CN1704758A - Method for realizing wheat behavior monitoring and forecasting by utilizing remote sensing and geographical information system technology - Google Patents
Method for realizing wheat behavior monitoring and forecasting by utilizing remote sensing and geographical information system technology Download PDFInfo
- Publication number
- CN1704758A CN1704758A CN 200410042625 CN200410042625A CN1704758A CN 1704758 A CN1704758 A CN 1704758A CN 200410042625 CN200410042625 CN 200410042625 CN 200410042625 A CN200410042625 A CN 200410042625A CN 1704758 A CN1704758 A CN 1704758A
- Authority
- CN
- China
- Prior art keywords
- wheat
- quality
- remote sensing
- monitoring
- model
- 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.)
- Pending
Links
Images
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
This invention relates to a method for monitoring and forecasting wheat quality by the remote sensing and geography information system technology, which applies a combined method to monitor the key factors for wheat quality, obstacle factors of plant diseases and insect pests, lodging and serious environment of key growing period and the wheat growing situation, sets up the coherent relations of remote sensing parameters of main factors for remote sensing parameters of main factors for wheat quality, its growing situation and in nutrition state, links the remote sensing model and agronomic model to construct a wheat quality remote sensing monitor linking model and utilizes the image data and geography information system data in different growing periods to realize forecast for its quality based on a quality evaluation model.
Description
Technical field
The present invention relates to a kind of technical scheme of utilizing rs and gis technical monitoring forecast wheat quality, belong to areas of information technology.Can directly apply to wheat cultivation management, wheat guidance purchase and classification processing.
Background technology
Wheat is China's staple food crop, and according to the needs of turn of the market and accession to WTO, China's agricultural planting structure is significantly adjusted, and Wheat Production has begun by simple pursuit high-yield mode to high-quality, special use and direction transformation efficiently.The quality of wheat grain quality is by breediness and the common decision of planting environment.Developed countries is mainly by selecting the high kind (antenatal) of high-quality biochemical component content for use; take the relatively uniform control measures of large-scale planting (in the product); make between the results seed quality discrepancy less, by implementing classification purchase, classification processing (postpartum), processing quality is improved at last.Although be no lack of various high-quality types in state's yield wheat, because shortage kind zoning and standardized cultivation degree are low, different ground interblock grain qualities differ greatly, and mix results processing quality is reduced greatly.Same fine quality is under the approaching prerequisite of yield level, and the quality grade price difference that brings because of different rich water combinations reaches 15-20%; The 2nd, flour processing enterprise is urgent to the large tracts of land fast and low-cost detection technique demand of wheat quality before gathering in the crops.
The principal ingredient of wheat seed is starch and protein, and for same kind, content of the two and component ratio have determined grain quality to a great extent.The aleuronat quality is the index that nutritional quality of wheat and processing quality is all had material impact, is the basic index of wheat international trade and quality evaluation.At present, powder is purchased, processes and is joined in difference classification according to the inherent biochemical component of wheat seed, can improve the flour added value more than 20%, though indoor check and analysis method is comparatively accurate, but because of its destructive sampling means, need the sampling point amount many and time-consuming, take a lot of work, the check and analysis cost is high and can't use.
Remote sensing (Remote Sensing, RS) be directly not contact object itself, survey and receive information (as information such as electric field, magnetic field, electromagnetic wave, seismic events) from afar from target object by instrument (sensor), through transmission of Information and Treatment Analysis thereof, the technology of features such as the attribute of recognition object and distribution thereof.In the last few years, RS developed rapidly, and a large amount of high spatials, time and spectral resolution image are widely used in fields such as resource environment, agricultural, military affairs.Utilize RS can be in real time, on a large scale, do not have the ground of destruction and survey surface condition, realized by " point-like information " revolution to " planar information ".Utilize the remote sensing monitoring crop quality that two kinds of methods can be arranged: the one, direct method, the 2nd, indirect method.For crops such as tealeaves, tobacco leaf, herbage, fodder maizes, its blade or stem stalk are the important component parts of economic yield, the biochemical component of blade or stem stalk inside such as nitrogen (can be converted into thick protein) etc. are the important indicators of estimating quality, descend the correlationship between remotely-sensed data and blade or stem stalk biochemical component in the time of can directly setting up certain mutually, and then assess its quality status.And paddy rice, wheat, common or high oil corn, Soybean and Other Crops, its seed is the results object that constitutes economic yield, the biochemical component of blade or stem stalk can not be directly as the index of estimating quality, therefore monitor quality by the remote sensing image that obtains the wheat seed maturity stage, also be close at present and can't realize.
Crop physiology studies show that, the mobilization again that the required nitrogen then about 80% of protein synthesis accumulates nitrogen from plant before blooming in the seed, and 20% from the nitrogen of back plant from the soil absorption of blooming.The former can calculate by the change dynamics of measuring nitrogen compound content in the plant, and the latter also can reflect in the nitrogen nutrition level from plant, and nitrogen nutrition level and change dynamics thereof can realize that this provides theoretical foundation for utilizing remote sensing indirect monitoring wheat seed protein by remote sensing monitoring in the plant.Yet the factor of decision wheat quality is complicated.Be the diversity that influences the biochemical component of wheat quality on the one hand; Be the complicacy of the nature-nurture effect of decision quality formation on the other hand.Can not get a desired effect by the simple monitoring of inverting indirectly like this.For this reason, the present invention adopts aggregative model, based on wheat cultivation agronomy knowledge, with rs and gis (Geographical Information System, GIS) be technological means, by setting up the correlationship between remotely-sensed data and blade or stem stalk biochemical component, is to link with blade or stem stalk biochemical component with non-Remote Sensing Model between the grain quality index, realizes the wheat quality monitoring and prediction according to image data and Geographic Information System background data that the wheat difference is obtained the period of growing.
Summary of the invention
Time-consuming in order to overcome at present artificial indoor sample test analysis, take a lot of work and the deficiency of expensive point source wheat quality monitoring, and utilize remote sensing image directly or indirectly to monitor the difficulty of wheat grain quality separately, the present invention is on the basis of existing wheat quality Study on influencing factors achievement, key factor and disease and pest that utilization rs and gis monitoring wheat quality forms, lodging waits obstruction factor and wheat growing way, set up the monitoring and prediction model that wheat grain quality forms, and utilize the main factor that influences quality formation to carry out the non-destructive monitoring forecast that comprehensive evaluation realizes wheat quality, realized in real time, large-area wheat quality monitoring and prediction, this monitoring and prediction precision as a result can reach plot level (promptly can monitor out the wheat quality situation in concrete plot), for the production decision of wheat high-quality and grain circulation and processing enterprise provide technology and information to instruct.
This method is support based on the physiology principle of wheat growth with the rs and gis technology, has solved real-time, large-area wheat quality monitoring and prediction.For realizing this goal, this method mainly solved the main affecting factors that is used for real-time, large-area wheat quality monitoring and prediction selection, set up and the remote sensing problem of implementation of quality influence factor based on the wheat quality monitoring model of agronomy knowledge, specifically comprise:
(1) influence factor and present stage remote sensing and the geographic information system technology level that forms by wheat quality, selected the main factor as the wheat quality monitoring and prediction such as the soil texture, wheat growth stage, wheat breed, plant water content, the effective water cut of soil, wheat growing way, and influence the sporadic obstruction factor that wheat quality forms, as wheat lodging, wheat disease and pest, the important environmental baseline of key developmental stages (as too high canopy surface temperature of wheat grain filling phase and too much rainfall etc.).
(2) utilize middle high-resolution satellite remote-sensing image monitoring wheat plant moisture, canopy surface temperature, wheat growing way and nutrition condition etc. to influence the correlation factor that wheat quality forms, set up relevant remote-sensing inversion model, this is the prerequisite and the basis of realizing the wheat quality monitoring.
The wheat late growth stage is the most important period that grain quality forms, and what wherein have the greatest impact is the effective water cut of soil of heading stage to milk stage, realize by monitoring upper soll layer moisture for the effective water cut monitoring of soil is general, and the current satellite remote sensing monitoring upper soll layer moisture that utilizes is primarily aimed at exposed soil or sparse vegetation coverage condition, covers the general using microwave remote sensing for high vegetation and realizes; On the other hand, the water stress situation (or damage caused by a drought situation) of the crop that can not accurately reflect of soil moisture.This patent is by the physiology principle of wheat growth, set up the piecewise function between the effective water cut of soil of different growing correspondence and plant moisture, the canopy surface temperature, solve the remote sensing monitoring problem of the effective water cut of soil, also solved wheat damage caused by a drought monitoring problem.
(3) effective link problems of agronomy model and Remote Sensing Model.That the advantage of remote sensing is is multiple dimensioned, multi-angle, multiband, provide large-scale earth observation data multidate, make us might in time obtain topographical features information---remote sensing parameters such as normalized differential vegetation index (NDVI), canopy surface temperature, brightness index and surface radiation temperature, and, further obtain the topographical features parameter by the remote sensing quantitative inversion that Remote Sensing Model and priori get involved---earth surface reflection rate, leaf area index (LAI), chlorophyll content, ground component temperature, soil water content, wheat plant and canopy physics and chemistry parameter etc.But the remote sensing image that directly obtains the wheat seed maturity stage is monitored quality, also is close at present and can't realizes, can only infer according to the image data that obtain other periods of growing.Physiology principle according to wheat quality formation, the achievement in research that wheat quality formative factor and existing remote sensing monitoring crop canopies temperature, crop growing state and biochemistry group are graded combines, and has set up the monitoring and prediction model based on the wheat grain quality formation of remote sensing parameter.This is that the present invention realizes the key issue that solves in the wheat quality monitoring and prediction.
This method is at first monitored the key factor (the effective water cut of soil, wheat growing way, plant water content) and obstruction factor (the condition of serious stress of soil environmental baseline of wheat lodging, wheat disease and pest, key developmental stages thereof that wheat quality forms that influence of each key developmental stages of wheat by remote sensing quantitative test means, as too high canopy surface temperature of wheat grain filling phase and too much rainfall etc.), and the plot is taken place obstruction factor from the good quality wheat zone, get rid of; Realize quality of wheat situation in the wheat quality normal region by wheat quality monitoring and prediction model then.Therefore, this method can detect simultaneously influences obstruction factor distributed areas and influence degree and the wheat grain quality characteristic that wheat quality forms.
Wheat quality monitoring and prediction technical scheme mainly is divided into following five steps realization:
The first step, the wheat planting area extracts, and extracts wheat planting area with the Geographic Information System background data with relevant priori according to multidate NDVI.The Geographic Information System background data comprises that wheat planting distribution over the years, soil utilization, soil cover and terrain data, (as the previous year is the zone in forest land in conjunction with priori, should not be the wheat planting zone current year), utilize the auxiliary sensor information classification of these data, improve the extraction precision that wheat planting distributes.
Second step, the relevant remote sensing parameter of wheat quality monitoring model the and influence monitoring of wheat quality formation principal element.Comprise that wheat canopy surface temperature, wheat plant water cut, blade and plant protein content, normalized differential vegetation index (NDVI) etc. reflect the index of wheat growing way and nutrition condition.Each remote sensing parameter inverse model of wheat is set up according to the wheat different growing.
In the 3rd step, the wheat quality obstruction factor is got rid of.According to Remote Sensing Model of having set up and the image data that obtains in real time, quantitative resolution is carried out in farmland in the monitored area and vegetation, to take place such as pustulation period excess moisture, the too high threshold value that surpasses of canopy surface temperature, than more serious field pieces of obstacle influence such as large tracts of land disease or lodging, from the normal region, get rid of.This operation both can be carried out separately, also can be used as the basis of additive method.
In the 4th step, Remote Sensing Model links with the agronomy model.This committed step is to set up wheat quality to form getting in touch between major influence factors and the remote sensing parameter that reflects wheat growing way, nutrition condition, can utilize remote sensing image in time to obtain to reflect wheat Physiology and biochemistry and soil environment situation by the model link, make up the peer link model.Most typical in this method is to set up based on model between the wheat canopy surface temperature of remote-sensing inversion and wheat plant moisture and the effective water cut of soil to link, and the realization key developmental stages is formed with the monitoring of the effective water cut of soil of material impact to wheat quality.
The 5th step, specifically use according to wheat flour, set up relevant quality comprehensive evaluation model, by the wheat quality comprehensive evaluation model wheat in the quality normal region is carried out comprehensive evaluation, and wheat quality monitoring and prediction result is exported at last as inferior zone in the zone that the obstruction factor generation is arranged.It is estimated factor obtain manner and comprises: obtain the background data (soil texture, wheat breed) stored in (the effective water cut of soil, wheat growing way, wheat plant water cut), the Geographic Information System, obtain (wheat growth stage) according to wheat growth physiology principle by remote sensing monitoring.Wheat growth stage does not participate in the comprehensive evaluation of wheat quality, but wheat growth stage directly influences the foundation of each remote sensing parameter inverse model of wheat.
This method can real-time, large-area monitoring wheat grain quality characteristic, and detects simultaneously and influence obstruction factor distributed areas and influence degree, wheat growing way and the nutrition condition that wheat quality forms.Because method helps government department to formulate rational policy, instruct the peasant to carry out high-quality production, and help grain dealer and grain processing enterprise to carry out correct business decision, economic benefit and social agency are obvious, and use cost is not high, government, bigger generalized grain dealer and grain processing enterprise are the potential users, should have good market outlook.Serial earth resources satellite is about to launch by China simultaneously, and this presses for the decipher technology of satellite information on the one hand, and application cost is further reduced, and this achievement in research has high potential commercial value undoubtedly.
Description of drawings
Fig. 1 is the technology path that utilizes rs and gis monitoring and prediction wheat quality.Its data source mainly contains remote sensing, Geographic Information System and knowledge (as obtaining wheat growth stage according to wheat growth physiology principle).Wherein the obstruction factor eliminating is optionally, just carries out this step when obstruction factor takes place; After quality forms the agronomy model and is the key factor analysis that forms according to quality, the quality evaluation model that utilizes the quality evaluation key factor chosen to set up.
Fig. 2 is at wheat heading stage, utilizes the aleuronat content distribution figure of TM Landsat remote sensing shadow prediction, and makes the protein rating distribution plan according to being divided into four social estate systems according to survey aleuronat content.
Fig. 3 is between the wheat whole growing, utilize the obstruction factor total score Butut of the TM Landsat remote sensing shadow monitoring of different growing, the expression wheat quality forms the obstruction factor distribution situation, comprises mainly that serious lodging, disease are serious, four factors such as pustulation period temperature too high (above 32 ℃) and growing way extreme difference.
Fig. 4 is at wheat heading stage, the wheat quality forecast ranking score Butut that utilizes the monitoring of TM Landsat remote sensing shadow to obtain.Consequently the form with natural plot provides, the inner wheat quality average level in reflection plot.
Embodiment
Now the invention will be further described in conjunction with example, promptly utilizes the TM satellite remote-sensing image that Beijing suburb is carried out the wheat quality monitoring and prediction:
At first, obtain 3 phase TM remote sensing images in winter wheat heading stage, pustulation period and milk stage, and with the normalized differential vegetation index of heading stage and pustulation period and wheat planting areal distribution in the past and the soil wheat planting zone that utilized information extraction.
Secondly, and utilize TM the 5th wave band (TM5) inverting respectively wheat plant moisture, TM the 6th wave band (TM6) inverting wheat canopy surface temperature, visible light and near-infrared band (TM2, TM3, TM4) inverting the index such as normalized differential vegetation index of reflection wheat growing way and the wheat plant biochemical indicators such as carbon nitrogen content of wheat plant and blade.
The 3rd, wheat quality forms the monitoring of obstruction factor generation area and extracts, and the concrete condition of Beijing suburb wheat is extracted the monitoring of three obstruction factors such as stripe rust of wheat, lodging and pustulation period excessive temperature district etc. respectively according to this year.
(1) monitoring of wheat lodging district is extracted.Utilize the visible light wave range (TM7) and the near-infrared band (TM4) of pustulation period and milk stage TM satellite remote-sensing image to monitor wheat lodging zone and disaster degree thereof.Wheat lodging back stem stalk and blade in detection viewing field ratio and the optical condition that is subjected to of plant component variation has taken place, its canopy spectra characteristic also changes thereupon.Spectral reflectivity increases with the increase of lodging angle, and the relative amplification of visible light wave range is higher than near-infrared band.Set up the model that utilizes normalization difference vegetation index NDVI (X) inverting lodging angle (Y1) and lodging index (Y2) based on this principle:
Y1＝-1998.2x+1784.7?R
2＝0.612
**(n＝22)
Y2＝-1.5462x+1.6868?R
2＝0.789
**(n＝11)
According to this model and utilize lodging take place before and after the LandSat ETM satellite image of phase 2 time, successful inverting the occurrence degree of wheat lodging, verify that through field investigation this model has higher reliability.
(2) monitoring of stripe rust of wheat evil district is extracted.The stripe rust of wheat district has characteristic difference with the canopy spectra in normal district at visible light and near infrared platform place, has set up the correlation model that utilizes position, red limit (X) inverting winter wheat stripe rust disease index (Y) in view of the above:
Y＝-3.031x+2262.7?R
2＝0.843
**(n＝20)
According to above model, the disease index of stripe rust of wheat that utilized the TM7 of pustulation period TM image and TM4 inverting, and utilize the disease index threshold value to extract the serious generating region of stripe rust of wheat.
(3) the too high district monitoring of pustulation period canopy surface temperature is extracted.Utilize TM the 6th wave band (TM6) the inverting wheat canopy surface temperature of pustulation period TM satellite remote-sensing image, and, the extracted region of canopy surface temperature greater than 35 ℃ gone out according to the physiology principle of wheat growth.
In the wheat quality monitoring, above-mentioned wheat quality can be formed the obstruction factor generation area and from the normal region, get rid of, thereby improve the accuracy and the efficient of wheat quality monitoring and prediction.
At last, Remote Sensing Model links and wheat comprehensive evaluation with the agronomy model, output wheat quality monitoring and prediction information.
Characteristics (mainly based on strong muscle and high protein wheat) according to the Beijing suburb wheat planting, the main factor and the ordering thereof of quality of wheat have been selected to influence, comprise kind, growing way, plant water content, the effective water cut of soil, the soil texture etc., and set up strong gluten wheat quality comprehensive evaluation model, give different weights according to the contribution rate size that above-mentioned factor pair grain quality forms.For the remote sensing parameter, test is set up according to large number of ground statistical model or mechanism model normalization assignment; Usually according to the priori assignment, as comprehensive evaluation assignment such as the main biochemical component content of kind basis, and soil texture normalization is according to GIS basic data assignment for non-remote sensing parameter.Wheat quality comprehensive monitoring model is as follows:
y＝Ax1+Bx2+Cx3+Dx4+Ex5
A, B, C, D, E are respectively the weight of kind, wheat growing way (expressing with NDVI), plant water content (canopy surface temperature), the effective water cut of soil and the soil texture in the formula, as the quality monitoring for strong gluten wheat, give weight coefficient: A=0.4; B=0.25; C=0.15; D=0.15; E=0.05.X1, X2, X3, X4, X5 represent the normalized value of kind, wheat growing way (NDVI), plant water content (canopy surface temperature), the effective water cut of soil and the soil texture respectively.
Image data is set up the biochemical component such as chlorophyll, nitrogen of wheatland canopy and temperature, moisture inverse model via satellite, and links with the agronomy model.In above-mentioned quality comprehensive evaluation model, the NDVI that the wheat growing way directly obtains with the TM remote sensing image, i.e. (TM4-TM3)/(TM4+TM3); Plant water content reflects that with canopy surface temperature (TC) canopy surface temperature is obtained by TM satellite remote-sensing image thermal infrared wave band TM6 (x) inverting, and its inverse model is as follows:
Y＝-1.0647x+39.449?R
2＝0.657
**(n＝20)
Upper soll layer water cut SM (Y) can pass through NDVI, the canopy surface temperature analysis-by-synthesis obtains, and normalization soil moisture index NDWI (x) inverting of passing through the acquisition of TM image here obtains, x=(TM4-TM5)/(TM4+TM5), and inverse model is:
Y＝-0.2843x+23.509?R
2＝0.2369
*(n＝20)
Kind normalization is according to the priori assignment, as in excellent 9507=5; Capital 9428=4; Capital winter 8=3; Capital 411=2.Soil texture normalization is according to GIS basic data assignment, as loam=5; Heavy loam=4; Sandy loam=3; Clay=2; Sandy soil=1.
Growing way normalized value and wheat growth stage have much relations, and pustulation period growing way normalization is as follows: when 0.2≤NDVI or NDVI＞0.8, value is 1; 0.2＜NDVI≤0.35 o'clock, value is 2; 0.35＜NDVI≤0.5 o'clock, value is 3; 0.5＜NDVI≤0.65 o'clock, value is 4; 0.65＜NDVI≤0.8 o'clock, value is 5.
The plant water content normalized value also has much relations with wheat growth stage, and pustulation period plant water content normalization is as follows: when TC＜20 ℃ or TC＞35 ℃, value is 0; When 20 ℃≤TC＞23 ℃, value is 1; When 23 ℃≤TC＞26 ℃, value is 2; When 26 ℃≤TC＞29 ℃, value is 3; When 29 ℃≤TC＞32 ℃, value is 4; When 32 ℃≤TC＞35 ℃, value is 5.
The effective water cut normalized value of soil is one all has the piecewise function that is closely related with the soil texture and wheat growth stage, and the effective water cut normalization of pustulation period soil is as follows:
(1) sandy soil: when SM＜2.7% or SM＞15.2%, value is 0; When 2.7%≤SM＞5.2%, value is 1; When 5.2%≤SM＞7.7%, value is 2; When 7.7%≤SM＞10.2%, value is 3; When 10.2%≤SM＞12.7%, value is 4; When 12.7%≤SM＞15.2%, value is 5;
(2) sandy loam: when SM＜5.4% or SM＞20%, value is 0; When 5.4%≤SM＞8.2%, value is 1; When 8.2%≤SM＞11%, value is 2; When 11%≤SM＞13.8%, value is 3; When 13.8%≤SM＞16.6%, value is 4; When 16.6%≤SM＞20%, value is 5;
(3) loam: when SM＜10.8% or SM＞20.8%, value is 0; When 10.8%≤SM＞12.8%, value is 1; When 12.8%≤SM＞14.8%, value is 2; When 14.8%≤SM＞16.8%, value is 3; When 16.8%≤SM＞18.8%, value is 4; When 18.8%≤SM＞20.8%, value is 5;
(4) heavy loam: when SM＜13.5% or SM＞22.4%, value is 0; When 13.5%≤SM＞15.2%, value is 1; When 15.2%≤SM＞17%, value is 2; When 17%≤SM＞18.8%, value is 3; When 18.8%≤SM＞20.7%, value is 4; When 20.7%≤SM＞22.4%, value is 5;
(5) clay: when SM＜17.3% or SM＞24%, value is 0; When 17.3%≤SM＞18.5%, value is 1; When 18.5%≤SM＞19.7%, value is 2; When 19.7%≤SM＞20.9%, value is 3; When 20.9%≤SM＞22.1%, value is 4; When 22.1%≤SM＞24%, value is 5;
Calculate the quality of wheat aggregative index by above comprehensive evaluation model, generated the wheat quality grade distributed intelligence of plot level.
Claims (7)
1. monitoring and prediction method of utilizing the rs and gis technology to realize wheat quality, it is characterized in that: utilize middle high-resolution multispectral remote sensing imaging monitor wheat biochemical component and obstruction factor, utilize geographic information system technology, in conjunction with wheat cultivation agronomy model and knowledge evaluation forecast wheat quality, may further comprise the steps successively:
(1) the wheat planting area extracts, and extracts the wheat planting area according to multidate NDVI and wheat planting distribution over the years, soil utilization, soil covering and landform;
(2) the relevant remote sensing parameter inverting of wheat quality monitoring model the and influence monitoring of wheat quality formation principal element;
(3) wheat quality obstruction factor monitoring with get rid of, obstruction factor comprises the condition of serious stress of soil environmental baseline of wheat lodging, wheat disease and pest, key developmental stages, as too high canopy surface temperature of wheat grain filling phase and too much rainfall;
(4) Remote Sensing Model links with the agronomy model, set up wheat quality and form getting in touch between major influence factors and the remote sensing parameter that reflects wheat growing way, nutrition condition, can utilize remote sensing image in time to obtain to reflect wheat Physiology and biochemistry and soil environment situation by the model link, make up the peer link model;
(5) specifically use according to wheat flour, carrying out wheat quality comprehensive evaluation in the monitoring and prediction zone, output wheat quality monitoring and prediction result.
2. according to the described monitoring and prediction method of utilizing the rs and gis technology to realize wheat quality of claim 1, it is characterized in that: by its seed is the crop quality monitoring and prediction that constitutes economic yield, specifically comprises paddy rice, common or high oil corn, soybean.
3. according to the described monitoring and prediction method of utilizing the rs and gis technology to realize wheat quality of claim 1, it is characterized in that: in the step (2), by the physiology principle of wheat growth, utilize the effective water cut of middle high-resolution multispectral remote sensing image wheat plant moisture and canopy surface temperature monitoring soil.
4. according to the described monitoring and prediction method of utilizing the rs and gis technology to realize wheat quality of claim 1, it is characterized in that: in the step (3), utilize the lodging of middle high-resolution multispectral remote sensing imaging monitor wheat.
5. according to the described monitoring and prediction method of utilizing the rs and gis technology to realize wheat quality of claim 1, it is characterized in that: in the step (3), to exist quality to form the obstruction factor zone from the wheat planting zone gets rid of from the quality normal region, obstruction factor comprises condition of serious stress of soil environmental baseline, the canopy surface temperature that the wheat grain filling phase is too high and the too much rainfall of wheat lodging, wheat disease and pest, key developmental stages.
6. according to the described monitoring and prediction method of utilizing the rs and gis technology to realize wheat quality of claim 1, it is characterized in that: in the step (4), set up wheat quality and form getting in touch between major influence factors and the remote sensing parameter that reflects wheat growing way, nutrition condition, link Remote Sensing Model and agronomy model make up wheat quality remote sensing monitoring link model.
7. according to the described monitoring and prediction method of utilizing the rs and gis technology to realize wheat quality of claim 1, it is characterized in that: in the step (5), select kind, growing way, plant water content, the effective water cut of soil, five factors of the soil texture as the different growing wheat quality comprehensive evaluation factor, set up the forecast of wheat quality comprehensive evaluation.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN 200410042625 CN1704758A (en) | 2004-05-28 | 2004-05-28 | Method for realizing wheat behavior monitoring and forecasting by utilizing remote sensing and geographical information system technology |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN 200410042625 CN1704758A (en) | 2004-05-28 | 2004-05-28 | Method for realizing wheat behavior monitoring and forecasting by utilizing remote sensing and geographical information system technology |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN1704758A true CN1704758A (en) | 2005-12-07 |
Family
ID=35576938
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN 200410042625 Pending CN1704758A (en) | 2004-05-28 | 2004-05-28 | Method for realizing wheat behavior monitoring and forecasting by utilizing remote sensing and geographical information system technology |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN1704758A (en) |
Cited By (25)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1794292A (en) * | 2006-01-09 | 2006-06-28 | 江苏省农业科学院 | Remote sensing estimation method of crop multimetadata crop rotation cycle |
| CN101858971A (en) * | 2010-06-02 | 2010-10-13 | 浙江大学 | Rice yield remote sensing estimation method based on MODIS data |
| CN101916337A (en) * | 2010-08-23 | 2010-12-15 | 湖南大学 | Method for dynamically predicting potential productivity of paddy rice based on geographical information system |
| CN101949916A (en) * | 2010-08-12 | 2011-01-19 | 中国农业大学 | Remote sensing quantitative inversion method for soil moisture supply amount |
| CN102162850A (en) * | 2010-04-12 | 2011-08-24 | 江苏省农业科学院 | Wheat yield remote sensing monitoring and forecasting method based on model |
| CN101776584B (en) * | 2010-01-18 | 2011-12-07 | 浙江林学院 | High-spectrum comprehensive analysis early detection method for Bursaphelenchus xylophilus |
| CN101699315B (en) * | 2009-10-23 | 2012-04-25 | 北京农业信息技术研究中心 | Monitoring device and method for crop growth uniformity |
| CN1804619B (en) * | 2005-12-29 | 2013-04-10 | 江苏省农业科学院 | Remote sensing diagnosis method for diseases and insect pests of crop rotation |
| CN1794287B (en) * | 2006-01-09 | 2013-06-26 | 江苏省农业科学院 | Remote sensing estimation method of yield loss of crop multimetadata crop rotation cycle |
| CN1794291B (en) * | 2006-01-09 | 2013-08-28 | 江苏省农业科学院 | Optimized remote sensing evaluation method of crop multielement crop rotation cycle |
| CN103674998A (en) * | 2013-10-24 | 2014-03-26 | 北京大学深圳研究生院 | Plant health state detection method based on thermal imaging technology |
| CN104850694A (en) * | 2015-05-13 | 2015-08-19 | 福州大学 | Winter wheat remote sensing monitoring method based on vegetation index increment in growing period |
| CN105631401A (en) * | 2015-12-11 | 2016-06-01 | 中国烟草总公司广东省公司 | Remote sensing and geographic information-based tobacco field tobacco quality monitoring and prediction method |
| CN108169138A (en) * | 2017-12-18 | 2018-06-15 | 扬州大学 | A kind of rice lodging monitoring method using thermal infrared images |
| CN108444838A (en) * | 2018-03-21 | 2018-08-24 | 大连理工大学 | A kind of trees evaluation method for early warning resistant to lodging |
| CN108665111A (en) * | 2018-05-16 | 2018-10-16 | 陈孟禹 | Phase prediction technique is prevented based on the development of worm state and the diversiform-leaved poplar spring looper of temperature relation |
| CN108985588A (en) * | 2018-06-28 | 2018-12-11 | 中国科学院遥感与数字地球研究所 | A kind of crop yield prediction remote sensing estimation method, device and system |
| CN109187356A (en) * | 2018-08-30 | 2019-01-11 | 南京都宁大数据科技有限公司 | Soybean growing way prediction technique based on satellite remote sensing date |
| CN109344892A (en) * | 2018-09-21 | 2019-02-15 | 北京航空航天大学 | A kind of lodging index extracting method based on Multi-source Information Fusion |
| CN110287944A (en) * | 2019-07-04 | 2019-09-27 | 吉林大学 | The crop pests monitoring method of multi-spectrum remote sensing image based on deep learning |
| CN112293181A (en) * | 2020-11-02 | 2021-02-02 | 内蒙古自治区农牧业科学院 | High-yield cultivation method for preventing barley and wheat from lodging |
| CN113449984A (en) * | 2021-06-25 | 2021-09-28 | 中国水利水电科学研究院 | Reed resource quantity evaluation method |
| CN114076955A (en) * | 2021-09-27 | 2022-02-22 | 农业农村部规划设计研究院 | Method for identifying lodging corn based on high-score first satellite data |
| CN116187100A (en) * | 2023-04-25 | 2023-05-30 | 吉林大学 | Method for estimating corn kernel protein content by combining crop growth model |
| CN118112189A (en) * | 2024-02-28 | 2024-05-31 | 淮北师范大学 | Wheat quality evaluation method and system |
-
2004
- 2004-05-28 CN CN 200410042625 patent/CN1704758A/en active Pending
Cited By (36)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1804619B (en) * | 2005-12-29 | 2013-04-10 | 江苏省农业科学院 | Remote sensing diagnosis method for diseases and insect pests of crop rotation |
| CN1794292B (en) * | 2006-01-09 | 2014-01-29 | 江苏省农业科学院 | Remote sensing estimation method of crop multimetadata crop rotation cycle |
| CN1794287B (en) * | 2006-01-09 | 2013-06-26 | 江苏省农业科学院 | Remote sensing estimation method of yield loss of crop multimetadata crop rotation cycle |
| CN1794292A (en) * | 2006-01-09 | 2006-06-28 | 江苏省农业科学院 | Remote sensing estimation method of crop multimetadata crop rotation cycle |
| CN1794291B (en) * | 2006-01-09 | 2013-08-28 | 江苏省农业科学院 | Optimized remote sensing evaluation method of crop multielement crop rotation cycle |
| CN101699315B (en) * | 2009-10-23 | 2012-04-25 | 北京农业信息技术研究中心 | Monitoring device and method for crop growth uniformity |
| CN101776584B (en) * | 2010-01-18 | 2011-12-07 | 浙江林学院 | High-spectrum comprehensive analysis early detection method for Bursaphelenchus xylophilus |
| CN102162850A (en) * | 2010-04-12 | 2011-08-24 | 江苏省农业科学院 | Wheat yield remote sensing monitoring and forecasting method based on model |
| CN101858971A (en) * | 2010-06-02 | 2010-10-13 | 浙江大学 | Rice yield remote sensing estimation method based on MODIS data |
| CN101949916B (en) * | 2010-08-12 | 2013-08-14 | 中国农业大学 | Remote sensing quantitative inversion method for soil moisture supply amount |
| CN101949916A (en) * | 2010-08-12 | 2011-01-19 | 中国农业大学 | Remote sensing quantitative inversion method for soil moisture supply amount |
| CN101916337B (en) * | 2010-08-23 | 2012-05-02 | 湖南大学 | Method for dynamically predicting potential productivity of paddy rice based on geographical information system |
| CN101916337A (en) * | 2010-08-23 | 2010-12-15 | 湖南大学 | Method for dynamically predicting potential productivity of paddy rice based on geographical information system |
| CN103674998A (en) * | 2013-10-24 | 2014-03-26 | 北京大学深圳研究生院 | Plant health state detection method based on thermal imaging technology |
| CN103674998B (en) * | 2013-10-24 | 2016-02-17 | 北京大学深圳研究生院 | Based on the plant health condition detection method of thermal imaging |
| CN104850694A (en) * | 2015-05-13 | 2015-08-19 | 福州大学 | Winter wheat remote sensing monitoring method based on vegetation index increment in growing period |
| CN104850694B (en) * | 2015-05-13 | 2018-05-04 | 福州大学 | Winter wheat remote-sensing monitoring method based on growth period vegetation index increment |
| CN105631401A (en) * | 2015-12-11 | 2016-06-01 | 中国烟草总公司广东省公司 | Remote sensing and geographic information-based tobacco field tobacco quality monitoring and prediction method |
| CN108169138A (en) * | 2017-12-18 | 2018-06-15 | 扬州大学 | A kind of rice lodging monitoring method using thermal infrared images |
| CN108169138B (en) * | 2017-12-18 | 2020-10-30 | 扬州大学 | Rice lodging monitoring method utilizing thermal infrared image |
| CN108444838B (en) * | 2018-03-21 | 2020-07-24 | 大连理工大学 | Tree lodging resistance evaluation early warning method |
| CN108444838A (en) * | 2018-03-21 | 2018-08-24 | 大连理工大学 | A kind of trees evaluation method for early warning resistant to lodging |
| CN108665111A (en) * | 2018-05-16 | 2018-10-16 | 陈孟禹 | Phase prediction technique is prevented based on the development of worm state and the diversiform-leaved poplar spring looper of temperature relation |
| CN108665111B (en) * | 2018-05-16 | 2021-08-13 | 陈孟禹 | Populus diversifolia spring inchworm control period prediction method based on insect state development and temperature relationship |
| CN108985588A (en) * | 2018-06-28 | 2018-12-11 | 中国科学院遥感与数字地球研究所 | A kind of crop yield prediction remote sensing estimation method, device and system |
| CN108985588B (en) * | 2018-06-28 | 2021-09-03 | 中国科学院遥感与数字地球研究所 | Crop yield per unit remote sensing estimation method, device and system |
| CN109187356A (en) * | 2018-08-30 | 2019-01-11 | 南京都宁大数据科技有限公司 | Soybean growing way prediction technique based on satellite remote sensing date |
| CN109344892A (en) * | 2018-09-21 | 2019-02-15 | 北京航空航天大学 | A kind of lodging index extracting method based on Multi-source Information Fusion |
| CN110287944B (en) * | 2019-07-04 | 2022-07-01 | 吉林大学 | Crop pest monitoring method based on multispectral remote sensing image of deep learning |
| CN110287944A (en) * | 2019-07-04 | 2019-09-27 | 吉林大学 | The crop pests monitoring method of multi-spectrum remote sensing image based on deep learning |
| CN112293181A (en) * | 2020-11-02 | 2021-02-02 | 内蒙古自治区农牧业科学院 | High-yield cultivation method for preventing barley and wheat from lodging |
| CN113449984A (en) * | 2021-06-25 | 2021-09-28 | 中国水利水电科学研究院 | Reed resource quantity evaluation method |
| CN113449984B (en) * | 2021-06-25 | 2023-12-29 | 中国水利水电科学研究院 | Reed resource quantity assessment method |
| CN114076955A (en) * | 2021-09-27 | 2022-02-22 | 农业农村部规划设计研究院 | Method for identifying lodging corn based on high-score first satellite data |
| CN116187100A (en) * | 2023-04-25 | 2023-05-30 | 吉林大学 | Method for estimating corn kernel protein content by combining crop growth model |
| CN118112189A (en) * | 2024-02-28 | 2024-05-31 | 淮北师范大学 | Wheat quality evaluation method and system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN1704758A (en) | Method for realizing wheat behavior monitoring and forecasting by utilizing remote sensing and geographical information system technology | |
| Basso et al. | Seasonal crop yield forecast: Methods, applications, and accuracies | |
| Stroppiana et al. | Early season weed mapping in rice crops using multi-spectral UAV data | |
| Pande et al. | Application of hyperspectral remote sensing role in precision farming and sustainable agriculture under climate change: A review | |
| Shao et al. | Rice monitoring and production estimation using multitemporal RADARSAT | |
| dela Torre et al. | Remote sensing-based estimation of rice yields using various models: A critical review | |
| CN104408307B (en) | The quick monitoring method of wheat powdery mildew occurring degree and its construction method of monitoring model | |
| Yu et al. | Prediction of soil properties using a hyperspectral remote sensing method | |
| Tatsumi et al. | Prediction of plant-level tomato biomass and yield using machine learning with unmanned aerial vehicle imagery | |
| CN111738066A (en) | Gridding rice sheath blight habitat evaluation method integrating multi-source remote sensing information | |
| de Lima et al. | Remote sensing monitoring of rice fields: Towards assessing water saving irrigation management practices | |
| Parihar et al. | FASAL: An integrated approach for crop assessment and production forecasting | |
| Song et al. | Estimating reed loss caused by Locusta migratoria manilensis using UAV-based hyperspectral data | |
| Jiao et al. | Monitoring crop growth using a canopy structure dynamic model and time series of synthetic aperture radar (SAR) data | |
| Orsini et al. | Testing vegetation index categories as influenced by soil management and nitrogen fertilization in cereal based cropping systems | |
| Karakoc et al. | Ratio-based vegetation indices for biomass estimation depending on grassland characteristics | |
| Saberioon et al. | Multi-spectral images tetracam agriculture digital camera to estimate nitrogen and grain yield of rice at different growth stages | |
| Mutlu et al. | UAV-based imaging for selection of turfgrass drought resistant cultivars in breeding trials | |
| Ye et al. | Estimation of citrus yield from canopy spectral features determined by airborne hyperspectral imagery | |
| Meng et al. | Evaluating soil available nitrogen status with remote sensing | |
| Luo et al. | Staple crop mapping with Chinese GaoFen-1 and GaoFen-6 satellite images: A case study in Yanshou County, Heilongjiang Province, China | |
| Kubiak et al. | Remote sensing semi-automatic measurements approach for monitoring bioenergetic crops of Miscanthus spp. | |
| Taylor et al. | Applications of optical sensing of crop health and vigour | |
| Gu et al. | Monitoring Maize Lodging Disaster Via Multi-Temporal Remote Sensing Images | |
| Mazis | Application of Proximal and Remote Sensing Methods for Estimating Important Morphological and Ecophysiological Plant Traits |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C02 | Deemed withdrawal of patent application after publication (patent law 2001) | ||
| WD01 | Invention patent application deemed withdrawn after publication |