稻作農(nóng)業(yè)對(duì)諸如溫室氣體排放、糧食安全、水資源利用和疾病傳播等問題有重要影響。盡管水稻分布制圖方法和應(yīng)用不斷改進(jìn)和深化,但現(xiàn)有的大區(qū)域水稻識(shí)別通常精度較低且數(shù)據(jù)稀缺。本論文旨在推進(jìn)遙感衛(wèi)星數(shù)據(jù)在水稻分布制圖方面的應(yīng)用,論文通過建立適合海量數(shù)據(jù)分析的有效方法,并在典型水稻種植地區(qū)進(jìn)行精度驗(yàn)證,主要包括以下幾個(gè)方面:（1）采用面向?qū)ο蟮挠跋穹治龇椒ǎ∣BIA）研究了物候?qū)λ痉植贾茍D的影響。利用高分辨率多光譜遙感數(shù)據(jù)結(jié)合作物物候信息,對(duì)水稻進(jìn)行了精細(xì)分類。利用遙感提取的農(nóng)作物物候的差異捕捉不同作物生長(zhǎng)過程的動(dòng)態(tài)特征,并利用高空間分辨率的多光譜數(shù)據(jù)探測(cè)不同地區(qū)農(nóng)作物的空間變異模式。論文利用MODIS20NDVI時(shí)間序列數(shù)據(jù)提取作物物候信息,而中國(guó)環(huán)境減災(zāi)小衛(wèi)星星座（HJ-1A/B）數(shù)據(jù)則主要用于水稻種植區(qū)的提取。論文的對(duì)比研究結(jié)果表明,將多光譜數(shù)據(jù)與作物物候信息結(jié)合能夠顯著提高水稻的分類精度。（2）論文探索比較了從中低分辨率遙感數(shù)據(jù)中提取的33種反映作物生長(zhǎng)過程的物候參數(shù)對(duì)基于高分辨率遙感數(shù)據(jù)開展水稻分類識(shí)別精度的提升效果。研究結(jié)果表明,運(yùn)用優(yōu)選的反映作物生長(zhǎng)過程的物候特征參數(shù)顯著地提高了由單... 

【文章來源】：中國(guó)科學(xué)院大學(xué)(中國(guó)科學(xué)院遙感與數(shù)字地球研究所)北京市

【文章頁(yè)數(shù)】：122 頁(yè)

【學(xué)位級(jí)別】：博士

【文章目錄】：
摘要
Thesis Abstract
Chapter 1. Introduction
    1.1. General Introduction
    1.2. Global Spatial Distribution of Paddy Rice Growing Areas
    1.3. Paddy Rice Phenology and Ecosystems
    1.4. Remote Sensing and Earth Observation Satellites
    1.5. Reflectance Characteristics of Paddy Rice Plants in Optical Remote Sensing Data
    1.6. Remote Sensing for Paddy Rice Agriculture
    1.7. Existing Paddy Rice Maps at National or Larger Scales
    1.8. Existing Paddy Rice Mapping Algorithms
    1.9. Thesis Objectives
Chapter 2. Paddy Rice Classification Using Multi-Source Spectral Data and Crop Phenology
    Abstract
    2.1. Introduction
    2.2. Study Area
    2.3. Datasets and Preprocessing
    2.4. Methods
    2.5. Results
    2.6. Discussion
    2.7. Conclusions and Future Works
Chapter 3. Evaluation of Temporal Features Extracted from Coarse Resolution Data for FineResolution mapping of Paddy Rice
    Abstract
    3.1. Introduction
    3.2. Study Area
    3.3. Dataset and Preprocessing
    3.4. Methods
    3.5. Results
    3.6. Discussion
    3.7. Conclusions and Future Works
Chapter 4. Incorporating Crop Phenological Trajectory and Texture for Paddy Rice Detectionwith Time Series MODIS, HJ-1A and ALOS PALSAR Images
    Abstract
    4.1. Introduction
    4.2. Study Site
    4.3. Dataset and Preprocessing
    4.4. Methodologies
    4.5. Results
    4.6. Discussion
    4.7. Conclusions
Chapter 5. Mapping Paddy Rice Based On Vegetation Phenology and Surface MoistureConditions
    Abstract
    5.1. Introduction
    5.2. Dataset and Study Area
    5.3. Methods
    5.4. Results and Discussion
    5.5. Conclusions
Chapter 6. Contribution and Outlook
    Contribution and Outlook
    Possible Future Studies
References
Acknowledgements
Publications related to this study
Annex I


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期刊論文
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