Study On Extracting The Cotton Field Management Information With Remote Sensing

Field managerment is one of the main farming measurements, and its level expresses the production ability comprehensively, the effection of managing field directly influence the yield, qualification, and benefit of crops.Therefore, it is evident that every step of field managerments should be armed with the new achievement of science and technology to meet the society and economy. The agruclutral machine take part in the field management under the industrialization, the resultion was that it liberated man hands and feet, and improve the production efficiency. Under the informatization society, which improvement would field management take? Evidence is mounting that the answer might be the collection, dealing, and decision of field management information automaticly, it would liberate our eyes and ears extremely, it would also increase the depth and breadth of monitoring the crop and field information in face of temporal and spac.Satellite remote sensing technology is the shining pearl and fruit of information science and technology. Some contents became the main application including crop growth, yield, and planting area after the technology of satellite remote sensing was used among the people, it provide the high efficient methods to let the agriculture managing unit gasp the field management information quickly and comprehensively. Following the development of rerearch and practice of remote sensing in agriculture, the works don't just stay in foretelling and concluding the macroscopical appearance, fortunately, some detail plots were payed attention to gradually, the conception, detailing the monior content and anticipate the process, was being breeded in the crop monitoring with remote sensing, and so the information acquainted by remote sensing would be more rich, which meet the informalization era in the field management.Based on the requirement discovery, in the research, many methods was used including field investigation and measurement, analysis indoor, interpretation with remote sensing, data estimation and comparision, and much assistant data was also used ,such as the expert knowledge of cotton planting, multi-temporal remote sensing data, field background information, and so on. The four content were researched including soil texture, existing plants of cotton, cotton growth condition based on LAI, and cotton field qualification, the detailed content, its analyzing precess, and the main conclusion as follows:1) In the research area, the spectrum with representative texture soil was measured, and the corresponding multi-band spectrum of LANDSAT-5 satellite was also distrilled, these reflections were analyzed to reveal the spectrum difference with different texture soil and make the classable texture type of soil with remote sensing clear. Furthermore, the 175 soil regions in the area were sampled, these textures were tested, and the multi-band reflections of the sample regions were schemed. As a result, the idea band and threshold value, recognizing the soil texture, was ascertained. In ters of the analyzing results above, the soil texture in the research area was interpreted, and the universal ways was used to check the interpretation accuracy. Three results of cartography methods implemented through plotting, GIS, and remote sensing were compared. water content, temperature, existing plants and their changes were analyzed, the soil attribute influencing physical basis of interpreting the soil texture was also analyzed, and the situation of cotton germination was analyzed because of the influence of soil texture. The research would conclude the optimal band interpreting the soil texture.For the soil with different texture, the spectral refection had the significant difference in the NIR and MIR, three type soil, sandy soil - silty loam, light loam– medium loam, and heavy loam– clay, could be devided. The interpretation accuracy of soil texture was high with LANDSAT-5 satellite, the probability is higher of dividing light loam– medium loam into other two types, and heavy loam– clay had high classification accuracy. Because of the strong relation between the soil texture and soil moisture, the difference of soil moisture is basis of recognizing the different texture soil. During about the planting time, the different texture soil influences the change of water, temperature, and decides the number of existing plants to some extent. The interpretation result of soil texuture with high space resolution could direct the cotton planting with suitable space order.2) In 2007, Sixty group sample data, consisting of the existing cotton-seedling density, longitude/latitude, sowing time, emergence time, were obtained through investigating the thirteen fields (630 hm2), and three sample dot data in every sample area were averaged. EVI and DEVI were retrieved up from the images of five times from sowing time to full-flowering. And then sixty group sample data were divided into two equal parts to establish and text models. The linear models were established by data of the middle sowing time and the all three sowing times on the basis of EVI and DEVI, respectively, and the model accuracy was tested by RMSE and REPE. At last, the existing cotton-seedling density at the country scale was retrieved by the best model. In the study, based on the Landsat-5 cell level, having analyzed the factors affecting the estimating accuracy, having explored vegetation indexes to clear up the space information difference of the non-cotton background, having ascertained the optimal time to monitoring the existing cotton-seedling density.Emergence time and soil background were the main factors influencing the accuracy of estimating the existing plants, and the estimation of dividing into the different time stages could obviously improve the accuracy. DEVI could raise the estimation in the ealy stage of cotton growing and advace the estimation time. The optimal time estimating the existing plants was from bud stage to flowering stage.3) The experiment was carried out in 2006-2007 in Xinjiang, and the eighteen cotton fields were validated as the standard observation station, 255 group data of LAI and NDVI, PVI, EVI from LANDSAT-5 were obtained, and 144 and 111 group data were used to establish the estimation models and test the accuracy of models, respectively. In the research, the estimation levels of three vegetation indexes were compared, and the expert knowledge of LAI was concuded. LAI on the about 70th day from the emergence time were retrieved by the optimal model at the regional scale, the cotton population growth was classified by the right of the 18 person-time expert knowledge of the classic LAI. To complish the qualified classification with the signifance of agriculture and field management, the method was researched through combining the remote sensing with agricultural knowledge. the two years in 2007 and 2008 was demonstrated to retrieve the LAI and classify the cotton growth. The index relation is expressed between LAI and NDVI, PVI, and EVI, and the saturation phenomena were evident when vegetation index estimate LAI. It was idea that the suitable vegetation index was chosen to improve the estimation of LAI at the different stages. The qualitative classification would be achieved through combining the expert knowledge and the retrieved result with remote sensing, which could provide the data support with the clear agronomy significance for the growing monitor of cotton.4) The cotton field quality conditions were divided into the three styles of the healthy cotton field, handicapped cotton field and suspected cotton field with handicap. The healthy cotton field was defined as the normal growing in the whole stage, the handicapped cotton field was defined as the unnormal growing in the whole stage, and the others was divided into the suspected area with handicap. Eighty cotton fields were used as the sample regions, multi-temporal remote sensing data was used to explore the optimal band and establish the model for classifying the cotton qualification. And then, the main factors, causing the cotton handicap, were proprosed through analyzing the LAI, soil texture, total salty, and exsiting plants. At last, the measurements improving the soil qualification were raised to direct complish the high and steady yield in the region.Multi-teporal remote sensing data had the prominent action to research the field qualification, the relation was discovered between the dynamic growth condition of cotton and cotton field qualification, and the model and process were estimated to diagnose the cotton qualification with multi-temporal data. Through the diagnosing result of cotton qualification, investigation and measurement in the field, the delailed facors leading to cotton field handicap chould be found out efficiently.