首页> 外文期刊>Precision Agriculture >The use of near infrared (NIR) spectroscopy to improve soil mapping at the farm scale. (Special Issue: Spatial variation in precision agriculture.)
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The use of near infrared (NIR) spectroscopy to improve soil mapping at the farm scale. (Special Issue: Spatial variation in precision agriculture.)

机译:使用近红外(NIR)光谱来改善农场规模的土壤测绘。 (特刊:精准农业的空间变异。)

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The creation of fine resolution soil maps is hampered by the increasing costs associated with conventional laboratory analyses of soil. In this study, near infrared (NIR) reflectance spectroscopy was used to reduce the number of conventional soil analyses required by the use of calibration models at the farm scale. Soil electrical conductivity and mid infrared reflection (MIR) from a satellite image were used and compared as ancillary data to guide the targeting of soil sampling. About 150 targeted samples were taken over a 97 hectare farm (approximately 1.5 samples per hectare) for each type of ancillary data. A sub-set of 25 samples was selected from each of the targeted data sets (150 points) to measure clay and soil organic matter (SOM) contents for calibration with NIR. For the remaining 125 samples only their NIR-spectra needed to be determined. The NIR calibration models for both SOM and clay contents resulted in predictions with small errors. Maps derived from the calibrated data were compared with a map based on 0.5 samples per hectare representing a conventional farm-scale soil map. The maps derived from the NIR-calibrated data are promising, and the potential for developing a cost-effective strategy to map soil from NIR-calibrated data at the farm-scale is considerable.
机译:与常规实验室土壤分析有关的成本不断增加,阻碍了高分辨率土壤图的创建。在这项研究中,使用近红外(NIR)反射光谱法减少了在农场规模使用校准模型所需的常规土壤分析次数。使用来自卫星图像的土壤电导率和中红外反射(MIR),并将其作为辅助数据进行比较,以指导目标土壤采样。对于每种类型的辅助数据,在97公顷的农场(每公顷约1.5个样品)中采集了约150个目标样品。从每个目标数据集中(150个点)选择25个样本的子集,以测量粘土和土壤有机质(SOM)含量,以进行NIR校准。对于其余的125个样本,只需确定其NIR光谱即可。针对SOM和黏土含量的NIR校准模型得出的预测误差很小。将来自校准数据的地图与基于每公顷0.5个样本的地图进行比较,该地图代表常规农场规模的土壤地图。从NIR校准的数据中得出的地图很有希望,在农场规模上开发具有成本效益的策略从NIR校准的数据中绘制土壤图的潜力是巨大的。

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