首页> 外文期刊>Intelligent automation and soft computing >REMOTE SENSING OF REGIONAL CROP TRANSPIRATION OF WINTER WHEAT BASED ON MODIS DATA AND FAO-56 CROP COEFFICIENT METHOD
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REMOTE SENSING OF REGIONAL CROP TRANSPIRATION OF WINTER WHEAT BASED ON MODIS DATA AND FAO-56 CROP COEFFICIENT METHOD

机译:基于MODIS数据和FAO-56作物系数法的冬小麦区域蒸腾遥感。

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摘要

Crop evapotranspiration is one of the most important parameters of farmland water cycle, which consists of crop transpiration (T_c) and soil evaporation. As the efficient component for crop production, T_c and its accurate determination, especially on a regional scale, is very critical for scientific design of irrigation scheduling and high-efficiency utilization of water resources. In this work, the T_c of winter wheat over an irrigation area located in the lower Yellow River of China was estimated by combining MODIS data and FAO-56 crop coefficient method. Specifically, the relationships between the single crop coefficient (K_c), basal crop coefficient (K_(cb)) and canopy vegetation indices were investigated and compared based on field data. Then, the actual K_(cb) map of winter wheat over the study area was estimated with MODIS-derived soil adjusted vegetation index (SAVI) using the relationship obtained from above field investigations. Finally, the T_c of winter wheat over the area was determined as the product of K_(cb) and reference crop evapotranspiration (ET_0). ET_0 was calculated from meteorological data, and then were spatially interpolated to obtain the regional map matching with the remotely sensed K_(cb). It was found that compared with K_c, K_(cb) was much more closely related to the vegetation indices of NDVI, SAVI, and EVI, even in the presence of nitrogen and water stress, with the coefficients of determination (R~2) being 0.60,0.67 and 0.68 respectively (n=195) which could be even higher without the water-stress points that had not reached the severity to make obvious changes in canopies. Results also demonstrated that it was feasible to utilize the K_(cb)-SAVI relationship to derive the K_(cb) of winter wheat over a large area by means of satellite remote sensing, and that it was effective to determine regional crop T_c using the above approach. It would be useful in practical application due to the advantages of easy operation and separating soil evaporation effectively.
机译:作物蒸散量是农田水循环的最重要参数之一,由作物蒸腾作用和土壤蒸发组成。作为作物生产的有效组成部分,T_c及其精确确定(尤其是在区域范围内)对于科学设计灌溉计划和高效利用水资源至关重要。在这项工作中,结合MODIS数据和FAO-56作物系数法,估算了位于中国黄河下游灌区的冬小麦的T_c。具体而言,根据田间数据调查并比较了单季作物系数(K_c),基础作物系数(K_(cb))和冠层植被指数之间的关系。然后,使用MODIS衍生的土壤调整植被指数(SAVI),使用从上述田间调查获得的关系,估算研究区域内冬小麦的实际K_(cb)图。最后,确定该地区冬小麦的T_c为K_(cb)与参考作物蒸散量(ET_0)的乘积。根据气象数据计算出ET_0,然后对其进行空间插值,以获得与遥感K_(cb)相匹配的区域地图。结果发现,与K_c相比,即使存在氮和水分胁迫,K_(cb)与NDVI,SAVI和EVI的植被指数关系更密切,其测定系数(R〜2)为如果缺水点没有达到造成冠层明显变化的严重程度,则分别为0.60、0.67和0.68(n = 195)甚至更高。结果还表明,利用K_(cb)-SAVI关系通过卫星遥感推导大面积冬小麦的K_(cb)是可行的,并且使用卫星遥感确定区域作物T_c是有效的以上方法。由于易于操作和有效地分离土壤蒸发的优点,在实际应用中将是有用的。

著录项

  • 来源
    《Intelligent automation and soft computing》 |2013年第3期|285-294|共10页
  • 作者单位

    State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China,Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China,Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China;

    Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China;

    Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China;

    Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Transpiration; MODIS Data; FAO-56 Crop Coefficient Method; Winter Wheat;

    机译:蒸腾;MODIS数据;FAO-56作物系数法;冬小麦;

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