本文選題：GWR模型 + 土壤重金屬��； 參考：《地理學(xué)報》2017年03期

【摘要】：目前土壤重金屬高光譜反演模型大多忽視了重金屬與光譜變量間相關(guān)關(guān)系的空間異質(zhì)性,這與實際情況不相吻合,而地理權(quán)重回歸(GWR)模型能有效地揭示變量間關(guān)系的空間異質(zhì)性。本文以福州市土壤重金屬Cd、Cu、Pb、Cr、Zn、Ni為對象,構(gòu)建土壤重金屬預(yù)測的GWR高光譜模型,并將預(yù)測結(jié)果與普通最小二乘法回歸(OLS)結(jié)果進行比較分析,探討GWR模型在土壤重金屬高光譜預(yù)測中的適用性及局限性。結(jié)果表明:(1)GWR模型在土壤重金屬高光譜預(yù)測中適用與否取決于重金屬對光譜變量影響的空間異質(zhì)性程度:對于Cr、Cu、Zn、Pb等對光譜變量影響空間異質(zhì)性大的元素,其GWR預(yù)測精度較OLS提高明顯,表現(xiàn)為GWR模型的調(diào)節(jié)R2較OLS模型有了明顯提高,分別為OLS模型的2.69倍、2.01倍、1.87倍和1.53倍;而AIC值以及殘差平方和較OLS模型卻明顯降低,AIC值減少量均大于3個單位,殘差平方和則僅分別為OLS模型的25.33%、30.09%、47.22%和86.84%;對于Cd和Ni等對光譜變量影響空間異質(zhì)性小的元素,相較于OLS模型,GWR模型的調(diào)節(jié)R2分別提高了0.015和0.007,殘差平方和分別減少了5.97%和4.18%,但AIC值卻分別增加了2.737和2.762,GWR預(yù)測效果改善不明顯;(2)光譜變換可以有效增強土壤重金屬的光譜特征,其中以光譜的倒數(shù)變換效果最好,而且該變換及其微分形式可以很好地提高模型的預(yù)測效果;(3)GWR模型的應(yīng)用前提是變量間關(guān)系的空間非平穩(wěn)性,適合在與土壤光譜變量間關(guān)系具有顯著空間異質(zhì)性的重金屬高光譜預(yù)測中推廣。
[Abstract]:At present, the spatial heterogeneity of the correlation between heavy metals and spectral variables is neglected in the hyperspectral inversion models of soil heavy metals, which is not consistent with the actual situation. The geographical weight regression (GWR) model can effectively reveal the spatial heterogeneity of the relationship between variables. In this paper, the GWR hyperspectral model of soil heavy metal prediction was constructed by taking the heavy metal CdCuCuPbPbPbCZZZN Ni in Fuzhou as an example, and the prediction results were compared with those obtained by the ordinary least square regression method (LLS). To discuss the applicability and limitation of GWR model in soil heavy metal hyperspectral prediction. The results showed that the applicability of the GWR model to the hyperspectral prediction of soil heavy metals depended on the spatial heterogeneity of the influence of heavy metals on the spectral variables. The prediction accuracy of GWR was significantly higher than that of OLS, and the regulating R2 of GWR model was significantly higher than that of OLS model, which was 2.69 times of OLS model, 1.87 times and 1.53 times of OLS model, respectively. However, the AIC value and the sum of squared residuals were significantly decreased by more than 3 units, and the sum of squared residuals were only 25.3330.09% and 86.84% of those of the OLS model, respectively, for elements with low spatial heterogeneity, such as CD and Ni. Compared with OLS model, the adjusted R2 of GWR model increased by 0.015 and 0.007, the sum of squared residuals decreased by 5.97% and 4.18%, but the AIC value increased 2.737 and 2.762 GWR respectively. Among them, the reciprocal transformation of spectrum is the best, and this transformation and its differential form can improve the prediction effect of the model and the application of the GWR model is based on the spatial nonstationarity of the relationship between variables. It is suitable for the hyperspectral prediction of heavy metals with significant spatial heterogeneity in relation to soil spectral variables.
【作者單位】： 福建師范大學(xué)地理科學(xué)學(xué)院;閩江學(xué)院地理科學(xué)系;
【基金】：國家自然科學(xué)基金項目(41601601) 福建省自然科學(xué)基金項目(2016J01194) 科技部國際合作重大專項(247608)~~
【分類號】：S151.9;O212.1

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