本文關(guān)鍵詞：主被動(dòng)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(xué)》2017年碩士論文　論文類(lèi)型：學(xué)位論文

  更多相關(guān)文章： 主被動(dòng)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(lèi)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(shí)效和經(jīng)濟(jì)性的特點(diǎn),且穿透性強(qiáng)、對(duì)水分敏感性強(qiáng),適合進(jìn)行土壤水分監(jiān)測(cè),比傳統(tǒng)的可見(jiàn)光和紅外遙感有很大的優(yōu)勢(shì)。是目前對(duì)地表土壤水分進(jìn)行監(jiān)測(cè)最有效的手段之一。在土壤水分反演研究中,被動(dòng)微波遙感對(duì)水分更為敏感,可用來(lái)反演土壤水分絕對(duì)值,而主動(dòng)微波遙感對(duì)地表形態(tài)與植被類(lèi)型比較敏感,常用來(lái)進(jìn)行土壤水分變化相對(duì)值的估算,且其空間分辨率較高。主動(dòng)和被動(dòng)微波遙感各有特點(diǎn),二者的聯(lián)合可充分發(fā)揮各自的優(yōu)勢(shì),達(dá)到優(yōu)勢(shì)互補(bǔ)的效果,特別是當(dāng)有農(nóng)作物覆蓋在土壤表面時(shí),主被動(dòng)微波的聯(lián)合反演方法能夠簡(jiǎn)化土壤水分監(jiān)測(cè)的過(guò)程,同時(shí)提高土壤水分估算的質(zhì)量。目前,光學(xué)與微波的協(xié)同遙感技術(shù)已經(jīng)受到廣大研究學(xué)者的重視,現(xiàn)在發(fā)射的衛(wèi)星基本都同時(shí)搭載著微波和光學(xué)傳感器。因此,光學(xué)和微波的協(xié)同遙感成為了遙感反演地表參數(shù)的一個(gè)重要方法,兩者的結(jié)合將能更準(zhǔn)確的實(shí)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(xué)與微波遙感的各自優(yōu)勢(shì),采用以主動(dòng)、被動(dòng)微波反演土壤水分模型為主,光學(xué)影像參數(shù)為輔的方法,將光學(xué)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(shí)現(xiàn)地表土壤水分的估算實(shí)驗(yàn),提高土壤水分的反演精度。本研究選擇地勢(shì)平坦區(qū)域且植被覆蓋均勻的黑河流域阿柔地區(qū)進(jìn)行土壤水分的反演試驗(yàn),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(duì)土壤水分信息獲取產(chǎn)生的影響,再結(jié)合主動(dòng)微波反演方法來(lái)實(shí)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(duì)土壤水分變化的影響,融合L波段輻射計(jì)地面觀測(cè)數(shù)據(jù)與L波段ALOS PALSAR雷達(dá)影像數(shù)據(jù),建立起土壤體積水分與雷達(dá)后向散射之間的關(guān)系,在不考慮土壤粗糙度影響的前提下來(lái)實(shí)現(xiàn)土壤水分監(jiān)測(cè)。本研究的主要成果包括以下幾個(gè)方面:(1)分析阿柔加密觀測(cè)區(qū)農(nóng)田水分的蒸發(fā)蒸騰機(jī)理,植被、土壤及粗糙度等參數(shù)對(duì)衛(wèi)星雷達(dá)回波信號(hào)的散射作用機(jī)理,以及光學(xué)衛(wèi)星Landsat-5影像、雷達(dá)衛(wèi)星ALOS-PLASAR數(shù)據(jù)以及輻射計(jì)遙感數(shù)據(jù)的特點(diǎn)。并分析植被、土壤等參數(shù)對(duì)衛(wèi)星雷達(dá)回波信號(hào)的散射作用機(jī)理。(2)針對(duì)觀測(cè)區(qū)擁有植被覆蓋,造成土壤對(duì)雷達(dá)后向散射影響的問(wèn)題,通過(guò)去除土壤散射模型中植被層的散射貢獻(xiàn),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(jī)制,利用半經(jīng)驗(yàn)水云模型,從雷達(dá)體散射信號(hào)中扣除掉植被水分的貢獻(xiàn),最后,開(kāi)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(dá)數(shù)據(jù)進(jìn)行主動(dòng)微波反演土壤水分算法,將校正后的雷達(dá)影像及TM影像通過(guò)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(duì)植被覆蓋地表,分別在HH和VV極化下,建立起針對(duì)土壤的后向散射系數(shù)同土壤水分之間的線性關(guān)系,進(jìn)行中分辨率下植被覆蓋地表的土壤水分估算實(shí)驗(yàn)。(4)選擇10m高分辨率的PLASAR雷達(dá)衛(wèi)星數(shù)據(jù)及120m低分辨率的輻射計(jì)數(shù)據(jù),以尺度融合為基礎(chǔ),考慮植被指數(shù)VI的基礎(chǔ)上,分析三者之間的數(shù)學(xué)關(guān)系,從而建立起主被動(dòng)微波聯(lián)合反演30m中分辨率下的土壤水分算法,來(lái)實(shí)現(xiàn)大范圍針對(duì)植被覆蓋的土壤水分估測(cè)。(5)利用觀測(cè)區(qū)中分辨率下的土壤水分實(shí)測(cè)數(shù)據(jù),分別進(jìn)行對(duì)主動(dòng)微波反演土壤水分方法和主被動(dòng)微波聯(lián)合反演算法進(jìn)行驗(yàn)證,并對(duì)其反演的土壤水分模擬值精度進(jìn)行比較和評(píng)價(jià)。
[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(xué)位授予單位】：山東農(nóng)業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：S152.7;S127

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 馬紅章;劉素美;彭愛(ài)華;孫林;孫根云;;L波段主被動(dòng)微波協(xié)同反演裸土土壤水分[J];農(nóng)業(yè)工程學(xué)報(bào);2016年19期

2 周壯;趙少杰;蔣玲梅;;被動(dòng)微波遙感土壤水分產(chǎn)品降尺度方法研究綜述[J];北京師范大學(xué)學(xué)報(bào)(自然科學(xué)版);2016年04期

3 柴莉華;魯夢(mèng)華;米嘉瑩;;利用被動(dòng)微波遙感提高土壤濕度分辨率[J];科技視界;2016年13期

4 李新;曾琪明;王心逸;黃江輝;焦健;;一種基于AMSR-E和ASAR數(shù)據(jù)的土壤水分協(xié)同反演方法[J];北京大學(xué)學(xué)報(bào)(自然科學(xué)版);2016年05期

5 張玲;安如;王U，

本文編號(hào)：1373936