生態農業：農業精準栽培管理技術開發【土壤環境科學系申雍教授】
| 論文篇名 | 英文：Effect of wetting on the determination of soil organic matter content using visible and near-infrared spectrometer 中文：潤濕對應用可見光和近紅外光譜儀測定土壤有機質含量的影響 |
| 期刊名稱 | Geoderma |
| 發表年份,卷數,起迄頁數 | 2020, 376, no.114528 |
| 作者 | Wang, Yi-Ping(王依蘋); Lee, Che-Kuan; Dai, Yi-Hao; Shen, Yuan(申雍)* |
| DOI | 10.1016/j.geoderma.2020.114528 |
| 中文摘要 | 使用可見光和近紅外光反射光譜儀，以近端感測技術於現地評估土壤樣品中有機質 (SOM) 含量的方法，會因土壤成分和水分含量的相互作用而變得複雜。 本研究用可攜式光譜儀量測了 486 個涵蓋廣泛土壤反射特性和土壤質地之農地壤樣品的反射光譜，樣品的濕度範圍從空氣乾燥到接近飽和。然後使用偏最小二乘回歸法(PLSR)，結合各種反射光譜預處理方法（標準正態變量轉換和去趨勢，以及光譜導數）開發土壤水分和 SOM 含量的預測模型。我們的結果表明，基於在足夠濕土壤樣品上測量的光譜的 PLSR 模型比基於風乾樣品的模型對 SOM 預測的準確性略高。除了還探索了潤濕以提高預測準確性的機制外，也通過分析它們的重要性，確定了與有機官能團（如芳烴、脂肪族和酰胺）相關的重要反射波長。本研究同時針對兩個獨立數據集（分別包含 126 和 99 個樣本）測試了開發模型的穩健性，達到平均偏差差 (MBD) = 0.02 %、均方根差 (RMSD) = 0.99 %、比率四分位數性能 (RPIQ) = 2.90 和 MBD = -0.23 %，RMSD= 1.35 %，RPIQ = 1.44。這些發現表明，基於充分潤濕的土壤樣本開發 SOM 預測 PLSR 模型可能是一種可行的方法，尤其是在開發用於現場操作的模型。 |
| 英文摘要 | The assessment of soil organic matter (SOM) content by proximal sensing using Visible and Near-Infrared (VNIR) reflectance spectroscopy of field soil samples is complicated by interactions with various soil constituents and moisture content. This study examined a total of 486 archived agricultural soil samples, covering a wide range of soil reflectance characteristics and soil textures. Spectral reflectance was measured with a spectrometer for samples with wetness ranges from air-dry to near saturation. Prediction models for soil water and SOM content were then developed using partial least square regressions (PLSR) combined with various reflectance spectrum pre-processing methods (standard normal variate transformation and detrend, as well as spectral derivatives). Our results indicate that the PLSR model based on spectra measured on sufficiently wet soil samples had slightly better accuracy for SOM predictions than models based on air-dried samples. The mechanisms of wetting to increase prediction accuracy were also explored. Important reflectance wavelengths associated with organic functional groups such as aromatics, aliphatics, and amides were identified through analysis of their variable importance in projections (VIP). Robustness of the developed models was tested against other two independent datasets (comprised of sample numbers 126 and 99 each), achieving prediction accuracies of mean bias difference (MBD) = 0.02 %, root mean square difference (RMSD) = 0.99 %, Ratio of Performance to Inter-Quartile (RPIQ) = 2.90 and MBD = -0.23 %, RMSD = 1.35 %, RPIQ = 1.44, respectively. These findings suggest that developing SOM prediction PLSR models based on sufficiently wetted soil samples may be a viable approach, particularly when developing models for operational use in the field. |
| 發表成果與本中心研究主題相關性 | 土壤有機質含量與土壤理化性質和生物活性有極重要的關聯。本研究開發應用土壤反射光譜偵測土壤有機質含量的技術，只需1分鐘即可獲知結果，大幅 縮短傳統化學分析的檢測時間與成本，輔助專家在現場做出精確的土壤診斷。 |
