CN108764688A - The wet stain of winter wheat of based on star multi-source precipitation data fusion does harm to remote-sensing monitoring method - Google Patents
The wet stain of winter wheat of based on star multi-source precipitation data fusion does harm to remote-sensing monitoring method Download PDFInfo
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Abstract
The invention discloses a kind of wet stains of winter wheat of based on star multi-source precipitation data fusion to do harm to remote-sensing monitoring method, including：Ground rainfall gauge observation precipitation data collection, satellite Retrieval precipitation data collection, NDVI data sets, dem data, crops phenology information, winter wheat planting area statistical data and winter wheat growing area sampled data on the spot are compiled, and NDVI data are pre-processed and are filtered；Winter wheat planting area spatial information is extracted using the NDVI time series datas after processing；Using the quantitative model of Geographical Weighted Regression Kriging method structure precipitation information fusion, precipitation data collection is obtained；Using the winter wheat planting area of precipitation data collection and extraction, index is done harm in conjunction with wet stain, winter wheat wet stain evil disaster area is monitored.The invention carries out the effective ways that the wet stain of large-scale crop does harm to space monitoring using the data, has stable technical feasibility.
Description
Technical field
The invention belongs to agrometeorological hazards to monitor field, and in particular to a kind of fusion of based on star multi-source precipitation data
The wet stain of winter wheat does harm to remote-sensing monitoring method.
Background technology
Under climate change background, Extreme Weather-climate Events increase so that agrometeorological hazard shows frequency height, intensity
Greatly, the situation of dangerous getting worse, the influence to agricultural production increasingly sharpen.Wet stain evil is the common agricultural weather of world wide
One of disaster, in the U.S., Australia, Russia, the African central and east, China, Pakistan, India, Nepal, Bangladesh
There is generation Deng country, and different growing periods can cause crops to endanger in various degree.There are about 10% in the whole world according to estimates
Soil is seriously influenced by soil accumulated water, however 20% is reached for specific region such as Eastern Europe and the Russian Federation (FAO,
2002), the wheat planting district of annual 15-20% is influenced by waterlogged disaster.In China, climate, landform, soil attribute, kind
The influence of the factors such as plant system, wheat planting district are easily subject to drought and waterlogging attack, and the southern region of rice field-upland field rotation is done harm to shadow by wet stain
It rings serious.Reinforce the wet stain of crop and do harm to study on monitoring, for preventing and reducing natural disasters, ensures that national food security has highly important reality
Meaning.
Precipitation is one of the meteorological index of the wet stain evil monitoring of crop, forecast and loss appraisal most critical, traditional utilization
Precipitation data is monitored into the wet stain evil of row crop mainly by the data at national surface weather observation station, but site density is sparse；
It region automatic weather station construction and comes into operation, the increase in density of meteorological site, but its quality of data, stability and its in wet stain
Evil monitoring and the application of loss appraisal need further to be studied.With the development of science and technology, many countries transmit one successively
Tropical rainfall observation satellite task (the Tropical Rainfall in serial earth observation satellite, the especially U.S.
Measurement Mission, TRMM), the epoch using active remote sensing retrieving precipitation are opened, which uses multisensor
Joint inversion precipitation technology produce 3h, day by day with moon Precipitation Products.Compared with ground observation and radar pinch-reflex ion diode method, satellite
Retrieving precipitation data has round-the-clock, all standing advantage, can relatively accurately reflect the spatial-temporal distribution characteristic of precipitation.But it defends
Star retrieving precipitation and radar pinch-reflex ion diode inherently belong to indirect operation means, and the raising of Product Precision has to pass through ground
Data is corrected, and the quality that effect directly determines fusion precipitation data is corrected.Respectively have in view of all kinds of observational datas excellent
Disadvantage, the advantage of comprehensive each data source is to obtain the Precipitation Products of high quality, and the way of mainstream is to cover space at present
The precipitation data of satellite Retrieval is merged with ground station observation precipitation data.
In view of non-stationary between the violent feature of rainfall change in time and space and satellite Retrieval precipitation and " true precipitation "
The fusion of spatial relationship, rainfall gauge and satellite Retrieval precipitation data is mainly merged using the method for local alignment, by ground
Face observational data carrys out the estimation of calibration satellite precipitation as a result, including probability density matching method (PDF) (space Jing Jing etc., 2013), objective
Analysis (Objective analysis, OA) (Barnes, 1964；Boushaki et al.,2009；Rozante et al.,
2010), probability density matching+optimum interpolation method (Optimum interpolation, OI) (Xie and Xiong, 2011；Pan
Yang etc., 2012), condition fusion (Baik etc., 2016), self-adaptive kernel density estimation (Li and Shao, 2010) and ground system
Meter method (Krajewski, 1987；Li and Shao,2010；Seo,1998；Teng et al., 2014) etc..These offices
Portion's fusion method combines ground rainfall gauge observation and the respective advantage of satellite Retrieval, can effectively improve the standard of precipitation Spatial outlier
True property.Consider between satellite Retrieval precipitation and " true precipitation " Space atmosphere relationship and geographical factors such as height above sea level,
Influence of the latitude and longitude information to precipitation, the present invention using Geographical Weighted Regression kriging method over the ground areal rainfall meter observation precipitation and
Satellite Retrieval precipitation data is merged.Due to relatively low (such as TRMM of spatial resolution of current satellite Retrieval precipitation data
The spatial resolution of 3B42/3B43V7 is 0.25 ° × 0.25 °), a pixel value of satellite Retrieval Precipitation Products is in the pixel
Average precipitation, and rainfall gauge obtain be an observation, there is apparent scale mismatch problem between the two.If by it
Directly merged with ground rainfall gauge observation precipitation as ambient field, the high resolution precipitation data of acquisition have it is larger not
Certainty, while apparent non-continuous event (Atkinson will be generated in the boundary of original satellite precipitation data grid cell
and Tate,2010；Li and Shao,2010；Park et al.,2017；Verdin et al.,2016).Therefore, this hair
It is bright into planet multi-source precipitation data fusion before, first use millet cake kriging method to original spatial resolution precipitation data
NO emissions reduction is carried out, makes it that there is better comparativity with ground rainfall gauge observation precipitation data.
Invention content
The purpose of the present invention is be directed to that ground rainfall gauge observation precipitation is used alone at present to winter wheat wet stain evil space prison
The difficulty of survey, and be used alone satellite Retrieval Precipitation Products to winter wheat wet stain evil space monitoring have larger error this
Outstanding problem, using Geographical Weighted Regression Kriging method, areal rainfall meter observes precipitation data and satellite Retrieval Precipitation Products over the ground
Merged, obtain high quality, high spatial resolution space precipitation data collection, and using the data to the wet stain of winter wheat do harm into
Row space monitoring.
The invention firstly uses the Seasonal variations of different atural object NDVI, study the side of the winter wheat planting area of extraction
Method determines the range of supporting body in the wet stain evil monitoring in region；Then original coarse resolution is defended using millet cake Kriging method
Star retrieving precipitation data carry out NO emissions reduction, and the scale solved between satellite Retrieval precipitation and ground rainfall gauge observation precipitation mismatches
Problem；Later using satellite precipitation, longitude and latitude and the height above sea level of NO emissions reduction as auxiliary variable, ground rainfall gauge is observed precipitation and is made
For dependent variable, the fusion of multi-source precipitation data is carried out using Geographical Weighted Regression Kriging method, obtains high quality, high-resolution
Precipitation data collection；The winter wheat planting area that finally using star precipitation fused data set and NDVI data are extracted, in conjunction with wet
Stain does harm to index, is done harm to the wet stain of winter wheat and carries out dynamic monitoring.
According to technical solution provided by the invention, a kind of wet stain evil of the winter wheat of based on star multi-source precipitation data fusion is distant
Feel monitoring method, the technical solution includes the following steps：
Step 1 obtains and arranges ground rainfall gauge observation precipitation data collection, satellite Retrieval precipitation data collection, NDVI data
Collection, dem data, During Growing Period of Winter Wheat data, crops phenology information, winter wheat planting area statistical data and winter wheat kind
Growing area sampled data on the spot；
Step 2, the dem data resampling for obtaining step 1 to 1km spatial resolutions, utilize the 1km grid numbers of resampling
According to the latitude and longitude information obtained under equal resolution, the dem data under 1km spatial resolutions and latitude and longitude information are obtained；
And pretreatment and time series reconstruct are carried out to NDVI data sets, obtain NDVI time series datas；
Step 3, the phenological calendar by analyzing the winter wheat in During Growing Period of Winter Wheat data, crops phenology information, winter are small
Wheat seeds plant the correspondence of area statistics data, winter wheat growing area sampled data and vegetation index on the spot, and step 2 is utilized to obtain
NDVI time series datas, using structure decision tree method extract winter wheat planting area spatial information, obtain winter wheat
Cultivated area；
Step 4 carries out NO emissions reduction to the satellite Retrieval precipitation data collection of original coarse resolution, obtains the satellite drop of NO emissions reduction
Water number evidence, and dropped using the satellite of Geographical Weighted Regression Kriging method structure ground rainfall gauge observation precipitation data and NO emissions reduction
Spatial Relational Model between water number evidence and geographical factors obtains high quality, high-resolution precipitation data collection；
Step 5, the precipitation data collection generated based on step 4 fusion do harm to precipitation guide line to winter wheat using the wet stain of winter wheat
Wet stain evil potential establishment domain carries out space monitoring；
Step 6 does harm to monitoring result based on the wet stain of winter wheat based on fusion precipitation that step 5 obtains, and is obtained in conjunction with step 3
Winter wheat planting area winter wheat disaster area is monitored.
The method of the present invention, on the basis of fusion generates high quality, high-resolution precipitation data collection, in conjunction with the winter of extraction
Wheat planting area is monitored winter wheat disaster area using the wet stain evil index of winter wheat.
In step 1, NDVI refers to vegetation-cover index, and DEM refers to digital elevation model (Digital Elevation
Model)；
In step 2, the pretreatment includes：Image joint, projection transform, resampling and cutting；Time series reconstructs
Be using Savizky-Golay filtering original NDVI data are filtered, denoising, rebuild the NDVI time sequences of high quality
Row curve obtains NDVI time series datas.
In step 4, the satellite Retrieval precipitation data to original coarse resolution carries out NO emissions reduction, the mesh of NO emissions reduction
It is 1km to mark resolution ratio；
The NO emissions reduction method is millet cake Kriging method, using the pixel of original resolution point as face data without
It is point data, being inferred by the experience variogram deconvolution process of face scale can be closer to the point scale of point scale spatial variability
Variogram, obtain remain initial data feature NO emissions reduction precipitation data collection (Kyriakidis, 2004；Kyriakidis
and Yoo,2005；Wang et al.,2016).
The geographical factors include longitude and latitude and height above sea level, and latitude and longitude information comes from the 1km of step 2 resampling
Dem data and latitude and longitude information.
The structure Geographical Weighted Regression Ke Lijin Spatial Relational Models specifically include：Geographical Weighted Regression is used first
Areal rainfall meter observation precipitation data and the corresponding auxiliary variable data of observation point carry out local fit to method over the ground, and observation point is corresponding
Auxiliary variable data include the satellite precipitation data of NO emissions reduction, step 2 obtain 1km spatial resolutions under dem data and warp
Latitude information obtains regression parameter and residual error at all observation point positions；Then it utilizes and obtains the spaces 1km closest to distribution method
The regression parameter spatial information of resolution ratio, and utilize the residual information of common Kriging technique interpolation generation 1km spatial resolutions；Most
Afterwards using regression parameter, auxiliary variable data and the residual information of the 1km resolution ratio obtained, by Geographical Weighted Regression gram
Golden formula fusion generates last 1km spatial resolutions high quality, high-resolution precipitation data collection.
In step 5, the wet stain evil precipitation guide line of the winter wheat is Meteorological Field standard ---《Winter wheat, rape flood stain
Grade》Winter wheat flood stain index (Q in (QX/T 107-2009)w)；
The winter wheat wet stain evil potential establishment domain is high quality, the high resolution precipitation data generated based on fusion
A situation arises with the stain evil of the space covering of winter wheat stain evil index selection.In practice, not all spatial dimension is all planted
Have winter wheat, only by the precipitation data of fusion monitor stain evil and the region for thering is winter wheat to plant be only winter wheat by
Disaster area.
Compared with prior art, the invention has the advantages that：
The present invention provides a kind of wet stains of winter wheat of based on star multi-source precipitation data fusion to do harm to remote-sensing monitoring method, knot
The observation of ground rainfall gauge and the respective advantage of satellite Retrieval precipitation data are closed, high quality, the high resolution precipitation number of generation are merged
Space monitoring is carried out according to can effectively be done harm to the wet stain of large-scale winter wheat.Precipitation data and satellite are observed for ground rainfall gauge
Existing scale mismatch problem between retrieving precipitation data alternative is defended coarse resolution the invention provides a kind of
The method that star Precipitation Products carry out NO emissions reduction.In addition, the high quality of fusion generation, high resolution precipitation data set can be used for
In the drought and waterlogging monitoring of other crops, provides effective data for crop drought and waterlogging space monitoring and support.
Description of the drawings
Fig. 1 is that the wet stain of winter wheat of the present invention is based on star multi-source precipitation data fusion does harm to remote-sensing monitoring method flow chart；
Fig. 2 is various typical feature NDVI time series variation features in 2012~2013 years During Growing Period of Winter Wheat in Hubei Province
Curve, abscissa DOY are year day of year, and 273 refer to the 273rd day 2012, and 17 refer to the 17th day 2013；
Fig. 3 is 2012~2013 years winter wheat planting area remote sensing recognition decision trees based on MODIS-NDVI time serieses
Disaggregated model；
Fig. 4 is that Hubei, Anhui and year winter wheat remote sensing of Jiangsu San Sheng 2001,2002,2003,2010,2013 and 2014 are estimated
Cultivated area is calculated compared with statistical result；
Fig. 5 is that the first tenday period of a month in May, 2013 TRMM retrieving precipitations and Geographical Weighted Regression Kriging technique merge spatial distribution of precipitation；
Fig. 6 based on fusion Hubei of precipitation data, the wet stain evil Typical Year in Anhui and Jiangsu Province (2001,2002,2003,
2010,2013 and 2014) the wet stain of winter wheat does harm to spatial distribution compared with meteorological station monitoring result；
Fig. 7 does harm to Typical Year based on the wet stain in Hubei, Anhui and Jiangsu Province for merging precipitation data and winter wheat planting area
The wet stain of (2001,2002,2003,2010,2013 and 2014) winter wheat does harm to disaster area remote sensing monitoring result.
Specific implementation mode
Below using China Jiangsu, Anhui and Hubei San Sheng as examples area, with document record and on-site inspection
For 2001~2014 years typical stain evils year (2001,2002,2003,2010,2013 and 2014), in conjunction with specific attached drawing to this
Invention is described in further detail.
As shown in Figure 1, the wet stain of winter wheat for the fusion of the present invention is based on star multi-source precipitation data does harm to remote-sensing monitoring method
Flow chart.It includes the following steps：
Step 1, obtain and arrange ground rainfall gauge observation precipitation data collection (national surface weather observation station daily precipitation),
Satellite Retrieval precipitation data collection (TRMM 3B42 daily precipitations data (0.25 °)), NDVI data sets (MOD13Q1NDVI data),
Dem data (90m), Google earth images, During Growing Period of Winter Wheat data, crops phenology information, winter wheat planting area
Statistical data and winter wheat growing area sampled data on the spot；
Specifically, Jiangsu, Anhui and three, Hubei have been compiled and has saved 2001~2014 years 203 national Ground Meteorologicals
Observation station daily precipitation observational data observes precipitation data collection as ground rainfall gauge.Respectively by Anhui Province weather information center, lake
North saves weather information and is provided with technical guarantee center, Jiangsu Province weather information center, and wherein Hubei Province shares 67 websites, Anhui
Province shares 75 websites, and Jiangsu Province shares 61 websites；Compiled covering Jiangsu, three provinces in Anhui and Hubei 2001~
The TRMM 3B42V7 daily precipitation products of 0.25 ° × 0.25 ° spatial resolution in 2014, as satellite Retrieval precipitation data collection；It receives
Collection arranged covering Jiangsu, synthesis in 16 days 2001~2014 years of three provinces in Anhui and Hubei MODIS 250m spatial resolutions
Vegetation index data product (MOD13Q1) (https://ladsweb.modaps.eosdis.nasa.gov/), as NDVI numbers
According to collection；SRTM 90m spatial resolution digital elevation data (DEM) (http is compiled://www.gscloud.cn/), make
For dem data；It has compiled in the Anhui Province 2001-2014 weather information center, Hubei Province's weather information and technical guarantee
The agricultural meteorological station During Growing Period of Winter Wheat data that the heart, Jiangsu Province weather information center provide, as During Growing Period of Winter Wheat data；It receives
Collection has arranged Jiangsu, Anhui and the Hubei Province's crops phenology information of document record, as crops phenology information；It compiles
Jiangsu, Anhui and the Hubei Province 2001-2014 winter wheat planting area statistical data that statistics bureau provides, as winter wheat kind
Plant area statistics data；It has compiled 2013~2014 years Jiangsu, Anhui and Hubei Province winter wheat and rape seed growing area GPS is real
Ground grab sampling data, as winter wheat and rape seed growing area sampled data on the spot.
According to national weather professional standard《Winter wheat, rape flood stain grade》(QX/T 107-2009) analyzes winter wheat
Precipitation data used by by stain situation is ten days scale, and this example is respectively dropped national surface weather observation station day using R language
Water observational data and TRMM 3B42V7 daily precipitation data are reunited into the precipitation data of ten days scale.
Step 2, the dem data resampling for obtaining step 1 to 1km spatial resolutions, utilize the 1km grid numbers of resampling
According to obtain equal resolution under latitude and longitude information, and to NDVI (vegetation-cover index) time series data carry out pretreatment and
Time series reconstructs；
Specifically, the dem data of the 90m resolution ratio obtained step 1 in ArcGIS 10.2 utilizes resampling to 1km
Spatial resolution (is averaged) in 1km grids, extracts the pixel central point latitude and longitude information of the 1km dem datas of resampling.
Image joint, projection transform and resampling are carried out to MOD13Q1 product data using MODIS data projection crossover tool MRT, and
And it isolates the NDVI layer datas product in each time MOD13Q1 products and has been used for extraction winter wheat area；Then it uses
The wave band calculating instrument of ENVI is converted to NDVI values between normal value range 0~1.Utilize Savizky-Golay filter methods
MODIS-NDVI time series datas are smoothed, noise is removed, the NDVI time serieses for rebuilding high quality are bent
Line chart.
Step 3, by analyze winter wheat phenological calendar, breeding time and growth conditions and vegetation index correspondence, profit
NDVI time series datas after being handled with step 2, using the method extraction winter wheat planting area space of structure decision tree
Information；
Specifically, using Google Earth platforms, and sampled data, winter are small on the spot for combination Jiangsu, Anhui and Hubei Province
The information such as wheat breeding time data and crops phenology information, obtain typical feature sample, and extraction sample point is rebuilding NDVI sequential
Change curve in data, and rejecting abnormalities curve choose the identification that optimal curve carries out winter wheat planting area.
Fig. 2 shows various typical feature NDVI time series variations in 2012~2013 years During Growing Period of Winter Wheat in Hubei Province
Indicatrix.Since winter wheat and rape seed and harvesting time differ, selected MODIS reference time datas than winter wheat and
Growth of rape phase range is slightly long (from totally 18 scape image in June in October last year to next year).The NDVI times of water body as seen from the figure
Sequence curve has notable difference, NDVI values to be less than 0 with other types of ground objects NDVI time-serial positions；The NDVI values in cities and towns
It is smaller, it is very gentle in the variation of entire winter Growing Season of Crops, risen in the 4-6 months；The NDVI curves in forest land are in centre low two
The characteristics of head height, winter, NDVI values were reduced due to leaf abscission, and spring begins to ramp up and reaches peak in the 5-6 months, and simultaneously
Between arable land NDVI values after harvesting reduce the variation tendency to form trough and have more apparent difference；On the arable land that do not plant in winter,
Its NDVI value minimum in winter (usually less than 0.3) and the NDVI values for having winter crop arable land significantly lower than kind, plant after the subsequent beginning of spring
It is started growth, NDVI is gradually increasing.Rape and the NDVI plots changes of winter wheat are almost the same, growth phase before the winter,
NDVI curves gently rise；Wintering Period, rape and winter wheat are in the 1-2 months since temperature is low, slow-growing and stopping sometimes being given birth to
Long, NDVI is varied less；After the 3-4 months turn green, rape and the equal fast-growth of winter wheat, NDVI curves rise rapidly；The 4-5 months are ripe
Harvest time, group turn yellow, and NDVI curves are begun to decline；The NDVI of rape is higher than the NDVI of winter wheat before winter.But in mid-March
Later, rape enters florescence, and NDVI values are declined, and the NDVI values of winter wheat are apparently higher than the NDVI values of rape, so March
In, the last ten-days period be identify winter wheat and rape Optimum temoral.According to these characteristic informations, Decision-Tree Classifier Model is established (as schemed
3), identification has water body, forest land and the cities and towns of notable separability with crops first, and these atural object masks is fallen, it is laggard
One step identifies winter crop, finally distinguishes winter wheat and rape emphatically on the basis of the winter crop identified, extracts winter wheat kind
Plant area.
According to the different year Decision-Tree Classifier Model of foundation, this example is extracted Hubei, Anhui and three, Jiangsu province typical case
Stain does harm to the cultivated area of year (2001,2002,2003,2010,2013 and 2014) winter wheat, and public using national statistics department
The statistical yearbook crop area data of cloth carry out it precision test, concrete outcome such as Fig. 4.As can be seen that the winter wheat of extraction
Cultivated area totally has higher precision, and compared with three province's winter wheat planting areas of statistical yearbook record, value all exists
1:Near 1 line；Compared with statistics, utilize the 2001 of MODIS data estimations, 2002,2003,2010,2013 and 2014
It studies area's winter wheat planting area relative error and is less than ± 5%.
Step 4 carries out NO emissions reduction to the satellite Retrieval precipitation data of original coarse resolution, and utilizes Geographical Weighted Regression gram
Satellite precipitation and geographical factors (the 1km DEM numbers of resampling of vertical gold method structure ground rainfall gauge observation precipitation and NO emissions reduction
According to and latitude and longitude information) between Spatial Relational Model, obtain high quality, high-resolution precipitation data collection；
Specifically, original spatial resolution (0.25 ° × 0.25 °) TRMM ten days precipitation datas to being recombined in step 1
Using millet cake Kriging method NO emissions reduction to 1km spatial resolutions, using original TRMM precipitation datas pixel as face data without
It is to be reduced to pixel central point to be handled, being inferred by the experience variogram deconvolution process of face scale can be closer to point ruler
The point scale variogram of spatial variability is spent, the NO emissions reduction precipitation data collection for remaining initial data feature is obtained；
Influence of the geographical factors to precipitation is considered, by longitude and latitude and height above sea level and the satellite Retrieval precipitation after NO emissions reduction
Collectively as auxiliary variable, ground rainfall gauge observes precipitation as dependent variable, builds the vertical grid space relationship of Geographical Weighted Regression gram
Model, fusion generate high quality, high-resolution precipitation data collection.Geographical Weighted Regression Kriging technique is first using geographical weighting
Areal rainfall meter the points of measurement evidence and corresponding auxiliary variable data carry out local fit to the Return Law over the ground, then to the residual of observation point
Difference carries out ordinary kriging interpolation.In this example, have at the position x for having rainfall gauge observation：
Wherein, Pobs(x) it is that observation point x upper ground surface rainfall gauges observe ten days precipitation value；β0(x) and βk(x) it is at observation point x
Local regression parameter is the function of spatial position；Xk(x) be corresponding k-th of the auxiliary variable in the places observation point x value；P is auxiliary
It includes four the TRMM ten days precipitation, longitude, latitude and height above sea level auxiliary variables of NO emissions reduction to help the number of variable, this example；ε
(x) it is the residual error at observation point x, it is assumed that it, which is obeyed, is independently just being distributed very much.Regression parameter in formula is by observation point x and its closest
Observation establishes the acquisition of local regression model, and solution formula is as follows：
In formula,It is the estimated value of local regression parameter at observation point x；X and P be respectively auxiliary variable (NO emissions reduction
TRMM ten days precipitation, longitude, latitude, height above sea level and constant-term variable) matrix and dependent variable (ground rainfall gauge observes precipitation) square
Battle array.W (x) be Spatial weight matrix, be the continuous monotonic decreasing function of distance between observation, can be used distance threshold function,
It is determined apart from inverse ratio function, Gauss threshold function tables and nearly Gauss truncated functions etc..In view of ground rainfall gauge observation point is whole
The sparse unevenness in a research area determines space weight, adaptive space in this example using adaptive nearly Gauss truncated functions
Weight function is：
In formula, dijBe observation point x with its j-th closest to point the distance between；B is bandwidth, and optimum bandwidth is logical in this example
Cross the determination of Generalized Cross Validation method.Assuming that regression parameter be a continuous surface, the adjacent regression parameter in position is similar, by with
After upper process determines regression parameter and residual error at all ground rainfall gauge observation positions, closest distribution method is used in this example
Obtain the regression parameter space distribution information of 1km spatial resolutionsThe spaces 1km are obtained using common Ke Lijin interpolation methods
The residual error space distribution information of resolution ratioSpatial position (μi,vi) at Geographical Weighted Regression Ke Lijin Precipitation estimations
ValueFor：
It is every that above-mentioned Geographical Weighted Regression Kriging method is applied to 2001~2014 years Anhui, Jiangsu and Hubei San Sheng
In the Precipitation estimation in ten days, the Geographical Weighted Regression Ke Lijin ten days precipitation fusion results in each ten days are obtained.This example is using cross validation
Mode is verified, and national surface weather observation station (203) are equally divided into 10 parts, per a about 20 websites, often
Secondary to be used to verify with 1 part therein, remaining 9 parts are merged as training sample.In order to ensure extracted website whole
It is substantially evenly distributed within the scope of a research area, by K mean cluster, all normal stations is divided into 20 classes, then do not weighed from every class
1 website is randomly selected again, forms 20 actual measurement verification websites of Fusion Model, and remaining website is melted as training sample
It closes, is repeated 10 times in this way, ensure that nearly all website is all selected as fusion check post mistake.The precision evaluation statistics of use refers to
Mark includes root-mean-square error (RMSE) and related coefficient (r).Table 1 is 2001~2014 years Anhui, Jiangsu and Hubei San Sheng TRMM
The root-mean-square error (RMSE) and related coefficient (r) of 3B42 ten days precipitation datas and Geographical Weighted Regression Ke Lijin fusion results are united
Meter, the results showed that compare original TRMM precipitation precision, Geographical Weighted Regression Ke Lijin fusion methods are to ten days precipitation Spatial outlier essence
Degree all improves a lot in each time.Fig. 5 shows the first tenday period of a month in May, 2013 TRMM retrieving precipitations and Geographical Weighted Regression gram
Li Jinfa merges spatial distribution of precipitation, it can be seen that the spatial distribution of precipitation of the two has larger difference, to the verification knot in the ten days
Fruit shows that the RMSE and r of TRMM retrieving precipitation data are respectively 28.95mm and 0.46, and passes through Geographical Weighted Regression Ke Lijin's
Fusion results its RMSE and r is respectively 12.62mm and 0.93, illustrates that Geographical Weighted Regression Kriging method can be merged effectively
Ground rainfall gauge observation and satellite Retrieval precipitation information, generate high quality, high-resolution ten days precipitation spatial data.
1 Anhui of table, 2001~2014 years TRMM 3B42 ten days precipitation datas in Jiangsu and Hubei San Sheng and Geographical Weighted Regression gram
The root-mean-square error (RMSE) and related coefficient (r) statistics of vertical gold fusion results
Step 5, the precipitation data collection generated based on step 4 fusion do harm to precipitation guide line to winter wheat using the wet stain of winter wheat
Wet stain evil potential establishment domain carries out space monitoring；
Specifically, in order to understand using fusion generate high resolution precipitation data to crop wet stain evil be monitored can
With property and accuracy.The 1km ten days precipitation datas generated using GWRK fusions, according to Meteorological Field standard ---《Winter wheat, rape
Flooded stain grade》(QX/T 107-2009) calculates annual winter wheat growth in 2001,2002,2003,2010,2013 and 2014
Season (May in October last year to next year) does harm to threshold value item per ten days winter wheat flood stain exponential quantity, further according to the winter wheat stain of the meteorology standard
Part analyzes the potential establishment domain space distribution of the wet stain evil of winter wheat.Table 2 is the precipitation data collection generated based on step 4 fusion,
The wet stain evil remote sensing monitoring result precision of winter wheat that precipitation guide line is done harm to using the wet stain of winter wheat is counted.Wherein remote sensing monitoring stain is done harm to
Station data are to be based on fusion precipitation data in the meteorological station position that stain evil occurs also to monitor that stain evil occurs；Fig. 6
Show the winter wheat wet stain evil space that 2001,2002,2003,2010,2013 and 2014 based on star merge precipitation data
Distribution is compared with station monitoring result.The stain evil monitoring result based on fusion precipitation data is can be seen that from table 2 and Fig. 6 not only
Can capture most of station stain evil a situation arises, while also can integrally reflect entire spatial dimension stain evil distribution feelings
Condition, monitoring accuracy whether stain evil occurs, other than remote sensing monitoring stain evil accuracy rate in 2013 is 74.70%, other times are accurate
True rate illustrates that the high resolution precipitation data generated using fusion can be used for monitoring crop wet stain evil space point 90% or more
Cloth situation.
The wet stain evil remote sensing monitoring result precision statistics of winter wheat of the table 2 based on fusion precipitation data
| Time | Stain does harm to station number | Remote sensing monitoring stain does harm to station number | Accuracy rate (%) |
| 2001 | 152 | 138 | 90.79 |
| 2002 | 171 | 161 | 94.15 |
| 2003 | 133 | 131 | 98.50 |
| 2010 | 125 | 120 | 96.00 |
| 2013 | 83 | 62 | 74.70 |
| 2014 | 67 | 62 | 92.54 |
Step 6 does harm to monitoring result based on the wet stain of winter wheat based on fusion precipitation that step 5 obtains, and is extracted in conjunction with step 3
Winter wheat planting area further winter wheat disaster area is monitored.
Specifically, using step 5 based on fusion ten days precipitation data obtain typical stain evil year (2001,2002,2003,
2010,2013 and 2014) the wet stain evil spatial distribution result of winter wheat, it is superimposed the correspondence time winter wheat that step 3 is extracted respectively
The wet stain of growing area monitoring winter wheat does harm to specific disaster area.
Table 3 and Fig. 7 are respectively 6 years Hubei, Anhui and Jiangsu Province's winter in 2001,2002,2003,2010,2013 and 2014
Wheat wet stain evil disaster area statistics and generation area spatial distribution.By table 3 and Fig. 7 it is found that 2001,2002,2003,2010,
Different degrees of stain evil, stain in 2001,2002,2003 and 2010 all has occurred in most of winter wheat growing area in 2013 and 2014
Evil situation is serious compared with 2013 and 2014.The main wet stain of slight and moderate that occurs is done harm within 2001, and moderate stain evil occurs mainly in
Hubei Province is northern, and it is regional to be slightly mainly distributed on Hubei Province Xiangfan, Jing Zhou, Qianjiang, Tianmen and Xiaogan etc., in the middle part of Anhui and Huaihe River
Northern area and South of Jiangsu Province；2002 in Hubei Province, the Anhui Province northwestward and south and South Jiangsu winter wheat kind
The slight and above stain evil generally has occurred in growing area, and wherein moderate and severe stain evil is mainly distributed on winter wheat plantation in the middle part of Hubei Province
Area, Anhui Province Fuyang, Bangbu, Chaozhou and Anqing and Nantong City are also distributed；2003 in Hubei Province's major part
Moderate stain evil has occurred in region, and slight stain evil mainly has occurred in the area such as Anhui Province Fuyang, Bangbu, Liu Qing, Hefei and Chuzhou,
Slight and moderate stain evil has occurred in South Jiangsu；The evil of winter wheat severe stain in 2010 be concentrated mainly on Hubei Province Xiangfan,
Slight and moderate stain evil, integrated distribution mainly has occurred in the south in Jing Zhou, Qianjiang, Tianmen and Xiaogan City and Jiangsu Province, Anhui
Also have slightly in Anhui Province Fuyang, Bozhou and Suzhou south and along Huaihe River counties and cities, Jiangsu Province's Yangzhou and with Taizhou City
Spend stain evil distribution；Slight stain evil mainly has occurred within 2013 and 2014, the region of stain evil occurs for winter wheat growing area within 2013
The less Jing Zhou for being mainly distributed on Hubei Province, Qianjiang, Jingmen and the Nantong City in Tianmen City and Jiangsu Province, stain evil hair in 2014
Raw region is bigger than 2013, other than the Jing Zhou in Hubei Province, Qianjiang, Jingmen and Tianmen City, Anhui and South Jiangsu winter wheat
Growing area also has stain evil.These monitoring results do harm to the statistical research knot of situation and forefathers with the stain obtained based on meteorological station
Fruit is almost the same, opens great equal (2015) to showing 2002 and 2003 in the loss appraisal research of Basin of Huaihe River winter wheat waterlogged disaster
Year is the typical stain evil year of Basin of Huaihe River (including Anhui and Jiangsu)；Liu Zhixiong and Xiao Ying (2012) are research shows that include Hubei Province
Yangtze upriver areas spring in 2002 and winter in 2003 it is partially flooded；The live system of winter wheat typical case's stain evil of Sheng Shaoxue etc. (2009)
Meter shows that slight stain evil has occurred in early and middle ten days in December, 2001 Huaihe River northern territory, and stain evil has occurred in early Febuary and last ten-days period south,
Stain evil generally has occurred in the area of Huaihe River in 2003.A kind of wet stain evil of the winter wheat of based on star multi-source precipitation data fusion of the present invention
Remote-sensing monitoring method can preferably reflect the wet stain evil spatial distribution characteristic of different year winter wheat concentrated planting area, have steady
Fixed technical feasibility.
Table 3 2001,6 years 2002,2003,2010,2013 and 2014 Hubei, Anhui and the wet stain evil of Jiangsu Province winter wheat
Disaster area counts
Claims (6)
1. a kind of wet stain of winter wheat of based on star multi-source precipitation data fusion does harm to remote-sensing monitoring method, which is characterized in that including
Following steps：
Step 1, obtain and arrange ground rainfall gauge observation precipitation data collection, satellite Retrieval precipitation data collection, NDVI data sets,
Dem data, During Growing Period of Winter Wheat data, crops phenology information, winter wheat planting area statistical data and winter wheat plantation
Area's sampled data on the spot；
Step 2, the dem data resampling for obtaining step 1 to 1km spatial resolutions, are obtained using the 1km raster datas of resampling
The latitude and longitude information under equal resolution is taken, the dem data under 1km spatial resolutions and latitude and longitude information are obtained；
And pretreatment and time series reconstruct are carried out to NDVI data sets, obtain NDVI time series datas；
Step 3, the phenological calendar by analyzing the winter wheat in During Growing Period of Winter Wheat data, crops phenology information, winter wheat kind
The correspondence for planting area statistics data, winter wheat growing area sampled data and vegetation index on the spot, utilizes step 2 to obtain
NDVI time series datas extract winter wheat planting area spatial information using the method for structure decision tree, obtain winter wheat kind
Plant area；
Step 4 carries out NO emissions reduction to the satellite Retrieval precipitation data collection of original coarse resolution, obtains the satellite precipitation number of NO emissions reduction
According to, and utilize the satellite precipitation number of Geographical Weighted Regression Kriging method structure ground rainfall gauge observation precipitation data and NO emissions reduction
According to the Spatial Relational Model between geographical factors, high quality, high-resolution precipitation data collection are obtained；
Step 5, the precipitation data collection generated based on step 4 fusion do harm to precipitation guide line to the wet stain of winter wheat using the wet stain of winter wheat
Evil potential establishment domain carries out space monitoring；
Step 6 does harm to monitoring result based on the wet stain of winter wheat based on fusion precipitation that step 5 obtains, the winter obtained in conjunction with step 3
Wheat planting area is monitored winter wheat disaster area.
2. the wet stain of winter wheat of based on star multi-source precipitation data fusion according to claim 1 does harm to remote-sensing monitoring method,
It is characterized in that, NDVI refers to vegetation-cover index, DEM refers to digital elevation model.
3. the wet stain of winter wheat of based on star multi-source precipitation data fusion according to claim 1 does harm to remote-sensing monitoring method,
It is characterized in that, in step 2, the pretreatment includes：Image joint, projection transform, resampling and cutting.
4. the wet stain of winter wheat of based on star multi-source precipitation data fusion according to claim 1 does harm to remote-sensing monitoring method,
It is characterized in that, in step 4, NO emissions reduction, the mesh of NO emissions reduction are carried out to the satellite Retrieval precipitation data collection of original coarse resolution
It is 1km to mark resolution ratio.
5. the wet stain of winter wheat of based on star multi-source precipitation data fusion according to claim 1 does harm to remote-sensing monitoring method,
It is characterized in that, in step 4, the geographical factors include longitude and latitude and height above sea level.
6. the wet stain of winter wheat of based on star multi-source precipitation data fusion according to claim 1 does harm to remote-sensing monitoring method,
It is characterized in that, in step 4, Geographical Weighted Regression Kriging method structure ground rainfall gauge observation precipitation data and drop ruler are utilized
Spatial Relational Model between the satellite precipitation data and geographical factors of degree, specifically includes：
First, using Geographical Weighted Regression method, areal rainfall meter observes precipitation data and the corresponding auxiliary variable data of observation point over the ground
Carry out local fit, the corresponding auxiliary variable data of observation point include the satellite precipitation data of NO emissions reduction, step 2 obtain 1km
Dem data under spatial resolution and latitude and longitude information obtain regression parameter and residual error at all observation point positions；
Then, using the regression parameter spatial information closest to distribution method acquisition 1km spatial resolutions, and common Ke Lijin is utilized
Method interpolation generates the residual information of 1km spatial resolutions；
Finally, it using the regression parameter, auxiliary variable data and residual information of the 1km resolution ratio of acquisition, is weighted by geography
Golden formula fusion generates last 1km spatial resolutions high quality, high-resolution precipitation data collection in recurrence gram.
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