are shown in Fig. 4 Long stage (178,226 and 249) is from the relationship between the actual measurement SM and SM in situ that BME algorithm is estimated.The X-axis of Fig. 4 is former Position.Measurement result, the Y-axis of Fig. 4 are the estimation SM from BME algorithm.Although there are excessively high estimation and that underestimates ask in Fig. 4 Topic, but most of points are distributed near 1:1 line.
In addition, we pass through each case (case 1, case 2, case 3 and the case for calculating every kind of method (MCA and PCA) 4) RMSE and R value and original position actual measurement SM carries out absolutely verifying work.In three periods (the 178th day, the 226th day and 249 days) in, RMSE and R value is calculated separately using 15 points, 16 points and 23 points of actual measurement in situ.RMSE the and R value of calculating is such as Shown in table 6.
As can be seen from Table 6, using PCA method, in case 1, the 178th day RMSE value is minimum, be 0.049 m3/ m3., less than 0.079 m3/m3 RMSE value, this is calculated between NO emissions reduction FY3-B SM and in-site measurement SM for it.It is on record The 178th day of PCA is used in example 1, R value maximum value is 0.639.It is significantly larger than 0.096 R value, this is in NO emissions reduction FY3-B It is calculated between SM and in-site measurement SM.At the 226th day, the minimum RMSE generated in case 2 and case 3 using MCA method Value is 0.062m3/m3, less than the 0.091m3/m3 generated by reducing FY3-B SM.It is given birth under case 1 using MCA method At R value maximum value be 0.53.At the 249th day, the minimum RMSE value that NO emissions reduction FY3-B SM is generated be 0.051 m3/ m3.During this period, the lower RMSE value of the estimation SM from BME algorithm is 0.053 m3/m3, is higher than NO emissions reduction FY3-B SM 0.051 m3/the m3 generated.Generally speaking, we conclude that, using PCA method, the estimation of BME algorithm The precision of SM is better than NO emissions reduction FY3-B SM, and minimum RMSE value and largest r value are respectively 0.049 m3/m3 and 0.639.Compare The precision of estimation SM based on MCA and PCA method, it has been found that the 178th day and the 249th day, the estimation SM based on PCA method RMSE value be less than the RMSE value based on MCA, the R value based on PCA method be higher than be based on MCA method.However, at the 226th day, base It is greater than the MCA method that is based in the RMSE value of the estimation SM of PCA method, the R value based on PCA method is lower than the R based on MCA method Value.In addition, estimating the accuracy of SM significantly lower than the 178th day and the 249th day at the 226th day.Reason may be serious vegetation It influences.Therefore, the 178th day based on PCA method and the 249th day estimation SM are best, other than the 249th day, based on PCA's Estimate that SM is better than the SM based on MCA.
The NO emissions reduction that table .6 measures SM in situ using RMSE the and R value and reduction FY3-B SM of BME algorithm estimation SM
5. conclusion
BME algorithm is used in SEKS-GUI software, SM content is estimated three corn growth stages, including three in this research A program, first program: HD and probability SD preparation；Step 2: SM estimates；Third program: estimate the verifying of SM.First In a program, MCA and PCA are for calculating the weight value of environmental factor.Then, by using NO emissions reduction FY3-B and auxiliary data (i.e. NDVI-LST-Albedo/MODIS, ASTER DEM, slope, aspect ratio, planar curvature, profile curvature, rough surface Degree, histogram form of probability) generating probability SD.In three growth phases of corn, the humidity index of 4 cases and The alleviation amplitude product that ASTER DEM is generated.Finally, obtaining 24 probability SD data sets.During second, BME is used Algorithm estimates SM content according to the HD data and probability SD that generate first during in SEKS-GUI software.The third side The SM of method, estimation is assessed for original position SM measurement (RMSE and R, i.e. equation 13-14).
Based on MCA and PCA method, principal element depends on the growth phase of corn in table 3 and 4.In the corn seeding phase, LST, NDVI, planar curvature and profile curvature are occupied an leading position.In the heading-stage, LST, NDVI, albedo and profile curvature are accounted for Leading position.In florescence, NDVI and profile curvature are dominant.NDVI is one of the principal element of all growth phases, should be by It is considered as the important parameter of SM spatial prediction.Secondly, PCA is a kind of effective space dimension reduction method, data can be subtracted from 11 kinds It is few to arrive only 4 kinds of main components.According to the SM estimated result (Fig. 3 and table 6) for using BME algorithm, the 178th day based on PCA and 249th day estimation SM is optimal.At the 226th day, the estimation SM based on MCA was better than the SM based on PCA method.In addition, I Draw a conclusion, the SM(estimated using BME algorithm for example, the case 1 based on PCA, RMSE=0.049 m3/m3, R= 0.639) consistent with in-site measurement SM, rather than NO emissions reduction FY3-B SM(RMSE=0.079m3/m3, R=0.096).
In our current research, we are estimated and are estimated SM using BME algorithm using three kinds of different programs.It is walked at first In rapid, we have prepared the HD and probability SD that estimate SM using BME algorithm in SEKS-GUI software.Number is measured with SM in situ According to as HD data.Probability SD is respectively by FY3-BSM, NDVI-LST-Albedo/MODIS product of NO emissions reduction, ASTER DEM Product and the gradient based on MCA (multivariate correlation analysis) and PCA (principal component analysis), slope aspect, planar curvature, section are bent Rate, surface roughness, humidity index and relief intensity data develop.Wherein, the gradient, slope aspect, planar curvature, section are bent Rate, surface roughness, humidity index and relief intensity data are from ASTER DEM product.MCA and PCA is for obtaining difference (i.e. NDVI, LST, Albedo, BME, the gradient, slope aspect, planar curvature, profile curvature, surface roughness, humidity refer to environmental factor Several and relief intensity) weighted value, this research is using distribution form of the histogram probability distribution as probabilistic standard difference.This point Cloth form assumes that the probability distribution data in each section are constants, and the size in all sections is all identical.This research uses Two methods (MCA and PCA) test three breeding times of 4 corns, obtain 24 probabilistic standard difference data collection.Second During a, estimation space SM is entered data to using HD and probability SD as SEKS-GUI software.On this basis, it utilizes BME has obtained the mapping graph of 16 SM estimation.During third, the SM estimated using BME algorithm is by using equation 13- 14 live SM measurement is verified.
During this investigation it turned out, since the accuracy for using regression model to reduce FY3-B SM is not high, the drop ruler with error Degree FY3-B SM generates probability SD(i.e. NDVI-LST-Albedo/MODIS with auxiliary data using only SD), ASTER DEM and the gradient, aspect ratio, planar curvature, profile curvature, surface roughness and are generated humidity index by ASTER DEM Relief intensity product.Histogram form of probability is considered as the form of probability SD distribution, wherein assuming probability distribution data It is steady state value in each section, and the size in all sections is identical.In the generating process of probability-weighted SD, we are used Two methods of multivariate correlation analysis (MCA) method and principal component analysis (PCA) method determine the weight between auxiliary data. In abrasion, since probability distribution is influenced by sample points, we used four case 4(1:500 soft datas of case Point；2:450 soft data point of case；3:400 soft data point of case；4:350 soft data point of case).As certainty value HD be used to correct the probability of the probability SD of SM to obtain SM after be distributed, that is, obtain the more high accuracy probability of SM.In the research In, actual measurement SM in situ is considered as HD, is distributed after being used to calibrate probability of the probability soft data to obtain SM.In program 2 In, by using BME algorithm (special-temporal slices knowledge synthesising pattern user interface) in SEKS-GUI, two kinds of weights based on HD The probability SD for determining method (MCA and PCA) and SM estimates that SM is in 3 corn growth stages at 4 kinds.Software, this is One software package provided free is currently scientific software library BMElib(Bayes's maximum entropy library) and GUI(graphical user circle Face) combination.
The above is only the preferred embodiments of the invention, are not intended to limit the invention creation, all in the present invention Any modifications, equivalent replacements, and improvements etc. done within the spirit and principle of creation, should be included in the guarantor of the invention Within the scope of shield.

Claims (2)

1. a kind of Bayes's soil moisture evaluation method based on multi- source Remote Sensing Data data, it is characterised in that: this method includes following Step:
Step 1 obtains soil moisture soft data and carries out data processing
Specified region, the soil moisture data of scheduled date are obtained using FY3-B monitoring soil moisture product；And to FY3-B soil Earth moisture data carries out NO emissions reduction processing, is divided 25km resolution ratio FY3-B soil moisture data space by using regression model Resolution rises to 1km resolution ratio soil moisture data；
(1)
WhereinA 25For 25 km average values of albedo (A),V 25For the 25 km average values of vegetation index NDVI(V),T 25For earth's surface Temperature LST(T) 25 km average values,mWithnIt is in FY3-B pixel respectivelyiRow and thejThe quantity of 1km pixel in column；A ijIt is In FY3-BiRow and thejThe albedo value of 1Km pixel in column,V ijIt is in FY3-BiRow and thej1Km pixel in column NDVI value,T ijIt is in FY3-B iRow and thejThe surface temperature LST value of 1Km pixel in column；
Linear regression model (LRM) are as follows: SM=a1+a2 A+a3 V+a4 T+a5 AV+a6 AT+a7 VT(2)
Wherein SM is soil moisture；a1、a2、a3、a4、a5、a6And a7It is regression coefficient；A is albedo, V is that vegetation index, T are ground Table temperature；
Firstly, the A that formula (1) is calculated25、V25And T25Formula (2) are brought into, according to corresponding grid cell size 25km resolution ratio FY3-B soil moisture data SM25, pass through the regression coefficient in regression analysis computation model；
Secondly, by the albedo (A) of MODIS product 1km resolution ratio, vegetation index NDVI(V) and surface temperature LST(T) bring into The Soil moisture SM of 1km resolution ratio has accordingly been calculated in model formation (2)1；
Step 2 obtains environmental factor soft data
Albedo (A) is obtained using MODIS product, vegetation index NDVI(V) and surface temperature LST(T)；Use ASTER product Obtain altitude data；And the gradient, slope aspect, planar curvature, section song are calculated by the altitude data that ASTER product obtains Rate, surface roughness, humidity index and relief intensity data；
Step 3 obtains NO emissions reduction FY3-B soil moisture data SM using step 11Environmental factor soft data is obtained with step 2, is adopted With histogram form of probability, probability soft data is obtained；
Step 4 determines environmental factor soft data in step 2 using multivariate correlation analysis method and principal component analytical method Weighted value, and obtain probability-weighted soft data；
Firstly, calculating thejA environmental factor and soil moisture data between quantitative probabilities, be denoted asP SD (EF j ):
(3)
Wherein Ef represents the environmental factor data obtained in step 2；nRepresent SM'snA section；kIndicate the of SMkIt is a Section,kValue from 1 ton；mRepresent Ef'smA section；It is the of SM in first of Ef intervalkIn a interval Sampling number；It is the of SMkTotal number of sample points in a section；EfmIt ismThe environmental factor in a section；Each In section, probability distribution data are constant values, and the size in all sections is identical；
Secondly, calculating wherein thejThe weighted value w of a environmental factor data j Are as follows:
(4)
Wherein,iFor the number of environmental factor in step 2,iFor value from 1 to 11, Rj isjA environmental factor and the FY3B soil water Related coefficient between divided data； EfjIt isjA environmental factor；NCC(Efj) it isjThe normalization phase relation of a environmental factor Number, the sum of NCC of all environmental factors are equal to 1, wherein NCC(Efj) value more than or equal to 0.18 environmental factor be leading environment The factor retains its environmental factor respective weights value, NCC(Efj) environmental factor weighted value of the value less than 0.18 be 0；
Again, the probability-weighted for calculating SM, is denoted as WPSD,
(5)
Step 5, actual measurement obtain soil moisture hard data
In step 1 the same area, phase same date, the pedotheque of 0-5cm depth is collected, and measures soil using aluminium box baking method Earth moisture content；All parametric cubics are measured, and average value is used for subsequent analysis, are denoted as；
Step 6, using Bayes's maximum entropy method to the soil moisture probability-weighted soft data in step 4And in step 5 Measured dataIt is merged as hard data, estimates soil moisture data, be as a result denoted as；
Step 7 assesses Prediction of Soil Water Content using root-mean-square error (RMSE) and related coefficient (R)As a result accurate Degree, wherein
(6)
(7)
Wherein,nIt is the total degree that soil moisture is surveyed in step 5,It is in step 5iSecondary actual measurement Soil moisture,It is in step 5nThe average value of secondary actual measurement soil moisture,It is in step 6iSecondary Bayes's maximum entropy Soil moisture,In step 6nThe average value of secondary Bayes's maximum entropy soil moisture.
2. a kind of Bayes's soil moisture evaluation method based on multi- source Remote Sensing Data data according to claim 1, feature It is, wherein probability-weighted soft data number is set as 4 classes, respectively 500,450,400 and 350 in step 6, obtains Soil moisture estimation result under four type different weights probability soft data numbers.
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Cited By (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111089827A (en) * 2020-01-07 2020-05-01 华南农业大学 Measuring system and measuring method for water penetration of citrus garden soil
CN111651411A (en) * 2020-04-21 2020-09-11 成都信息工程大学 Complex terrain remote sensing soil moisture product downscaling method
CN111742329A (en) * 2020-05-15 2020-10-02 安徽中科智能感知产业技术研究院有限责任公司 Mining typical ground object dynamic monitoring method and platform based on multi-source remote sensing data fusion and deep neural network
CN111861222A (en) * 2020-07-22 2020-10-30 中国水利水电科学研究院 Method for acquiring farmland and grassland roughness facing regional scale wind erosion
CN112528555A (en) * 2020-11-23 2021-03-19 中国科学院空天信息创新研究院 Soil moisture map drawing method and device
CN112560570A (en) * 2020-09-29 2021-03-26 中国科学院大学 High-resolution earth surface temperature estimation method based on cooperative downscaling and data fusion
CN113033262A (en) * 2019-12-25 2021-06-25 中移(成都)信息通信科技有限公司 Model training method and crop yield estimation method
CN113419046A (en) * 2021-06-17 2021-09-21 北京大学 Improved soil humidity product bivariate fusion method
CN113763409A (en) * 2021-08-27 2021-12-07 南通大学 Gauss filtering-based multi-threshold segmentation method for high-dimensional brain nuclear magnetic image
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
CN115204479A (en) * 2022-07-01 2022-10-18 南京信息工程大学 Crop survival area prediction method and system based on multi-source remote sensing data
CN117235607A (en) * 2023-11-13 2023-12-15 天津市农业发展服务中心 Soil moisture content real-time monitoring system and monitoring method

Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105550423A (en) * 2015-12-09 2016-05-04 浙江大学 CMORPH satellite precipitation data downscaling method based on Fuzzy-OLS (Ordinary Least Squares)
CN106019408A (en) * 2016-05-10 2016-10-12 浙江大学 Multi-source-remote-sensing-data-based high-resolution-ratio satellite remote-sensing estimation method
CN106446444A (en) * 2016-10-14 2017-02-22 中国科学院遥感与数字地球研究所 Soil moisture spatial predication research based on Bayes maximum entropy and priori knowledge
CN106483147A (en) * 2016-10-14 2017-03-08 中国科学院遥感与数字地球研究所 The long-term sequence passive microwave soil moisture accuracy improvements research worked in coordination with based on MODIS and measured data
US20170122889A1 (en) * 2014-06-18 2017-05-04 Texas Tech University System Portable Apparatus for Soil Chemical Characterization
CN108169161A (en) * 2017-12-12 2018-06-15 武汉大学 A kind of corn planting regional soil humidity appraisal procedure based on modified MODIS indexes
CN108268735A (en) * 2018-01-29 2018-07-10 浙江大学 Soil moisture NO emissions reduction method based on multi-source remote sensing satellite fused data
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
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

Patent Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20170122889A1 (en) * 2014-06-18 2017-05-04 Texas Tech University System Portable Apparatus for Soil Chemical Characterization
CN105550423A (en) * 2015-12-09 2016-05-04 浙江大学 CMORPH satellite precipitation data downscaling method based on Fuzzy-OLS (Ordinary Least Squares)
CN106019408A (en) * 2016-05-10 2016-10-12 浙江大学 Multi-source-remote-sensing-data-based high-resolution-ratio satellite remote-sensing estimation method
CN106446444A (en) * 2016-10-14 2017-02-22 中国科学院遥感与数字地球研究所 Soil moisture spatial predication research based on Bayes maximum entropy and priori knowledge
CN106483147A (en) * 2016-10-14 2017-03-08 中国科学院遥感与数字地球研究所 The long-term sequence passive microwave soil moisture accuracy improvements research worked in coordination with based on MODIS and measured data
CN108169161A (en) * 2017-12-12 2018-06-15 武汉大学 A kind of corn planting regional soil humidity appraisal procedure based on modified MODIS indexes
CN108268735A (en) * 2018-01-29 2018-07-10 浙江大学 Soil moisture NO emissions reduction method based on multi-source remote sensing satellite fused data
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
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

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
王春梅 等: "被动微波土壤水分产品真实性检验研究进展", 《浙江农业学报》 *
王春梅 等: "被动微波土壤水分产品真实性检验研究进展", 《浙江农业学报》, vol. 31, no. 05, 31 May 2019 (2019-05-31), pages 846 - 854 *

Cited By (20)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113033262B (en) * 2019-12-25 2022-08-05 中移(成都)信息通信科技有限公司 Model training method and crop yield estimation method
CN113033262A (en) * 2019-12-25 2021-06-25 中移(成都)信息通信科技有限公司 Model training method and crop yield estimation method
CN111089827A (en) * 2020-01-07 2020-05-01 华南农业大学 Measuring system and measuring method for water penetration of citrus garden soil
CN111651411A (en) * 2020-04-21 2020-09-11 成都信息工程大学 Complex terrain remote sensing soil moisture product downscaling method
CN111651411B (en) * 2020-04-21 2023-03-10 成都信息工程大学 Complex terrain remote sensing soil moisture product downscaling method
CN111742329A (en) * 2020-05-15 2020-10-02 安徽中科智能感知产业技术研究院有限责任公司 Mining typical ground object dynamic monitoring method and platform based on multi-source remote sensing data fusion and deep neural network
CN111742329B (en) * 2020-05-15 2023-09-12 安徽中科智能感知科技股份有限公司 Mining typical feature dynamic monitoring method and platform based on multi-source remote sensing data fusion and deep neural network
CN111861222A (en) * 2020-07-22 2020-10-30 中国水利水电科学研究院 Method for acquiring farmland and grassland roughness facing regional scale wind erosion
CN111861222B (en) * 2020-07-22 2023-11-14 中国水利水电科学研究院 Method for obtaining roughness of cultivated land and grassland facing regional scale wind erosion
CN112560570B (en) * 2020-09-29 2023-07-25 中国科学院大学 High-resolution earth surface temperature estimation method for collaborative downscaling and data fusion
CN112560570A (en) * 2020-09-29 2021-03-26 中国科学院大学 High-resolution earth surface temperature estimation method based on cooperative downscaling and data fusion
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
CN112528555A (en) * 2020-11-23 2021-03-19 中国科学院空天信息创新研究院 Soil moisture map drawing method and device
CN113419046B (en) * 2021-06-17 2022-02-18 北京大学 Improved soil humidity product bivariate fusion method
CN113419046A (en) * 2021-06-17 2021-09-21 北京大学 Improved soil humidity product bivariate fusion method
CN113763409A (en) * 2021-08-27 2021-12-07 南通大学 Gauss filtering-based multi-threshold segmentation method for high-dimensional brain nuclear magnetic image
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
CN115204479A (en) * 2022-07-01 2022-10-18 南京信息工程大学 Crop survival area prediction method and system based on multi-source remote sensing data
CN117235607A (en) * 2023-11-13 2023-12-15 天津市农业发展服务中心 Soil moisture content real-time monitoring system and monitoring method
CN117235607B (en) * 2023-11-13 2024-02-13 天津市农业发展服务中心 Soil moisture content real-time monitoring system and monitoring method

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