本文關(guān)鍵詞：引入地形因子的黑土區(qū)大豆干生物量遙感反演模型及驗(yàn)證　出處：《農(nóng)業(yè)工程學(xué)報(bào)》2017年16期 　論文類型：期刊論文

  更多相關(guān)文章： 遙感 作物 模型 大豆 地上干生物量 地形因子

【摘要】：為了對(duì)田塊尺度農(nóng)作物地上干生物量進(jìn)行估測(cè),提高大豆地上干生物量反演模型的精度和穩(wěn)定性,該文獲取了研究區(qū)地塊2016年7、8月份的SPOT-6多光譜數(shù)據(jù),并測(cè)定不同地形坡位的大豆地上干生物量,以歸一化植被指數(shù)(normalized difference vegetation index,NDVI)和增強(qiáng)型植被指數(shù)(enhanced vegetation index,EVI)為輸入量,建立田塊尺度大豆地上干生物量一元線性回歸模型;加入與地上干生物量相關(guān)的地形因子,建立逐步多元回歸和神經(jīng)網(wǎng)絡(luò)多層感知反演模型。結(jié)果表明:1)使用傳統(tǒng)的單一植被指數(shù)模型預(yù)測(cè)大豆地上干生物量有可行性,但模型精度和穩(wěn)定性不高。2)加入地形因子(海拔、坡度、坡向)的神經(jīng)網(wǎng)絡(luò)多層感知器模型,有較高的精度和可靠性,模型準(zhǔn)確度達(dá)到90.4%,驗(yàn)證結(jié)果顯示預(yù)估精度為96.2%。反演結(jié)果與地塊的地形、地貌、氣溫和降水特征基本吻合,反映了作物長(zhǎng)勢(shì)的空間分布特征,可以為田塊尺度大豆地上干生物量動(dòng)態(tài)監(jiān)測(cè)和精準(zhǔn)管理,提供借科學(xué)依據(jù)。
[Abstract]:In order to estimate the aboveground dry biomass of field scale crops and improve the accuracy and stability of soybean aboveground dry biomass inversion model, this paper obtained the study area block on 2016 7. In August, the SPOT-6 multispectral data were obtained, and the dry aboveground biomass of soybean at different topographic slopes was measured. Normalized difference vegetation index is used as a normalized vegetation index. NDVI) and enhanced vegetation index (EVI) are the inputs. A linear regression model of aboveground dry biomass of field scale soybean was established. Topographic factors related to aboveground dry biomass were added. The stepwise multivariate regression and neural network multilayer perception inversion models were established. The results showed that the traditional single vegetation index model was feasible to predict the aboveground dry biomass of soybean. However, the model accuracy and stability are not high. 2) the neural network multilayer perceptron model with terrain factors (elevation, slope, slope direction) has high accuracy and reliability, and the accuracy of the model reaches 90.4%. The results show that the prediction accuracy is 96.2.The inversion results are basically consistent with the terrain, geomorphology, temperature and precipitation characteristics of the block, reflecting the spatial distribution characteristics of crop growth. It can provide scientific basis for dynamic monitoring and accurate management of field scale soybean aboveground dry biomass.
【作者單位】： 東北農(nóng)業(yè)大學(xué)資源與環(huán)境學(xué)院;中國(guó)科學(xué)院東北地理與農(nóng)業(yè)生態(tài)研究所;
【基金】：國(guó)家自然科學(xué)基金項(xiàng)目(41671438;41501357) “中國(guó)科學(xué)院東北地理與農(nóng)業(yè)生態(tài)研究所”引進(jìn)優(yōu)秀人才項(xiàng)目
【分類號(hào)】：S565.1;TP79
【正文快照】： 0引言作物地上生物量是反映作物生長(zhǎng)狀況的重要指標(biāo),作物生物量估算是服務(wù)現(xiàn)代農(nóng)業(yè)的一項(xiàng)重要內(nèi)容,及時(shí)準(zhǔn)確的生物量模擬對(duì)國(guó)家農(nóng)業(yè)決策、農(nóng)田生產(chǎn)管理、糧食倉(cāng)儲(chǔ)安全等都有重要意義[1]。傳統(tǒng)地面調(diào)查監(jiān)測(cè)的統(tǒng)計(jì)模型與物理模型難以實(shí)用化,無論是從時(shí)間還是從空間角度來獲取生，

本文編號(hào)：1433065