CN109992863A - A kind of LAI inversion method and device - Google Patents
A kind of LAI inversion method and device Download PDFInfo
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Abstract
The invention discloses LAI inversion method and devices.The method comprise the steps that the LAI model prediction value that there are high-resolution Reflectivity for Growing Season data, obtain under low point of rate scale and under high-resolution scale obtained using the more years mean values of LAI of the low resolution image picture element obtained from GLASS LAI product and from Landsat sensor；According to the LAI model prediction value under low point of rate scale and under high-resolution scale, the LAI predicted value of each node in set multi-resolution tree model is obtained；According to the Satellite Observations of the corresponding LAI predicted value of the node and the multi-source got, using the state vector set matrix of the set each node of multi-scale filtering technology innovation, the LAI inverting value of the image picture element under every kind of spatial resolution is obtained using the state vector set matrix after the node updates.The present invention can be obtained under multiple spatial resolutions with inverting, and spatial distribution is complete, the continuous LAI inverting value of time series, and promotes the precision of LAI inverting value.
Description
Technical field
The present invention relates to remote sensing information extractive technique field, in particular to a kind of LAI inversion method and device.
Background technique
Leaf area index (Leaf Area Index, LAI) is the important parameter for describing Vegetation canopy, to plant biological object
The research of the problems such as reason process, Global climate change, ecological model and energy cycle of matter has significant impact.Currently, in order to
Meet time series, different spaces scale LAI application demand, mainly by by the Satellite Observations of multiple sensors
It is fused to Land Surface Parameters remote-sensing inversion and estimates LAI in the process.
Estimate that LAI is main by the way that the Satellite Observations of multiple sensors are fused to during Land Surface Parameters remote-sensing inversion
Refer to: utilizing the same phase in target area, the sight with different remote sensing informations that obtains from different satellite sensors
Measured data is mutually assisted, is complementary to one another, and is based on same remote sensing estimation model, is given full play to the advantage of different sensors data, melt
It closes inverting and obtains the preferable LAI of consistency.But most of this method all uses the same or similar observation number of spatial resolution
According to though taking full advantage of different remote sensing information, the LAI product that inverting obtains still is unable to satisfy different spaces scale
Application demand, and the data merged are the observation data of same phase mostly, LAI product remains discontinuous in time series
's.
Summary of the invention
The present invention provides a kind of LAI inversion method and devices, at least partly to solve the above problems.
In a first aspect, the present invention provides a kind of LAI inversion methods, comprising: utilize what is obtained from GLASS LAI product
More years mean values of the LAI of low resolution image picture element and obtained from Landsat sensor have high-resolution earth surface reflection
Rate data obtain the LAI model prediction value under the LAI model prediction value and high-resolution scale under low point of rate scale；According to institute
The LAI model prediction value under the LAI model prediction value and high-resolution scale under low point of rate scale is stated, set multi-resolution tree is obtained
The LAI predicted value of each node in model；Wherein, the set multi-resolution tree model includes multiple nodes, and each node is corresponding
The image picture element of additional space resolution ratio, the identical node of the spatial resolution of image picture element are in the set multi-resolution tree mould
The same level of type, the node of the different levels in corresponding same geographical location constitute father and son's node, and each node association is joined by LAI
Array at state vector set matrix；According to the moonscope of the corresponding LAI predicted value of the node and the multi-source got
Data, using the state vector set matrix of the set each node of multi-scale filtering technology innovation, after the node updates
State vector set matrix obtain the LAI inverting value of the image picture element under every kind of spatial resolution.
Second aspect, the present invention provides a kind of LAI inverting devices, comprising: model prediction value computing unit, for utilizing
It the more years mean values of LAI of the low resolution image picture element obtained from GLASS LAI product and is obtained from Landsat sensor
Have high-resolution Reflectivity for Growing Season data, obtain low point of rate scale under LAI model prediction value and high-resolution scale
Under LAI model prediction value；Gather multi-resolution tree computing unit, for according to the LAI model prediction under the low point of rate scale
LAI model prediction value under value and high-resolution scale obtains the LAI predicted value of each node in set multi-resolution tree model；
Wherein, the set multi-resolution tree model includes multiple nodes, and each node corresponds to the image picture element of additional space resolution ratio, shadow
As the identical node of the spatial resolution of pixel is in the same level of the set multi-resolution tree model, corresponding same geographical position
The node for the different levels set constitutes father and son's node, and each node is associated with the state vector set matrix being made of LAI parameter；It is more
Source Satellite Observations assimilate computing unit, for defending according to the corresponding LAI predicted value of the node and the multi-source that gets
Star observes data, using the state vector set matrix of the set each node of multi-scale filtering technology innovation, utilizes the node
Updated state vector set matrix obtains the LAI inverting value of the image picture element under every kind of spatial resolution.
It is spatial scaling model that the present invention, which constructs set multi-resolution tree, by assimilation multi-source Reflectivity for Growing Season data, such as
Assimilate TM/ETM+ and MODIS Reflectivity for Growing Season data, the continuous different spatial resolutions of time series can be obtained with inverting
LAI inverting value；When i.e. the present invention comprehensively utilizes the Reflectivity for Growing Season data of spatial resolution with higher and has preferable
Between resolution ratio Reflectivity for Growing Season data, the advantage of two kinds of Reflectivity for Growing Season data is able to complementation, eliminates and single biography is used only
The limitation of traditional refutation strategy of sensor observation, and satellite as much as possible is sufficiently used based on set multi-scale filtering technology
Data are observed, so that the precision for the different spatial resolutions LAI inverting value that inverting obtains also gets a promotion.
Detailed description of the invention
Fig. 1 is the LAI inversion method flow chart shown in the embodiment of the present invention；
Fig. 2 is the schematic diagram of the set multi-resolution tree model shown in the embodiment of the present invention；
Fig. 3 is the schematic diagram being updated to multi-resolution tree model shown in the embodiment of the present invention；
Fig. 4 is that time series LAI of the Bondville website center pel on each scale shown in the embodiment of the present invention is anti-
Drill result schematic diagram；
Fig. 5 is the LAI inverting in the region Bondville website 7km × 7km shown in the embodiment of the present invention on each scale
Result schematic diagram；
Fig. 6 is the ground ginseng in the region Bondville website 7km × 7km shown in the embodiment of the present invention on each scale
Examine image schematic diagram；
Fig. 7 be the embodiment of the present invention shown in the region Bondville website 7km × 7km on each scale LAI inverting knot
Scatterplot density schematic diagram between fruit and ground reference image；
Fig. 8 is the structural block diagram of the LAI inverting device shown in the embodiment of the present invention.
Specific embodiment
To make the object, technical solutions and advantages of the present invention clearer, below in conjunction with attached drawing to embodiment party of the present invention
Formula is described in further detail.
Hereinafter, will be described with reference to the accompanying drawings the embodiment of the present invention.However, it should be understood that these descriptions are only exemplary
, and be not intended to limit the scope of the invention.In addition, in the following description, descriptions of well-known structures and technologies are omitted, with
Avoid unnecessarily obscuring idea of the invention.
Term as used herein is not intended to limit the present invention just for the sake of description specific embodiment.Used here as
Word " one ", " one (kind) " and "the" etc. also should include " multiple ", " a variety of " the meaning, unless in addition context clearly refers to
Out.In addition, the terms "include", "comprise" as used herein etc. show the presence of the feature, step, operation and/or component,
But it is not excluded that in the presence of or add other one or more features, step, operation or component.
There are all terms (including technical and scientific term) as used herein those skilled in the art to be generally understood
Meaning, unless otherwise defined.It should be noted that term used herein should be interpreted that with consistent with the context of this specification
Meaning, without that should be explained with idealization or excessively mechanical mode.
Shown in the drawings of some block diagrams and/or flow chart.It should be understood that some sides in block diagram and/or flow chart
Frame or combinations thereof can be realized by computer program instructions.These computer program instructions can be supplied to general purpose computer,
The processor of special purpose computer or other programmable data processing units, so that these instructions are when executed by this processor can be with
Creation is for realizing function/operation device illustrated in these block diagrams and/or flow chart.
Therefore, technology of the invention can be realized in the form of hardware and/or software (including firmware, microcode etc.).Separately
Outside, technology of the invention can take the form of the computer program product on the computer-readable medium for being stored with instruction, should
Computer program product uses for instruction execution system or instruction execution system is combined to use.In context of the invention
In, computer-readable medium, which can be, can include, store, transmitting, propagating or transmitting the arbitrary medium of instruction.For example, calculating
Machine readable medium can include but is not limited to electricity, magnetic, optical, electromagnetic, infrared or semiconductor system, device, device or propagation medium.
The specific example of computer-readable medium includes: magnetic memory apparatus, such as tape or hard disk (HDD)；Light storage device, such as CD
(CD-ROM)；Memory, such as random access memory (RAM) or flash memory；And/or wire/wireless communication link.
The embodiment of the embodiment of the present invention for ease of understanding, first to the present embodiments relate to abbreviation solve
Release explanation.
LAI, (leaf area index, leaf area index) describe a dimensionless group of vegetation structure, definition
For the half of blade area total on unit surface area；
TM, (Thematic Mapper, thematic mapper), one be mounted on Landsat Landsat 4-5
A sensor, Xiang Quanqiu provide free data；
ETM+, (Enhanced Thematic Mapper Plus, Enhanced Thematic Mapper), is mounted in US Terrestrial
A sensor on satellite Landsat 7, Xiang Quanqiu provide free data；
MODIS, (Moderate-Resolution Imaging Spectroradiometer, intermediate-resolution imaging spectral
Instrument), a sensor being mounted on the satellite Terra/Aqua of U.S.'s earth observing system in the works, Xiang Quanqiu is provided free
Data；
EnMsT, (Ensemble Multiscale Tree gathers multi-resolution tree), a kind of scale provided in this embodiment
Transformation model；
GLASS LAI, Global Land Surface Satellite LAI product, a kind of LAI Universal Product；
NDVI, (Normalized Difference Vegetation Index, normalized differential vegetation index) are reflection agricultures
One of crop growing state and the important parameter of nutritional information；
MKF, (Multiscale Kalman Filter, Multiscale Kalman Filtering), a kind of linear assimilation method；
EnMsF, (Ensemble Multiscale Filter gathers multi-scale filtering), a kind of non-linear assimilation method；
ACRM, (A two-layer Canopy Reflectance Model, the double-deck canopy reflectance model), canopy spoke
One kind for penetrating mode, for simulating canopy reflectance spectrum；
GEOV2LAI, the second version of the Geoland2 (GEOV2) LAI product, a kind of LAI
Universal Product.
Currently, the Mono temporal moonscope inverting gained that whole world LAI product is usually obtained by single sensor.In many areas
Domain, these LAI products are discontinuous, imperfect in spatial distribution usually in time series, and for certain vegetation patterns
For LAI product be it is not accurate enough, these problems seriously limit application of the LAI product in geoscience.
Based on this embodiment of the present invention to gather multi-resolution tree as spatial scaling model, by assimilating TM/ETM+ and MODIS
Reflectivity for Growing Season data, Simultaneous Inversion obtain the LAI inverting value of the continuous different spatial resolutions of time series.The present invention is implemented
Example utilizes TM/ETM+ Reflectivity for Growing Season data spatial resolution with higher, and MODIS Reflectivity for Growing Season data have preferable
Temporal resolution, the advantage of the two are able to complementation, eliminate be used only the limitation of traditional refutation strategy of single-sensor observation with
And effective observation data as much as possible are sufficiently used based on assimilation algorithm, so that the different spatial resolutions LAI that inverting obtains
Precision also get a promotion.
The embodiment of the invention provides a kind of LAI inversion methods.
Fig. 1 is the LAI inversion method flow chart shown in the embodiment of the present invention, as shown in Figure 1, the method packet of the present embodiment
It includes:
S110, using the more years mean values of LAI of the low resolution image picture element obtained from GLASS LAI product and from land
The LAI model prediction that there are high-resolution Reflectivity for Growing Season data, obtain under low point of rate scale obtained in satellite sensor
LAI model prediction value under value and high-resolution scale.
S120, according to the LAI model prediction under the LAI model prediction value and high-resolution scale under the low point of rate scale
Value obtains the LAI predicted value of each node in set multi-resolution tree model；Wherein, set multi-resolution tree model includes multiple sections
Point, each node correspond to the image picture element of additional space resolution ratio, and the identical node of the spatial resolution of image picture element is in collection
The same level of multi-resolution tree model is closed, the node of the different levels in corresponding same geographical location constitutes father and son's node, Mei Gejie
The state vector set matrix that point association is made of LAI parameter.
S130, according to the corresponding LAI predicted value of the node and Satellite Observations, using set multi-scale filtering technology
The state vector set matrix for updating each node obtains every kind of sky using the state vector set matrix after the node updates
Between image picture element under resolution ratio LAI inverting value.
The present embodiment building set multi-resolution tree is spatial scaling model, by assimilating multi-source Reflectivity for Growing Season data, example
Such as assimilate TM/ETM+ and MODIS Reflectivity for Growing Season data, the continuous different spatial resolutions of time series can be obtained with inverting
LAI inverting value；I.e. the present embodiment comprehensively utilizes the Reflectivity for Growing Season data of spatial resolution with higher and has preferable
The advantage of two kinds of Reflectivity for Growing Season data is able to complementation, eliminated using only single by the Reflectivity for Growing Season data of temporal resolution
The limitation of traditional refutation strategy of sensor observation, and sufficiently defended using as much as possible based on set multi-scale filtering technology
Star observes data, so that the precision for the different spatial resolutions LAI inverting value that inverting obtains also gets a promotion.
Combination of embodiment of the present invention Fig. 2-6, are described in detail above-mentioned steps S110-S130.
Step S110 is first carried out, that is, utilizes the LAI of the low resolution image picture element obtained from GLASS LAI product more
Year mean value and what is obtained from Landsat sensor have high-resolution Reflectivity for Growing Season data, obtains under low point of rate scale
LAI model prediction value and high-resolution scale under LAI model prediction value.
Due to MODIS Reflectivity for Growing Season data have global, time continuity, the available any time, region and
The sample data of vegetation pattern, and the historical data with long-term sequence, product have quality to control file, Product Precision warp
The inspection of particular study group is crossed, in some embodiments, the GLASS of low resolution is obtained from MODIS reflectivity image
LAI product.
In some embodiments, LAI model prediction value and the high-resolution under low point of rate scale are obtained by following methods
LAI model prediction value under scale: LAI more years of the low resolution image picture element obtained from GLASS LAI product first are equal
Value, and using the Annual variations rule of more years mean values of the LAI, construct the LAI process model under low point of rate scale；Then according to
With high-resolution Reflectivity for Growing Season data, the LAI value of high resolution image pixel is obtained, is constructed using the LAI value high
LAI process model under resolution-scale；Then using under the LAI process model and high score rate scale under low point of rate scale
LAI process model obtains the LAI model prediction value under the LAI model prediction value and high-resolution scale under low point of rate scale.
In some embodiments, each resolution-scale for gathering multi-resolution tree model is all made of the LAI mistake of same form
Journey model, LAI process model are as follows:
In formula (1), LAIt fFor the LAI model prediction value at current time, ZtFor the more years mean values of LAI at current time, 0
< ε < 1 is the constant for preventing invalid operation, illustratively, ε=0.001, KtIt is bent for the corresponding time series of more years mean values of LAI
Slope of the line in moment t, LAIt-1Initial value for the LAI inverting value of previous moment, LAI can be obtained according to statistical value.Not
Isospace resolution-scale, ZtAnd KtValue it is different.
Using the GLASS LAI product of time series, many years of the available LAI on low resolution (1-km) scale are equal
Value constructs LAI process model, according to the Annual variations of many years mean time sequence curve rule to obtain using LAI process model
Obtain the LAI model prediction value under the corresponding low point of rate scale of LAI value of each pixel inverting of low resolution scale.Due to there is no height
The LAI product of resolution ratio, the more years mean values of LAI being unable to get on corresponding high-resolution scale, therefore on high-resolution scale
LAI priori value can not directly obtain.In view of LAI and NDVI is there are apparent correlation, the present embodiment passes through following formula (2)
Conversion method obtain the LAI priori value of high resolution image pixel indirectly:
In formula (2), LAIHIt is the LAI priori value of high resolution image pixel, LAIGExpression GLASS product 1km ×
The LAI value of 1km pixel, NDVIGIndicate the NDVI value of the corresponding 1km × 1km pixel of MODIS product, NDVIHExpression is projected in
High-resolution NDVI value in MODIS NDVI pixel, the value can be by the Landsat TM/ETM+ earth's surfaces of high-resolution (30-m)
Reflectivity data is calculated through following formula (3):
In formula (3), NIR and R respectively indicate the reflectance value of high-resolution near infrared band and infrared band.
After the LAI value of high resolution image pixel is calculated using formula (2) and (3), can both it be based on formula (1)
Obtain the LAI model prediction value under high score rate scale.
After obtaining the LAI model prediction value under the LAI model prediction value and high-resolution scale under low point of rate scale,
Continue to execute step S120, i.e., it is pre- according to the LAI model under the LAI model prediction value and high-resolution scale under low point of rate scale
Report value obtains the LAI predicted value of each node in set multi-resolution tree model；Wherein, set multi-resolution tree model includes multiple
Node, each node correspond to the image picture element of additional space resolution ratio, and the identical node of the spatial resolution of image picture element is in
Gather the same level of multi-resolution tree model, the node of the different levels in corresponding same geographical location constitutes father and son's node, each
Node is associated with the state vector set matrix being made of LAI parameter.
The structure of the set multi-resolution tree of the present embodiment building is as shown in Fig. 2, set multi-resolution tree is by a series of section
Point composition, and the node on different layers corresponds to different spatial resolutions, possessing the most layer of node is most thin resolution ratio
(i.e. resolution ratio highest), positioned at the bottommost of tree；And possessing the least layer of node is most thick resolution ratio (i.e. resolution ratio is minimum),
Positioned at the top of tree.As shown in Fig. 2, set multi-resolution tree is from bottom to top, spatial resolution corresponding to each layer is gradually decreased.
In a set multi-resolution tree, the corresponding node of each layer be not directly interrelated, but passes through high one layer of node structure
At the corresponding relationship of father node and child node.With reference to Fig. 2, the corresponding relationship between this node is indicated by solid black lines.
Therefore, in addition to most thin resolution ratio, the node in other resolution ratio has i child node.Equally, in addition to most thick resolution ratio,
Node in other resolution ratio has a single father node, each node s is associated with one, and by parameter to be asked, (ACRM's is quick
Feel parameter, including parameters such as leaf area index, leaf specific gravity, chlorophyll content and spectral reflectance parameters) composition state vector
Gather matrix X (s), since the purpose of the present embodiment is the state vector set matrix for inverting LAI value, in the present embodiment
X (s) is mainly LAI parameter, when in order to obtain other sensitive parameters, such as leaf specific gravity, chlorophyll content and spectral reflectance etc.
When the inverting value of parameter, it can use corresponding parameter and construct each associated state vector set matrix X (s) of node.One
In a little examples, it can utilize in the sensitive parameters such as leaf area index, leaf specific gravity, chlorophyll content and spectral reflectance parameter simultaneously
The associated state vector set matrix X (s) of each node of one or more buildings, it is possible thereby to obtain simultaneously one or more
The inverting value of sensitive parameter.
In some embodiments, the LAI predicted value of each node in multi-resolution tree model is obtained by following methods: being obtained
Each child node of the node；The LAI estimated value of the node, the height are obtained according to the LAI predicted value of each child node
The LAI model prediction value of each image picture element under resolution-scale is each bottom node in set multi-resolution tree model
LAI estimated value；According to the LAI model prediction value of the LAI estimated value of the node and the node, the LAI of the node is obtained
First predicted value；The LAI predicted value of the node is obtained using the first predicted value of LAI of the node.It, can in some embodiments
To obtain the father node of the node, forecast according to the LAI second of the first predicted value of LAI of the node and the father node
Value, obtains the second predicted value of LAI of the node, and the second predicted value of LAI of the node is the LAI forecast of the node
Value；Wherein, the first predicted value of LAI for gathering top mode in multi-resolution tree model is identical as the second predicted value of LAI.
The present embodiment utilizes MKF technology, the LAI model prediction value in highest and lowest resolution-scale is merged, to obtain
The LAI model prediction value of each scale, to construct set multi-resolution tree.
As shown in figure 3, MKF technology includes two processes, upward renewal process and downwards update update.Upward renewal process
Since the bottom of set multi-resolution tree, terminate in top；Upward renewal process by formula (4) merge present node and
The LAI model prediction value of its all child node obtains the first predicted value of LAI of present node.
In formula (4),Indicate the first predicted value of LAI of node s, Yf(s) indicate node s by process model meter
Obtained LAI model prediction value, error covariance Rf(s), H indicates Systems with Linear Observation operator,Table respectively
Show the LAI estimated value and its error covariance of node s,
Operator cov
Identity covariance operation, q indicate the child node number of node s, and F and Q ' respectively indicate spatial scaling coefficient, P (s) indicate into
Before the upward renewal process of row, the error covariance of present node itself can be according to actual setting, illustratively, with LAI phase
The element of pass can be set to 0.01.
Downward renewal process terminates since the top of set multi-resolution tree in the bottom.According to set multi-resolution tree
The LAI model prediction value of upper all nodes, downward renewal process is top-down to the progress of each node smooth, is obtained often with this
The corresponding LAI predicted value of a node.
In formula (5), Xf(s) the LAI predicted value of node s, X are indicatedf(s γ) indicates the LAI of the father node s γ of node s
Second predicted value.
In obtaining set multi-resolution tree model after the LAI predicted value of each node, step S130, i.e. root are continued to execute
According to the corresponding LAI predicted value of the node and Satellite Observations, using the set each node of multi-scale filtering technology innovation
State vector set matrix obtains the shadow under every kind of spatial resolution using the state vector set matrix after the node updates
As the LAI inverting value of pixel.
In some embodiments, according to the corresponding spatial resolution of the node, the node is obtained with the presence or absence of corresponding
Satellite Observations；If corresponding Satellite Observations are not present in the node, the LAI using the child node of the node is pre-
Report value updates the state vector set matrix of the node；If that there are corresponding Satellite Observations is (such as corresponding for the node
The node of high-resolution scale Satellite Observations if it exists, the corresponding Satellite Observations of the node are TM/ETM+'s at this time
Reflectivity for Growing Season data, if node in such as 500 meters of corresponding low resolution scale of resolution-scale there are Satellite Observations,
The corresponding Satellite Observations of the node are the Reflectivity for Growing Season data of MODIS at this time, it is possible thereby to assimilate multi-source earth surface reflection
Rate data), the node is updated using the LAI predicted value of the child node of the Satellite Observations and node of the node
State vector set matrix；The shadow under every kind of spatial resolution is obtained using the state vector set matrix after the node updates
As the LAI inverting value of pixel.
When corresponding Satellite Observations are not present in node, some embodiments are forecast using the LAI of the child node of node
The process that value updates the state vector set matrix of the node is as follows:
Obtain the corresponding first set matrix of the node and second set matrix, each member of the first set matrix
Element corresponds to the observation vector of each child node, each element of second set matrix correspond to the forecast of each child node to
Amount；Wherein, there are the observation vectors of the child node of Satellite Observations is made of the Satellite Observations of the child node, is existed
Input parameter institute of the forecast vector of the child node of Satellite Observations by the LAI predicted value of the child node as ACRM model
Obtained reflectivity data is constituted, and there is no the observation vectors of the child node of Satellite Observations by the corresponding scale of the node
By the LAI predicted value of the child node, the obtained LAI conversion value after conversion is constituted conversion coefficient, and moonscope is not present
The forecast vector of the child node of data is made of the LAI predicted value of the node；Utilize the LAI predicted value of the node, described
First set matrix and the second set matrix carry out set multi-scale filtering to the node, after obtaining the node filtering
State vector set matrix；According to the filtered state vector set matrix to each section in set multi-resolution tree model
Point is smoothed, and the state vector set matrix after obtaining smoothing processing is the updated state vector set
Matrix.
When node is there are when corresponding Satellite Observations, the Satellite Observations and the node of some embodiment nodes
Child node the LAI predicted value state vector set matrix that updates the node process it is as follows:
The corresponding third set matrix of the node and the 4th set matrix are obtained, the third set matrix includes first
Element and second element, first element are the observation vector being made of the Satellite Observations of the node, described second
Element is the corresponding observation vector of each child node of the node, and each element of the 4th set matrix includes every height
The reflectivity data of the forecast vector of node, the LAI predicted value of the node and ACRM model output；Utilize the node
LAI predicted value, the third set matrix and it is described 4th set matrix set multi-scale filtering is carried out to the node, obtain
Obtain the filtered state vector set matrix of node；According to the filtered state vector set matrix to the more rulers of set
Each node is smoothed in degree tree-model, and the state vector set matrix after obtaining smoothing processing is the update
State vector set matrix afterwards.
In some embodiments, the father node for obtaining the node, according to the corresponding filtered state of the node to
State vector set matrix after duration set matrix and the corresponding smoothing processing of the father node, obtains the node smoothing processing
State vector set matrix afterwards；Wherein, the state in the multi-resolution tree model after the corresponding smoothing processing of top mode
Vector set matrix with filtered state vector set matrix it is identical.
As shown in figure 3, each in set multi-resolution tree model to update using ENMsF method assimilation multi-source observation data
The state vector set matrix of node, renewal process include upward renewal process and downward renewal process.Renewal process is upwards
Whole tree of bottom-up scanning, since most thin resolution ratio, terminates in most thick resolution ratio, right in upward renewal process
There is no the nodes of Satellite Observations, then the state vector set matrix update of the node comes solely from the corresponding institute of the node
There is the state vector set of child node.
In formula (6),Indicate the filtered state vector set matrix of node s, Yj(s) indicate that node s is corresponding
First set matrix, error covariance Rj(s),Indicate the corresponding second set matrix of node s.
If there are Satellite Observations, Y for the child node of node sj(s) element of the observation vector of child nodes is seen by satellite
Reflectivity data is surveyed to constitute,Child nodes forecast that the element of vector is defeated as ACRM model by the LAI predicted value of child node
Enter the reflectivity that parameter obtains to constitute.
If Satellite Observations, Y is not present in the child node of node sj(s) element of observation vector is by child node in
LAI predicted value is constituted by the LAI conversion value that spatial scaling coefficient F is transformed into node s,It is middle forecast vector element be
The LAI predicted value of node s itself.
In upward renewal process, to there are the node of Satellite Observations, then the state vector set matrix of the node
It updates simultaneously from the corresponding Satellite Observations of the node and the state vector collection of the corresponding all child nodes of the node
It closes.
In formula (7), Ys(s) indicate that the corresponding third set matrix of node s, error covariance are R (R),
Indicate the corresponding 4th set matrix of node s,Indicate the linear transformation square of the 4th set matrix and third set matrix of connection
Battle array, YsIt (s) include the first element and second element, the first element is the observation vector being made of the Satellite Observations of node s,
Second element is the corresponding observation vector of each child node of node s,Each element include each child node forecast to
The reflectivity data of amount, the LAI predicted value of node s and the output of ACRM model, wherein in formula (7), Ys(s) withIt is involved
Child node observation vector and forecast the uniform formula of vector (6) interior joint s be not present Satellite Observations the case where to keep one
It causes.
Downward renewal process is whole set multi-resolution tree of scanning from up to down, since root node, to most thin point
Resolution terminates.During downward update, gather all nodes on multi-resolution tree based on all observations by from upper and
Under it is smooth.
In formula (8), Xa(s) the state vector set matrix after node s smoothing processing, X are indicateda(s γ) indicates node
State vector set matrix after the corresponding smoothing processing of father node s γ of s.
Since each element of the state vector set matrix after smoothing processing includes multiple components, this multiple component is put down
Mean value is the final result of the LAI inverting value of each node.
Due to the set multi-resolution tree in Fig. 2 structure from carefully to it is thick and from coarse to fine direction be it is asymmetric, from carefully to
Thick upward renewal process only considers the Satellite Observations of layer where node and lower level, and from coarse to fine downward updated
Cheng Ze is the Satellite Observations for considering whole all layers of tree, meets the strategy of first local entirety again.Therefore, the present embodiment
The renewal process of MKF and EnMsF is all first to update upwards, is updated still further below.
The embodiment of the present invention based on above-mentioned steps S110~S130 be capable of time of fusion sequence, have different spaces differentiate
The multi-source Satellite Observations of rate, inverting obtain under multiple spatial resolutions, and spatial distribution is complete, the continuous LAI of time series
The precision of inverting value, obtained LAI inverting value gets a promotion.
By taking U.S.'s Bondville website as an example, TM/ETM+ and MODIS is merged using the method that the embodiment of the present invention proposes
The multiple dimensioned LAI of Reflectivity for Growing Season data inversion time series, inversion result is as shown in figs. 4-7.
Fig. 4 is (the corresponding spatial discrimination of a-f in Fig. 4 on each scale of Bondville website center pel in 2000
Rate are as follows: (a) 28.9m；(b)57.8m；(c)115.6m；(d)231.2m；(e)462.4m；(f) 924.8m) LAI time series
Inversion result (Retrieved LAI), wherein having ground survey data as reference the 186th day and the 224th day
(Reference LAI).Original ground measurement data is located at the set multi-resolution tree bottom.Every upper layer, ground survey number
It is averagely obtained according to by the corresponding 4 sub- node aggregations of lower layer, corresponding standard deviation is also plotted in Fig. 4.
As a comparison, the time series of the GEOV2 LAI product of the MODIS LAI and 1 km resolution ratio of 500 meters of resolution ratio
LAI has also been drawn in corresponding scale (Fig. 4-e and Fig. 4-f) and has come out.On each space scale, the embodiment of the present invention is proposed
Method can obtain complete LAI time-serial position, and it is very close with ground reference value.In Fig. 4-e, inverting
LAI and MODIS LAI product have similar variation tendency in time series, but MODIS LAI has in vegetation growing season
Apparent unusual fluctuations；In Fig. 4-f, the seasonal variations of the LAI of inverting are consistent with GEOV2LAI, but there are bright by GEOV2 LAI
Aobvious over-evaluates phenomenon.
Fig. 5-7 is inversion result in the 186th day 2000 region Bondville website 7km × 7km, corresponding ground
With reference to image and the scatterplot density map of the two.In Fig. 5-6, each column represents the different layers of multi-resolution tree, i.e. different resolution, from a left side
Resolution ratio is thicker at double to the right, and the corresponding spatial resolution of first row is 28.9m, the corresponding spatial discrimination of secondary series in Fig. 5-6
Rate is 57.8m, and it is 115.6m that third, which arranges corresponding spatial resolution, and the corresponding spatial resolution of the 4th column is 231.2m, the 5th
Arranging corresponding spatial resolution is 462.4m, and the corresponding spatial resolution of the 6th column is 924.8m.On each scale, LAI is anti-
The spatial distribution, variation tendency and ground reference image for drilling result are consistent.Under different spatial resolutions, scatterplot density
The tropic of figure has similar trend, and all very close 1:1 line of all these tropic, shows that the LAI value of inverting has
Higher precision has good consistency with reference point.
The embodiment of the invention also provides a kind of LAI inverting devices.
Fig. 8 is the structural block diagram of the LAI inverting device shown in the embodiment of the present invention, as shown in figure 8, the device of the present embodiment
Include:
Model prediction value computing unit, for utilizing the low resolution image picture element obtained from GLASS LAI product
More years mean values of LAI and obtained from Landsat sensor have high-resolution Reflectivity for Growing Season data, obtain low point of rate
LAI model prediction value under LAI model prediction value and high-resolution scale under scale；
Gather multi-resolution tree computing unit, for according to the LAI model prediction value and high-resolution under the low point of rate scale
LAI model prediction value under rate scale obtains the LAI predicted value of each node in set multi-resolution tree model；Wherein, the collection
Closing multi-resolution tree model includes multiple nodes, and each node corresponds to the image picture element of additional space resolution ratio, the sky of image picture element
Between the identical node of resolution ratio be in the same level of the set multi-resolution tree model, the different layers in corresponding same geographical location
The node of grade constitutes father and son's node, and each node is associated with the state vector set matrix being made of LAI parameter；
Multi-source Satellite Observations assimilate computing unit, for according to the corresponding LAI predicted value of the node and getting
Multi-source Satellite Observations, using set each node of multi-scale filtering technology innovation state vector set matrix, benefit
The LAI inverting value of the image picture element under every kind of spatial resolution is obtained with the state vector set matrix after the node updates.
In some embodiments, multi-source Satellite Observations assimilate computing unit, for according to the corresponding sky of the node
Between resolution ratio, obtain the node with the presence or absence of corresponding Satellite Observations；If there is no the sights of corresponding satellite for the node
Measured data updates the state vector set matrix of the node using the LAI predicted value of the child node of the node；If the section
For point there are corresponding Satellite Observations, the LAI using the child node of the Satellite Observations and node of the node is pre-
Report value updates the state vector set matrix of the node；It is obtained using the state vector set matrix after the node updates every
The LAI inverting value of image picture element under kind spatial resolution.
Multi-source Satellite Observations assimilation computing unit includes: the first assimilation module, the second assimilation module and third assimilation
Module；
First assimilation module is for obtaining the corresponding first set matrix of the node and second set matrix, and described first
Each element of set matrix corresponds to the observation vector of each child node, and each element of second set matrix corresponds to often
The forecast vector of a child node；Wherein, there are the observation vectors of the child node of Satellite Observations by the satellite of the child node
It observes data to constitute, there are the forecast vectors of the child node of Satellite Observations by the LAI predicted value conduct of the child node
The obtained reflectivity data of the input parameter of ACRM model is constituted, and there is no the observation vectors of the child node of Satellite Observations
By the corresponding spatial scaling coefficient of the node, by the LAI predicted value of the child node, the obtained LAI after conversion is converted
Value is constituted, and there is no the forecast vectors of the child node of Satellite Observations to be made of the LAI predicted value of the node；Using described
The LAI predicted value of node, the first set matrix and the second set matrix carry out the node to gather multiple dimensioned filter
Wave obtains the filtered state vector set matrix of the node；According to the filtered state vector set matrix to institute
It states each node in set multi-resolution tree model to be smoothed, the state vector set matrix after obtaining smoothing processing is
For the updated state vector set matrix.
Second assimilation module is for obtaining the corresponding third set matrix of the node and the 4th set matrix, the third
Gathering matrix includes the first element and second element, and first element is the sight being made of the Satellite Observations of the node
Direction finding amount, the second element are the corresponding observation vector of each child node of the node, and the described 4th gathers the every of matrix
A element includes the reflectivity of the forecast vector of each child node, the LAI predicted value of the node and ACRM model output
Data；The node is carried out using the LAI predicted value of the node, the third set matrix and the 4th set matrix
Gather multi-scale filtering, obtains the filtered state vector set matrix of the node；According to the filtered state vector
Set matrix each node in the set multi-resolution tree model is smoothed, acquisition smoothing processing after state to
Duration set matrix is the updated state vector set matrix.
Third assimilation module is used to obtain the father node of the node；According to the corresponding filtered state of the node to
State vector set matrix after duration set matrix and the corresponding smoothing processing of the father node, obtains the node smoothing processing
State vector set matrix afterwards；Wherein, the state in the multi-resolution tree model after the corresponding smoothing processing of top mode
Vector set matrix with filtered state vector set matrix it is identical.
In some embodiments, set multi-resolution tree computing unit includes the first computing module and the second computing module；
First computing module is used to obtain each child node of the node；It is obtained according to the LAI predicted value of each child node
The LAI estimated value of the node is obtained, the LAI model prediction value of each image picture element under the high-resolution scale is the collection
Close the LAI estimated value of each bottom node in multi-resolution tree model；According to the LAI estimated value of the node and the node
LAI model prediction value obtains the first predicted value of LAI of the node；Described in the first predicted value of LAI acquisition using the node
The LAI predicted value of node.
Second computing module is used to obtain the father node of the node；According to the first predicted value of LAI of the node and institute
The second predicted value of LAI for stating father node, obtains the second predicted value of LAI of the node, and the second predicted value of LAI of the node is
For the LAI predicted value of the node；Wherein, the first predicted value of LAI of top mode and LAI second in the multi-resolution tree model
Predicted value is identical.
In some embodiments, model prediction value computing unit is also used to the low resolution obtained from GLASS LAI product
More years mean values of the LAI of rate image picture element, and using the Annual variations rule of more years mean values of the LAI, it constructs under low point of rate scale
LAI process model；According to high-resolution Reflectivity for Growing Season data, the LAI value of high resolution image pixel, benefit are obtained
The LAI process model under high score rate scale is constructed with the LAI value；Using under the low point of rate scale LAI process model and
LAI process model under the high score rate scale obtains under the LAI model prediction value and high-resolution scale under low point of rate scale
LAI model prediction value.
LAI inverting device provided by the present application can also pass through hardware or software and hardware combining by software realization
Mode realize.Taking software implementation as an example, LAI inverting device provided by the present application may include processor, be stored with machine and can hold
The machine readable storage medium of row instruction.Processor can be communicated with machine readable storage medium via system bus.Also, pass through
Machine-executable instruction corresponding with LAI inverting logic in machine readable storage medium is read and executes, on processor is executable
The LAI inversion method of text description.
The machine readable storage medium mentioned in the application can be any electronics, magnetism, optics or other physical stores
Device may include or store information, such as executable instruction, data, etc..For example, machine readable storage medium may is that
RAM (Radom Access Memory, random access memory), volatile memory, nonvolatile memory, flash memory, storage are driven
Dynamic device (such as hard disk drive), solid state hard disk, any kind of storage dish (such as CD, DVD) or similar storage are situated between
Matter or their combination.
According to example disclosed in the present application, present invention also provides a kind of including machine-executable instruction machine readable is deposited
Storage media, such as above-described machine readable storage medium, the machine-executable instruction can be by LAI inverting devices
Device is managed to execute to realize LAI inversion method described above.
For the ease of clearly describing the technical solution of the embodiment of the present invention, in the embodiment of invention, use " first ",
Printed words such as " second " distinguish function and the essentially identical identical entry of effect or similar item, and those skilled in the art can manage
The printed words such as solution " first ", " second " are not defined quantity and execution order.
The above description is merely a specific embodiment, under above-mentioned introduction of the invention, those skilled in the art
Other improvement or deformation can be carried out on the basis of the above embodiments.It will be understood by those skilled in the art that above-mentioned tool
Body description only preferably explains that the purpose of the present invention, protection scope of the present invention should be subject to the protection scope in claims.
Claims (10)
1. a kind of LAI inversion method characterized by comprising
It is sensed using the more years mean values of LAI of the low resolution image picture element obtained from GLASS LAI product and from Landsat
What is obtained in device has high-resolution Reflectivity for Growing Season data, obtains LAI model prediction value and high score under low point of rate scale
LAI model prediction value under resolution scale；
According to the LAI model prediction value under the LAI model prediction value and high-resolution scale under the low point of rate scale, collected
Close the LAI predicted value of each node in multi-resolution tree model；Wherein, the set multi-resolution tree model includes multiple nodes, often
A node corresponds to the image picture element of additional space resolution ratio, and the identical node of the spatial resolution of image picture element is in the set
The node of the same level of multi-resolution tree model, the different levels in corresponding same geographical location constitutes father and son's node, each node
It is associated with the state vector set matrix being made of LAI parameter；
According to the Satellite Observations of the corresponding LAI predicted value of the node and the multi-source got, using the multiple dimensioned filter of set
Wave technology updates the state vector set matrix of each node, is obtained using the state vector set matrix after the node updates
The LAI inverting value of image picture element under every kind of spatial resolution.
2. LAI inversion method according to claim 1, which is characterized in that described to be forecast according to the corresponding LAI of the node
The Satellite Observations of multi-source for being worth and getting, using the state vector collection of the set each node of multi-scale filtering technology innovation
Close matrix, comprising:
According to the corresponding spatial resolution of the node, the node is obtained with the presence or absence of corresponding Satellite Observations；
If corresponding Satellite Observations are not present in the node, institute is updated using the LAI predicted value of the child node of the node
State the state vector set matrix of node；If there are corresponding Satellite Observations for the node, the satellite of the node is utilized
The LAI predicted value of observation data and the child node of the node updates the state vector set matrix of the node；
The LAI for obtaining the image picture element under every kind of spatial resolution using the state vector set matrix after the node updates is anti-
Drill value.
3. LAI inversion method according to claim 2, which is characterized in that if there is no defend the node accordingly
Star observes data, and the state vector set matrix of the node is updated using the LAI predicted value of the child node of the node, wraps
It includes:
Obtain the corresponding first set matrix of the node and second set matrix, each element pair of the first set matrix
It should be the observation vector of each child node, each element of second set matrix corresponds to the forecast vector of each child node；
Wherein, there are the observation vectors of the child node of Satellite Observations is made of the Satellite Observations of the child node, is existed and is defended
Star observes input parameter gained of the forecast vector of the child node of data by the LAI predicted value of the child node as ACRM model
The reflectivity data arrived is constituted, and there is no the observation vectors of the child node of Satellite Observations to be turned by the corresponding scale of the node
Changing coefficient, the obtained LAI conversion value after conversion is constituted by the LAI predicted value of the child node, and moonscope number is not present
According to the forecast vector of child node be made of the LAI predicted value of the node；
The node is carried out using the LAI predicted value of the node, the first set matrix and the second set matrix
Gather multi-scale filtering, obtains the filtered state vector set matrix of the node；
According to the filtered state vector set matrix to it is described set multi-resolution tree model in each node carry out it is flat
Sliding processing, the state vector set matrix after obtaining smoothing processing is the updated state vector set matrix.
4. LAI inversion method according to claim 3, which is characterized in that if there are corresponding satellites for the node
Data are observed, update the node using the LAI predicted value of the child node of the Satellite Observations and node of the node
State vector set matrix, comprising:
The corresponding third set matrix of the node and the 4th set matrix are obtained, the third set matrix includes the first element
And second element, first element are the observation vector being made of the Satellite Observations of the node, the second element
Each element for the corresponding observation vector of each child node of the node, the 4th set matrix includes each child node
Forecast vector, the node LAI predicted value and the ACRM model output reflectivity data；
The node is carried out using the LAI predicted value of the node, the third set matrix and the 4th set matrix
Gather multi-scale filtering, obtains the filtered state vector set matrix of the node；
According to the filtered state vector set matrix to it is described set multi-resolution tree model in each node carry out it is flat
Sliding processing, the state vector set matrix after obtaining smoothing processing is the updated state vector set matrix.
5. LAI inversion method according to claim 3 or 4, which is characterized in that it is described according to the filtered state to
Duration set matrix is smoothed each node in the multi-resolution tree model, comprising:
Obtain the father node of the node；
After the corresponding filtered state vector set matrix of the node and the corresponding smoothing processing of the father node
State vector set matrix, the state vector set matrix after obtaining the node smoothing processing；Wherein, the multi-resolution tree mould
State vector set matrix in type after the corresponding smoothing processing of top mode and filtered state vector set matrix
It is identical.
6. LAI inversion method according to claim 1, which is characterized in that the LAI according under the low point of rate scale
LAI model prediction value under model prediction value and high-resolution scale obtains the LAI of each node in set multi-resolution tree model
Predicted value, comprising:
Obtain each child node of the node；
The LAI estimated value of the node is obtained according to the LAI predicted value of each child node, each of under the high-resolution scale
The LAI model prediction value of image picture element is the LAI estimated value of each bottom node in the set multi-resolution tree model；
According to the LAI model prediction value of the LAI estimated value of the node and the node, the LAI first for obtaining the node is pre-
Report value；
The LAI predicted value of the node is obtained using the first predicted value of LAI of the node.
7. LAI inversion method according to claim 6, which is characterized in that the LAI first using the node is forecast
Value obtains the LAI predicted value of the node, comprising:
Obtain the father node of the node；
According to the second predicted value of LAI of the first predicted value of LAI of the node and the father node, the LAI of the node is obtained
Second predicted value, the second predicted value of LAI of the node are the LAI predicted value of the node；Wherein, the multi-resolution tree mould
The first predicted value of LAI of top mode is identical as the second predicted value of LAI in type.
8. LAI inversion method according to claim 1, which is characterized in that described utilize obtains from GLASS LAI product
Low resolution image picture element more years mean values of LAI and obtained from Landsat sensor have high-resolution earth's surface it is anti-
Rate data are penetrated, the LAI model prediction value under the LAI model prediction value and high-resolution scale under low point of rate scale is obtained, comprising:
The more years mean values of LAI of the low resolution image picture element obtained from GLASS LAI product, and it is equal using the LAI more years
The Annual variations rule of value, constructs the LAI process model under low point of rate scale；
According to high-resolution Reflectivity for Growing Season data, the LAI value of high resolution image pixel is obtained, the LAI is utilized
LAI process model under value building high score rate scale；
Using the LAI process model under the LAI process model and the high score rate scale under the low point of rate scale, low point is obtained
LAI model prediction value under LAI model prediction value and high-resolution scale under rate scale.
9. LAI inversion method according to claim 8, which is characterized in that each point of the set multi-resolution tree model
Resolution scale is all made of the LAI process model of same form:
Wherein,For the LAI model prediction value at current time, ZtFor the more years mean values of LAI at current time, 0 < ε < 1 is anti-
The only constant of invalid operation, KtSlope for the corresponding time-serial position of more years mean values of LAI in moment t, LAIt-1When being previous
The LAI inverting value at quarter.
10. a kind of LAI inverting device characterized by comprising
Model prediction value computing unit is more for the LAI using the low resolution image picture element obtained from GLASS LAI product
Year mean value and what is obtained from Landsat sensor have high-resolution Reflectivity for Growing Season data, obtains under low point of rate scale
LAI model prediction value and high-resolution scale under LAI model prediction value；
Gather multi-resolution tree computing unit, for according to the LAI model prediction value and high-resolution ruler under the low point of rate scale
LAI model prediction value under degree obtains the LAI predicted value of each node in set multi-resolution tree model；Wherein, the set is more
Scale tree-model includes multiple nodes, and each node corresponds to the image picture element of additional space resolution ratio, the space point of image picture element
The identical node of resolution is in the same level of the set multi-resolution tree model, the different levels in corresponding same geographical location
Node constitutes father and son's node, and each node is associated with the state vector set matrix being made of LAI parameter；
Multi-source Satellite Observations assimilate computing unit, for according to the corresponding LAI predicted value of the node and get it is more
The Satellite Observations in source utilize institute using the state vector set matrix of the set each node of multi-scale filtering technology innovation
State vector set matrix after stating node updates obtains the LAI inverting value of the image picture element under every kind of spatial resolution.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113505486A (en) * | 2021-07-14 | 2021-10-15 | 中国科学院空天信息创新研究院 | Leaf area index inversion method and system for three-dimensional complex earth surface |
| CN115615936A (en) * | 2022-12-05 | 2023-01-17 | 中关村睿宸卫星创新应用研究院 | Method for calculating maximum chlorophyll index of inland water area based on multi-source satellite data |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102798851A (en) * | 2012-08-27 | 2012-11-28 | 电子科技大学 | Geometric-imaging-based MODIS (Moderate Resolution Imaging Spectroradiometer) LAI product verification method |
| CN105004320A (en) * | 2015-06-09 | 2015-10-28 | 北京师范大学 | Gaofen satellite data land surface vegetation coverage retrieval method and system |
| CN105303063A (en) * | 2015-12-03 | 2016-02-03 | 武汉大学 | Leaf area index inversion method and system of merged phenological data and remote sensing data |
| CN106296630A (en) * | 2015-05-18 | 2017-01-04 | 中国科学院遥感与数字地球研究所 | A kind of website LAI observes spatial representative evaluation methodology in Remote Sensing Products grid cell size |
| CN108761456A (en) * | 2018-05-07 | 2018-11-06 | 中国农业科学院农业资源与农业区划研究所 | A kind of inversion method of leaf area index of crop |
| CN108761034A (en) * | 2018-05-16 | 2018-11-06 | 河海大学 | A kind of multi-source satellite soil moisture retrieval method |
| CN108981617A (en) * | 2018-09-27 | 2018-12-11 | 北京师范大学 | A kind of winter wheat inversion method of leaf area index and system |
-
2019
- 2019-03-22 CN CN201910221265.7A patent/CN109992863B/en not_active Expired - Fee Related
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102798851A (en) * | 2012-08-27 | 2012-11-28 | 电子科技大学 | Geometric-imaging-based MODIS (Moderate Resolution Imaging Spectroradiometer) LAI product verification method |
| CN106296630A (en) * | 2015-05-18 | 2017-01-04 | 中国科学院遥感与数字地球研究所 | A kind of website LAI observes spatial representative evaluation methodology in Remote Sensing Products grid cell size |
| CN105004320A (en) * | 2015-06-09 | 2015-10-28 | 北京师范大学 | Gaofen satellite data land surface vegetation coverage retrieval method and system |
| CN105303063A (en) * | 2015-12-03 | 2016-02-03 | 武汉大学 | Leaf area index inversion method and system of merged phenological data and remote sensing data |
| CN108761456A (en) * | 2018-05-07 | 2018-11-06 | 中国农业科学院农业资源与农业区划研究所 | A kind of inversion method of leaf area index of crop |
| CN108761034A (en) * | 2018-05-16 | 2018-11-06 | 河海大学 | A kind of multi-source satellite soil moisture retrieval method |
| CN108981617A (en) * | 2018-09-27 | 2018-12-11 | 北京师范大学 | A kind of winter wheat inversion method of leaf area index and system |
Non-Patent Citations (3)
| Title |
|---|
| XUCHEN ZHAN: ""A METHOD FOR MULTISCALE ESTIMATION OF LEAF AREA INDEX FROM TIME-SERIES MULTI-SOURCE REMOTE SENSING DATA"", 《IEEE XPLORE》 * |
| YONGHUA QU 等: ""Preliminary study on integrated wireless smart terminals for leaf area index measurement"", 《COMPUTERS AND ELECTRONICS IN AGRICULTURE》 * |
| 靳华安 等: ""遥感反演时间序列叶面积指数的集合卡尔曼平滑算法"", 《光谱学与光谱分析》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113505486A (en) * | 2021-07-14 | 2021-10-15 | 中国科学院空天信息创新研究院 | Leaf area index inversion method and system for three-dimensional complex earth surface |
| CN113505486B (en) * | 2021-07-14 | 2023-12-29 | 中国科学院空天信息创新研究院 | Three-dimensional complex earth surface leaf area index inversion method and system |
| CN115615936A (en) * | 2022-12-05 | 2023-01-17 | 中关村睿宸卫星创新应用研究院 | Method for calculating maximum chlorophyll index of inland water area based on multi-source satellite data |
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|---|---|
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