首页> 外文期刊>Precision Agriculture >Utility of remote sensing in predicting crop and soil characteristics
【24h】

Utility of remote sensing in predicting crop and soil characteristics

机译:遥感在预测作物和土壤特征中的作用

获取原文
获取原文并翻译 | 示例
           

摘要

Remote sensing during the production season can provide visual indications of crop growth along with the geographic locations of those areas. A grid coordinate system was used to sample cotton and soybean fields to determine the relationship betweenspectral radiance, soil parameters, and cotton and soybean yield. During the 2 years of this study, mid- to late-season correlation coefficients between spectral radiance and yield generally ranged from 0.52 to 0.87. These correlation coefficients wereobtained using the green-red ratio and a vegetation index similar to the normalized difference vegetation index (NDVI) using the green and red bands. After 102 days after planting (DAP), the ratio vegetation index (RVI). difference vegetation index (DVI), NDVI, and soil-adjusted vegetation index (SAVI) generally provided correlation coefficients from 0.54 to 0.87. Correlation coefficients for cotton plant height measurements taken 57 and 66 DAP during 2000 ranged from 0.51 to 0.76 for all bands, ratios,and indices examined, with the exception of Band 4 (720 nm). The most consistent correlation coefficients for soybean yield were obtained 89-93 DAP. corresponding to peak vegetative production and early pod set. using RVI, DVI, NDVI, and SAVI. Correlation coefficients generally ranged from 0.52 to 0.86. When the topographic features and soil nutrient data were analyzed using principal component analysis (PCA), the interaction between the crop canopy, topographic features, and soil parameters captured in the imagery allowed the formation of predictive models, indicating soil factors were influencing crop growth and could be observed by the imagery. The optimum time during 1999 and 2000 for explaining the largest amount of variability for cotton growthoccurred during the period from first bloom to first open boll, with R values ranging from 0.28 to 0.70. When the PCA-stepwise regression analysis was performed on the soybean fields. R~2 values were obtained ranging from 0.43 to 0.82, 15 DAP, and rangedfrom 0.27 to 0.78, 55-130 DAP. The use of individual bands located in the green, red, and MR, ratios such as RVI and DVI, indices such as NDVI, and stepwise regression procedures performed on the cotton and soybean fields performed well during the cotton and soybean production season, though none of these single bands, ratios, or indices was consistent in the ability to correlate well with crop and soil characteristics over multiple dates within a production season. More research needs to be conductedto determine whether a certain image analysis method will be needed on a field-by-field basis, or whether multiple analysis procedures will need to be performed for each imagery date in order to provide reliable estimates of crop and soil characteristics
机译:在生产季节进行遥感可以提供作物生长以及这些地区地理位置的视觉指示。使用网格坐标系对棉花和大豆田进行采样,以确定光谱辐射度,土壤参数与棉花和大豆产量之间的关系。在本研究的2年中,光谱辐射度与产量之间的中后期相关系数通常在0.52至0.87之间。这些相关系数是使用绿-红比和植被指数(使用绿色和红色波段的归一化植被指数(NDVI))获得的。播种后102天(DAP),比率植被指数(RVI)。差异植被指数(DVI),NDVI和土壤调整植被指数(SAVI)通常提供的相关系数为0.54至0.87。 2000年进行的57和66 DAP测得的所有株带,比率和指数的棉花株高测量的相关系数在0.51至0.76范围内,第4波段(720 nm)除外。获得的大豆产量最一致的相关系数为89-93 DAP。对应于植物高峰生产和豆荚早熟。使用RVI,DVI,NDVI和SAVI。相关系数通常在0.52至0.86的范围内。当使用主成分分析(PCA)分析地形特征和土壤养分数据时,作物冠层,地形特征和图像中捕获的土壤参数之间的相互作用允许形成预测模型,表明土壤因素正在影响作物生长和生长。可以通过图像观察到。 1999年和2000年的最佳时间用于解释从初花到初开铃期间棉花生长的最大变化量,R值在0.28到0.70之间。当对大豆田进行PCA逐步回归分析时。获得的R〜2值范围为0.43至0.82、15 DAP,范围为0.27至0.78、55-130 DAP。在棉花和大豆生产季节,使用位于绿色,红色和MR的单个条带,比率(例如RVI和DVI),指数(例如NDVI)以及在棉田和大豆田上进行的逐步回归程序效果很好,尽管没有这些单个波段,比率或指数中的一个在生产季节内多个日期与农作物和土壤特性良好相关的能力是一致的。需要进行更多的研究以确定是否需要逐场进行某种图像分析方法,或者是否需要对每个图像日期执行多种分析程序以提供可靠的农作物和土壤特征估计

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号