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首页> 外文会议>Conference on Ecosystems' Dynamics, Agricultural Remote Sensing and Modeling, and Site-Specific Agriculture; Aug 7, 2003; San Diego, California, USA >Remote Sensing in Dryland Cotton: Relation to-Yield Potential and Soil Properties
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Remote Sensing in Dryland Cotton: Relation to-Yield Potential and Soil Properties

机译：旱地棉花遥感：与潜力和土壤特性的关系

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摘要

The use of soil and topography information to explain crop yield variation across fields is often applied for crop management purposes. Remote sensed data is a potential source of information for site-specific crop management, providing both spatial and temporal information about soil and crop condition. Studies were conducted in a 104-acre (42-hectare) dryland cotton field in 2001 and 2002 in order to (1) qualitatively assess the spatial variability of soil physical properties from kriged estimates, (2) compare actual yields with normalized difference vegetation reflectance indices (NDVI) obtained from multispectral imagery and from in situ radiometer data, and (3) predict site-specific cotton yields using a crop simulation model, GOSSYM. An NDVI map of soybean in 2000 obtained from a multispectral image was used to establish four sites in each low, medium and high NDVI class. These 12 sites were studied in 2001 and 12 more sites selected at random were studied in 2002 (n=24). Site-specific measurements included leaf area index (LAI), canopy hyperspectral reflectance, and three-band multispectral image data for green, red, and near-infrared reflectance wavebands at spatial resolutions of 2 m in 2001 and 0.5 m in 2002. Imagery was imported into the image analysis software Imagine (ERDAS, v. 8.5) for georegistration and image analysis. A 6x6 pixels (144 m~2) area of interest was established on top of each field plot site and digital numbers (DN) from reflectance imagery were extracted from each band for derivation of NDVI maps for each of four sampling dates. Lint yield from each plot site was collected by hand and also by a cotton picker equipped with AgLeader yield monitor and OmniStar differential global positioning system. We found plant height, leaf area index, and lint yield were closely associated with NDVI maps and with MR band values acquired from either an aircraft or handheld (GER-1500) sensor during peak bloom in mid July. Results indicate NDVI and NIR bands could be used to produce estimated field maps of plant height, leaf area index and yield, which offer a potentially attractive mid-season management tool for site specific farming in dryland cotton.
机译：使用土壤和地形信息来解释田间作物产量的变化通常用于作物管理目的。遥感数据是特定地点作物管理的潜在信息源，可提供有关土壤和作物状况的时空信息。为了在2001年和2002年于104英亩（42公顷）的旱地棉田中进行研究，以（1）通过克立格法的估计定性评估土壤物理性质的空间变异性，（2）将实际产量与标准化差异植被反射率进行比较从多光谱图像和原位辐射计数据获得的指数（NDVI），以及（3）使用作物模拟模型GOSSYM预测特定地点的棉花产量。从多光谱图像获得的2000年大豆NDVI图用于建立低，中和高NDVI类别中的四个位点。 2001年对这12个站点进行了研究，2002年又对12个随机选择的站点进行了研究（n = 24）。特定于站点的测量包括叶面积指数（LAI），冠层高光谱反射率以及绿色，红色和近红外反射波段的三波段多光谱图像数据，其空间分辨率分别为2001年和2002年分别为2 m和0.5 m。导入到图像分析软件Imagine（ERDAS，第8.5版）中，以进行地理配准和图像分析。在每个现场绘图站点的顶部建立一个6x6像素（144 m〜2）的关注区域，并从每个波段中提取反射率图像的数字（DN），以得出四个采样日期的每个NDVI图。手工收集每个样地的皮棉产量，并通过配备有AgLeader产量监测器和OmniStar差分全球定位系统的棉花采摘机进行收集。我们发现植物高度，叶面积指数和皮棉产量与NDVI图和7月中旬高峰期从飞机或手持式（GER-1500）传感器获取的MR波段值密切相关。结果表明，NDVI和NIR波段可用于生成估计的植物身高，叶面积指数和单产的田间地图，这为旱地棉花的特定地点种植提供了潜在的有吸引力的中期管理工具。

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