本文關鍵詞：主被動微波協(xié)同反演植被覆蓋地表土壤水分方法研究　出處：《山東農(nóng)業(yè)大學》2017年碩士論文　論文類型：學位論文

  更多相關文章： 主被動遙感 土壤水分 L波段 植被指數(shù) 水云模型

【摘要】：水是地球上最重要的資源,是人類生命存活的基本元素。土壤水分作為全球陸地自然環(huán)境生態(tài)系統(tǒng)中的重要組成部分,控制著陸地與大氣間水熱能量交換。微波遙感具有宏觀、高時效和經(jīng)濟性的特點,且穿透性強、對水分敏感性強,適合進行土壤水分監(jiān)測,比傳統(tǒng)的可見光和紅外遙感有很大的優(yōu)勢。是目前對地表土壤水分進行監(jiān)測最有效的手段之一。在土壤水分反演研究中,被動微波遙感對水分更為敏感,可用來反演土壤水分絕對值,而主動微波遙感對地表形態(tài)與植被類型比較敏感,常用來進行土壤水分變化相對值的估算,且其空間分辨率較高。主動和被動微波遙感各有特點,二者的聯(lián)合可充分發(fā)揮各自的優(yōu)勢,達到優(yōu)勢互補的效果,特別是當有農(nóng)作物覆蓋在土壤表面時,主被動微波的聯(lián)合反演方法能夠簡化土壤水分監(jiān)測的過程,同時提高土壤水分估算的質(zhì)量。目前,光學與微波的協(xié)同遙感技術已經(jīng)受到廣大研究學者的重視,現(xiàn)在發(fā)射的衛(wèi)星基本都同時搭載著微波和光學傳感器。因此,光學和微波的協(xié)同遙感成為了遙感反演地表參數(shù)的一個重要方法,兩者的結(jié)合將能更準確的實現(xiàn)地表參數(shù)的反演,提高其反演精度,充分發(fā)揮遙感衛(wèi)星的作用。本文聯(lián)合了光學與微波遙感的各自優(yōu)勢,采用以主動、被動微波反演土壤水分模型為主,光學影像參數(shù)為輔的方法,將光學模型反演的植被參數(shù)(植被指數(shù)VI、葉面積指數(shù)LAI)輸入到微波模型中,共同實現(xiàn)地表土壤水分的估算實驗,提高土壤水分的反演精度。本研究選擇地勢平坦區(qū)域且植被覆蓋均勻的黑河流域阿柔地區(qū)進行土壤水分的反演試驗,基于此本文提出兩種土壤水分反演方法:1.利用水云模型去除掉植被層含水量對土壤水分信息獲取產(chǎn)生的影響,再結(jié)合主動微波反演方法來實現(xiàn)土壤水分的估算。2.在單頻率、單入射角的條件下,考慮植被指數(shù)VI對土壤水分變化的影響,融合L波段輻射計地面觀測數(shù)據(jù)與L波段ALOS PALSAR雷達影像數(shù)據(jù),建立起土壤體積水分與雷達后向散射之間的關系,在不考慮土壤粗糙度影響的前提下來實現(xiàn)土壤水分監(jiān)測。本研究的主要成果包括以下幾個方面:(1)分析阿柔加密觀測區(qū)農(nóng)田水分的蒸發(fā)蒸騰機理,植被、土壤及粗糙度等參數(shù)對衛(wèi)星雷達回波信號的散射作用機理,以及光學衛(wèi)星Landsat-5影像、雷達衛(wèi)星ALOS-PLASAR數(shù)據(jù)以及輻射計遙感數(shù)據(jù)的特點。并分析植被、土壤等參數(shù)對衛(wèi)星雷達回波信號的散射作用機理。(2)針對觀測區(qū)擁有植被覆蓋,造成土壤對雷達后向散射影響的問題,通過去除土壤散射模型中植被層的散射貢獻,建立了參數(shù)優(yōu)化的水云模型。本文基于農(nóng)作物覆蓋地表的后向散射機制,利用半經(jīng)驗水云模型,從雷達體散射信號中扣除掉植被水分的貢獻,最后,開展植被覆蓋下土壤水分的高分辨率遙感反演。(3)基于PLASAR雷達數(shù)據(jù)進行主動微波反演土壤水分算法,將校正后的雷達影像及TM影像通過重采樣的方法做降尺度處理,形成30m分辨率的影像數(shù)據(jù)。隨后針對植被覆蓋地表,分別在HH和VV極化下,建立起針對土壤的后向散射系數(shù)同土壤水分之間的線性關系,進行中分辨率下植被覆蓋地表的土壤水分估算實驗。(4)選擇10m高分辨率的PLASAR雷達衛(wèi)星數(shù)據(jù)及120m低分辨率的輻射計數(shù)據(jù),以尺度融合為基礎,考慮植被指數(shù)VI的基礎上,分析三者之間的數(shù)學關系,從而建立起主被動微波聯(lián)合反演30m中分辨率下的土壤水分算法,來實現(xiàn)大范圍針對植被覆蓋的土壤水分估測。(5)利用觀測區(qū)中分辨率下的土壤水分實測數(shù)據(jù),分別進行對主動微波反演土壤水分方法和主被動微波聯(lián)合反演算法進行驗證,并對其反演的土壤水分模擬值精度進行比較和評價。
[Abstract]:Water is the most important resource on the earth, is the basic element of human survival. Soil moisture is an important part of the global terrestrial ecosystem in the natural environment, control of water and heat energy exchange between land and atmospheric. Microwave remote sensing has the characteristics of high efficiency and macro economy, and strong penetration of water strong sensitivity, suitable for monitoring soil moisture, compared with the traditional optical and infrared remote sensing has great advantage. Is present on the surface soil moisture is one of the most effective means of monitoring. In the study of soil moisture retrieval, passive microwave remote sensing of water is more sensitive, and can be used to inverse the soil moisture content, and active microwave remote sensing the surface morphology and the vegetation types are more sensitive, commonly used in estimating soil moisture change relative value, and its high spatial resolution. The active and passive microwave remote sensing with different characteristics, the two joint He can give full play to their strengths, to achieve complementary effects, especially when there are crop mulch on the soil surface, the process of the joint inversion method of active and passive microwave can simplify the monitoring of soil moisture, and improve the quality of soil moisture estimation. At present, optical and microwave remote sensing technology cooperation has attracted the attention of scholars research now, the basic satellite are also equipped with microwave and optical sensors. Therefore, optical and microwave remote sensing coordination has become an important method for remote sensing inversion of surface parameters, the combination of the two will be able to realize more accurate surface parameter retrieval to improve the retrieval accuracy, and give full play to the role of remote sensing satellite. This paper combined with the the advantages of optical and microwave remote sensing, using active, passive microwave soil moisture retrieval model, supplemented by optical imaging parameters, optical model The inversion of vegetation parameters (leaf area index LAI vegetation index VI, microwave) input to the model, to realize estimation of experimental soil moisture, improve the inversion precision of soil moisture inversion test. A soft area of Heihe basin of flat area and vegetation coverage in uniform soil moisture, this paper put forward two soil moisture inversion method based on the cloud model: 1. remove vegetation layer moisture generated effects on soil moisture information, estimation of.2. combined with active microwave inversion method to achieve the soil moisture in the single frequency, single incident angle under the condition of considering the influence of vegetation on soil moisture index VI, fusion L band radiometer ground observation data and L band ALOS PALSAR radar image data, establish the volume of soil moisture and the relationship between radar backward scattering, without considering the soil roughness. Ring down the premise of implementation of soil moisture monitoring. The main results of this study include the following aspects: (1) analysis of a soft encryption evaporation mechanism, observation area of farmland water vegetation, soil and roughness parameters on the scattering mechanism of satellite radar echo signal, and optical characteristics of Landsat-5 satellite image, radar satellite ALOS-PLASAR the data and radiometer data. And analysis of vegetation, soil and other parameters on the scattering mechanism of satellite radar echo signal. (2) in the observation area with vegetation cover, causing the radar backward scattering effect of soil, soil removal contribution by scattering scattering model of vegetation layer, established cloud model parameters optimization. Based on the crop covers the surface of the back scattering mechanism, using semi empirical water cloud model, radar scattering signal from the body after deducting the vegetation water contribution, finally, High resolution remote sensing inversion of vegetation cover soil moisture. (3) active microwave soil moisture retrieval algorithm based on PLASAR radar data, the radar images and TM images corrected by means of resampling the downscaling image data processing, forming the 30m resolution. Then according to the vegetation cover, respectively in HH and VV polarization, establish a linear relationship between the soil backscattering coefficient with soil moisture, soil moisture in the surface vegetation cover resolution estimation experiment. (4) choose radiation 10m high resolution PLASAR radar satellite data and 120m low resolution data fusion based on scale, considering the vegetation the VI index, a mathematical analysis of the relationship between the three, so as to establish the soil moisture resolution of active and passive microwave inversion in the 30m, to achieve a wide range for vegetation coverage Estimation of soil moisture. (5) the use of soil moisture data resolution observation area under, respectively, to validate the active microwave soil moisture retrieval method and passive microwave combined inversion algorithm, and soil moisture on the retrieval precision of simulation value comparison and evaluation.

【學位授予單位】：山東農(nóng)業(yè)大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：S152.7;S127

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本文編號：1373936