农业工程学报
農業工程學報
농업공정학보
2014年
13期
185-193
,共9页
张晓倩%郭琳%马尚杰%赵占营%裴志远
張曉倩%郭琳%馬尚傑%趙佔營%裴誌遠
장효천%곽림%마상걸%조점영%배지원
雷达%极化%监测%SAR%水稻%叶面积指数%时序分析%生育期
雷達%極化%鑑測%SAR%水稻%葉麵積指數%時序分析%生育期
뢰체%겁화%감측%SAR%수도%협면적지수%시서분석%생육기
radar%polarization%monitoring%SAR%rice%LAI%temporal signature analysis%growth stage
为了确定全极化雷达数据监测水稻叶面积指数动态变化的精度，该文对水稻叶面积指数与后向散射系数进行了各生长阶段建模比较。采用广东雷州地区多时相多入射角精细全极化 Radarsat-2数据，结合水稻全生育期地面样方实测数据，首先分析多入射角归一化后四极化（vertical-horizontal polarization，VH；vertical-vertical polarization，VV；horizontal-horizontal polarization，HH；horizontal-vertical polarization，HV）、比值极化HH/VV后向散射系数与水稻叶面积指数（leaf area index，LAI）随时间变化特征以及在营养生长阶段、生殖生长阶段和全生育期的相关关系，提取相关系数高于0.8的极化与生长阶段进行水云模型建模，最终生成多期水稻LAI反演分布图，并验证该数据反演水稻各生长阶段 LAI 的精度，探索 SAR 数据追踪区域尺度水稻长势的可行性。结果表明，在地形较为平坦的水稻集中连片种植区，VV、HH/VV后向散射系数与LAI在营养生长期、全生育期极显著相关（P＜0.01），相关系数均高于0.83。营养生长阶段VV、HH/VV水云模型拟合决定系数分别为0.77、0.87，全生育期VV、HH/VV水云模型拟合决定系数分别为0.73、0.8，营养生长阶段模型优于全生育期模型。精细四极化SAR数据监测区域尺度水稻LAI动态变化具有应用潜力，优选的极化模型为进一步的水稻长势监测提供依据。
為瞭確定全極化雷達數據鑑測水稻葉麵積指數動態變化的精度，該文對水稻葉麵積指數與後嚮散射繫數進行瞭各生長階段建模比較。採用廣東雷州地區多時相多入射角精細全極化 Radarsat-2數據，結閤水稻全生育期地麵樣方實測數據，首先分析多入射角歸一化後四極化（vertical-horizontal polarization，VH；vertical-vertical polarization，VV；horizontal-horizontal polarization，HH；horizontal-vertical polarization，HV）、比值極化HH/VV後嚮散射繫數與水稻葉麵積指數（leaf area index，LAI）隨時間變化特徵以及在營養生長階段、生殖生長階段和全生育期的相關關繫，提取相關繫數高于0.8的極化與生長階段進行水雲模型建模，最終生成多期水稻LAI反縯分佈圖，併驗證該數據反縯水稻各生長階段 LAI 的精度，探索 SAR 數據追蹤區域呎度水稻長勢的可行性。結果錶明，在地形較為平坦的水稻集中連片種植區，VV、HH/VV後嚮散射繫數與LAI在營養生長期、全生育期極顯著相關（P＜0.01），相關繫數均高于0.83。營養生長階段VV、HH/VV水雲模型擬閤決定繫數分彆為0.77、0.87，全生育期VV、HH/VV水雲模型擬閤決定繫數分彆為0.73、0.8，營養生長階段模型優于全生育期模型。精細四極化SAR數據鑑測區域呎度水稻LAI動態變化具有應用潛力，優選的極化模型為進一步的水稻長勢鑑測提供依據。
위료학정전겁화뢰체수거감측수도협면적지수동태변화적정도，해문대수도협면적지수여후향산사계수진행료각생장계단건모비교。채용엄동뇌주지구다시상다입사각정세전겁화 Radarsat-2수거，결합수도전생육기지면양방실측수거，수선분석다입사각귀일화후사겁화（vertical-horizontal polarization，VH；vertical-vertical polarization，VV；horizontal-horizontal polarization，HH；horizontal-vertical polarization，HV）、비치겁화HH/VV후향산사계수여수도협면적지수（leaf area index，LAI）수시간변화특정이급재영양생장계단、생식생장계단화전생육기적상관관계，제취상관계수고우0.8적겁화여생장계단진행수운모형건모，최종생성다기수도LAI반연분포도，병험증해수거반연수도각생장계단 LAI 적정도，탐색 SAR 수거추종구역척도수도장세적가행성。결과표명，재지형교위평탄적수도집중련편충식구，VV、HH/VV후향산사계수여LAI재영양생장기、전생육기겁현저상관（P＜0.01），상관계수균고우0.83。영양생장계단VV、HH/VV수운모형의합결정계수분별위0.77、0.87，전생육기VV、HH/VV수운모형의합결정계수분별위0.73、0.8，영양생장계단모형우우전생육기모형。정세사겁화SAR수거감측구역척도수도LAI동태변화구유응용잠력，우선적겁화모형위진일보적수도장세감측제공의거。
Rice is one of the most important food crops in China, so timely obtaining accurate rice growth information in regional scale is highly significant for crop management and decision making. However, there are plenty of rain and dense cloud cover in rice growth season, that makes it difficult to monitor rice paddy information by optical remote sensing data. It is an accepted fact that Synthetic Aperture Radar (SAR) data is a suitable alternative for crop monitoring in cloud-prone and raining area. In this paper, we investigated the potential of Radarsat-2 SAR data for rice paddy growth monitoring at regional scale. Four C-band SAR backscattering coefficients (VH, VV, HH, HV) and HH/VV ratio backscattering coefficient were employed for setting up the relationship with leaf area index (LAI) in rice growing season. The test site located in Leizhou (20°52′N, 110°05′E), Guangdong province. Four Radarsat-2 products in fine quad polarization mode were acquired during the critical rice growth stage. In situ measurement in year 2013 was also made concurrently with the satellite pass. 25 plots were selected for measuring the rice growth parameters, such as LAI, plant height, sowing date, etc. The backscattering coefficients with multi-incidence angle were extracted from multi-temporal SAR images and then normalized to same angle. Firstly, the study analyzed the temporal behavior of microwave backscattering coefficients (VH, VV, HH, HV, HH/VV) and LAI in rice growth season. Secondly, correlation analyses between the backscattering coefficients in different polarization and LAI were also carried out in vegetative stage, reproductive stage and whole growth period respectively, then picking the polarization and growth stage corresponding with high correlation coefficient which was above 0.8 to build water cloud model and evaluate the performance of each model. Finally, based on the previous result, the LAI distribution map in time domain was generated using the best model of entire growth period. The results showed that, (1) In flat area, the correlation coefficient in different stage from high to low is: vegetative stage, whole growth period, reproductive stage. There is a positive correlation between HH, HV (VH) and LAI, a negative correlation between VV, HH/VV and LAI. The correlation coefficients of VV, HH/VV and LAI were both above 0.8; (2) The VV, HH/VV water cloud models (R2=0.77,R2=0.87 respectively) in vegetative stage performed better than in full rice growth stage (R2=0.73,R2=0.8 respectively); (3) The better prediction model was applied in multi-temporal SAR image for computing LAI value in rice area. The LAI distribution map of each single date can point out the rice growth condition in different area, while multi-temporal LAI distribution maps in rice growth season can point out the LAI changes in the same region. In conclusion, the study proved the potential of timely rice monitoring by multi-temporal FQ mode SAR data in regional scale, and also provided a reliable approach for rice LAI prediction. As the development of precision agriculture, C-band SAR data can be used in quantitative rice crop monitoring and it will become an increasingly important data source.
