15 March 2001, Volume 16 Issue 1

    

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  TM Remote Sensing and Yield Variability of Wheat within Fields

  Remote Sensing Technology and Application. 2001, 16(1): 23-27. https://doi.org/10.11873/j.issn.1004-0323.2001.1.23

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  JIANG Cheng, JIN Ji-yun, ZHANG Wei-li(Key Laboratory of Plant Nutrition Research of MOA,Soil and FertilizerInstitute of CAAS,Beijing100081,China)Abstract:The intention of site-specific management is to optimize grower inputs on areas much smallerthan the entire field. These areas may be as small as a few square meters in size. To manage a field onsuch a scale, data would have to be collected on a similar or smaller scale. To collect the data by handwould be very time consuming, labor intensive and destructive. In recent years, as an important compo-nent of site-specific soil management in precision agriculture, remote sensing developed greatly. Satelliteremote sensing is a promising technique, which could provide the essential, real-time and spatially continu-ous crop information for site-specific crop management. Specially, recent advances in the spatial, spectraland temporal resolution of remote sensing as well as potential positive changes in availability of remotelysensed data may make it a profitable tool for more farmers. However, higher cost still hinders itswidespread application in agriculture production. In this paper, TM remote sensing image with relativelylow cost was studied for the feasibility as a important information resource for evaluating crop yield vari-ability and designing the management unit of soil nutrients. The results showed that Vegetation Index(VI) could reflect the wheat characteristics of each growth stage. Two VI (NDVI and RVI) showedgreater spatial variability in accordance with the wheat yield in the field and the largest spatial variabilityoccurred in the late growth period of wheat. NDVI and RVI of three important growth stages of wheat(heading, stooling and jointing stages) were significantly correlated and the significant correlation betweenwheat yield and NDVI on Nov.18 (stooling stage) was found. Moreover, NDVI and RVI could supply im-portant information on some yield parameters, such as 1000 kernel weight and spike number. This re-search was preliminary and some other detailed studies would be needed in future.Key words:Precision agriculture, Yield variability, TM remote sensing, Vegetation index
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  A Study on Monitoring Frost of Main Crop in the Area ofNingxia by Using Remote Sensing

  Remote Sensing Technology and Application. 2001, 16(1): 32-36. https://doi.org/10.11873/j.issn.1004-0323.2001.1.32

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  ZHANG Xiao-yu1, CHEN Yu-ying2, SU Zhan-sheng1,ZHOU Hui-qin1, MA Yu-ping3(1.Ningxia Meteorological Institute, 2.Ningxia Meteorological Observatory,3Ningxia Meteorological office,Yinchuan750002,China)Abstract:The frost occurred is lies on many factors, such as condition of weather, landform, soil texture,bacterial population and vegetation itself etc. To monitor the frost by using remote sensing, we have tosolve many problems, for example, the precision of temperature retrieval and distribution of vegetation byusing NOAA data. In addition, we have to consider to both the aerosol and humidity effect on the temper-ature retrieval. Due to above mentioned reasons, monitoring the frost by remote sensing is rather compli-cated and difficult.Based on the analysis of the characteristic and the law of the frost, using the data of NOAA andweather station during 1991 to 2000,as well as using the data of the frost damage, there are three methodswere introduced to monitor the frost in Ningxia in this paper. The first is difference vegetation index(DVI) which monitoring the frost by means of the difference of vegetation index between fore-frost and af-ter-frost. The second is temperature reference (TR) which monitoring the frost by compare to the differ-ence between the temperature retrieval and frost index of crops. The third is the area of cold valley(ACV) which obtained ACV by integral the temperature below the frost index. We have examined themonitoring precision of those methods, the results show that each is available to monitor the different typeof frost in Ningxia.Key words:Vegetation index, AVHRR, Minimum temperature, Frost, Remote sensing