基于MODIS數(shù)據(jù)的東北地區(qū)氣溫反演及玉米冷害監(jiān)測(cè)研究
發(fā)布時(shí)間：2018-11-10 07:41
【摘要】：低溫冷害是我國(guó)農(nóng)作物的主要?dú)庀鬄?zāi)害,玉米是低溫冷害的主要作物之一。玉米作為東北三省主要的糧食作物,其產(chǎn)量嚴(yán)重影響著畜牧業(yè)的發(fā)展及人們的生活水平。全球氣候變暖導(dǎo)致東北地區(qū)糧食的種植界限逐漸在向北移動(dòng),且隨著種植面積和規(guī)模的逐漸擴(kuò)大,倘若東北地區(qū)發(fā)生冷害,影響非常嚴(yán)重。應(yīng)用遙感數(shù)據(jù)對(duì)于玉米冷害的監(jiān)測(cè)目前并不多見。本文利用2005-2014年MODIS雙星平臺(tái)數(shù)據(jù),選取MODIS高質(zhì)量的LST數(shù)據(jù)建立關(guān)于氣溫的遙感估算模型,將MODIS傳感器每日4次對(duì)地觀測(cè)高質(zhì)量LST數(shù)據(jù)進(jìn)行融合,獲取全天候氣溫遙感數(shù)據(jù),經(jīng)數(shù)據(jù)的時(shí)間插補(bǔ)獲取連續(xù)的時(shí)間序列日數(shù)據(jù)。為得到缺失站點(diǎn)的數(shù)據(jù)需要對(duì)時(shí)間序列進(jìn)行空間插補(bǔ)。最后參照氣象行業(yè)標(biāo)準(zhǔn)中冷害的指標(biāo),基于空間插補(bǔ)后的數(shù)據(jù)對(duì)東北地區(qū)玉米低溫冷害年份進(jìn)行判別。本研究主要結(jié)論如下:(1)以氣象臺(tái)站觀測(cè)日平均氣溫作為因變量,引入陸地表面溫度(LST)、經(jīng)度(LON)、緯度(LAT)、歸一化植被指數(shù)(NDVI)、太陽(yáng)天頂角(SAZ)、高程(ALT)以及日序數(shù)(N)作為模型的自變量,所選因子均達(dá)到0.01水平顯著相關(guān)。選取四次觀測(cè)的高質(zhì)量LST數(shù)據(jù)作為數(shù)據(jù)源,利用多變量線性回歸方法構(gòu)建的氣溫遙感模型調(diào)整R2分別達(dá)到了 0.632、0.824、0.53及0.706,經(jīng)2013-2014年的數(shù)據(jù)進(jìn)行模型驗(yàn)證,估算模型大部分樣本均落在(1:1)線附近。(2)考慮到數(shù)據(jù)的完整性,對(duì)雙星平臺(tái)每日4次過境數(shù)據(jù)進(jìn)行融合,根據(jù)氣溫遙感模型的調(diào)整R2大小,以雙星平臺(tái)夜間數(shù)據(jù)優(yōu)先,白天數(shù)據(jù)次之的規(guī)則進(jìn)行融合,融合前后誤差均滿足正態(tài)分布;應(yīng)用鄰近時(shí)間氣溫的插補(bǔ)方法對(duì)缺失的氣溫進(jìn)行時(shí)間序列的時(shí)間融合。融合插補(bǔ)后數(shù)據(jù)量增長(zhǎng)1倍以上,插補(bǔ)后平均誤差增加均不超過0.5℃。(3)采用生長(zhǎng)季內(nèi)≥10℃活動(dòng)積溫的距平指標(biāo)(指標(biāo)1)和5-9月平均氣溫之和的距平指標(biāo)(指標(biāo)2)對(duì)2005-2014年?yáng)|北地區(qū)玉米低溫冷害進(jìn)行判別,兩個(gè)判別指標(biāo)對(duì)冷害年份及站點(diǎn)的判別大體上相同,但也略有差異。2005、2006、2009和2011年均發(fā)生冷害,2005年為冷害多發(fā)年份,冷害多集中在遼寧和吉林。指標(biāo)2監(jiān)測(cè)出的冷害年份多于指標(biāo)1所監(jiān)測(cè)的結(jié)果,在2006年和2011年指標(biāo)2監(jiān)測(cè)出的冷害站點(diǎn)數(shù)少于指標(biāo)1。(4)用農(nóng)業(yè)氣象災(zāi)害數(shù)據(jù)和氣象臺(tái)站數(shù)據(jù)進(jìn)行遙感估算冷害的指標(biāo)驗(yàn)證,氣象數(shù)據(jù)計(jì)算出的兩個(gè)冷害指標(biāo)對(duì)冷害年份和站點(diǎn)的判別一致性很高,但是在冷害等級(jí)劃分上存在差異;用氣象指標(biāo)對(duì)遙感估算冷害進(jìn)行驗(yàn)證,部分年份及站點(diǎn)表現(xiàn)出不一致現(xiàn)象;用農(nóng)業(yè)氣象災(zāi)害數(shù)據(jù)進(jìn)行遙感估算冷害指標(biāo)及臺(tái)站冷害指標(biāo)的驗(yàn)證,部分冷害年份和站點(diǎn)一致。
[Abstract]:Low temperature chilling injury is the main meteorological disaster of crops in China, and maize is one of the main crops of low temperature chilling injury. Corn is the main food crop in the three provinces of Northeast China, and its yield seriously affects the development of animal husbandry and people's living standard. Global warming causes the grain planting boundary to move northward gradually, and with the growing area and scale expanding gradually, if chilling damage occurs in Northeast China, the effect will be very serious. It is rare to use remote sensing data to monitor maize chilling damage. In this paper, the MODIS binary platform data from 2005 to 2014 are used to establish the remote sensing estimation model of air temperature with MODIS high quality LST data. The MODIS sensor fuses the high quality LST data of earth observation four times a day to obtain the all-weather temperature remote sensing data. Continuous time series daily data are obtained by time interpolation of data. In order to obtain missing site data, time series need to be spatially interpolated. Finally, referring to the index of chilling injury in meteorological industry standard, based on the data of space interpolation, the cold damage years of maize in Northeast China were judged. The main conclusions of this study are as follows: (1) with the daily mean temperature observed by meteorological stations as dependent variables, the land surface temperature (LST), longitude and latitude (LON), latitude (LAT), normalized vegetation index (NDVI),) and solar zenith angle (SAZ),) are introduced. The elevation (ALT) and the daily ordinal (N) are the independent variables of the model, and the selected factors are significantly related to each other at the level of 0. 01. Four high quality LST data were selected as data sources, and the adjusted R2 of temperature remote sensing model constructed by multivariate linear regression method reached 0.632n0.8240.53 and 0.706, respectively. The model was verified by the data from 2013-2014. Most of the samples of the estimation model fall near the (1:1) line. (2) considering the integrity of the data, the binary platform transiting data four times a day is fused, and R2 is adjusted according to the temperature remote sensing model. In the case of binary satellite platform, the night data is first used, then the daytime data is fused, and the errors before and after fusion are normal distribution. The time series of the missing temperature is fused by the interpolation method of the adjacent time temperature. After fusion interpolation, the amount of data has more than doubled. The mean error increased by less than 0.5 鈩，
本文編號(hào)：2321827
[Abstract]:Low temperature chilling injury is the main meteorological disaster of crops in China, and maize is one of the main crops of low temperature chilling injury. Corn is the main food crop in the three provinces of Northeast China, and its yield seriously affects the development of animal husbandry and people's living standard. Global warming causes the grain planting boundary to move northward gradually, and with the growing area and scale expanding gradually, if chilling damage occurs in Northeast China, the effect will be very serious. It is rare to use remote sensing data to monitor maize chilling damage. In this paper, the MODIS binary platform data from 2005 to 2014 are used to establish the remote sensing estimation model of air temperature with MODIS high quality LST data. The MODIS sensor fuses the high quality LST data of earth observation four times a day to obtain the all-weather temperature remote sensing data. Continuous time series daily data are obtained by time interpolation of data. In order to obtain missing site data, time series need to be spatially interpolated. Finally, referring to the index of chilling injury in meteorological industry standard, based on the data of space interpolation, the cold damage years of maize in Northeast China were judged. The main conclusions of this study are as follows: (1) with the daily mean temperature observed by meteorological stations as dependent variables, the land surface temperature (LST), longitude and latitude (LON), latitude (LAT), normalized vegetation index (NDVI),) and solar zenith angle (SAZ),) are introduced. The elevation (ALT) and the daily ordinal (N) are the independent variables of the model, and the selected factors are significantly related to each other at the level of 0. 01. Four high quality LST data were selected as data sources, and the adjusted R2 of temperature remote sensing model constructed by multivariate linear regression method reached 0.632n0.8240.53 and 0.706, respectively. The model was verified by the data from 2013-2014. Most of the samples of the estimation model fall near the (1:1) line. (2) considering the integrity of the data, the binary platform transiting data four times a day is fused, and R2 is adjusted according to the temperature remote sensing model. In the case of binary satellite platform, the night data is first used, then the daytime data is fused, and the errors before and after fusion are normal distribution. The time series of the missing temperature is fused by the interpolation method of the adjacent time temperature. After fusion interpolation, the amount of data has more than doubled. The mean error increased by less than 0.5 鈩，
本文編號(hào)：2321827
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