【摘要】：【目的】衛(wèi)星遙感具有覆蓋范圍廣、獲取速度快、信息量大、動態(tài)性強(qiáng)等優(yōu)勢,能夠及時(shí)準(zhǔn)確地獲取作物產(chǎn)量信息,反映作物產(chǎn)量空間變化趨勢。遙感技術(shù)作物估產(chǎn)已成為現(xiàn)代農(nóng)業(yè)生產(chǎn)中研究熱點(diǎn)。通過改善遙感估產(chǎn)建模方法,以實(shí)現(xiàn)進(jìn)一步提高大田作物遙感估產(chǎn)精度,為宏觀了解不同區(qū)域作物產(chǎn)量形成情況及變化趨勢提供直觀、可靠的參考�！痉椒ā空撐慕Y(jié)合2011—2012年江蘇省大豐、興化、姜堰、泰興、儀征5個(gè)縣區(qū)的定點(diǎn)觀測試驗(yàn),以國產(chǎn)衛(wèi)星產(chǎn)品HJ-1A/1B影像為遙感數(shù)據(jù),于小麥開花期開展大田定位觀測區(qū)衛(wèi)星遙感植被指數(shù)、關(guān)鍵生長指標(biāo)與收獲期單產(chǎn)間的定量分析。通過對產(chǎn)量與小麥生長指標(biāo)以及植被指數(shù)進(jìn)行定量關(guān)系分析,進(jìn)一步增強(qiáng)遙感反演的機(jī)理性和重演性。將衛(wèi)星遙感變量與小麥產(chǎn)量進(jìn)行相關(guān)關(guān)系分析作為遙感估產(chǎn)的直接建模方法,間接建模方法則是選取與產(chǎn)量相關(guān)性較好的遙感變量以及與遙感變量相關(guān)性較好的主要苗情指標(biāo),利用篩選得到的敏感遙感變量,首先監(jiān)測對應(yīng)的小麥生長指標(biāo),結(jié)合該小麥生長指標(biāo)與產(chǎn)量間的定量關(guān)系,進(jìn)而建立間接估產(chǎn)模型,利用此模型進(jìn)行小麥遙感間接估產(chǎn)。利用直接和間接建模方法,以相關(guān)性最高為原則,篩選估算產(chǎn)量的敏感衛(wèi)星遙感變量。以2012年試驗(yàn)數(shù)據(jù)為建模樣本,采用線性回歸分析方法,分析小麥開花期苗情指標(biāo)、產(chǎn)量與衛(wèi)星遙感變量兩兩之間的相關(guān)性,分別構(gòu)建以遙感植被指數(shù)為基礎(chǔ)的大田小麥估產(chǎn)模型,與地面實(shí)測結(jié)果一起建立模型共同分析。以2011年試驗(yàn)數(shù)據(jù)為驗(yàn)證樣本,選取評價(jià)指標(biāo)擬合度(R2)和均方根誤差(RMSE),對兩類模型的估算精度進(jìn)行驗(yàn)證和比較,以提高遙感反演的定量化水平和可信度�！窘Y(jié)果】分別以差值植被指數(shù)(difference vegetation index,DVI)和比值植被指數(shù)(ratio vegetation index,RVI)為基礎(chǔ)的單因子直接估產(chǎn)模型的均方根誤差(root mean square error,RMSE)為918 kg·hm-2和1 399.5 kg·hm-2,以DVI和RVI遙感變量構(gòu)建雙變量估產(chǎn)模型的RMSE為1 036.5 kg·hm-2,以歸一化植被指數(shù)(normalized difference vegetation index,NDVI)和葉片氮積累量為基礎(chǔ)構(gòu)建的間接估產(chǎn)模型的RMSE為805.5 kg·hm-2,說明開花期HJ-1A/1B影像估算小麥區(qū)域產(chǎn)量是可行的,且精度較高;經(jīng)比較,以NDVI和葉片氮積累量為基礎(chǔ)的間接估產(chǎn)模型精度明顯高于直接估產(chǎn)模型,相較于DVI直接估產(chǎn)模型RMSE降低了112.5 kg·hm-2,相較于RVI直接估產(chǎn)模型RMSE降低了594 kg·hm-2,相較于雙因子模型RMSE降低了231 kg·hm-2�！窘Y(jié)論】國產(chǎn)衛(wèi)星HJ-1A/B可以較好滿足估測小麥產(chǎn)量要求,且利用間接方法建立作物遙感估產(chǎn)模型要好于直接方法,研究結(jié)果為利用遙感技術(shù)更為準(zhǔn)確估算大田小麥產(chǎn)量提供了一種新的途徑。
[Abstract]:[objective] Satellite remote sensing has the advantages of wide coverage, fast acquisition speed, large amount of information, strong dynamic and so on. It can obtain crop yield information accurately and timely, and reflect the spatial variation trend of crop yield. Crop yield estimation by remote sensing has become a hotspot in modern agricultural production. By improving the modeling method of crop yield estimation by remote sensing, the precision of crop yield estimation can be further improved, which can provide a visual view for the macroscopic understanding of crop yield formation and change trend in different regions. [methods] based on the fixed-point observation experiments in five counties of Dafeng, Xinghua, Jiangyan, Taixing and Yizheng in Jiangsu Province from 2011-2012, the HJ-1A/1B images of domestic satellite products were used as remote sensing data. The quantitative analysis of satellite remote sensing vegetation index, key growth index and harvest yield was carried out in the field location observation area during the flowering period of wheat (Triticum aestivum L.). Through quantitative analysis of yield, wheat growth index and vegetation index, the mechanism and repeatability of remote sensing inversion are further enhanced. The correlation analysis between satellite remote sensing variables and wheat yield was taken as the direct modeling method for estimating yield by remote sensing, while indirect modeling method was used to select remote sensing variables with good correlation with yield and main seedling condition indexes with good correlation with remote sensing variables. Based on the sensitive remote sensing variables obtained by screening, the corresponding wheat growth index was first monitored, and the quantitative relationship between the wheat growth index and the yield was combined, and then an indirect yield estimation model was established, which was used to estimate the yield indirectly by remote sensing. By using direct and indirect modeling methods, sensitive satellite remote sensing variables for estimating yield are screened based on the principle of maximum correlation. Based on the experimental data in 2012, the correlation between seedling growth index, yield and satellite remote sensing variables in flowering stage of wheat was analyzed by linear regression analysis. The field wheat yield estimation models based on remote sensing vegetation index were constructed, and the model was analyzed together with the measured results on the ground. Taking the experimental data of 2011 as the validation sample, the estimation accuracy of the two models is verified and compared with the evaluation index fitting (R2) and root mean square error (RMSE),). [results] based on the difference vegetation index (difference vegetation index,DVI) and the ratio vegetation index (ratio vegetation index,RVI), the root mean square error (root mean square error,) of the single factor direct yield estimation model was obtained. RMSE) for 918 kg hm-2 and 1 399.5 kg hm-2, using DVI and RVI remote sensing variables to construct a bivariate yield estimation model with a RMSE of 1 036.5 kg hm-2, to normalize the vegetation index (normalized difference vegetation index, The RMSE of the indirect yield estimation model based on NDVI) and leaf nitrogen accumulation was 805.5 kg hm-2, which indicated that the HJ-1A/1B image at flowering stage was feasible and accurate in estimating wheat regional yield. By comparison, the precision of indirect yield estimation model based on NDVI and leaf nitrogen accumulation was significantly higher than that of direct yield estimation model. Compared with DVI direct yield estimation model, RMSE decreased by 112.5 kg hm-2,. Compared with RVI direct yield estimation model, RMSE decreased by 594 kg hm-2, compared with two-factor model RMSE by 231 kg hm-2. [conclusion] domestic satellite HJ-1A/B can better meet the requirement of wheat yield estimation. The indirect method is better than the direct method in establishing crop yield estimation model by remote sensing. The results provide a new way to estimate wheat yield more accurately by remote sensing.
【作者單位】： 揚(yáng)州大學(xué)江蘇省作物遺傳生理國家重點(diǎn)實(shí)驗(yàn)室培育點(diǎn)/糧食作物現(xiàn)代產(chǎn)業(yè)技術(shù)協(xié)同創(chuàng)新中心;
【基金】：國家自然科學(xué)基金(41271415) 江蘇高校優(yōu)勢學(xué)科建設(shè)工程(PAPD) 江蘇省農(nóng)業(yè)自主創(chuàng)新資金(CX(16)1042) 蘇州市農(nóng)業(yè)科技創(chuàng)新項(xiàng)目(SNG201643) 揚(yáng)州市科技計(jì)劃(YZ2016242) 揚(yáng)州大學(xué)科技創(chuàng)新團(tuán)隊(duì)
【分類號】：S127;S512.1

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