CN113762383B - Vegetation index fusion method based on multi-source data - Google Patents
Vegetation index fusion method based on multi-source data Download PDFInfo
- Publication number
- CN113762383B CN113762383B CN202111044340.0A CN202111044340A CN113762383B CN 113762383 B CN113762383 B CN 113762383B CN 202111044340 A CN202111044340 A CN 202111044340A CN 113762383 B CN113762383 B CN 113762383B
- Authority
- CN
- China
- Prior art keywords
- vegetation coverage
- data
- vfc
- vegetation
- scale
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000007500 overflow downdraw method Methods 0.000 title claims abstract description 10
- 230000004927 fusion Effects 0.000 claims abstract description 17
- 238000000034 method Methods 0.000 claims abstract description 16
- 238000011160 research Methods 0.000 claims abstract description 11
- 238000006243 chemical reaction Methods 0.000 claims abstract description 4
- 239000002689 soil Substances 0.000 claims description 13
- 238000010606 normalization Methods 0.000 claims description 6
- 239000004927 clay Substances 0.000 claims description 3
- 239000000843 powder Substances 0.000 claims description 3
- 239000004576 sand Substances 0.000 claims description 3
- 238000011835 investigation Methods 0.000 description 2
- 238000002310 reflectometry Methods 0.000 description 2
- 230000002776 aggregation Effects 0.000 description 1
- 238000004220 aggregation Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000004807 localization Effects 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 238000012549 training Methods 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/25—Fusion techniques
- G06F18/251—Fusion techniques of input or preprocessed data
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N2021/1793—Remote sensing
- G01N2021/1797—Remote sensing in landscape, e.g. crops
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Life Sciences & Earth Sciences (AREA)
- Remote Sensing (AREA)
- Radar, Positioning & Navigation (AREA)
- Theoretical Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Computer Networks & Wireless Communication (AREA)
- General Engineering & Computer Science (AREA)
- Evolutionary Biology (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Artificial Intelligence (AREA)
- Evolutionary Computation (AREA)
- Health & Medical Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Image Processing (AREA)
Abstract
The invention discloses a vegetation index fusion method based on multi-source data, which comprises the following steps: s1, performing spatial rasterization on a field vegetation coverage data set by adopting a geographic regression weighting model to obtain the field vegetation coverage raster data set; s2, acquiring related data of the research area affecting the surface vegetation coverage, and calibrating a bio-geochemical model Biome-BGC based on the related data to obtain a daily-scale vegetation coverage grid data set; s3, acquiring vegetation coverage acquired by AVHRR and MODIS sensors of a terrra satellite in a research area, and performing scale conversion on the vegetation coverage to obtain a ten-day scale vegetation coverage grid data set; s4, constructing and solving a vegetation index fusion model based on the 3 data sets to realize fusion of multi-source vegetation data; the method solves the problem that the space-time resolution and the estimation accuracy of the surface vegetation coverage are not high.
Description
Technical Field
The invention relates to the technical field of ecological remote sensing, in particular to a vegetation index fusion method based on multi-source data.
Background
The vegetation coverage is an important index for representing the coverage degree of the surface vegetation, has close relations with the coverage degree of the surface vegetation, water and soil loss, land desertification, global climate change and the like, and is an important parameter of ecological environment change, global climate model and regional climate model. Therefore, the method for acquiring the surface vegetation coverage and the change information thereof with higher space-time resolution has important practical significance for revealing the surface space change rule, discussing the changed driving factors and analyzing and evaluating the regional ecological environment.
Disclosure of Invention
Aiming at the defects in the prior art, the vegetation index fusion method based on the multi-source data solves the problem that the space-time resolution and estimation accuracy of the surface vegetation coverage are not high.
In order to achieve the aim of the invention, the invention adopts the following technical scheme: a vegetation index fusion method based on multi-source data comprises the following steps:
s1, acquiring a field vegetation coverage data set of a research area, and performing spatial rasterization on the field vegetation coverage data set by adopting a geographic regression weighting model to obtain a field vegetation coverage raster data set;
s2, acquiring related data of the research area affecting the surface vegetation coverage, and calibrating a bio-geochemical model Biome-BGC based on the related data to obtain a daily-scale vegetation coverage grid data set;
s3, acquiring vegetation coverage acquired by AVHRR and MODIS sensors of a terrra satellite in a research area, and performing scale conversion on the vegetation coverage to obtain a ten-day scale vegetation coverage grid data set;
s4, constructing and solving a vegetation index fusion model according to the field vegetation coverage grid data set, the daily-scale vegetation coverage grid data set and the ten-day-scale vegetation coverage grid data set, so as to realize fusion of the multi-source vegetation data.
Further, the relevant data in step S2 includes: soil depth data, soil sand content data, soil powder content data, soil clay content data, DEM data, longitude and latitude gradient data and slope direction data.
Further, in step S4, the vegetation index fusion model is:
vfc ys ＝a y ×vfc y +β y
vfc zs ＝a z ×vfc z +β z
therein, vfc true Vfc for true surface vegetation coverage xs For normalized field vegetation coverage raster data vfc ys Vegetation coverage raster data for normalized daily scale vfc zs Vegetation coverage raster data for the late ten-day scale of normalization vfc x Raster data for field vegetation coverage vfc y Vegetation coverage raster data for daily scale vfc z Raster data of vegetation coverage for ten-day scale, a y And beta y Vegetation coverage raster data vfc for daily scale y A) normalized coefficient of a z And beta z Vegetation coverage raster data vfc for ten-day scale z Is used for the normalization coefficient of (a),raster data vfc for field vegetation coverage x Standard deviation of>Vegetation coverage raster data vfc for daily scale y Is set in the standard deviation of (2),vegetation coverage raster data vfc for ten-day scale z Standard deviation of>Raster data vfc for field vegetation coverage x Mean value of->Vegetation coverage raster data vfc for daily scale y Mean value of->Vegetation coverage raster data vfc for ten-day scale z Mean, omega 1 Normalized weight, ω, for normalized field vegetation coverage raster data 2 Weights, ω, for normalized day scale vegetation coverage raster data 3 Weights for vegetation coverage raster data for the late ten-day scale are normalized.
In summary, the invention has the following beneficial effects:
a vegetation index fusion method based on multi-source data utilizes a grid data set 1 based on field investigation data, utilizes a calibrated and verified Biome-BGC model pair to generate a grid data set 2, and utilizes Terra satellite AVHRR and MODIS sensor images to generate a grid data set 3. Then, error variances of three vegetation coverage data sets are estimated respectively by means of an uncertainty estimation method (Triple-allocation) method, fusion analysis is carried out on the three data sets on the basis of an improved least square method principle, and data fusion of satellite-ground multi-source vegetation data is achieved. The obtained result is superior to the estimation precision of a result used independently, the support of long-time sequence data is obtained, the space-time resolution and the estimation precision of the surface vegetation coverage are improved, and more accurate data support is provided for the refined regional ecological environment change and the regional climate model thereof.
Drawings
FIG. 1 is a flow chart of a vegetation index fusion method based on multi-source data.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and all the inventions which make use of the inventive concept are protected by the spirit and scope of the present invention as defined and defined in the appended claims to those skilled in the art.
As shown in fig. 1, a vegetation index fusion method based on multi-source data includes the following steps:
s1, acquiring a field vegetation coverage data set of a research area, and performing spatial rasterization on the field vegetation coverage data set by adopting a geographic regression weighting model to obtain a field vegetation coverage raster data set;
in this embodiment, step S1 specifically includes:
the method comprises the steps of spatially classifying observation data of 162 sites of inner mongolia, sorting regional field site investigation data, introducing DEM and longitude and latitude as explanatory variables, and rasterizing regional vegetation coverage by adopting a geographic weighted regression model to obtain a field vegetation coverage grid data set.
S2, acquiring related data of the research area affecting the surface vegetation coverage, and calibrating a bio-geochemical model Biome-BGC based on the related data to obtain a daily-scale vegetation coverage grid data set;
the relevant data in step S2 includes: soil depth data, soil sand content data, soil powder content data, soil clay content data, DEM data, longitude and latitude gradient data and slope direction data.
In this embodiment, step S2 specifically includes:
and (3) carrying out parameter localization on the BIOME_BGC model, wherein the grid meteorological data acquisition method required to be input is an APSIM interpolation method, the data source is site meteorological observation data, and the spatial resolution after interpolation is 0.5km multiplied by 0.5km. And simulating a vegetation coverage grid data set of a daily scale by using the calibrated and verified BIOME_BGC model.
S3, acquiring vegetation coverage acquired by AVHRR and MODIS sensors of a terrra satellite in a research area, and performing scale conversion on the vegetation coverage to obtain a ten-day scale vegetation coverage grid data set;
in this embodiment, the data sources of the vegetation coverage in step S3 are: vegetation coverage (Fractional Vegetation Cover, FVC) data and digital elevation (Data Elevation model, DEM) data for the MODIS of the tera satellite.
The remote sensing vegetation coverage data is obtained by training a relation model from the preprocessed reflectivity to the FVC value based on a machine learning method, and the data is resampled through aggregation grouping to obtain the ten-day-value vegetation coverage. The data source is the reflectivity and vegetation coverage products of AVHRR and MODIS sensors of Terra satellite, the time resolution is 8 days, and the total monitoring is 46 times a year. The vegetation coverage remote sensing data set time range is 2000-2015 years, and the spatial resolution is 0.5km multiplied by 0.5km by adopting the SIN projection mode.
S4, constructing and solving a vegetation index fusion model according to the field vegetation coverage grid data set, the daily-scale vegetation coverage grid data set and the ten-day-scale vegetation coverage grid data set, so as to realize fusion of the multi-source vegetation data.
In this embodiment, step S4 specifically includes:
error estimation is carried out on 3 data sets respectively by using a Triple-collocation (TC) method, and in order to avoid numerical problems in the error estimation process, the number of samples of each independent data set is selected to be more than 100. After 3 kinds of data are sorted in time and space, the data of 3 surface vegetation coverage on the same space grid point at the same time are all presentReconstructing a vegetation index fusion model, and determining the weight omega in the vegetation index fusion model by adopting an improved least square method 1 、ω 2 And omega 3 。
The vegetation index fusion model in the step S4 is as follows:
vfc ys ＝a y ×vfc y +β y
vfc zs ＝a z ×vfc z +β z
therein, vfc true Vfc for true surface vegetation coverage xs For normalized field vegetation coverage raster data vfc ys Vegetation coverage raster data for normalized daily scale vfc zs Vegetation coverage raster data for the late ten-day scale of normalization vfc x Raster data for field vegetation coverage vfc y Vegetation coverage raster data for daily scale vfc z Raster data of vegetation coverage for ten-day scale, a y And beta y Vegetation coverage raster data vfc for daily scale y A) normalized coefficient of a z And beta z Vegetation coverage for ten-day scaleRaster data vfc z Is used for the normalization coefficient of (a),raster data vfc for field vegetation coverage x Standard deviation of>Vegetation coverage raster data vfc for daily scale y Is set in the standard deviation of (2),vegetation coverage raster data vfc for ten-day scale z Standard deviation of>Raster data vfc for field vegetation coverage x Mean value of->Vegetation coverage raster data vfc for daily scale y Mean value of->Vegetation coverage raster data vfc for ten-day scale z Mean, omega 1 Normalized weight, ω, for normalized field vegetation coverage raster data 2 Weights, ω, for normalized day scale vegetation coverage raster data 3 Weights for vegetation coverage raster data for the late ten-day scale are normalized.
The modified least square method means that: for three vegetation coverage data, wherein the field vegetation coverage raster data is accurate, so that the optimal solution is not obtained by using a least square method for three data sets at the same time, but the weight omega is solved by using a least square method for the daily-scale vegetation coverage raster data set and the ten-day-scale vegetation coverage raster data set 2 And omega 3 Obtaining omega 2 And omega 3 Re-using a daily scale vegetation coverage grid dataset and a ten-day scale vegetation coverageThe image after the fusion of the grid data sets is then covered with the field vegetation grid data, and the weight omega is solved 1 Sum (omega) 2 +ω 3 ) Finally to omega 1 、ω 2 And omega 3 Normalized to obtain the weight omega 1 、ω 2 And omega 3 The method can fuse the obtained fusion result of the star-ground multi-source surface vegetation coverage, has better data quality than the vegetation coverage data quality of a single source, can better reflect the real surface vegetation coverage, and has better application prospect.
Claims (2)
1. The vegetation index fusion method based on the multi-source data is characterized by comprising the following steps of:
s1, acquiring a field vegetation coverage data set of a research area, and performing spatial rasterization on the field vegetation coverage data set by adopting a geographic regression weighting model to obtain a field vegetation coverage raster data set;
s2, acquiring related data of the research area affecting the surface vegetation coverage, and calibrating a bio-geochemical model Biome-BGC based on the related data to obtain a daily-scale vegetation coverage grid data set;
s3, acquiring vegetation coverage acquired by AVHRR and MODIS sensors of a terrra satellite in a research area, and performing scale conversion on the vegetation coverage to obtain a ten-day scale vegetation coverage grid data set;
s4, constructing and solving a vegetation index fusion model according to the field vegetation coverage grid data set, the daily-scale vegetation coverage grid data set and the ten-day-scale vegetation coverage grid data set to realize fusion of multi-source vegetation data;
the vegetation index fusion model in the step S4 is as follows:
vfc ys ＝a y ×vfc y +β y
vfc zs ＝a z ×vfc z +β z
therein, vfc true Vfc for true surface vegetation coverage xs For normalized field vegetation coverage raster data vfc ys Vegetation coverage raster data for normalized daily scale vfc zs Vegetation coverage raster data for the late ten-day scale of normalization vfc x Raster data for field vegetation coverage vfc y Vegetation coverage raster data for daily scale vfc z Raster data of vegetation coverage for ten-day scale, a y And beta y Vegetation coverage raster data vfc for daily scale y A) normalized coefficient of a z And beta z Vegetation coverage raster data vfc for ten-day scale z Is used for the normalization coefficient of (a),raster data vfc for field vegetation coverage x Standard deviation of>Vegetation coverage raster data vfc for daily scale y Standard deviation of>Vegetation coverage raster data vfc for ten-day scale z Standard deviation of>Raster data vfc for field vegetation coverage x Mean value of->Vegetation coverage raster data vfc for daily scale y Mean value of->Vegetation coverage raster data vfc for ten-day scale z Mean, omega 1 Normalized weight, ω, for normalized field vegetation coverage raster data 2 Weights, ω, for normalized day scale vegetation coverage raster data 3 Weights for vegetation coverage raster data for the late ten-day scale are normalized.
2. The method of claim 1, wherein the related data in step S2 includes: soil depth data, soil sand content data, soil powder content data, soil clay content data, DEM data, longitude and latitude gradient data and slope direction data.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111044340.0A CN113762383B (en) | 2021-09-07 | 2021-09-07 | Vegetation index fusion method based on multi-source data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111044340.0A CN113762383B (en) | 2021-09-07 | 2021-09-07 | Vegetation index fusion method based on multi-source data |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113762383A CN113762383A (en) | 2021-12-07 |
| CN113762383B true CN113762383B (en) | 2024-04-05 |
Family
ID=78793494
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111044340.0A Active CN113762383B (en) | 2021-09-07 | 2021-09-07 | Vegetation index fusion method based on multi-source data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113762383B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114419455B (en) * | 2021-12-14 | 2024-10-15 | 大连理工大学 | Regional desertification degree judging method based on multi-source remote sensing data |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104142142A (en) * | 2014-07-01 | 2014-11-12 | 北京师范大学 | Method for estimating global vegetation coverage |
| CN109359411A (en) * | 2018-11-01 | 2019-02-19 | 中国科学院东北地理与农业生态研究所 | A kind of Marsh Wetland vegetation fraction estimation method under climate change effect |
| CN110348314A (en) * | 2019-06-14 | 2019-10-18 | 中国资源卫星应用中心 | A kind of method and system using multi- source Remote Sensing Data data monitoring vegetation growing way |
| CN110927120A (en) * | 2019-11-30 | 2020-03-27 | 内蒙古蒙草生命共同体大数据有限公司 | Early warning method for coverage degree of planting |
| CN112633588A (en) * | 2020-12-30 | 2021-04-09 | 中国林业科学研究院资源信息研究所 | Forest fire behavior potential prediction method based on multi-source data fusion |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106295576B (en) * | 2016-08-12 | 2017-12-12 | 中国水利水电科学研究院 | A kind of water source type analytic method based on nature geography characteristic |
| WO2019157348A1 (en) * | 2018-02-09 | 2019-08-15 | The Board Of Trustees Of The University Of Illinois | A system and method to fuse multiple sources of optical data to generate a high-resolution, frequent and cloud-/gap-free surface reflectance product |
-
2021
- 2021-09-07 CN CN202111044340.0A patent/CN113762383B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104142142A (en) * | 2014-07-01 | 2014-11-12 | 北京师范大学 | Method for estimating global vegetation coverage |
| CN109359411A (en) * | 2018-11-01 | 2019-02-19 | 中国科学院东北地理与农业生态研究所 | A kind of Marsh Wetland vegetation fraction estimation method under climate change effect |
| CN110348314A (en) * | 2019-06-14 | 2019-10-18 | 中国资源卫星应用中心 | A kind of method and system using multi- source Remote Sensing Data data monitoring vegetation growing way |
| CN110927120A (en) * | 2019-11-30 | 2020-03-27 | 内蒙古蒙草生命共同体大数据有限公司 | Early warning method for coverage degree of planting |
| CN112633588A (en) * | 2020-12-30 | 2021-04-09 | 中国林业科学研究院资源信息研究所 | Forest fire behavior potential prediction method based on multi-source data fusion |
Non-Patent Citations (1)
| Title |
|---|
| 基于灵活的时空融合模型的植被覆盖度与植被指数关系;王杰等;草业科学;第34卷(第02期);264-272 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113762383A (en) | 2021-12-07 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Arsenault et al. | A comprehensive, multisource database for hydrometeorological modeling of 14,425 North American watersheds | |
| CN111553245A (en) | Vegetation classification method based on machine learning algorithm and multi-source remote sensing data fusion | |
| CN108982548B (en) | Surface soil moisture retrieval method based on passive microwave remote sensing data | |
| CN111982294B (en) | All-weather earth surface temperature generation method integrating thermal infrared and reanalysis data | |
| Moeslund et al. | Light detection and ranging explains diversity of plants, fungi, lichens, and bryophytes across multiple habitats and large geographic extent | |
| CN114740180A (en) | Soil organic carbon estimation method and device based on multi-source remote sensing data | |
| CN108647401B (en) | Watershed nitrogen and phosphorus pollution assessment method based on space remote sensing technology | |
| Saritha et al. | Digital elevation model and terrain mapping using LiDAR | |
| CN103529189A (en) | Soil organic matter space distribution predication method based on qualitative and quantitative auxiliary variables | |
| CN106021868A (en) | Multi-rule algorithm-based remote sensing data downscaling method | |
| EP3794386B1 (en) | Determination of location-specific weather information for agronomic decision support | |
| CN114819737B (en) | Method, system and storage medium for estimating carbon reserves of highway road vegetation | |
| Yin et al. | Estimation of grassland height based on the random forest algorithm and remote sensing in the Tibetan Plateau | |
| Yun et al. | Mitigating atmospheric effects in InSAR measurements through high-resolution data assimilation and numerical simulations with a weather prediction model | |
| Kvamme | Geographic information systems and archaeology | |
| CN113762383B (en) | Vegetation index fusion method based on multi-source data | |
| CN113408111A (en) | Atmospheric degradable water yield inversion method and system, electronic equipment and storage medium | |
| CN112285808A (en) | Method for reducing scale of APHRODITE precipitation data | |
| Wang et al. | Soil moisture retrieval from sentinel-1 and sentinel-2 data using ensemble learning over vegetated fields | |
| Gong et al. | Long-term precipitation estimation combining time-series retrospective forecasting and downscaling-calibration procedure | |
| CN114184173A (en) | Comprehensive evaluation method for mapping capability of three-dimensional image of remote sensing satellite | |
| Huo et al. | Assessing the effect of scaling methods on retrieval of soil moisture based on MODIS images in arid regions | |
| CN106019310A (en) | Resolution improvement method based on remote sensing data of satellite | |
| CN112270293A (en) | Rice field CH day by day4Remote sensing estimation method of flux | |
| Karamouz et al. | Soil moisture data: From using citizen science to satellite technology |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |