本文選題：土壤有機質(zhì) + 高光譜��； 參考：《中國農(nóng)業(yè)科學院》2015年碩士論文

【摘要】：土壤有機質(zhì)(SOM)是土壤肥力的重要指標,不僅能提供作物養(yǎng)分,改善土壤物理性質(zhì),還具有土壤保水和保肥的作用�？焖佟蚀_地估算SOM含量,可以為耕地質(zhì)量評價、培肥地力、提高糧食產(chǎn)量提供重要的決策依據(jù)。土壤光譜特性是SOM含量、含水量、氧化鐵含量、土壤質(zhì)地等屬性的綜合反映,利用土壤的光譜信息,可以快速地估算出SOM的含量。但土壤含水量等因素對高光譜估算SOM含量的精度有很大的影響,因此,研究確定高光譜估算SOM的含水量適宜范圍具有理論和實踐意義。本研究以東北耕地土壤為研究對象,利用美國ASD Fieldspec Pro FR地物高光譜儀在室內(nèi)條件下對烘干土、風干土和含水量為5~40%(按5%遞增)的土壤進行光譜測量,對光譜數(shù)據(jù)進行反射率(R)、反射率一階導數(shù)(R’)和反射率倒數(shù)對數(shù)(Log(1/R))3種光譜數(shù)據(jù)數(shù)學變換,然后對全部樣本和不同土壤類型,運用偏最小二乘回歸法(PLSR)、支持向量機(SVM)和二者的結合方法建立相應的SOM含量估算模型。研究結論如下:1.用全部土壤樣本建立的SOM含量估算模型中,風干土光譜數(shù)據(jù)在PLSR和PLSR-SVM方法建立的SOM含量模型結果最好。SVM建立的模型中,含水量15%和20%的土樣光譜數(shù)據(jù)建立的SOM含量模型結果最好,風干土結果次之。2.當土壤含水量大于25%時,不適宜利用高光譜數(shù)據(jù)進行SOM含量估算。3.PLSR和PLSR-SVM建立的SOM含量模型中,光譜數(shù)據(jù)Log(1/R)變換形式的土壤含水量水平建立的SOM含量模型精度都比較高;SVM建立的SOM含量模型中,光譜數(shù)據(jù)R和Log(1/R)變換形式的土壤含水量水平建立的SOM含量模型相對較好。4.對于單個土壤類型,進行PLSR和SVM的SOM含量建模時,黑土的估算模型結果最好,而草甸土和黑鈣土的SOM含量結果不太理想。
[Abstract]:Soil organic matter (SOM) is an important index of soil fertility, which can not only provide crop nutrients, improve the physical properties of soil, but also have the function of soil moisture and fertilizer conservation. Estimating SOM content quickly and accurately can provide important decision basis for evaluating cultivated land quality, increasing fertility and increasing grain yield. The soil spectral characteristic is the comprehensive reflection of SOM content, water content, iron oxide content, soil texture and so on. Using the soil spectral information, the SOM content can be estimated quickly. However, soil water content and other factors have great influence on the accuracy of estimating SOM content by hyperspectral method. Therefore, it is of theoretical and practical significance to study and determine the suitable range of soil moisture content in hyperspectral estimation. In this study, the soil of northeast cultivated land was used as the research object. The dry soil, dry soil and soil with water content of 5 ~ 40% (increasing by 5%) were measured by ASD Fieldspec Pro FR hyper spectrometer under indoor conditions. The spectral data are mathematically transformed into three kinds of spectral data: reflectivity (R), first derivative of reflectivity (R') and log (1 / R), then all samples and different soil types are transformed. Using partial least square regression (PLSR), support vector machine (SVM) and the combination of the two methods, a corresponding SOM content estimation model is established. The conclusion of the study is as follows: 1. In the SOM content estimation model based on all soil samples, the dry soil spectral data is best in the model established by PLSR and PLSR-SVM. The SOM content model with 15% and 20% soil moisture content data was the best, followed by air-dried soil. When the soil moisture content is greater than 25, it is not suitable to use hyperspectral data to estimate SOM content. 3. PLSR and PLSR-SVM established SOM content model. The SOM content model based on log (1 / R) transformation is more accurate than SOM content model established by SVM. The SOM content model based on spectral data R and Log (1 / R) transformation is better than SOM content model based on log (1 / R) transformation. For a single soil type, when the SOM content of PLSR and SVM is modeled, the estimation model of black soil is the best, but the SOM content of meadow soil and calcareous soil is not very good.
【學位授予單位】：中國農(nóng)業(yè)科學院
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：S153.6;S127

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