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The status of agricultural ecosystem examined by microwave data

机译：微波数据检查的农业生态系统状况

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摘要

The Remote Sensing Centre in the Institute of Geodesy and Cartography has undertaken the investigation of applying ENVISAT.ASAR, ALOS.PALSAR, and TerraSAR-X data for modelling of soil moisture under various crops. Radar data are independent of weather conditions and that is why they are very useful in monitoring the dynamic of various soil-vegetation parameters. The aim of the study was to examine the usefulness of radar data for describing the soil-vegetation parameters of agri-environment ecosystem in the Wielk-opolska region of Poland. The research on agriculture area has been carried out at ground level by taking measurements of soil moisture, Leaf Area Index, height of the vegetation, and wet and dry biomass. The TerraSAR-X and ALOS.PALSAR images have been applied for classification of agriculture area. The images taken in X band were used for discriminating the structure of vegetation, while the images taken in L-band turned out to be very effective at mapping structure of forests and distinguishing them from non-forest areas. Therefore synergy of images acquired in bands X and L gave good results in classification of forested areas and crops. The influence of soil moisture on backscattering coefficient calculated from ENVISAT.ASAR under various polarisations and incidence angles for different crops distinguished from classification of ENVISAT.ASAR IS6 and IS4 has been examined to find correlations between satellite derived data and soil-vegetation parameters. The best correlation between backscatter and LAI has been obtained by applying ASAR IS6 HH or ASAR IS4 HH images, while the best correlation between backscatter and soil moisture has been obtained applying ASAR IS6 W or ASAR IS4 W images. The influence of the crop descriptor such as LAI on backscatter coefficient calculated from ENVISAT.ASAR IS2 HH under various soil moisture conditions as well as the influence of the crop descriptor such as soil moisture on backscatter coefficient calculated from ENVISAT.ASAR IS2 HV under various LAI conditions was examined for wheat using the "water-cloud model". The relationships between modelled values of soil moisture and LAI and measured values have been derived and discussed.
机译：大地测量与制图研究所的遥感中心已进行了研究，将ENVISAT.ASAR，ALOS.PALSAR和TerraSAR-X数据用于各种作物的土壤水分建模。雷达数据与天气条件无关，这就是为什么它们在监视各种土壤植被参数的动态方面非常有用。这项研究的目的是检验雷达数据对描述波兰Wielk-opolska地区农业环境生态系统土壤植被参数的有用性。通过测量土壤湿度，叶面积指数，植被高度以及干湿生物量，在地面上进行了农业面积研究。 TerraSAR-X和ALOS.PALSAR图像已应用于农业区域分类。 X波段拍摄的图像被用来区分植被的结构，而L波段拍摄的图像被证明对映射森林结构并将其与非森林区域区分开来非常有效。因此，在X波段和L波段获得的图像的协同作用在森林区域和农作物的分类中提供了良好的结果。根据ENVISAT.ASAR IS6和IS4的分类，研究了土壤水分对ENVISAT.ASAR在不同极化和入射角下不同作物在不同极化和入射角下计算出的反向散射系数的影响，以发现卫星数据与土壤植被参数之间的相关性。通过应用ASAR IS6 HH或ASAR IS4 HH图像可获得反向散射与LAI的最佳相关性，而通过使用ASAR IS6 W或ASAR IS4 W图像可获得反向散射与土壤水分的最佳相关性。在不同土壤水分条件下，LAI等作物描述符对从ENVISAT.ASAR IS2 HH计算的反向散射系数的影响，以及在不同LAI条件下，作物描述符例如ENVISAT.ASAR IS2 HV对反向散射系数的影响使用“水云模型”检查了小麦的条件。推导并讨论了土壤水分和LAI的模型值与测量值之间的关系。

著录项

  • 来源
    《Acta astronautica》 |2010年第8期|P.721-730|共10页
  • 作者

    Katarzyna Dabrowska-Zielinska; rnMaria Budzynska; rnAndrzej Ciolkosz; rnWanda Kowalik; rnZbigniew Bochenek;

  • 作者单位

    Institute of Geodesy and Cartography, Remote Sensing Centre, Modzelewskiego 27, 02-679 Warsaw, Poland;

    rnInstitute of Geodesy and Cartography, Remote Sensing Centre, Modzelewskiego 27, 02-679 Warsaw, Poland;

    rnInstitute of Geodesy and Cartography, Remote Sensing Centre, Modzelewskiego 27, 02-679 Warsaw, Poland;

    rnInstitute of Geodesy and Cartography, Remote Sensing Centre, Modzelewskiego 27, 02-679 Warsaw, Poland;

    rnInstitute of Geodesy and Cartography, Remote Sensing Centre, Modzelewskiego 27, 02-679 Warsaw, Poland;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    ENVISAT.ASAR; ALOS.PALSAR; TerraSAR-X; LAI; soil moisture; water-cloud model;

    机译：ENVISAT.ASAR;ALOS.PALSAR;TerraSAR-X;赖;土壤湿度;水云模型;

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