本文選題：土壤濕度　切入點(diǎn)：反演　出處：《山西農(nóng)業(yè)大學(xué)》2015年碩士論文

【摘要】：土壤水是陸地和大氣之間能量互換過程中的一個(gè)重要因素,是極其重要的環(huán)境因子。晉中地區(qū)地處黃土高原東緣,水資源較為短缺屬于國(guó)際公認(rèn)的極度缺水地區(qū),干旱對(duì)農(nóng)業(yè)造成了重要影響。遙感技術(shù)監(jiān)測(cè)土壤濕度具有快速、宏觀和動(dòng)態(tài)等特點(diǎn),且已經(jīng)發(fā)展為大區(qū)域干旱監(jiān)測(cè)評(píng)估和區(qū)域土壤水資源評(píng)價(jià)糧食安全問題解決的主要手段。本研究利用多源遙感影像反演晉中地區(qū)2014年5-9月土壤濕度,對(duì)快速、適時(shí)獲取大范圍土壤濕度空間分布信息,有效預(yù)防和應(yīng)對(duì)干旱有理論意義和現(xiàn)實(shí)意義。晉中地區(qū)是山西省糧食、蔬菜、水果、畜牧品等的重要產(chǎn)區(qū)之一,農(nóng)業(yè)居山西省領(lǐng)先位置生產(chǎn)條件十分優(yōu)越。文化旅游業(yè)發(fā)達(dá),是人口與經(jīng)濟(jì)密集分布區(qū),氣候?qū)倥瘻貛Т箨懶约撅L(fēng)氣候,季節(jié)變化明顯,降雨在時(shí)空上分布不均,呈現(xiàn)東部丘陵地區(qū)降水多西部平原地帶降水少趨勢(shì)。遙感技術(shù)反演區(qū)域土壤水分的研究是定量遙感研究中的前沿方向,目前,在區(qū)域土壤水遙感監(jiān)測(cè)研究中,基于表觀熱慣量和歸一化植被干旱指數(shù)的反演模型應(yīng)用較為廣泛且精度較高,但是單一模型的反演往往會(huì)忽略模型的適用范圍。當(dāng)前山西省遙感監(jiān)測(cè)的研究比較少且都是使用單一反演模型,急需補(bǔ)充這一領(lǐng)域研究。本文以山西省晉中市為研究區(qū)，以MODIS數(shù)據(jù)、TM8數(shù)據(jù)和實(shí)測(cè)土壤濕度數(shù)據(jù)為數(shù)據(jù)源,參照優(yōu)勢(shì)互補(bǔ)的設(shè)計(jì)思想,運(yùn)用溫度植被指數(shù)模型(TVDI)和表觀熱慣量模型(ATI)聯(lián)合反演晉中5-9月土壤濕度,通過NDVI值分區(qū)將NDVI0.37區(qū)域采用歸一化植被干旱指數(shù)(TVDI)方法反演土壤相對(duì)濕度,NDVI0.37區(qū)域采用表觀熱慣量模型(ATI)反演土壤濕度,并反演出2014年5-9月晉中每月土壤濕度,最后結(jié)合反演圖像,對(duì)研究區(qū)土壤相對(duì)濕度的空間和時(shí)間分布特征進(jìn)行分析,其中模型擬合結(jié)果均通過0.01顯著性差異檢驗(yàn)。研究結(jié)果表明：1晉中地區(qū)的土壤相對(duì)濕度在植物生長(zhǎng)期間呈現(xiàn)兩個(gè)由降到升的變化周期,第一個(gè)周期在5月到7月中旬,5-6月土壤相對(duì)濕度為下降,6-7月中旬土壤相對(duì)濕度為上升,7月中旬為土壤相對(duì)濕度峰值,6月初為生長(zhǎng)期土壤濕度最低值,同樣6月初也是降雨均值最低時(shí)期。第二個(gè)周期是在7月中旬到9月下旬,其中,7月中旬到8月中旬土壤相對(duì)濕度為下降,9月土壤相對(duì)濕度為上升期,峰值出現(xiàn)在8月初。2模型反演精度檢驗(yàn)結(jié)果表明反演的土壤水分與實(shí)測(cè)的土壤水分具有良好的相關(guān)性,反演精度較高。
[Abstract]:Soil water is an important factor in the process of energy exchange between land and atmosphere, and is an extremely important environmental factor. Jinzhong region is located in the eastern edge of the Loess Plateau. Drought has an important impact on agriculture. Remote sensing technology is characterized by rapid, macroscopic and dynamic monitoring of soil moisture. And it has been developed as the main means to solve the food security problem of drought monitoring and assessment of regional soil water resources. This study uses multi-source remote sensing images to retrieve soil moisture in Jinzhong region from May to September 2014. It is of theoretical and practical significance to obtain the spatial distribution information of soil moisture on a large scale and to effectively prevent and cope with drought. Jinzhong region is one of the important producing areas of grain, vegetables, fruits and livestock products in Shanxi Province. Agriculture occupies the leading position in Shanxi Province in terms of production conditions. Cultural tourism is developed, population and economy are densely distributed, climate is continental monsoon climate in warm temperate zone, seasonal changes are obvious, and rainfall is unevenly distributed in time and space. The research of retrieving regional soil moisture by remote sensing is the forward direction of quantitative remote sensing research. At present, in the research of remote sensing monitoring of regional soil water, the trend of precipitation in the eastern hilly region is more than that in the western plain. The inversion model based on apparent thermal inertia and normalized vegetation drought index is widely used and has high accuracy. However, the inversion of a single model often ignores the scope of application of the model. At present, the research on remote sensing monitoring in Shanxi Province is less and uses a single inversion model, so it is urgent to supplement the research in this field. In this paper, Jinzhong City, Shanxi Province, is taken as the research area. Based on MODIS data (TM8) and measured soil moisture data, and referring to the design idea of complementary advantages, soil moisture was retrieved from May to September in Jinzhong by using TVDI-based model of temperature vegetation index (TVI) and apparent thermal inertia model (ATI). The NDVI0.37 region was divided into NDVI values and the normalized vegetation drought index (TVDI) method was used to invert the soil relative humidity and the apparent thermal inertia model was used to retrieve the soil moisture in the area of NDVI 0.37, and the monthly soil moisture in Jinzhong period from May to September 2014 was reversed. Finally, the spatial and temporal distribution characteristics of soil relative humidity in the study area are analyzed by using the inversion image. The results of model fitting all passed 0.01 significant difference test. The results showed that the soil relative humidity in Jinzhong area of 1: 1 showed two periods of change from decreasing to rising during plant growth. The first cycle was from May to the middle of July when the relative humidity of the soil decreased from June to the middle of July, and the peak value of the soil relative humidity in the middle of July and the beginning of June was the lowest value of soil moisture in the growing period. The second cycle was from mid-July to late September, in which the relative soil moisture decreased from mid-July to mid-August, and the soil relative humidity increased in September. The peak value appeared in the early August .2 model inversion accuracy test results show that the inversion of soil moisture and measured soil moisture has a good correlation, and the inversion accuracy is high.
【學(xué)位授予單位】：山西農(nóng)業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：S152.7;S127

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