Purpose

The main purpose of this study is to realize the rapid and non-destructive determination of soil urease activity, so as to provide guidance for soil nitrogen transformation in time.

Methods

In this study, five gradient experiments of water regulation were set up under the conditions of multiple cropping of winter wheat and summer soybean. The data of soil urease activity and hyperspectral reflectance were collected. We explored the influence of water regulation on soil urease activity. And based on a variety of spectral transformation algorithms and modeling algorithms, hyperspectral monitoring models of soil urease activity were constructed.

Results

Soil urease activity increased first and then decreased with the aggravation of drought stress. FD, CR, MSC, and SNV transformation can improve the correlation between spectral reflectance and soil urease activity. The accuracy of the models constructed by PLSR and SMLR was high. In the nonlinear algorithm, SPA-ANN based on SNV had the highest accuracy. Among all the models, the PLSR model based on FD had the highest accuracy, with R²_v of 0.8564, RMSE_v of 0.4013, and RPD of 2.5667. This study can provide technical support for the rapid determination of soil urease activity and provide a theoretical basis for further rational management of farmland.

中文翻译：

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不同水分调控下土壤脲酶活性的高光谱监测

目的

本研究的主要目的是实现土壤脲酶活性的快速、无损测定，为及时进行土壤氮素转化提供指导。

方法

本研究在冬小麦和夏大豆复种条件下建立了5个梯度调节水分试验。收集土壤脲酶活性和高光谱反射率数据。我们探讨了水分调节对土壤脲酶活性的影响。并基于多种光谱变换算法和建模算法，构建了土壤脲酶活性的高光谱监测模型。

结果

随着干旱胁迫的加剧，土壤脲酶活性先升高后降低。FD、CR、MSC和SNV变换可以提高光谱反射率与土壤脲酶活性之间的相关性。PLSR和SMLR构建的模型精度高。在非线性算法中，基于SNV的SPA-ANN精度最高。在所有模型中，基于FD的PLSR模型精度最高，R ² _v为0.8564，RMSE _v为0.4013，RPD为2.5667。该研究可为土壤脲酶活性的快速测定提供技术支持，为进一步合理管理农田提供理论依据。