CN109033962A - Monthly effective coverage index synthetic method based on GF-1/WFV data - Google Patents

Monthly effective coverage index synthetic method based on GF-1/WFV data Download PDF

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CN109033962A
CN109033962A CN201810648221.8A CN201810648221A CN109033962A CN 109033962 A CN109033962 A CN 109033962A CN 201810648221 A CN201810648221 A CN 201810648221A CN 109033962 A CN109033962 A CN 109033962A
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ndvi
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袁烨城
雷鸣
雷一鸣
丁青
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Apocalypse Remote Sensing Science And Technology Ltd Of Section In Suzhou
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Abstract

Monthly effective coverage index synthetic method based on GF-1/WFV data of the invention, with NDVI(Normalized Difference Vegetation Index, normalized differential vegetation index) for, specific steps are as follows: 1) radiation calibration is completed to 1A grades of products of GF-1/WFV；2) cloud, Yun Ying detection are carried out, is extracted for subsequent valid data；3) Atmospheric Correction is completed；4) it completes geometry and is just penetrating correction；5) based on the GF-1 valid data NDVI data of maximum method synthesis one phase of January target area；6) check that target area whether there is shortage of data, if so, then being supplemented with MODIS 13Q1 product.

Description

Monthly effective coverage index synthetic method based on GF-1/WFV data
Technical field
The present invention relates to remote-sensing inversion field, espespecially a kind of vegetation index calculation method based on remote-sensing inversion technology.
Background technique
The vegetation index being calculated using remote sensing technology, such as NDVI(Normalized Difference Vegetation Index, normalized differential vegetation index), it is for parametric inversions such as vegetation growth monitoring, resource investigation, biomass One of most important index.In common satellite resource, MODIS data are the most widely used.MODIS sensor carries It on TERRA and AQUA satellite, can pass by within one day 4 times, average 8-16 days complete coverage goal areas are primary, but spatial resolution It is lower, up to 250 meters, therefore MODIS data and product are usually used in the vegetation resources analysis of large scale, long-term sequence On.Although being greatly improved in Landsat TM, ETM+, OLI series of products spatial resolution, temporal resolution is not Foot, it is difficult to meet high frequency time detection requirement, such as the requirement of one phase of January frequency in grassland monitoring.In addition, SPOT is serial, ALOS, Although the commercial satellites such as WorldWiew, RapidEye spatial resolution returns to the period, has respective advantage on spectrum channel, It is that wide cut (visual field) is usually smaller, high frequency time requirement is often not achieved in actual imaging.
With the implementation of " high-resolution earth observation systems key special subjects ", first high-resolution of Chinese independent research Optical satellite GF-1 was succeeded in sending up in 2013.The Seeds of First Post-flight one 2 m resolution panchromatic camera (PMS), 8 m Resolution multi-spectral camera (MMS) and 4 16 m resolution multi-spectral cameras (WFV).With MODIS, LandSat series, ZY-3 It is compared etc. domestic and international traditional optical satellite, GF-1 WFV sensor uses wide visual field (800km), and it improves and returns to the period, and There is higher spatial resolution (16m), this enables GF1 repeatedly to obtain the effective image in target area in short period of time, also makes The high frequency time NDVI data inversion based on GF-1 WFV data is obtained to be possibly realized.
Summary of the invention
In view of the problems of the existing technology, the present invention is to propose a set of monthly effective plant based on GF-1/WFV data By index synthetic method, a month primary mesh in complete coverage goal area on high spatial resolution (16m) scale is realized Mark.
[1] to achieve the above object, the monthly effective coverage index synthetic method of the invention based on GF-1/WFV data, Specific steps are as follows: 1) radiation calibration is completed to 1A grades of products of GF-1/WFV；2) cloud, Yun Ying detection are carried out, subsequent significant figure is used for According to extraction；3) Atmospheric Correction is completed；4) it completes geometry and is just penetrating correction；5) it is based on GF-1/WFV valid data maximum value synthetic method Synthesize the NDVI data of one phase of January target area；6) check that target area whether there is shortage of data, if so, then using MODIS 13Q1 Product is supplemented.
[2] further, GF-1/WFV 1A grades of product radiation calibration processing in step 1), according to Chinese Resources satellite application The annual radiation terrestrial reference coefficient that center (http://www.cresda.com/CN/) is announced is calculated；
[3] further, step 2 medium cloud, Yun Ying detection, are handled, specific steps for GF-1/WFV radiation calibration result Are as follows: the thick result of cloud detection is obtained with wave band thresholding method；Water body testing result is obtained with wave band threshold method, and for correcting cloud Detect thick result；Cloud shadow is detected with wave band threshold method, the 2 kinds of situations of cloud shadow for being divided into the cloud shadow and land in water body are examined respectively Consider, obtains final cloud, cloud shadow testing result.
[4] further, GF-1/WFV radiation calibration result will carry out atmospheric radiation correction in step 3), with the 6S of ENVI or FLASSH module is corrected.
[5] further, geometry is just penetrating correction in step 4), using the dem data of SRTM 30m resolution ratio, for GF-1/ WFV Atmospheric Correction with the Create Ortho Corrected Raster Dataset of ArcGIS as a result, calculated；
[6] further, one phase of January target area is synthesized based on GF-1/WFV valid data maximum value synthetic method in step 5) NDVI data, specific steps are as follows: the geometric correction to GF-1/WFV is as a result, be calculated single scape with the raster symbol-base device of ArcGIS GF-1/WFV NDVI；With the cloud in step 2, cloud shadow testing result, exposure mask calculating is carried out with NDVI, removes cloud, Yun Ying is blocked Part obtains single scape GF-1/WFV NDVI valid data；By the of that month all GF-1/WFV NDVI valid data in target area into The synthesis of row maximum value, is calculated with the Mosaic module of ArcGIS.
[7] further, specific processing step in step 6) are as follows: to target area this month GF-1/WFV NDVI composite result into Row checks, has checked whether area data missing；If processing terminate without shortage of data；If there is shortage of data, MODIS is used 13Q1 NDVI product supplements absent region.
[8] present invention is directed to remote sensing NDVI, proposes a kind of monthly effective coverage index synthesis side of GF-1/WFV data Method.Algorithm of the invention takes full advantage of GF-1/WFV sensor high spatial resolution and MODIS 13Q1 product high time point The advantage of resolution can completely synthesize the NDVI product of a target area monthly phase 16m spatial resolution, can be further used for Vegetation growth status monitoring, vegetation biomass inverting etc..
Detailed description of the invention
[9] Fig. 1 is that the present invention is based on the flow charts of the monthly effective coverage index synthetic method of GF-1/WFV data
Specific embodiment
[10] the present invention is based on monthly effective coverage index synthetic method calculation process such as Fig. 1 institutes of GF-1/WFV data Show.This method algorithm principle is specifically described as follows:
[11] 1. GF-1/WFV clouds, cloud detection principle
[12] since GF-1/WFV only has 3 visible lights and 1 near-infrared totally 4 wave bands, lack Thermal infrared bands or to gas The short-wave band of colloidal sol, water vapor sensitive, therefore the cloud of GF-1/WFV image, Yun Ying detection are mainly using cloud, Yun Ying in each wave The Reflectivity of section, textural characteristics, geometrical characteristic are calculated:
[13] (1) cloud detection
[14] the thick result of cloud detection is obtained first with HOT, VBR index, in which:
[15]
[16] in formula:B 1 For blue wave wave band,B 3 For red wave wave band, in additionB 2 For green wave wave band,B 4 For near infrared band, similarly hereinafter.
[17]
[18] the thick result of cloud detection are as follows:
[19]
[20] wherein,t 1 、t 2 、t 3 Respectively judge whether it is the threshold value of cloud, similarly hereinafter.
[21] the water body part in image is secondly extracted, it is thick as a result, wherein water body can express for refining cloud detection Are as follows:
[22]
[23] wherein:
[24]
[25] finally, cloud detection result are as follows:
[26]
[27] (2) Yun Ying is detected
[28] wave band threshold value is utilized, considers land part cloud shadow and 2 kinds of situations of water body part cloud shadow respectively, extracts Yun Ying detection As a result:
[29]
[30] whereinfillholeFor in mathematical morphologyfillholeIt calculates, land part refers to water body part Water Number identification, simultaneously:
[31]
[32]t 1 -t 10 For above-mentioned each metrics-thresholds, specific value can optimize according to the actual situation, can also be with reference to following value:t 1 = 0.13、t 2 = 0.7、t 3 = 0.07、t 4 = 0.15、t 5 = 0.2、t 6 = 0.2、t 7 = 0.15、t 8 = 0.08、t 9 = 0.06、t 10 = 0.01。
[33] 2. carry out supplement principle to missing data using MODIS 13Q1 NDVI product
[34] when GF-1/WFV NDVI valid data synthesis monthly NDVI there are when partial region data missing, can use MODIS 13Q1 NDVI product is supplemented.Here there are 2 important hypotheses: (1) in shorter time interval, absolutely Most of object spectrum variations are linear；(2) in the same time, the reflectivity of similar wave band can be used between different sensors Linear model conversion.
[35] MODIS 13Q1 NDVI product is 16 days phases, spatial resolution 250m.It is corresponding to target month first 2 phase MODIS 13Q1 NDVI products carry out maximum value synthesis, synthesize the monthly maximum value product of MODIS NDVI.
[36] secondly, carrying out resampling to the monthly maximum value product of MODIS NDVI, resampling is at 16m resolution ratio, with GF- 1/WFV spatial resolution is consistent.
[37] third has the area of data to sample by land use pattern stratified random, fits in two kinds of sensors Linear transformation relationship between the two:
[38]
[39] in formula, F, C respectively represent the NDVI value of GF-1/WFV and MODIS image, and x, y are spatial position, tkIt is that image obtains The time is taken, a, b are PARAMETERS IN THE LINEAR MODELs.
[40] it is finally mended according to fitting parameter with data of the monthly maximum value product of MODIS NDVI to missing area It fills.
[41] it is to be noted that any deformation that specific embodiment according to the present invention is made, all without departing from this hair The range that bright spirit and claim are recorded.

Claims (7)

1. the monthly effective coverage index synthetic method based on GF-1/WFV data, specific steps are as follows:
Radiation calibration is completed to 1A grades of products of GF-1/WFV.
2. the monthly effective coverage index synthetic method based on GF-1/WFV data, specific steps are as follows:
Cloud, Yun Ying detection are carried out, is extracted for subsequent valid data, identifies cloud CMGWhen, utilize wave band characteristic threshold value, such as formula (1) shown in-(6):
(1)
CM in formulaRAre as follows:
(2)
(3)
(4)
(5)
(6)
In formula:B 1 For blue wave wave band,B 2 For green wave wave band,B 3 For red wave wave band,B 4 For near infrared band,t 1 -t 8 For above-mentioned each finger Threshold value is marked, specific value can optimize according to the actual situation, and it can also be with reference to following value:t 1 = 0.13、t 2 = 0.7、t 3 = 0.07、t 4 = 0.15、t 5 = 0.2、t 6 = 0.2、t 7 = 0.15、t 8 = 0.08；When identifying cloud shadow Shadow, wave band spy is utilized Threshold value is levied, as shown in formula (7)-(8):
(7)
WhereinfillholeFor in mathematical morphologyfillholeIt calculates, land part and water body part are known with Water index Not, simultaneously:
(8)
Whereint 9 -t 10 For above-mentioned each metrics-thresholds, specific value can optimize according to the actual situation, can also be with reference to following value:t 9 = 0.06、t 10 = 0.01。
3. the monthly effective coverage index synthetic method based on GF-1/WFV data, specific steps are as follows:
Atmospheric Correction is completed, is corrected with 6S the or FLASSH module of ENVI.
4. the monthly effective coverage index synthetic method based on GF-1/WFV data, specific steps are as follows:
Complete geometry just penetrating correction, using the dem data of SRTM 30m resolution ratio, for GF-1/WFV Atmospheric Correction as a result, with The Create Ortho Corrected Raster Dataset of ArcGIS is calculated.
5. the monthly effective coverage index synthetic method based on GF-1/WFV data, specific steps are as follows:
The NDVI data for synthesizing one phase of January target area with maximum value synthetic method based on GF-1/WFV valid data, to GF-1/WFV Geometric correction as a result, single scape GF-1/WFV NDVI is calculated with the raster symbol-base device of ArcGIS；With in step 2 cloud, Cloud shadow testing result carries out exposure mask calculating with NDVI, removes cloud, cloud shadow shield portions, it is effective to obtain single scape GF-1/WFV NDVI Data；The of that month all GF-1/WFV NDVI valid data in target area are subjected to maximum value synthesis, with the Mosaic mould of ArcGIS Block is calculated.
6. the monthly effective coverage index synthetic method based on GF-1/WFV data, specific steps are as follows:
Check that target area whether there is shortage of data, if so, then supplemented with MODIS 13Q1 product, GF-1/WFV with MODIS 13Q1 has the area of data to sample by land use pattern stratified random, and the linear transformation fitted between the two is closed System:
(9)
In formula, F, C respectively represent the NDVI value of GF-1/WFV and MODIS image, and x, y are spatial position, tkWhen being image capturing Between, a, b are PARAMETERS IN THE LINEAR MODELs.
7. finally being supplemented according to fitting parameter with data of the monthly maximum value product of MODIS NDVI to missing area.
CN201810648221.8A 2018-06-22 2018-06-22 Monthly effective coverage index synthetic method based on GF-1/WFV data Pending CN109033962A (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110378290A (en) * 2019-07-22 2019-10-25 中国水利水电科学研究院 A kind of cloudy optical image data flood Water-Body Information rapid extracting method and system
CN111125277A (en) * 2019-11-14 2020-05-08 国家气候中心 Landsat remote sensing vegetation index restoration method based on cube technology

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102901563A (en) * 2012-11-01 2013-01-30 中国科学院地理科学与资源研究所 Method and device for determining land surface emissivity of narrow band and broad band simultaneously
CN106780091A (en) * 2016-12-30 2017-05-31 黑龙江禾禾遥感科技有限公司 Agricultural disaster information remote sensing extracting method based on vegetation index time space statistical nature
CN108152212A (en) * 2017-12-12 2018-06-12 中国科学院地理科学与资源研究所 Meadow ground biomass inversion method based on high time resolution and spatial resolution Multi-spectral Remote Sensing Data

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102901563A (en) * 2012-11-01 2013-01-30 中国科学院地理科学与资源研究所 Method and device for determining land surface emissivity of narrow band and broad band simultaneously
CN106780091A (en) * 2016-12-30 2017-05-31 黑龙江禾禾遥感科技有限公司 Agricultural disaster information remote sensing extracting method based on vegetation index time space statistical nature
CN108152212A (en) * 2017-12-12 2018-06-12 中国科学院地理科学与资源研究所 Meadow ground biomass inversion method based on high time resolution and spatial resolution Multi-spectral Remote Sensing Data

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110378290A (en) * 2019-07-22 2019-10-25 中国水利水电科学研究院 A kind of cloudy optical image data flood Water-Body Information rapid extracting method and system
CN111125277A (en) * 2019-11-14 2020-05-08 国家气候中心 Landsat remote sensing vegetation index restoration method based on cube technology

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Application publication date: 20181218