水稻冠层反射光谱特征与籽粒品质指标的相关性研究

系统分析了水稻籽粒加工品质、外观品质、营养品质等与水稻冠层反射光谱特征之间的相关性。结果表明,成熟期冠层反射光谱指数与水稻籽粒加工品质呈显著负相关,尤其是1650 nm和660 nm的比值和归一化组合,决定系数高达0.8以上。籽粒的外观品质与冠层光谱特征之间的关系不明显。籽粒蛋白质含量则与大多数生育时期的冠层光谱比值指数和归一化指数呈极显著相关,相关系数多在0.80以上;而淀粉含量与灌浆盛期的710 nm和660 nm的比值和归一化组合相关性最好。说明通过遥感手段来预测水稻籽粒的加工品质和营养品质指标是可行的。     

Impact of land‐use intensity on the relationships between vegetation indices,photosynthesis and biomass of intensively and extensively managed grassland fens

Vegetation indices are widely used as model inputs and for non‐destructive estimation of biomass and photosynthesis, but there have been few validation studies of the underlying relationships. To test their applicability on temperate fens and the impact of management intensity, we investigated the relationships between normalized difference vegetation index (NDVI), leaf area index (LAI), brown and green above‐ground biomass and photosynthesis potential (PP). Only the linear relationship between NDVI and PP was management independent (R² = 0·53). LAI to PP was described by a site‐specific and negative logarithmic function (R² = 0·07–0·68). The hyperbolic relationship of LAI versus NDVI showed a high residual standard error (s.e.) of 1·71–1·84 and differed between extensive and intensive meadows. Biomass and LAI correlated poorly (R² = 0·30), with high species‐specific variability. Intensive meadows had a higher ratio of LAI to biomass than extensive grasslands. The fraction of green to total biomass versus NDVI showed considerable noise (s.e. = 0·13). These relationships were relatively weak compared with results from other ecosystems. A likely explanation could be the high amount of standing litter, which was unevenly distributed within the vegetation canopy depending on the season and on the timing of cutting events. Our results show there is high uncertainty in the application of the relationships on temperate fen meadows. For reliable estimations, management intensity needs to be taken into account and several direct measurements throughout the year are required for site‐specific correction of the relationships, especially under extensive management. Using NDVI instead of LAI could reduce uncertainty in photosynthesis models.     

基于多光谱图像的水稻叶片叶绿素和籽粒氮素含量检测研究

先利用常规技术分析了水稻的叶片叶绿素和籽粒氮素含量,然后用包含绿(G)、红(R)和近红外(NIR)三波段通道的电荷耦合器件(CCD)成像技术对水稻叶片和籽粒进行了无损检测。试验结果显示,水稻叶片叶绿素a、叶绿素b分别与G、NIR通道图像灰度呈极显著线性相关,叶绿素(a+b)含量则与上述两通道图像灰度呈显著线性相关;而且,水稻籽粒氮素含量与G、NIR通道、归一化植被指数(NDVI)灰度呈显著线性相关。由此建立了水稻叶片叶绿素和籽粒氮素含量的多光谱图像预测模型,并分别用21个样本对模型进行检验,其中线性显著相关的7个模型的相对误差RE(%)介于9.36%~157%,实现了对水稻叶片叶绿素和籽粒氮素含量的快速、准确、非破坏性检测。     

基于冠层高光谱遥感的杂交水稻植被指数氮素营养诊断模型

以杂交水稻为研究对象,进行两因素裂区试验,主区为品种,副区为施氮水平,分析了4个植被指数(VIs)分别与叶片氮素含量(LNC)、叶片氮素积累量(LNA)和地上部氮素积累量(APNA)之间的相关性,并建立了以VIs为自变量的氮素营养诊断模型.结果 表明,4个VIs和LNC、LNA之间均存在决定系数大于0.7的波段区域且波...     

New Vegetation Index and Its Application in Estimating Leaf Area Index of Rice

Leaf area index (LAI) is an important characteristic of land surface vegetation system, and is also a key parameter for the models of global water balancing and carbon circulation. By using the reflectance values of Landsat-5 blue, green and red channels simulated from rice reflectance spectrum, the sensitivities of the bands to LAI were analyzed, and the response and capability to estimate LAI of various NDVIs (normalized difference vegetation indices), which were established by substituting the red band of general NDVI with all possible combinations of red, green and blue bands, were assessed. Finally, the conclusion was tested by rice data at different conditions. The sensitivities of red, green and blue bands to LAI were different under various conditions. When LAI was less than 3, red and blue bands were more sensitive to LAI. Though green band in the circumstances was less sensitive to LAI than red and blue bands, it was sensitive to LAI in a wider range. When the vegetation indices were constituted by all kinds of combinations of red, green and blue bands, the premise for making the sensitivity of these vegetation indices to LAI be meaningful was that the value of one of the combinations was greater than 0.024, i.e. visible reflectance (VIS)>0.024. Otherwise, the vegetation indices would be saturated, resulting in lower estimation accuracy of LAI. Comparison on the capabilities of the vegetation indices derived from all kinds of combinations of red, green and blue bands to LAI estimation showed that GNDVI (Green NDVI) and GBNDVI (Green-Blue NDVI) had the best relations with LAI. The capabilities of GNDVI and GBNDVI to LAI estimation were tested under different circumstances, and the same result was acquired. It suggested that GNDVI and GBNDVI performed better to predict LAI than the conventional NDVI.     

新型植被指数及其在水稻叶面积指数估算上的应用

叶面积指数LAI不仅是陆表植被系统的一个重要属性,而且是全球水平衡、碳循环等模型中的重要输入参数。首先通过使用水稻小区试验冠层光谱数据模拟Landsat 5卫星蓝、绿、红光波段;其次分析了各个波段对LAI的敏感性;然后分析了由这个3个波段的所有组合分别代替常规NDVI中的红光波段构成的VNDVI对LAI变化的反应和对LAI的估算能力;最后使用不同条件下的水稻数据进行验证。结果表明,在不同的LAI范围,红绿蓝光3个波段对LAI有不同的敏感性。当LAI<3时,红蓝光波段敏感性较高。虽然这时绿光波段的敏感性不如红蓝光波段,然而绿光波段在更大的范围对LAI都有相当的敏感性。当采用红绿蓝光波段的各种组合构成植被指数时,如果要使这些植被指数不出现饱和现象,并使对LAI的敏感性有意义,其前提是要求这个波段或是波段组合的值要大于0.024,即VNDI(visible NDVI)公式中的VIS>0024,否则将可能产生饱和现象,而使LAI估算准确度降低。综合比较所有由红绿蓝光波段各种组合构成的植被指数对LAI的估算能力,认为GNDVI和GBNDVI与LAI有比较好的关系。使用其他条件下的水稻数据对各种NDVI的LAI估算能力进行了验证,仍然得到了同样的结论。可见,GNDVI和GBNDVI在估算LAI时确实比传统NDVI具有更好的效果。     

基于中分辨率成像光谱仪(MODIS)数据的水稻遥感估产研究——以江苏省为例

选择以水稻为传统优势粮食产业的江苏省作为研究区,采用MODIS09数据作为数据源,根据水稻移栽期稻田土壤湿度较大的特点,利用不同植被指数间的关系,按照一定的算法排除地表干扰像元,提取水稻像元,并在此基础上结合统计资料,分析水稻单产与提取的水稻植被指数之间的关系,并利用水稻植被指数预测全省水稻单产。研究表明,在条件时间序列插值算法(CTIF)处理的基础上提取水稻像元,并基于提取的水稻像元进行遥感估产的方法能取得较好的估算效果。拟合的2004-2006年单产平均精度高于99%,预测的2007年各地级市水稻单产精度在95%左右,全省平均单产相对误差为0·38%,精度较高,具有一定可行性,可利用该方法对不同年份和不同地区进行水稻产量估算。     

基于高光谱遥感的水稻冠层吸收光合有效辐射的估算研究

 利用高光谱遥感数据,对光谱反射率、一阶微分光谱及二阶微分光谱与吸收光合有效辐射（APAR）进行相关分析,并利用反射率、微分光谱、植被指数法研究了水稻冠层APAR的估算效果。结果表明,高光谱反射率、一阶微分光谱及二阶微分光谱的最优波段与APAR的相关性均极显著,一阶微分光谱能够更好的估算APAR,它与APAR在769 nm处的拟合决定系数为05218;5种植被指数所选的最优波段组合拟合方程的决定系数在0.67以上,其中以复归一化差值植被指数（758, 781）估算效果最好,决定系数达0.7585。     

Estimating N status of winter wheat using a handheld spectrometer in the North China Plain

Excessive nitrogen (N) fertilizer application is very common in the North China Plain. Diagnosis of in-season N status in crops is critical for precision N management in this area. Remote sensing, as a timely and nondestructive tool, could be an alternative to traditional plant testing for diagnosing crop N status. The objectives of this study were to determine which vegetation indices could be used to estimate N status in winter wheat (Triticum aestivum L.) under high N input conditions, develop models to predict winter wheat N uptake using spectral vegetation indices and validate the models with data from farmers’ fields. An N rate experiment and a variety-N experiment were conducted in Huimin, Shandong Province from 2005/2006 to 2006/2007 to develop the models. Positive linear relationships between simple ratio vegetation indices (red vegetation index, RVI and green vegetation index, GVI) and N uptake were observed independent of growth stages and varieties (R², 0.48–0.74). In contrast, the relationships between normalized difference vegetation indices (NDVI and GNDVI), red and green normalized difference vegetation index (RGNDI), and red and green ratio vegetation index (RGVI) were exponentially related to N uptake (R², 0.43–0.79). Subsequently, 69 farmers’ fields in four different villages were selected as datasets to validate the developed models. The results indicated that the prediction using RVI had the highest coefficient of determination (R², 0.60), the lowest root mean square error (RMSE, 39.7 kg N ha⁻¹) and relative error (RE, 30.5%) across different years, varieties and growth stages. We conclude that RVI can be used to estimate nitrogen status for winter wheat in over-fertilized farmers’ fields before heading.     

基于Terra与Aqua MODIS增强型植被指数的县级水稻总产遥感估算

以湖南省醴陵市为研究区域,在比较分析Terra卫星与Aqua卫星中分辨率成像光谱仪增强型植被指数的基础上,结合这两个卫星提供的空间分辨率为250m、时间分辨率为16d的植被指数产品,建立水稻各主要生育期增强型植被指数平均值乘以水稻面积的结果与乡镇级水稻总产的一次线性、二次非线性及逐步回归模型。通过误差分析,选择最优遥感拟合模型,并在此基础上,预测下一年水稻总产。结果显示,水稻种植区Terra卫星与Aqua卫星中分辨率成像光谱仪增强型植被指数有超过50%的偏差绝对值小于0.03;93.22%、99.50%的偏差绝对值分别小于0.08、0.10。水稻遥感拟合模型模拟结果相对误差小于0.1%,预测模型的估产结果比遥感拟合模型的拟合结果的误差大,但相对误差仍然小于5%。     

基于无人机多光谱遥感的冬小麦冠层叶绿素含量估测研究

为探讨利用无人机多光谱影像监测冬小麦叶绿素含量的可行性,基于北京市大兴区中国水科院试验基地的2019年冬小麦无人机多光谱影像和田间实测冠层叶绿素含量数据,选取16种光谱植被指数,确定对冬小麦冠层叶绿素含量显著相关的植被指数,采用一元二次线性回归和逐步回归分析方法建立各生育时期及全生育期的SPAD值估测模型,通过精度检验确定对冬小麦冠层叶绿素含量监测的最优模型。结果表明,两种分析方法中逐步回归建模效果最佳。拔节期选取4个植被指数(MSR、CARI、NGBDI、TVI)建模效果最好,模型率定的决定系数(r~2)为0.73,模型验证的r~2、相对误差(RE)和均方根误差(RMSE)分别为0.63、2.83%、1.68;抽穗期选取3个植被指数(GNDVI、GOSAVI、CARI)建模效果最好,模型率定的r~2为0.81,模型验证的r~2、RE、RMSE分别为0.63、2.83%、1.68;灌浆期选取2个植被指数(MSR、NGBDI)建模效果最好,模型率定的r~2为0.67,模型验证的r~2、RE、RMSE分别为0.65、2.83%、1.88。因此,无人机多光谱影像结合逐步回归模型可以很好地监测冬小麦SPAD值动态变化。     

不同生育时期冬小麦叶片相对含水量高光谱监测

为实现冬小麦不同生育时期叶片水分含量的快速监测,以冬小麦冠层高光谱数据和红外热成像数据为基础,计算得到5种光谱参数,通过对不同生育时期叶片相对含水量与光谱参数拟合状况进行分析和筛选,分别构建了基于光谱参数的叶片相对含水量反演模型,并对模型进行检验。结果表明,不同生育时期叶片相对含水量与比值指数（RVI）、归一化差值植被指数（NDVI）、比值/归一化植被指数（R/ND）、优化土壤调整植被指数（OSAVI）、冠气温差（TDc-a）均呈极显著相关（P＜0.01）;拔节期、抽穗期、开花期、灌浆前期和灌浆后期叶片相对含水量分别与NDVI、OSAVI、R/ND、TDc-a和TDc-a拟合效果较好,决定系数分别为0.842、0.884、0.831、0.864和0.945;预测模型的均方根误差分别为0.019、0.016、0.027、0.032和0.024,相对误差分别为2.16%、1.80%、3.30%、3.81%和3.53%。因此,在拔节期、抽穗期、开花期、灌浆前期和灌浆后期,可以分别利用NDVI、OSAVI、R/ND、TDc-a和TDc-a估测冬小麦叶片相对含水量。     

水稻不同发育时期高光谱与叶绿素和类胡萝卜素的变化规律

通过大田和室内试验,测定了2个品种、3个供氮水平处理的水稻冠层、完全展开倒1叶、倒3叶和穗在不同发育时期的高光谱反射率及对应叶片和穗的叶绿素、类胡萝卜素含量。结果表明,不同供氮水平的水稻冠层和叶片光谱差异明显,冠层光谱反射率随发育期推迟,抽穗前在可见光范围逐渐降低、在近红外区域逐渐增大,抽穗后在可见光范围逐渐增大,在近红外区域逐渐降低;抽穗后,冠层、叶片和穗光谱的红边位置存在“蓝移”现象;叶片叶绿素、类胡萝卜素含量呈S形变化;高光谱植被指数R990/R553、R1200/R553、R750/R553、R553/R670、R800/R553、R800/R680、（R800-R680）/（R800+R680）［R为反射率,下标为对应波长值（nm)］和红边位置λred与叶绿素、类胡萝卜素含量之间存在极显著相关,说明能用它们来估算水稻冠层、叶片和穗的叶绿素、类胡萝卜素含量。     

应用隔年卫星遥感影像评估区域麦稻连作水平

为探讨利用稀缺或隔年卫星遥感影像估算大地理区域麦稻连作水平的可行性,以江苏省常熟市为例,利用隔年TM卫星遥感影像,提取小麦和水稻种植面积并分析了麦稻连作水平。在解译隔年稻麦卫星遥感影像获取麦稻连作面积信息的基础上,运用马尔可夫链建模方法构建麦稻连作水平评估模型来估算麦稻连作水平,并用GPS 实地取样调查对估算精度进行验证。结果表明,常熟市麦稻连作指数为0.76,即有76%的小麦连作水稻,估算精度在95%以上,与传统的地面调查方法相比较,统计麦稻连作指数或水平更能反映大地理区域麦稻连作的现状。     

Estimation of Rice Evapotranspiration Using Reflective Images of Landsat Satellite in Sefidrood Irrigation and Drainage Network

More accurate estimation of crop evapotranspiration(ET_c)in a regional scale has always been one of the most important challenges.Temporal and spatial monitoring of ET_(c )using satellite images can help to enhance accuracy of estimations.In this study,the(ET_c)_(rice) maps were produced by using statistical/experimental methods based on crop coefficient(K_c)maps derived from vegetation index(Ⅵ).K_c was estimated using four methods,including linear relationship between K_c and Ⅵ(K_c-Ⅵ),calibrated model of K_c-Ⅵ,linear relationship between K_(cb)(the basal crop coefficient)and Ⅵ(K_(cb)-Ⅵ),and calibrated model of K_(cb)-Ⅵ.The results showed that calibrated model of K_c-Ⅵ had a better performance compared to the other methods,with normalized root mean square errors(NRMSE),mean absolute error and root mean square error being 5.7%,0.05 mm/d and 0.06mm/d,respectively.(ET_c)_(rice) maps were produced by using calibrated model of K_c-Ⅵ and reference evapotranspiration(ET_0)from FAO Penman-Monteith method.The NRMSE was 21.3%for using FAO Penman-Monteith method.Therefore,calibrated K_c-Ⅵ model in combining with ET_0 based on the Landsat 7 ETM+images could be provided a good estimation of(ET_c)_(rice) in regional scale,and can be applied to estimate water requirement due to the free and facilitate access.     

褐飞虱危害在水稻植株光谱反射率上的表现

利用手持式光谱仪测定了褐飞虱为害后稻株的光谱反射率。在可见光和近红外区,随褐飞虱虫量和虫龄的增加稻株光谱反射率呈下降趋势,近红外光区的光谱反射率可很好地表征褐飞虱不同龄期、不同虫量及成虫产卵对稻株的危害程度。各波长处的光谱反射率与褐飞虱虫量间存在明显的负相关,并且在520～570nm和700～1000nm波长范围内的相关性达到了极显著水平。褐飞虱为害后稻株光谱的红边斜率和红边面积也分别与虫量存在极显著相关。利用虫害后稻株在可见光波长550nm处的反射率(R550)和近红外光区波长760nm处的反射率(R760)、红边参数值与未受害稻株相应值的比值建立了褐飞虱虫量的预测模型,发现在19次预测中,各模型的预测正确率为53%～79%,且R760因子对褐飞虱虫量有相对较好的预测效果。     

Large-scale screening for maize drought resistance using multiple selection criteria evaluated under water-stressed and well-watered environments

A total of 550 maize inbred lines collected from global breeding programs were evaluated for drought resistance under both well-watered and water-stressed environments. The evaluation was based on multiple measurements of biomass taken before and after the drought stress was applied using the normalized difference vegetation index (NDVI), along with other selection criteria including anthesis-silking interval, leaf senescence, chlorophyll content, root capacitance, final grain yield, and grain yield components. Kernel weight was the most stable trait under drought stress. Correlations between the primary trait (grain yield) and the secondary traits, except the root capacitance and ASI under water-stressed condition, were all significant. Root capacitance had relatively low heritability and low genetic correlation with other drought resistance criteria, and is not recommended as a drought resistance criterion. Significant reduction of NDVI values measured in the afternoon when the leaves became rolling, compared to those measured in the morning when the leaves were open, provides a reliable index for leaf rolling, which however was not significantly correlated with grain yield. NDVIs measured across different developmental stages were highly correlated with each other and with most of the secondary traits as well as, grain yield, indicating that NDVI can be used as a secondary trait for large-scale drought resistance screening. Regression models built based on non-yield drought criteria and yield components explained about 40% and 95% of the variation for the grain yield, respectively. Some maize lines developed in China for temperate regions showed strong drought resistance comparable to tropical maize lines when tested under tropical condition, indicating that temperate lines with a wide adaptability can be used in drought resistance breeding for both temperate and tropical environments.