基于GOCI影像的長(zhǎng)江口及鄰近海域有色溶解有機(jī)物（CDOM）遙感反演及其逐時(shí)變化分析
發(fā)布時(shí)間：2019-06-28 09:39
【摘要】：有色溶解有機(jī)物(CDOM)是海洋水色組分之一,認(rèn)識(shí)河口近岸海域CDOM的分布、遷移和轉(zhuǎn)化不僅對(duì)于海洋水色遙感具有重要意義,而且有著顯著的生物學(xué)意義和光譜學(xué)意義,其與生物地球化學(xué)循環(huán)有重要聯(lián)系。本文以CDOM吸收系數(shù)為CDOM濃度指標(biāo),對(duì)CDOM的遙感反演應(yīng)用進(jìn)行研究,并在此基礎(chǔ)上探究長(zhǎng)江口和鄰近海域的CDOM分布特征及其日變化特征。對(duì)于長(zhǎng)江口高濁度水體,已有的反演模型對(duì)于CDOM的變化非常不敏感,反演精度很低,因此本文選擇BP神經(jīng)網(wǎng)絡(luò)算法進(jìn)行研究,其優(yōu)勢(shì)是得到的模型結(jié)果中失效的數(shù)據(jù)比較少,而其他模型可能存在大面積的算法失效。從精度結(jié)果看,BP神經(jīng)網(wǎng)絡(luò)算法的精度高于GOCI標(biāo)準(zhǔn)軟件GDPS提供的Moon算法與YOC算法,但同時(shí)BP神經(jīng)網(wǎng)絡(luò)算法也存在需要人為參與學(xué)習(xí)判斷的缺點(diǎn)。本文以長(zhǎng)江口及其鄰近海域野外實(shí)測(cè)數(shù)據(jù)為基礎(chǔ),在QAA算法及QAA-E算法基礎(chǔ)上,建立了基于BP神經(jīng)網(wǎng)絡(luò)算法反演bbp(555)與ap(443)的關(guān)系,適用于GOCI衛(wèi)星數(shù)據(jù)的反演模型。利用2012年4月26日星地同步數(shù)據(jù)對(duì)模型進(jìn)行驗(yàn)證,驗(yàn)證結(jié)果表明該模型可以應(yīng)用于GOCI衛(wèi)星數(shù)據(jù)的CDOM吸收系數(shù)反演。在此基礎(chǔ)上分析了長(zhǎng)江口及其鄰近海域CDOM吸收系數(shù)分布及CDOM日變化情況,得到以下結(jié)論。(1)基于QAA算法的BP神經(jīng)元網(wǎng)絡(luò)法對(duì)CDOM吸收系數(shù)的反演效果較好,適用于長(zhǎng)江口及其鄰近海域CDOM反演。但總體而言,高濁度水域CDOM反演精度仍有待提高,原因是由于長(zhǎng)江口及其鄰近海域沿岸水體中含較高濃度的懸浮物,而懸浮物對(duì)后向散射光譜的影響占主導(dǎo)作用,對(duì)葉綠素和CDOM光譜的影響較大,從而減弱了 CDOM與后向散射光譜之間的相關(guān)性,最終導(dǎo)致算法在復(fù)雜水體的反演精度降低。(2)利用2014年3月15日的GOCI影像反演長(zhǎng)江口及其鄰近海域CDOM吸收系數(shù),并對(duì)其日內(nèi)變化時(shí)空特征進(jìn)行了分析。結(jié)果表明,GOCI數(shù)據(jù)能夠清晰展現(xiàn)CDOM吸收系數(shù)的空間分布,體現(xiàn)出水體受潮汐等外界因素影響而導(dǎo)致的CDOM吸收系數(shù)的變化。從日內(nèi)變化來(lái)看,在漲潮期間,CDOM吸收系數(shù)的空間分布為長(zhǎng)江口內(nèi)北支高于南支濃度,北支濃度與口外接近;而在退潮期間,北支CDOM吸收系數(shù)明顯下降,且低于口外CDOM吸收系數(shù),南港,北港的CDOM吸收系數(shù)也出現(xiàn)逐漸下降現(xiàn)象。而海水與淡水的混合稀釋,使CDOM的濃度梯度從長(zhǎng)江口往外海區(qū)呈現(xiàn)沿西北-東南方向降低的趨勢(shì)。(3)利用GOCI數(shù)據(jù)高時(shí)間分辨率的特點(diǎn),可以捕捉一天內(nèi)CDOM的變化特征,這有利于對(duì)CDOM循環(huán)過程進(jìn)行實(shí)時(shí)監(jiān)測(cè),為進(jìn)一步研究長(zhǎng)江口及其鄰近海域CDOM日循環(huán)變化特性及其驅(qū)動(dòng)機(jī)制及河口演化規(guī)律提供了重要的觀測(cè)數(shù)據(jù)。(4)定量遙感中分類、校正、反演模型的誤差需要進(jìn)行統(tǒng)計(jì)評(píng)估,從而確定其性能與效果,而這些評(píng)估通�；谝恍┏Ｒ姷慕y(tǒng)計(jì)參數(shù)。本文通過計(jì)算機(jī)模擬了誤差的各種近似分布,研究了樣本量n和統(tǒng)計(jì)指標(biāo)RMSE、MAE與UA之間的關(guān)系。結(jié)果表明RMSE、MAE與UA隨著樣本量n的變化呈現(xiàn)不同的趨勢(shì):在n小于40左右時(shí),RMSE和MAE往往隨樣本量增大呈上升趨勢(shì),隨后趨于平緩;而UA隨樣本量n的增加總是平滑下降。由此可以看出,在樣本量少的情況下,UA比RMSE、MAE更適合評(píng)價(jià)遙感模型的不確定性(可信賴度),因?yàn)榛诮y(tǒng)計(jì)學(xué)常識(shí),樣本量越大,建立的模型就越可靠(不確定度越小)。
[Abstract]:The color-dissolved organic matter (CDOM) is one of the marine water-color components. It is recognized that the distribution, migration and transformation of the CDOM in the nearshore area of the estuary are not only important for the remote sensing of marine water color, but also have significant biological significance and spectroscopy significance. It is closely related to the biogeochemical cycle. In this paper, the CDOM absorption coefficient is used as the index of the concentration of the CDOM, and the application of the remote sensing inversion of the CDOM is studied, and the distribution characteristics and the diurnal variation of the CDOM in the Changjiang estuary and the adjacent sea area are explored. For the high-turbidity water body of the Yangtze River estuary, the existing inversion model is very insensitive to the change of the CDOM, and the inversion precision is very low, so the BP neural network algorithm is selected for research, and the advantage is that the data of the failure in the obtained model result is less, While other models may have a large area of algorithm failure. The accuracy of the BP neural network algorithm is higher than that of the Moon algorithm and the YOC algorithm provided by the GCI standard software GDPS from the accuracy results, but the BP neural network algorithm also has the disadvantage that the artificial participation of the learning judgment is required. The relationship between bp (555) and ap (443) on the basis of the QAA algorithm and the QAA-E algorithm is established based on the field data of the Changjiang River estuary and its adjacent sea area, and the relationship between bp (555) and ap (443) is established based on the BP neural network algorithm, which is suitable for the inversion model of GOCI satellite data. The results show that the model can be applied to the inversion of the CDOM absorption coefficient of GOCI satellite data. On this basis, the distribution of the absorption coefficient of the CDOM and the diurnal variation of the CDOM in the Changjiang Estuary and its adjacent sea area are analyzed, and the following conclusions are obtained. (1) The BP neural network method based on the QAA algorithm has better inversion effect on the absorption coefficient of the CDOM, and is suitable for the inversion of the CDOM in the Changjiang estuary and its adjacent sea area. However, in general, that accuracy of the inversion of the CDOM in the high-turbidity water area is still to be improved due to the high concentration of suspended matter in the water body along the Yangtze estuary and its adjacent sea area, and the effect of the suspended matter on the back-scattering spectrum of the suspended matter is dominant, and the effect on the spectrum of the chlorophyll and the CDOM is large. So that the correlation between the CDOM and the backward scattering spectrum is reduced, and finally, the inversion accuracy of the algorithm in the complex water body is reduced. (2) Using the GCI image on March 15,2014, the absorption coefficient of the CDOM in the Changjiang Estuary and its adjacent sea area was inverted, and the spatial and temporal characteristics of the diurnal variation were analyzed. The results show that the spatial distribution of the absorption coefficient of the CDOM can be clearly displayed by the GOCI data, and the change of the absorption coefficient of the CDOM due to the influence of the external factors such as the deliquescence of the water is reflected. In the diurnal variation, the spatial distribution of the absorption coefficient of the CDOM is higher than that of the south branch in the Yangtze River estuary during the flood tide, and the concentration of the north branch is close to the outside of the mouth; and during the ebb tide, the absorption coefficient of the CDOM of the north branch is obviously lower, and the absorption coefficient of the CDOM is lower than the external CDOM absorption coefficient, and the south port, The absorption coefficient of CDOM in the northern port also appears to be decreasing. The mixture of seawater and fresh water is diluted, so that the concentration gradient of the CDOM is decreased in the north-west-southeast direction from the outside of the Yangtze River estuary. and (3) utilizing the characteristics of high time resolution of the GOCI data, can capture the change characteristics of the CDOM in a day, which is favorable for real-time monitoring of the CDOM circulation process, In order to further study the characteristics of the cycle change of the CDOM in the Yangtze River estuary and its adjacent sea area and its driving mechanism and the estuary evolution law, it provides important observation data. (4) The error of classification, correction and inversion model in quantitative remote sensing requires statistical evaluation to determine its performance and effect, which is usually based on some common statistical parameters. The relationship between the sample size n and the statistical index RMSE, MAE and UA is studied by computer simulation of various approximate distributions of the error. The results show that RMSE, MAE and UA show a different trend with the change of sample size n: when n is less than 40, the RMSE and MAE tend to increase with the sample size, and then tend to be gentle; and UA increases with the sample size n. As a result, it can be seen that, in the case of a small sample size, the UA is more suitable for evaluating the uncertainty (reliability) of the remote sensing model than the RMSE and MAE, as the larger the sample size is based on the statistical common sense, the more reliable the model is established (the less the degree of certainty).
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：P734
本文編號(hào)：2507195
[Abstract]:The color-dissolved organic matter (CDOM) is one of the marine water-color components. It is recognized that the distribution, migration and transformation of the CDOM in the nearshore area of the estuary are not only important for the remote sensing of marine water color, but also have significant biological significance and spectroscopy significance. It is closely related to the biogeochemical cycle. In this paper, the CDOM absorption coefficient is used as the index of the concentration of the CDOM, and the application of the remote sensing inversion of the CDOM is studied, and the distribution characteristics and the diurnal variation of the CDOM in the Changjiang estuary and the adjacent sea area are explored. For the high-turbidity water body of the Yangtze River estuary, the existing inversion model is very insensitive to the change of the CDOM, and the inversion precision is very low, so the BP neural network algorithm is selected for research, and the advantage is that the data of the failure in the obtained model result is less, While other models may have a large area of algorithm failure. The accuracy of the BP neural network algorithm is higher than that of the Moon algorithm and the YOC algorithm provided by the GCI standard software GDPS from the accuracy results, but the BP neural network algorithm also has the disadvantage that the artificial participation of the learning judgment is required. The relationship between bp (555) and ap (443) on the basis of the QAA algorithm and the QAA-E algorithm is established based on the field data of the Changjiang River estuary and its adjacent sea area, and the relationship between bp (555) and ap (443) is established based on the BP neural network algorithm, which is suitable for the inversion model of GOCI satellite data. The results show that the model can be applied to the inversion of the CDOM absorption coefficient of GOCI satellite data. On this basis, the distribution of the absorption coefficient of the CDOM and the diurnal variation of the CDOM in the Changjiang Estuary and its adjacent sea area are analyzed, and the following conclusions are obtained. (1) The BP neural network method based on the QAA algorithm has better inversion effect on the absorption coefficient of the CDOM, and is suitable for the inversion of the CDOM in the Changjiang estuary and its adjacent sea area. However, in general, that accuracy of the inversion of the CDOM in the high-turbidity water area is still to be improved due to the high concentration of suspended matter in the water body along the Yangtze estuary and its adjacent sea area, and the effect of the suspended matter on the back-scattering spectrum of the suspended matter is dominant, and the effect on the spectrum of the chlorophyll and the CDOM is large. So that the correlation between the CDOM and the backward scattering spectrum is reduced, and finally, the inversion accuracy of the algorithm in the complex water body is reduced. (2) Using the GCI image on March 15,2014, the absorption coefficient of the CDOM in the Changjiang Estuary and its adjacent sea area was inverted, and the spatial and temporal characteristics of the diurnal variation were analyzed. The results show that the spatial distribution of the absorption coefficient of the CDOM can be clearly displayed by the GOCI data, and the change of the absorption coefficient of the CDOM due to the influence of the external factors such as the deliquescence of the water is reflected. In the diurnal variation, the spatial distribution of the absorption coefficient of the CDOM is higher than that of the south branch in the Yangtze River estuary during the flood tide, and the concentration of the north branch is close to the outside of the mouth; and during the ebb tide, the absorption coefficient of the CDOM of the north branch is obviously lower, and the absorption coefficient of the CDOM is lower than the external CDOM absorption coefficient, and the south port, The absorption coefficient of CDOM in the northern port also appears to be decreasing. The mixture of seawater and fresh water is diluted, so that the concentration gradient of the CDOM is decreased in the north-west-southeast direction from the outside of the Yangtze River estuary. and (3) utilizing the characteristics of high time resolution of the GOCI data, can capture the change characteristics of the CDOM in a day, which is favorable for real-time monitoring of the CDOM circulation process, In order to further study the characteristics of the cycle change of the CDOM in the Yangtze River estuary and its adjacent sea area and its driving mechanism and the estuary evolution law, it provides important observation data. (4) The error of classification, correction and inversion model in quantitative remote sensing requires statistical evaluation to determine its performance and effect, which is usually based on some common statistical parameters. The relationship between the sample size n and the statistical index RMSE, MAE and UA is studied by computer simulation of various approximate distributions of the error. The results show that RMSE, MAE and UA show a different trend with the change of sample size n: when n is less than 40, the RMSE and MAE tend to increase with the sample size, and then tend to be gentle; and UA increases with the sample size n. As a result, it can be seen that, in the case of a small sample size, the UA is more suitable for evaluating the uncertainty (reliability) of the remote sensing model than the RMSE and MAE, as the larger the sample size is based on the statistical common sense, the more reliable the model is established (the less the degree of certainty).
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：P734
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