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首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >Ad Hoc Modeling of Root Zone Soil Water with Landsat Imagery and Terrain and Soils Data
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Ad Hoc Modeling of Root Zone Soil Water with Landsat Imagery and Terrain and Soils Data

机译：利用Landsat影像和地形和土壤数据对根区土壤水进行Ad Hoc建模

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摘要

Agricultural producers require knowledge of soil water at plant rooting depths, while many remote sensing studies have focused on surface soil water or mechanistic models that are not easily parameterized. We developed site-specific empirical models to predict spring soil water content for two Montana ranches. Calibration data sample sizes were based on the estimated variability of soil water and the desired level of precision for the soil water estimates. Models used Landsat imagery, a digital elevation model, and a soil survey as predictor variables. Our objectives were to see whether soil water could be predicted accurately with easily obtainable calibration data and predictor variables and to consider the relative influence of the three sources of predictor variables. Independent validation showed that multiple regression models predicted soil water with average error (RMSD) within 0.04 mass water content. This was similar to the accuracy expected based on a statistical power test based on our sample size (n = 41 and n = 50). Improved prediction precision could be achieved with additional calibration samples, and range managers can readily balance the desired level of precision with the amount of effort to collect calibration data. Spring soil water prediction effectively utilized a combination of land surface imagery, terrain data, and subsurface soil characterization data. Ranchers could use accurate spring soil water content predictions to set stocking rates. Such management can help ensure that water, soil, and vegetation resources are used conservatively in irrigated and non-irrigated rangeland systems.
机译：农业生产者需要了解植物生根深度的土壤水，而许多遥感研究都集中在不容易参数化的地表土壤水或机械模型上。我们开发了针对特定地点的经验模型，以预测两个蒙大拿州牧场的春季土壤含水量。校准数据样本的大小基于土壤水的估计变异性和土壤水估计值所需的精确度。模型使用Landsat影像，数字高程模型和土壤调查作为预测变量。我们的目标是查看是否可以使用容易获得的校准数据和预测变量来准确预测土壤水，并考虑三种预测变量源的相对影响。独立验证显示，多个回归模型预测的土壤水的平均误差（RMSD）在0.04质量含水量之内。这类似于根据我们的样本量（n = 41和n = 50）进行的统计功效测试所期望的准确性。可以使用其他校准样本来提高预测精度，并且范围管理器可以轻松地在所需的精度水平与收集校准数据的工作量之间取得平衡。春季土壤水的预测有效地利用了地面图像，地形数据和地下土壤特征数据的组合。牧场主可以使用准确的春季土壤含水量预测来设定放养率。这种管理可以帮助确保在灌溉和非灌溉牧场系统中保守地使用水，土壤和植被资源。

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