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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 and weather information in cotton yield prediction
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Remote sensing and weather information in cotton yield prediction

机译：棉花产量预报中的遥感和天气信息

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摘要

If farmers could predict yield on a spatially variable basis, they could better understand risks and returns in applying costly inputs such as fertilizers, etc. To this end, several remotely sensed images of a cotton field were collected during the 2002 growing season, along with daily high and low temperatures. Image data were converted to normalized-difference vegetation index (NDVI), and temperature data were used to normalize NDVI changes over periods between image collections. Remote-sensing and weather data were overlaid in a geographic information system (GIS) with data from the field: topography, soil texture, and historical cotton yield. All these data were used to develop relationships with yield data collected at the end of the 2002 season. Stepwise regression was conducted at grid-cell sizes from 10 m square (100 m~2) to 100 m square (10,000 m~2) in 10-m increments. Relationships at each cell size were calculated with data available at the beginning of the season, at the first image date, at the second image date, and so on. Stepwise linear regression was used to select variables at each date that would constitute an appropriate model to predict yield. Results indicated that, at most dates, model accuracy was highest at the 100-m cell size. Remotely sensed data combined with weather data contributed much information to the models, particularly with data collected within 2.5 months of planting. The most appropriate model had an R~2 value of 0.63, and its average prediction error was about 0.5 bale/ha (0.2 bale/ac, or roughly 100 1b/ac).
机译：如果农民能够在空间可变的基础上预测产量，那么他们可以更好地理解使用肥料等昂贵投入的风险和回报。为此，在2002年生长季节期间，收集了几幅遥感的棉田图像，以及每天高温和低温。将图像数据转换为归一化植被指数（NDVI），并使用温度数据归一化图像采集之间各个时期的NDVI变化。遥感和天气数据被覆盖在地理信息系统（GIS）中，其中包含以下领域的数据：地形，土壤质地和棉花历史产量。所有这些数据都用于与2002季末收集的产量数据建立关系。从10 m平方（100 m〜2）到100 m平方（10,000 m〜2）的网格单元大小以10 m的增量逐步回归。使用季节开始时，第一个拍摄日期，第二个拍摄日期等可用的数据计算每个像元大小的关系。使用逐步线性回归来选择每个日期的变量，这些变量将构成预测产量的合适模型。结果表明，在大多数情况下，模型精度在100米的像元大小中最高。遥感数据与天气数据相结合，为模型提供了很多信息，尤其是在种植2.5个月内收集到的数据。最合适的模型的R〜2值为0.63，其平均预测误差约为0.5包/公顷（0.2包/英亩，或大约100 1b / ac）。

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