基于高光谱遥感的水稻氮素营养参数监测研究
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摘要
作物生长遥感的光谱参数与定量模型是作物生长实时监测与精确诊断研究的重要内容。数字农业技术的发展迫切需要低成本、高密度、高精度和高可靠性的作物氮素营养信息获取技术。而地空遥感技术的农业应用使大面积快速监测作物长势及估测化学组分成为可能。本研究的目的是以水稻为研究对象,基于地面ASD高光谱、CROPSCAN多光谱信息和星载高(多)光谱影像数据,综合运用高光谱信提取技术、遥感影像解译技术、水稻生理生态测试技术,系统分析不同年份、不同试验条件下水稻冠层高(多)光谱反射特征及其与氮素营养指标的相关关系,比较不同宽窄波段指数预测水稻氮营养状况的效果,构建地—空不同尺度下水稻氮素营养状况相关指标的敏感光谱特征参量及定量估测模型。预期研究结果有助于确立基于地面和空间高光谱技术进行水稻氮素营养和生长状况实时监测的关键技术。
首先系统分析了水稻的红边区域光谱、红边位置特征和红边面积形状特征及其与冠层叶片氮素状况的关系。结果表明,水稻红边区域光谱受施氮水平和品种影响较大,导数光谱在红边区域的700nm、720nm和730nm附近出现“三峰”现象。经典的红边位置由于“三峰”特征现象而导致其对水稻氮浓度变化不敏感,不适合水稻氮素状况的定量监测。倒高斯模型、线性内插方法和线性外推法构造的红边位置随水稻氮浓度连续变化,能用于水稻氮浓度的定量监测；另外,基于695nm,700nnm和705nm等3个波段的拉格朗日算法也可应用于水稻冠层叶片氮浓度的估测。比较不同红边位置发现,改进型线性外推法在水稻冠层叶片氮浓度监测应用上较其他几种算法有较明显优势,是适合于水稻冠层叶片氮浓度监测的最佳红边位置拟合方法。同时,基于两两峰值波段划分所得红边子面积所构成的比值(双峰对称度)、归一化差值(归一化对称度)参数与氮浓度之间的关系密切,是2种良好的估测水稻叶层氮浓度的红边面积形状参数,其中,双峰对称度DPS(A675-700,A675-755),即由675-700nm区域面积与675-755nm区域面积的比值,以及双峰归一化指数(?)DPS(A675-700, A700.755),即由675-700nm区域面积和700-755nm区域面积构成的归一化值,对水稻叶层氮浓度的估测效果最好,因而可用于不同水稻品种和生长条件下的叶层氮浓度估测。而光谱参数DPS (A720-730, A700-720)可用于预测水稻叶层的氮积累量。
定量研究了水稻冠层高光谱反射率对叶层氮素状况的敏感性,确立了叶层氮素状况与冠层高光谱植被指数的定量关系。结果显示,对水稻氮状况反应最敏感的波段是红光665-675nm、蓝光490-500nm和红边区域波段680-760nm。两波段植被指数与水稻叶层氮浓度相关最好的是绿光波段组合,为550-600nm与500-550nm；三波段植被指数的入选波段为蓝光波段组合或红边波段与蓝光波段组合。两波段植被指数中与叶层氮浓度相关性最好的是比值指数R(533,565)。三波段组合植被指数显著提高了对叶层氮浓度的预测性,新型蓝光氮指数R434/(R496+R401)和R705/(R717+R491)的模型预测精度和普适性较R(533,565)显著提高。同时,通过分析已有的色素或氮素相关植被指数与水稻叶层氮浓度的关系,发现部分两波段和三波段植被指数在水稻上有较好的应用,其中两波段植被指数有ZM、GM-2、RI-1dB、RI-2dB和NDRE,三波段植被指数有mND705和PRIc,且三波段植被指数表现优于两波段指数。然而,与本文所得新型蓝光氮指数R434/(R496+R401)和R705/(R717+R491)相比,已有光谱参数的预测性和普适性均较低,说明R434/(R496+R401)和R705/(R717+R491)是一种良好的水稻叶层氮浓度新型估测参数。结果还显示,比值植被指数RVI(827,742)可以有效预测不同生长条件下水稻叶片氮积累量的动态变化。
基于ASD高光谱数据,系统模拟了不同光谱分辨率对350-1000nm波段范围水稻冠层反射光谱、敏感植被指数的效应,比较了ASD和Cropscan两种传感器在水稻叶层氮浓度估测中的差异。结果表明,光谱分辨率在20nm以内时,对冠层反射光谱影响较小,而当光谱分辨率低于50nm后则显著影响冠层光谱反射率,主要体现在红谷变浅,绿峰变矮,红边陡峭程度变小。不同叶片氮浓度条件下,光谱分辨率对近红外光谱影响较小,对可见光影响较大,红光区区分不同氮水平的能力逐渐降低,200nm分辨率内绿光区均可较好的区分不同氮水平。近红外波段(NIR)与红边(Red edge)、红光(Red)和绿光(Green)波段组成的比值、归一化差值和差值指数值随光谱分辨率的降低均呈下降趋势。其中NIR与Red edge、 Red组成的植被指数值低于20nm分辨率后下降趋势较明显,但20nm内数值变化较小；而RI(NIR,Green)和NI(NIR,Green)在200nnm分辨率内均较稳定,之后才逐渐下降,但DI(NIR,Green)在低于20nm分辨率时降趋势较明显。另外,水稻氮敏感参数R434/(R496+R401)、R705/(R717+R491)和R533/R565随光谱分辨率的变化均较平缓,在300nm分辨率内值变化较小,说明这些参数对不同分辨率传感器有一定的普适性。
通过分析不同光谱分辨率反射光谱的两波段组合指数与水稻冠层叶片氮浓度的关系,发现光谱分辨率在10nm以内,对波段组合范围及其与氮浓度的相关关系没有显著影响；当分辨率降低到15nm后,与氮素相关显著的波段组合区域开始减少,至150nnm分辨率,相关显著的波段组合仅剩绿绿组合；而小于150nm分辨率后,则变为绿蓝或蓝蓝组合,同时相关性显著降低。光谱分辨率对不同的植被指数影响程度不一样。如对于归一化指数ND(760,710),近红外参考波段760nm波段的分辨率在3-200nm间对预测效果影响不大,而红边波段710nm,则要求分辨率在20nnm内才具有较好效果。对于绿光波段比值指数R(533,565),当565nm的分辨率在50nm以内、533nm分辨率在25nnm以内时,两波段任意分辨率的组合均有较好预测效果,超出此范围,特定的分辨率组合才能获得较好预测效果。另外,比较Cropscan与ASD两种传感器的光谱反射率的差异显示,不同波长范围的宽窄波段反射率可能除了受光谱分辨率的影响外,还受施氮水平、水稻品种及生育时期等因素的影响。水稻生育前后期,两种传感器的差异较大,而在水稻旺盛生长期(抽穗期)差异较小。总体上看,可见光和短波红外波段的宽窄传感器的反射率差异较大,同时也易受施氮水平、品种和生育期的影响。另外,基于ASD窄波段的预测精度仅稍高于基于Cropscan的预测效果,但前者对拔节前(未封行)氮浓度拟合较差,而对封行后氮浓度拟合好于后者。
分析确立了水稻叶片色素含量状况、叶面积指数(LAI)、叶片光合速率与冠层高光谱参数的关系及定量监测模型。结果表明,与CHLa和CHLa+b浓度相关较好的原始光谱比值指数是红边波段714nm和近红外波段760nm组成的RI(714,760);归一化指数和差值指数均为绿光波段组合,ND(543,565)和DI(562,543).与CHLa浓度相关较好的导数光谱指数为导数比值指数R(D_(744),D_(761))和归一化指数ND(D_(748),D_(761))。与原始光谱植被指数相比,导数比值和归一化指数与CHL浓度的拟合决定系数大幅提高。三波段组合而成的植被指数(修正型归一化指数gmND705)与水稻冠层叶片叶绿素浓度呈极显著相关关系,相关程度显著高于2波段植被指数。而与CHLa和CHLa+b密度相关较好的波段组合均为红边末端波段743nm和近红外波段822nm组成的植被指数。比值指数RI(743,822)、归一化指数ND(743,822)和均与水稻叶层叶绿素密度呈极显著直线负相关关系。与叶层CHL密度直线相关程度最高导数光谱指数是归一化指数ND(D511,D771)和差值指数DI(D549,D779)。在CHL密度估测中,与原始光谱指数相比,导数光谱指数并未提高相关性。综合比较基于不同植被参数的叶绿素状况模型预测的R2、RMSE和RE值,发现绿色改进型线归一化指数gmND705用于预测叶绿素a和叶绿素a+b浓度,归一化指数ND(743,822)用于预测叶绿素a和叶绿素a+b密度的效果最佳。另外,原始光谱组成的2波段差值指数形式估测水稻叶面积指数效果最好,其次为比值和归一化植被指数。同样,一阶导数光谱组成的2波段差值形式与LAI的组合相关性显著高于比值和归一化两种形式,相关最好的植被参数是红光和近红外光组成的导数差值指数DVI(D676,D778),但总体上导数光谱指数不如原始光谱指数与LAI关系密切。独立试验数据的检验结果表明,以差值指数DVI(854,760)为变量建立的水稻LAI监测模型可有效地用于水稻LAI的估测。另外,比值指数R(810,680)可以较好地监测不同水氮条件下水稻叶片的光合特征。
综合研究了不同氮素水平下水稻地面冠层高光谱数据、星载高光谱影像(Hyperion)和多光谱影像数据(TM和ALOS)特征及其与水稻叶层氮状况的定量关系。结果表明,大气显著影响水稻冠层地面光谱反射率,大气影响使可见光反射率值变大,使短波红外部分波段反射率值变小。FLAASH大气校正模型对Hyperion和TM卫星图像、经验线性法对ALOS图像有良好的大气校正结果。通过地、空不同水平估测水稻叶层氮素状况的高光谱参数基本一致,Hyperion光谱与水稻叶层氮浓度相关较好的植被参数有绿光修正归一化指数gmND(760,710)、改进型线性外推红边位置、比值植被指数RVI(884,690)和归一化植被指数NDVI(884,690).与Hyperion相比,TM和ALOS无法获取红边参数等高光谱参数,但由篮、红和近红外等三个波段组合而成的蓝色修正归一化植被指数(R830-R660)/(R830+R660-2×R485)和(R_(825)-R_(650))/(R_(825)+R_(650)-2×R_(460))与氮浓度相关性相关明显高于2波段(?)DVI(NIR,R),且与Hyperion的估测精度类似。另外,Hyperion光谱与水稻叶层氮积累量相关较好的植被参数是RI(830,742),而TM和ALOS均为ND(NIR,Green),这2个参数可作为大尺度水稻叶层氮积累量估测的适宜参数。ALOS在水稻氮素状况的光谱参数估测能力上与TM类似,但其空间分辨率更高,在植被生长监测上展示良好的应用前景。
The spectral parameters and monitoring models in remote sensing studies are of key importance for information acquisition and diagnosis on crop growth status. At present, development of digital agriculture presents an urgent need for low-cost, reliable, consistent and precise techniques for monitoring nitrogen status in crop plants. Recent success of remote sensing in agricultural application has made it possible to rapidly monitor growth status and biochemical components in large-area field crops. Implication of various ground-based and space-borne remote sensed information and instruments is receiving more and more attention in existing research. This study was undertaken to make a systematic analysis on the characteristics of the reflectance spectra, compare prediction ability of nitrogen status with vegetation indices based on narrow and wide bands, and develop key spectral indices and quantitative models for nitrogen parameter estimation at ground and space levels, based on canopy hyper-spectral (multi-spectral) and image spectral reflectance of field-grown rice with varied nitrogen levels and rice varieties in different years. This would help to establish the key technology for real-time monitoring of plant nitrogen status with ground and satellite hyper-spectral sensors in rice production.
Firstly, a systematic analysis was made on the characteristics of the first-derivative reflectance spectra in red edge area, and the quantitative relationship of red edge position (REP) and red edge area shape parameters to canopy leaf nitrogen concentrations under varied nitrogen rates and rice varieties in different field experiments. The results showed that spectrum in red edge area was significantly affected by different nitrogen levels and different rice varieties, and "three-peak" feature could be observed with the first derivative spectrum at about700nm,720nm and730nm bands, respectively. Traditional REP was not sensitive to canopy leaf nitrogen concentration because of the three-peak feature, but REPs based on inverted Gaussian fitting technique, linear four-point interpolation technique, linear extrapolation method and adjusted linear extrapolation method generated continuous REP data, and could be used to estimate canopy leaf nitrogen concentration. Besides, REP from a three-point Lagrangian interpolation with three first-derivatives bands (695nm, 700nm and705nm) also had a good relationship with canopy leaf nitrogen concentration. Comparison of these REP revealed that the adjusted linear extrapolation method (755FD73o+675FD700)/(FD730+FD700) had the best prediction performance on canopy leaf nitrogen concentration, with a relative simple algorithm, so it is a proper REP parameter for estimating canopy leaf nitrogen concentration in rice.
In addition, the peak heights of the3peak bands changed alternatively with different nitrogen levels, so child areas and shapes surrounded by the first derivative spectra curve and x coordinate changed accordingly. Double peak symmetry (DPS) based on the ratio of2different red edge child areas divided by "peak band line", and normalized double peak symmetry (NDPS) with normalization of the2different red edge child areas were significantly related to canopy leaf nitrogen concentrations in rice. Results of model calibration and validation indicated that DPS(A675-700,A675-755) and NDPS(A675-700, A675-755), ratio and normalized difference of area in675-700nm to675-755nm red edge region, respectively, performed best in estimating canopy leaf nitrogen concentration, so these two spectral indices were good red edge area shape parameters for monitoring canopy leaf nitrogen concentration in rice. Furthermore, the spectral parameter DPS(A72o-730A700-720) was found to be a good indicator for leaf nitrogen accumulation in rice.
Then, the sensitivity of reflectance spectra to canopy leaf nitrogen status was examined, and the quantitative relationships of different hyper-spectral vegetation indices to canopy leaf nitrogen concentrations were evaluated. The results showed that the reflectance of red region (665-675nm), blue region (490-500nm) and red edge region (680-760nm) was highly sensitive to canopy leaf nitrogen status. Two bands-based vegetation indices combined with550-600nm and500-550nm in green region had good relationships with canopy leaf nitrogen concentrations, and ratio index R(533,565) exhibited the best performance in all two bands vegetation indices. Yet prediction ability of nitrogen concentration was significantly improved using three bands vegetation indices. Novel three bands indices, blue nitrogen indices R434/(R496+R401) and R705/(R717+R491) were developed for monitoring canopy leaf nitrogen concentration, and the test results indicated that R434/(R496+R401) and R705/(R717+R491) had better prediction precision and suitable application than R(533,565). In addition, some reported vegetation indices also had good relationships to canopy leaf nitrogen concentrations, such as two bands indices ZM, GM-2, RI-1dB, RI-2dB, NDRE and three bands indices mND705, PRIc, with three band indices better than two band indices, although less satisfactory prediction than blue nitrogen indices and R.705/(R705+R491). Comparison of all previous indices and present indices indicated that novel blue nitrogen indices R434/(R496+R401) and R705/(R717+R491) had the best prediction capability for estimating canopy leaf nitrogen concentration in rice. Besides, RVI(827,742) was identified as a good indicator for leaf nitrogen accumulation in rice.
A knowledge on the responses of reflectance spectra and vegetation indices to bandwidth can contribute to proper selection of sensitive bands and development of spectral indices for nitrogen status monitoring. Effects of different bandwidths on canopy reflectance spectra in the range of350to1000nm bands and sensitive vegetation indices were studied with simulated spectra based on ASD data, and then prediction powers of canopy nitrogen concentrations based on two different remote sensors, ASD and Cropscan were compared. The results showed that there was less effect on reflectance spectra if bandwidth was within20nm, on the contrary, when it was larger than50nm, several phenomena happened that the red vale turned shallow, green peak got lower and slope of red edge reached flat. Bandwidth had smaller impact on visible light and larger effect on near infrared light under different nitrogen levels, discrimination power of nitrogen levels based on red light decreased with reduced spectral resolution, but green light could well discriminate nitrogen levels within200nm spectral resolution. Ratio indices, normalized indices and differential indices developed from reflectance of NIR and red edge, red and green bands also decreased with reduced spectral resolution, especially when bandwidth was broader than20nm. Yet RI(NIR,Green) and NI(NIR,Green) just had similar values when bandwidth was narrower than200nm. Several sensitive vegetation indices such as R434/(R496+R401), R705/(R717+R491) and R533/R565were found to be very stable within300nm bandwidth, thus indicating a wide applicability with different remote sensors.
Relationships between canopy leaf nitrogen concentration and spectral indices composed of arbitrary two bands with different spectral resolution in the region of350to1000nm were also explored. The results indicated that the band combinations with good correlation to nitrogen status were constant within10nm bandwidth, and then reduced in number. For the purpose of reliable estimation of nitrogen status, the bandwidths of vegetation index such as ND (760,710) maybe diverse. The200nm bandwidth for760nm contributed identically to nitrogen estimation, but20nm bandwidth was requested for710nm, and different vegetation indices exhibited different powers. Comparison of Cropscan and ASD sensors indicated that reflectance spectra were not only controlled by bandwidth, but also by applied nitrogen levels, cultivar types and growth stages. The ASD sensor had higher prediction power than Cropscan sensor with the same vegetation index at the growth stages with higher coverage, whereas vice versa at growth stages with lower population coverage.
Further, the relationships of leaf chlorophyll status, leaf area index and leaf photosynthesis to hyper-spectral reflectance and derivative parameters were quantified for deriving monitoring models on leaf pigment status with key hyper-spectral indices in rice. The results indicated that ratio index RI(714,760), normalized index ND(543,565) and difference index DI(562,543) had good relationships with chlorophyll a or chlorophyll a+b concentrations. The first derivative spectral ratio and normalized indices R(D744,D761) and ND(D748,D761) had better relationships with chlorophyll a or chlorophyll a+b concentrations than RI(714,760), ND(543,565) and DI(562,543), and the similar results were obtained with three bands index gmND705. The correlation results showed that vegetation indices composed of spectral reflectance of bottom band in red edge area743nm and NIR band822nm were significantly related to chlorophyll density, and both RI(743,822) and ND(743,822) had negative linear relationships with canopy leaf chlorophyll density. The first derivative spectral normalized and difference indices ND(D511,D771) and DI(D549,D779) also had better relationships with chlorophyll density, although not much improved over the RI(743,822) and ND(743,822). Green modified normalized index gmND705was good indicator for canopy leaf chlorophyll concentration, and normalized index ND(743,822) for chlorophyll density estimation from comparison of determination coefficients, RMSEs and REs of monitoring models with different vegetation indices. In addition, good relationships existed between LAI and vegetation indices, the correlation sequence of LAI to different index types was DVI>RVI>NDVI which consisted of spectral reflectance or the first derivative spectra. The best vegetation index for LAI monitoring were found to be the difference index of850nm to760nm DVI(854,760) and the first derivative difference index of676nm to778nm DVI(D676,D778), respectively. The vegetation index composed of spectral reflectance was better than that of the first derivative spectra for LAI monitoring in rice. Testing of the monitoring models with independent dataset also proved that the spectral index of DVI(854,760) gave accurate LAI estimation, so can be considered as a good indicator for LAI monitoring in rice. In addition, ratio index R(810,680) can be used to monitor leaf photosynthetic characteristics at different growth stages of rice.
Finally, an integrative analysis was made on the characteristics of reflectance spectra of ASD, Hyperion, TM and ALOS, and on the quantitative relationships of spectral parameters to canopy leaf nitrogen concentrations under different nitrogen levels. The results showed that real spectral reflectance in rice was significantly influenced by atmosphere, which caused the reflectance of VIS to increase and NIR to decrease. Atmospheric correction model FLAASH and empirical line calibration were good methods for Hyperion, TM and ALOS, respectively. The hyper-spectral parameters for canopy leaf nitrogen estimation were essentially consistent at ground and space levels, and green modified normalized difference index gmND(760,710), adjusted linear extrapolation red edge position, RVI(884,690) and NDVI(884,690) were good predictors for canopy leaf nitrogen concentrations in rice. As compared to Hyperion data, hyper-spectral parameters such as REP could not be extracted from TM and ALOS data, but blue modified normalized vegetation index (RNIR-RR)/(RNIR+RR-2×RB) composed of reflectance of blue, red and NIR was better than NDVI(NIR,R) for canopy leaf nitrogen concentration evaluation with the same prediction precision. In addition, RI(830,742) and ND(NIR,Green) derived from Hyperion and TM or ALOS respectively were two good parameters for estimation of canopy nitrogen accumulation at ground or space level. Since ALOS had the similar ability of estimating canopy leaf nitrogen concentration as TM with higher spatial resolution, it could be quite promising for plant growth monitoring in the future.
首先系统分析了水稻的红边区域光谱、红边位置特征和红边面积形状特征及其与冠层叶片氮素状况的关系。结果表明,水稻红边区域光谱受施氮水平和品种影响较大,导数光谱在红边区域的700nm、720nm和730nm附近出现“三峰”现象。经典的红边位置由于“三峰”特征现象而导致其对水稻氮浓度变化不敏感,不适合水稻氮素状况的定量监测。倒高斯模型、线性内插方法和线性外推法构造的红边位置随水稻氮浓度连续变化,能用于水稻氮浓度的定量监测；另外,基于695nm,700nnm和705nm等3个波段的拉格朗日算法也可应用于水稻冠层叶片氮浓度的估测。比较不同红边位置发现,改进型线性外推法在水稻冠层叶片氮浓度监测应用上较其他几种算法有较明显优势,是适合于水稻冠层叶片氮浓度监测的最佳红边位置拟合方法。同时,基于两两峰值波段划分所得红边子面积所构成的比值(双峰对称度)、归一化差值(归一化对称度)参数与氮浓度之间的关系密切,是2种良好的估测水稻叶层氮浓度的红边面积形状参数,其中,双峰对称度DPS(A675-700,A675-755),即由675-700nm区域面积与675-755nm区域面积的比值,以及双峰归一化指数(?)DPS(A675-700, A700.755),即由675-700nm区域面积和700-755nm区域面积构成的归一化值,对水稻叶层氮浓度的估测效果最好,因而可用于不同水稻品种和生长条件下的叶层氮浓度估测。而光谱参数DPS (A720-730, A700-720)可用于预测水稻叶层的氮积累量。
定量研究了水稻冠层高光谱反射率对叶层氮素状况的敏感性,确立了叶层氮素状况与冠层高光谱植被指数的定量关系。结果显示,对水稻氮状况反应最敏感的波段是红光665-675nm、蓝光490-500nm和红边区域波段680-760nm。两波段植被指数与水稻叶层氮浓度相关最好的是绿光波段组合,为550-600nm与500-550nm；三波段植被指数的入选波段为蓝光波段组合或红边波段与蓝光波段组合。两波段植被指数中与叶层氮浓度相关性最好的是比值指数R(533,565)。三波段组合植被指数显著提高了对叶层氮浓度的预测性,新型蓝光氮指数R434/(R496+R401)和R705/(R717+R491)的模型预测精度和普适性较R(533,565)显著提高。同时,通过分析已有的色素或氮素相关植被指数与水稻叶层氮浓度的关系,发现部分两波段和三波段植被指数在水稻上有较好的应用,其中两波段植被指数有ZM、GM-2、RI-1dB、RI-2dB和NDRE,三波段植被指数有mND705和PRIc,且三波段植被指数表现优于两波段指数。然而,与本文所得新型蓝光氮指数R434/(R496+R401)和R705/(R717+R491)相比,已有光谱参数的预测性和普适性均较低,说明R434/(R496+R401)和R705/(R717+R491)是一种良好的水稻叶层氮浓度新型估测参数。结果还显示,比值植被指数RVI(827,742)可以有效预测不同生长条件下水稻叶片氮积累量的动态变化。
基于ASD高光谱数据,系统模拟了不同光谱分辨率对350-1000nm波段范围水稻冠层反射光谱、敏感植被指数的效应,比较了ASD和Cropscan两种传感器在水稻叶层氮浓度估测中的差异。结果表明,光谱分辨率在20nm以内时,对冠层反射光谱影响较小,而当光谱分辨率低于50nm后则显著影响冠层光谱反射率,主要体现在红谷变浅,绿峰变矮,红边陡峭程度变小。不同叶片氮浓度条件下,光谱分辨率对近红外光谱影响较小,对可见光影响较大,红光区区分不同氮水平的能力逐渐降低,200nm分辨率内绿光区均可较好的区分不同氮水平。近红外波段(NIR)与红边(Red edge)、红光(Red)和绿光(Green)波段组成的比值、归一化差值和差值指数值随光谱分辨率的降低均呈下降趋势。其中NIR与Red edge、 Red组成的植被指数值低于20nm分辨率后下降趋势较明显,但20nm内数值变化较小；而RI(NIR,Green)和NI(NIR,Green)在200nnm分辨率内均较稳定,之后才逐渐下降,但DI(NIR,Green)在低于20nm分辨率时降趋势较明显。另外,水稻氮敏感参数R434/(R496+R401)、R705/(R717+R491)和R533/R565随光谱分辨率的变化均较平缓,在300nm分辨率内值变化较小,说明这些参数对不同分辨率传感器有一定的普适性。
通过分析不同光谱分辨率反射光谱的两波段组合指数与水稻冠层叶片氮浓度的关系,发现光谱分辨率在10nm以内,对波段组合范围及其与氮浓度的相关关系没有显著影响；当分辨率降低到15nm后,与氮素相关显著的波段组合区域开始减少,至150nnm分辨率,相关显著的波段组合仅剩绿绿组合；而小于150nm分辨率后,则变为绿蓝或蓝蓝组合,同时相关性显著降低。光谱分辨率对不同的植被指数影响程度不一样。如对于归一化指数ND(760,710),近红外参考波段760nm波段的分辨率在3-200nm间对预测效果影响不大,而红边波段710nm,则要求分辨率在20nnm内才具有较好效果。对于绿光波段比值指数R(533,565),当565nm的分辨率在50nm以内、533nm分辨率在25nnm以内时,两波段任意分辨率的组合均有较好预测效果,超出此范围,特定的分辨率组合才能获得较好预测效果。另外,比较Cropscan与ASD两种传感器的光谱反射率的差异显示,不同波长范围的宽窄波段反射率可能除了受光谱分辨率的影响外,还受施氮水平、水稻品种及生育时期等因素的影响。水稻生育前后期,两种传感器的差异较大,而在水稻旺盛生长期(抽穗期)差异较小。总体上看,可见光和短波红外波段的宽窄传感器的反射率差异较大,同时也易受施氮水平、品种和生育期的影响。另外,基于ASD窄波段的预测精度仅稍高于基于Cropscan的预测效果,但前者对拔节前(未封行)氮浓度拟合较差,而对封行后氮浓度拟合好于后者。
分析确立了水稻叶片色素含量状况、叶面积指数(LAI)、叶片光合速率与冠层高光谱参数的关系及定量监测模型。结果表明,与CHLa和CHLa+b浓度相关较好的原始光谱比值指数是红边波段714nm和近红外波段760nm组成的RI(714,760);归一化指数和差值指数均为绿光波段组合,ND(543,565)和DI(562,543).与CHLa浓度相关较好的导数光谱指数为导数比值指数R(D_(744),D_(761))和归一化指数ND(D_(748),D_(761))。与原始光谱植被指数相比,导数比值和归一化指数与CHL浓度的拟合决定系数大幅提高。三波段组合而成的植被指数(修正型归一化指数gmND705)与水稻冠层叶片叶绿素浓度呈极显著相关关系,相关程度显著高于2波段植被指数。而与CHLa和CHLa+b密度相关较好的波段组合均为红边末端波段743nm和近红外波段822nm组成的植被指数。比值指数RI(743,822)、归一化指数ND(743,822)和均与水稻叶层叶绿素密度呈极显著直线负相关关系。与叶层CHL密度直线相关程度最高导数光谱指数是归一化指数ND(D511,D771)和差值指数DI(D549,D779)。在CHL密度估测中,与原始光谱指数相比,导数光谱指数并未提高相关性。综合比较基于不同植被参数的叶绿素状况模型预测的R2、RMSE和RE值,发现绿色改进型线归一化指数gmND705用于预测叶绿素a和叶绿素a+b浓度,归一化指数ND(743,822)用于预测叶绿素a和叶绿素a+b密度的效果最佳。另外,原始光谱组成的2波段差值指数形式估测水稻叶面积指数效果最好,其次为比值和归一化植被指数。同样,一阶导数光谱组成的2波段差值形式与LAI的组合相关性显著高于比值和归一化两种形式,相关最好的植被参数是红光和近红外光组成的导数差值指数DVI(D676,D778),但总体上导数光谱指数不如原始光谱指数与LAI关系密切。独立试验数据的检验结果表明,以差值指数DVI(854,760)为变量建立的水稻LAI监测模型可有效地用于水稻LAI的估测。另外,比值指数R(810,680)可以较好地监测不同水氮条件下水稻叶片的光合特征。
综合研究了不同氮素水平下水稻地面冠层高光谱数据、星载高光谱影像(Hyperion)和多光谱影像数据(TM和ALOS)特征及其与水稻叶层氮状况的定量关系。结果表明,大气显著影响水稻冠层地面光谱反射率,大气影响使可见光反射率值变大,使短波红外部分波段反射率值变小。FLAASH大气校正模型对Hyperion和TM卫星图像、经验线性法对ALOS图像有良好的大气校正结果。通过地、空不同水平估测水稻叶层氮素状况的高光谱参数基本一致,Hyperion光谱与水稻叶层氮浓度相关较好的植被参数有绿光修正归一化指数gmND(760,710)、改进型线性外推红边位置、比值植被指数RVI(884,690)和归一化植被指数NDVI(884,690).与Hyperion相比,TM和ALOS无法获取红边参数等高光谱参数,但由篮、红和近红外等三个波段组合而成的蓝色修正归一化植被指数(R830-R660)/(R830+R660-2×R485)和(R_(825)-R_(650))/(R_(825)+R_(650)-2×R_(460))与氮浓度相关性相关明显高于2波段(?)DVI(NIR,R),且与Hyperion的估测精度类似。另外,Hyperion光谱与水稻叶层氮积累量相关较好的植被参数是RI(830,742),而TM和ALOS均为ND(NIR,Green),这2个参数可作为大尺度水稻叶层氮积累量估测的适宜参数。ALOS在水稻氮素状况的光谱参数估测能力上与TM类似,但其空间分辨率更高,在植被生长监测上展示良好的应用前景。
The spectral parameters and monitoring models in remote sensing studies are of key importance for information acquisition and diagnosis on crop growth status. At present, development of digital agriculture presents an urgent need for low-cost, reliable, consistent and precise techniques for monitoring nitrogen status in crop plants. Recent success of remote sensing in agricultural application has made it possible to rapidly monitor growth status and biochemical components in large-area field crops. Implication of various ground-based and space-borne remote sensed information and instruments is receiving more and more attention in existing research. This study was undertaken to make a systematic analysis on the characteristics of the reflectance spectra, compare prediction ability of nitrogen status with vegetation indices based on narrow and wide bands, and develop key spectral indices and quantitative models for nitrogen parameter estimation at ground and space levels, based on canopy hyper-spectral (multi-spectral) and image spectral reflectance of field-grown rice with varied nitrogen levels and rice varieties in different years. This would help to establish the key technology for real-time monitoring of plant nitrogen status with ground and satellite hyper-spectral sensors in rice production.
Firstly, a systematic analysis was made on the characteristics of the first-derivative reflectance spectra in red edge area, and the quantitative relationship of red edge position (REP) and red edge area shape parameters to canopy leaf nitrogen concentrations under varied nitrogen rates and rice varieties in different field experiments. The results showed that spectrum in red edge area was significantly affected by different nitrogen levels and different rice varieties, and "three-peak" feature could be observed with the first derivative spectrum at about700nm,720nm and730nm bands, respectively. Traditional REP was not sensitive to canopy leaf nitrogen concentration because of the three-peak feature, but REPs based on inverted Gaussian fitting technique, linear four-point interpolation technique, linear extrapolation method and adjusted linear extrapolation method generated continuous REP data, and could be used to estimate canopy leaf nitrogen concentration. Besides, REP from a three-point Lagrangian interpolation with three first-derivatives bands (695nm, 700nm and705nm) also had a good relationship with canopy leaf nitrogen concentration. Comparison of these REP revealed that the adjusted linear extrapolation method (755FD73o+675FD700)/(FD730+FD700) had the best prediction performance on canopy leaf nitrogen concentration, with a relative simple algorithm, so it is a proper REP parameter for estimating canopy leaf nitrogen concentration in rice.
In addition, the peak heights of the3peak bands changed alternatively with different nitrogen levels, so child areas and shapes surrounded by the first derivative spectra curve and x coordinate changed accordingly. Double peak symmetry (DPS) based on the ratio of2different red edge child areas divided by "peak band line", and normalized double peak symmetry (NDPS) with normalization of the2different red edge child areas were significantly related to canopy leaf nitrogen concentrations in rice. Results of model calibration and validation indicated that DPS(A675-700,A675-755) and NDPS(A675-700, A675-755), ratio and normalized difference of area in675-700nm to675-755nm red edge region, respectively, performed best in estimating canopy leaf nitrogen concentration, so these two spectral indices were good red edge area shape parameters for monitoring canopy leaf nitrogen concentration in rice. Furthermore, the spectral parameter DPS(A72o-730A700-720) was found to be a good indicator for leaf nitrogen accumulation in rice.
Then, the sensitivity of reflectance spectra to canopy leaf nitrogen status was examined, and the quantitative relationships of different hyper-spectral vegetation indices to canopy leaf nitrogen concentrations were evaluated. The results showed that the reflectance of red region (665-675nm), blue region (490-500nm) and red edge region (680-760nm) was highly sensitive to canopy leaf nitrogen status. Two bands-based vegetation indices combined with550-600nm and500-550nm in green region had good relationships with canopy leaf nitrogen concentrations, and ratio index R(533,565) exhibited the best performance in all two bands vegetation indices. Yet prediction ability of nitrogen concentration was significantly improved using three bands vegetation indices. Novel three bands indices, blue nitrogen indices R434/(R496+R401) and R705/(R717+R491) were developed for monitoring canopy leaf nitrogen concentration, and the test results indicated that R434/(R496+R401) and R705/(R717+R491) had better prediction precision and suitable application than R(533,565). In addition, some reported vegetation indices also had good relationships to canopy leaf nitrogen concentrations, such as two bands indices ZM, GM-2, RI-1dB, RI-2dB, NDRE and three bands indices mND705, PRIc, with three band indices better than two band indices, although less satisfactory prediction than blue nitrogen indices and R.705/(R705+R491). Comparison of all previous indices and present indices indicated that novel blue nitrogen indices R434/(R496+R401) and R705/(R717+R491) had the best prediction capability for estimating canopy leaf nitrogen concentration in rice. Besides, RVI(827,742) was identified as a good indicator for leaf nitrogen accumulation in rice.
A knowledge on the responses of reflectance spectra and vegetation indices to bandwidth can contribute to proper selection of sensitive bands and development of spectral indices for nitrogen status monitoring. Effects of different bandwidths on canopy reflectance spectra in the range of350to1000nm bands and sensitive vegetation indices were studied with simulated spectra based on ASD data, and then prediction powers of canopy nitrogen concentrations based on two different remote sensors, ASD and Cropscan were compared. The results showed that there was less effect on reflectance spectra if bandwidth was within20nm, on the contrary, when it was larger than50nm, several phenomena happened that the red vale turned shallow, green peak got lower and slope of red edge reached flat. Bandwidth had smaller impact on visible light and larger effect on near infrared light under different nitrogen levels, discrimination power of nitrogen levels based on red light decreased with reduced spectral resolution, but green light could well discriminate nitrogen levels within200nm spectral resolution. Ratio indices, normalized indices and differential indices developed from reflectance of NIR and red edge, red and green bands also decreased with reduced spectral resolution, especially when bandwidth was broader than20nm. Yet RI(NIR,Green) and NI(NIR,Green) just had similar values when bandwidth was narrower than200nm. Several sensitive vegetation indices such as R434/(R496+R401), R705/(R717+R491) and R533/R565were found to be very stable within300nm bandwidth, thus indicating a wide applicability with different remote sensors.
Relationships between canopy leaf nitrogen concentration and spectral indices composed of arbitrary two bands with different spectral resolution in the region of350to1000nm were also explored. The results indicated that the band combinations with good correlation to nitrogen status were constant within10nm bandwidth, and then reduced in number. For the purpose of reliable estimation of nitrogen status, the bandwidths of vegetation index such as ND (760,710) maybe diverse. The200nm bandwidth for760nm contributed identically to nitrogen estimation, but20nm bandwidth was requested for710nm, and different vegetation indices exhibited different powers. Comparison of Cropscan and ASD sensors indicated that reflectance spectra were not only controlled by bandwidth, but also by applied nitrogen levels, cultivar types and growth stages. The ASD sensor had higher prediction power than Cropscan sensor with the same vegetation index at the growth stages with higher coverage, whereas vice versa at growth stages with lower population coverage.
Further, the relationships of leaf chlorophyll status, leaf area index and leaf photosynthesis to hyper-spectral reflectance and derivative parameters were quantified for deriving monitoring models on leaf pigment status with key hyper-spectral indices in rice. The results indicated that ratio index RI(714,760), normalized index ND(543,565) and difference index DI(562,543) had good relationships with chlorophyll a or chlorophyll a+b concentrations. The first derivative spectral ratio and normalized indices R(D744,D761) and ND(D748,D761) had better relationships with chlorophyll a or chlorophyll a+b concentrations than RI(714,760), ND(543,565) and DI(562,543), and the similar results were obtained with three bands index gmND705. The correlation results showed that vegetation indices composed of spectral reflectance of bottom band in red edge area743nm and NIR band822nm were significantly related to chlorophyll density, and both RI(743,822) and ND(743,822) had negative linear relationships with canopy leaf chlorophyll density. The first derivative spectral normalized and difference indices ND(D511,D771) and DI(D549,D779) also had better relationships with chlorophyll density, although not much improved over the RI(743,822) and ND(743,822). Green modified normalized index gmND705was good indicator for canopy leaf chlorophyll concentration, and normalized index ND(743,822) for chlorophyll density estimation from comparison of determination coefficients, RMSEs and REs of monitoring models with different vegetation indices. In addition, good relationships existed between LAI and vegetation indices, the correlation sequence of LAI to different index types was DVI>RVI>NDVI which consisted of spectral reflectance or the first derivative spectra. The best vegetation index for LAI monitoring were found to be the difference index of850nm to760nm DVI(854,760) and the first derivative difference index of676nm to778nm DVI(D676,D778), respectively. The vegetation index composed of spectral reflectance was better than that of the first derivative spectra for LAI monitoring in rice. Testing of the monitoring models with independent dataset also proved that the spectral index of DVI(854,760) gave accurate LAI estimation, so can be considered as a good indicator for LAI monitoring in rice. In addition, ratio index R(810,680) can be used to monitor leaf photosynthetic characteristics at different growth stages of rice.
Finally, an integrative analysis was made on the characteristics of reflectance spectra of ASD, Hyperion, TM and ALOS, and on the quantitative relationships of spectral parameters to canopy leaf nitrogen concentrations under different nitrogen levels. The results showed that real spectral reflectance in rice was significantly influenced by atmosphere, which caused the reflectance of VIS to increase and NIR to decrease. Atmospheric correction model FLAASH and empirical line calibration were good methods for Hyperion, TM and ALOS, respectively. The hyper-spectral parameters for canopy leaf nitrogen estimation were essentially consistent at ground and space levels, and green modified normalized difference index gmND(760,710), adjusted linear extrapolation red edge position, RVI(884,690) and NDVI(884,690) were good predictors for canopy leaf nitrogen concentrations in rice. As compared to Hyperion data, hyper-spectral parameters such as REP could not be extracted from TM and ALOS data, but blue modified normalized vegetation index (RNIR-RR)/(RNIR+RR-2×RB) composed of reflectance of blue, red and NIR was better than NDVI(NIR,R) for canopy leaf nitrogen concentration evaluation with the same prediction precision. In addition, RI(830,742) and ND(NIR,Green) derived from Hyperion and TM or ALOS respectively were two good parameters for estimation of canopy nitrogen accumulation at ground or space level. Since ALOS had the similar ability of estimating canopy leaf nitrogen concentration as TM with higher spatial resolution, it could be quite promising for plant growth monitoring in the future.
引文
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