Predicting pasture root density from soil spectral reflectance: field measurement
Predicting pasture root density from soil spectral reflectance: field measurement
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DOI：
10.1111/j.1365-2389.2009.01199.x
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发表时间：
2010-02-01
影响因子：
4.2
通讯作者：
Tuohy, M. P.
中科院分区：
文献类型：
--
作者：
Kusumo, B. H.;Hedley, M. J.;Tuohy, M. P.
本文报道了一种利用便携式光谱辐射计现场测量根密度的田间技术的发展和评价。这项技术在新西兰马纳瓦图地区两个永久牧场上的对比土壤(新近形成的土壤和河流土壤)上进行了评估。使用改进的土壤探头，在直径80 mm的土芯的三个深度(15，30和60 mm)的水平表面上获得了350-2500 nm的反射光谱，总共108个样品。扫描结束后，在每个深度取3 mm土片进行根密度测量和土壤碳(C)、氮(N)分析。两种土壤的根密度变化范围很大，从1.53~37.03 mg干根g~(-1)土。河流相土壤的平均根密度(13.21mgg-1)是潮土(6.88mgg-1)的两倍。利用一阶导数光谱和参考数据的偏最小二乘回归(PLSR)建立的校准模型，能够使用留一法交叉验证程序预测未知样本的根密度。当来自两种土壤类型的样本分开(而不是分组)以提供具有更相似属性的光谱数据的子总体(n=54)时，根密度预测更准确。与河流相土壤(R2=0.75，RPD=1.98，RMSECV=5.11 mg g-1)相比，四季相土壤(R2=0.83，预测偏差比(RPD)=2.44，交叉验证均方根误差(RMSECV)=1.96 mg g-1)对根密度的预测效果更好。结果表明，利用田间土芯获取的土壤反射光谱可以预测牧草的根系密度。通过从具有与验证集相似的光谱属性的田间数据源中选择校准数据，可以产生用于预测田间根密度的改进的PLSR模型。根密度和土壤碳含量可以独立预测，这在研究土壤有机质变化的潜在速率时可能特别有用。
This paper reports the development and evaluation of a field technique for in situ measurement of root density using a portable spectroradiometer. The technique was evaluated at two sites in permanent pasture on contrasting soils (an Allophanic and a Fluvial Recent soil) in the Manawatu region, New Zealand. Using a modified soil probe, reflectance spectra (350-2500 nm) were acquired from horizontal surfaces at three depths (15, 30 and 60 mm) of an 80-mm diameter soil core, totalling 108 samples for both soils. After scanning, 3-mm soil slices were taken at each depth for root density measurement and soil carbon (C) and nitrogen (N) analysis. The two soils exhibited a wide range of root densities from 1.53 to 37.03 mg dry root g-1 soil. The average root density in the Fluvial soil (13.21 mg g-1) was twice that in the Allophanic soil (6.88 mg g-1). Calibration models, developed using partial least squares regression (PLSR) of the first derivative spectra and reference data, were able to predict root density on unknown samples using a leave-one-out cross-validation procedure. The root density predictions were more accurate when the samples from the two soil types were separated (rather than grouped) to give sub-populations (n = 54) of spectral data with more similar attributes. A better prediction of root density was achieved in the Allophanic soil (r2 = 0.83, ratio prediction to deviation (RPD ) = 2.44, root mean square error of cross-validation (RMSECV ) = 1.96 mg g -1) than in the Fluvial soil (r2 = 0.75, RPD = 1.98, RMSECV = 5.11 mg g -1). It is concluded that pasture root density can be predicted from soil reflectance spectra acquired from field soil cores. Improved PLSR models for predicting field root density can be produced by selecting calibration data from field data sources with similar spectral attributes to the validation set. Root density and soil C content can be predicted independently, which could be particularly useful in studies examining potential rates of soil organic matter change.