封育条件下草地光谱反射特征及地上生物量估测

2005年7月至2006年4月,对云南省马龙县封育草地和过牧草地的光谱反射率、草层高度、覆盖度和地上生物量进行了测定,分析了归一化植被指数(NDVI)及比值植被指数(RVI)与地上生物量之间的相关性。结果表明:过牧草地封育1年之后,其草层高度、覆盖度和地上生物量显著增加,光谱反射特征也相应地发生明显变化。450~850 nm范围内,两种草地不同季相条件下在各波段的光谱反射率差异均达到极显著水平(P<0.01),覆盖度及季节变化对近红外波段的影响明显大于可见光波段。旺盛生长期(7月)和枯黄期(11月),封育草地具有植被反射型特征,而自由放牧草地表现为植被-土壤型;返青期(4月)两种草地均表现为土壤型。过牧草地地上生物量与两种植被指数之间无显著相关性。封育草地地上生物量与NDVI,RVI之间存在显著的(P<0.05)非线性相关,旺盛生长期和返青期NDVI与地上生物量的相关性强于RVI,枯黄期RVI与地上生物量相关性强于NDVI。     

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GreenSeeker的水稻氮素估测

为研究水稻植株氮素指标与GreenSeeker植被指数的定量关系,通过设置不同年份、不同氮肥水平的田间试验,于移栽后定期使用GreenSeeker获取冠层归一化差值植被指数(NDVI)和比值植被指数(RVI),并同步破坏性取样获取植株生物量和氮含量,分析不同品种和不同氮营养条件下氮素和植被指数(NDVI和RVI)变化规律,建立基于NDVI和RVI的氮素监测模型。结果表明,植株氮含量可以基于NDVI和RVI分时期进行估算,植株氮积累量可以被分阶段反演。利用GreenSeeker可以实现水稻氮素快速无损监测,为水稻氮肥精确管理提供技术支持。     

基于主动冠层光谱仪的莴苣生物量及氮素营养状况估测

  【目的】  研究冠层光谱技术在蔬菜氮素营养诊断中应用的可行性和提高其准确性的方法,为推进蔬菜氮素营养管理与施肥推荐提供快速无损检测技术。  【方法】  以茎菜类蔬菜—莴苣 (Lactuca sativa L.) 为研究对象进行田间试验。设置5个化肥年施用梯度:0、108、162、216、270 kg/hm2,在莴苣幼苗期、莲座期、茎形成期和收获期,利用GreenSeeker冠层光谱仪获取冠层光谱特征值—植被归一化指数 (NDVI) 和比值植被指数 (RVI),并测定植株生物量和含氮量。评估用生育期NDVI和RVI值预测蔬菜生物量和氮素营养的可行性与准确性,并验证用移栽天数校正提高全生育期光谱值预测精度的可行性。  【结果】  NDVI和RVI与莴苣地上部生物量 (AGB)、根冠比 (RTS)、植株吸氮量 (PNU) 和植株氮浓度 (PNC) 等指标间均存在显著相关关系,尤其以NDVI相关性更高。相关性分析结果表明,NDVI与AGB和PNU呈正相关,相关系数分别介于0.779～0.945和0.819～0.938;与RTS和PNC呈负相关,相关性系数介于–0.367～–0.844和–0.328～–0.732。对比不同时期,莲座期和茎形成期的NDVI值对莴苣生物量和氮素营养指标预测的准确性较高,对AGB、RTS、PNU和PNC预测准确性分别为0.76～0.92、0.37～0.71、0.77～0.88和0.34～0.54。利用两年NDVI值建立各时期莴苣生物量和氮素营养状况统一预测方程,莲座期方程最为准确,对AGB、RTS、PNU、PNC预测准确性分别为73%、48%、52%、31%。综合全生育预测方程,冠层光谱仪测定的NDVI值对莴苣生物量和氮素营养预测指标的准确性较高,基于NDVI值的AGB、RTS、PNU和PNC预测方程准确度分别为54%、43%、57%和26%。引入移栽天数 (DAT) 对该预测方程进行校正后,AGB、PNU和PNC预测方程的准确度分别提高至62%、71%和34%。  【结论】  基于冠层光谱仪测定的各生育期的植被归一化指数 (NDVI) 可准确预测莴苣的生物量和氮素营养状况,尤以莲座期的预测结果最为准确。经移栽天数 (DAT) 校正后,基于全生育期的NDVI值建立的预测方程对AGB、PNU的预测准确度可分别提高到62%和71%,基本满足莴苣类低覆盖度蔬菜作物的氮素营养管理。     

Analysis of Common Canopy Reflectance Spectra for Indicating Leaf Nitrogen Concentrations in Wheat and Rice

Abstract

Non-destructive monitoring and diagnosis of plant nitrogen (N) concentration are of significant importance for precise N management and productivity forecasting in field crops. The present study was conducted to identify the common spectra wavebands and canopy reflectance spectral parameters for indicating leaf nitrogen concentration (LNC, mg N g-¹ DW) and to determine quantitative relationships of LNC to canopy reflectance spectra in both rice (Oryza sativa L.) and wheat (Triticum aestivum L.). Ground-based canopy spectral reflectance and LNC were measured with seven field experiments consisting of seven different wheat cultivars and five different rice cultivars and varied N fertilization levels across three growing seasons for wheat and four growing seasons for rice. All possible ratio vegetation indices (RVI), difference vegetation indices (DVI), and normalized difference vegetation indices (NDVI) of key wavebands from the MSR16 radiometer were calculated. The results showed that LNC of wheat and rice increased with increasing N fertilization rates. Canopy reflectance, however, was a more complicated relationship under different N application rates. In the near infrared portion of the spectrum (760?1220 nm), canopy spectral reflectance increased with increasing N supply, whereas in the visible region (460?710 nm), canopy reflectance decreased with increasing N supply. For both rice and wheat, LNC was best estimated at 610, 660 and 680 nm. Among all possible RVI, DVI and NDVI of key bands from the MSR16 radiometer, NDVI(1220, 610) and RVI(1220, 610) were most highly correlated to LNC in both wheat and rice. In addition, the correlations of NDVI(1220, 610) and RVI(1220, 610) to LNC were found to be higher than those of individual wavebands at 610, 660 and 680 nm in both wheat and rice. Thus LNC in both wheat and rice could be indicated with common wavebands and vegetation indices, but separate regression equations are necessary for precisely describing the dynamic change patterns of LNC in wheat and rice. When independent data were fit to the derived equations, the root mean square error (RMSE) values for the predicted LNC with NDVI(1220, 610) and RVI(1220, 610) relative to the observed values were 10.50% and 10.52% in wheat, and 13.04% and 12.61% in rice, respectively, indicating a good fit. These results should improve the knowledge on non-destructive monitoring of leaf N status in cereal crops.     

植被指数与作物叶面积指数的相关关系研究

作物长势参数是精细农业遥感监测的重要对象,叶面积指数(LAI)是作物长势最重要的参数之一,利用遥感手段快速获取作物的LAI具有重要的意义。为此,考虑到波段组合方式对LAI的反演效果的不可忽略性,采用4种不同的波段组合,结合PROSPECT和SAIL的模拟数据、地面实测数据和高光谱影像数据,从植被指数的饱和性和模型拟合精度两个角度对6个植被指数展开了评价。结果表明:TVI、MSAVI和MCARI23个植被指数在以上3个方面均表现较优,750～680 nm波段组合更加适合于LAI的反演。     

基于水稻冠层植被指数的龟裂碱土盐碱化信息预测研究

土壤盐碱化是危及农业生产的重大生态环境问题,遥感技术可以通过植被冠层光谱来估测土壤的盐碱化程度。试验结果表明:土壤pH与ESP(exchangeable sodium saturation percentage)之间有极显著相关关系;随着生育期的推移,水稻冠层NDVI逐渐增大,RVI逐渐减小,孕穗期水稻冠层NDVI值最高,RVI值最低。从拔节期到乳熟期的水稻冠层NDVI和RVI与相应时期的土壤pH和ESP都有较好的相关性,而且随着作物生长期的延伸,NDVI估测土壤pH和ESP的准确性有增加的趋势;返青期和分蘖期相关性不稳定。NDVI和RVI对pH和ESP估测的精度无显著性差异,但NDVI对ESP的估测效果更好一些。所以,从拔节期到乳熟期,通过水稻冠层植被指数可以较准确地预测龟裂碱土的碱化程度。     

基于双波段作物长势分析仪的东北水稻长势监测

为了实现水稻精细栽培和变量管理的目的,利用独立开发的双波段作物长势分析仪,进行了水稻生长监测的试验与分析。传感器分别在610与1220 nm处测量太阳光与作物冠层反射光的强度,进而计算光谱反射率。利用双波段作物长势分析仪于2008年在黑龙江省农垦总局建三江分局2处水稻试验田,在分蘖期与抽穗早期进行了氮肥胁迫试验,结果表明水稻叶片氮浓度及生物质干质量与RVI、NDVI都具有很高的相关性,但与NDVI的相关性比与RVI的更高。分蘖期的测量结果表明,NDVI与施肥量的相关性非常显著,R2大于0.94。但NDVI并不与施肥量成线性相关,过量的施氮量反而会引起NDVI值的降低。分蘖期、抽穗早期的NDVI值都与最终产量有着显著的相关,其中抽穗早期的NDVI与产量的决定系数（R2）达到了0.96。分析结果显示利用双波段作物长势分析仪监测水稻冠层,可达到控制投入和提高产量的目的,为水稻的精细栽培提供理论与技术支持。     

关中地区小麦冠层光谱与氮素的定量关系

【目的】分析不同生育期及整个生育期小麦叶片氮含量(LNC)与冠层光谱反射特征的关系,以实现对田间小麦活体氮素营养状况的监测,为小麦叶片氮素状况的精确诊断提供依据。【方法】以位于陕西关中地区杨凌揉谷镇、扶风马席村和巨良农场的3个小麦试验田为研究对象,测定不同长势及生育期小麦LNC及冠层光谱反射率,分析不同长势下小麦LNC和反射率的变化,并研究氮含量与冠层光谱反射率的相关性,以及小麦LNC与比值植被指数(RVI)、归一化植被指数(NDVI)的相关性,建立小麦LNC的敏感波段及光谱监测模型。【结果】在同一生育期,长势差的小麦叶片氮含量较低,长势较好的叶片氮含量高。与单波段相比,组合波段构成的植被指数RVI、NDVI与LNC的相关性明显提高,近红外波段(730~1 075nm)和红波段630,660,690nm组成组合波段的RVI、NDVI与LNC呈极显著正相关,其中LNC与RVI的相关性较高。利用独立的小麦田间试验数据,采用通用的均方根差(RMSE)、决定系数(R2)、准确度(斜率)3个指标对所建立的模型进行检验,最终选取RVI(970,690)为监测小麦LNC的最佳光谱参数,构建的最佳模型为LNC=0.176 3×RVI(970,690)0.775 6,R2为0.863,RMSE为0.137,准确度为0.979,接近于1。【结论】利用小麦冠层光谱反射率构建了预测小麦LNC的最佳模型,该模型具有较好的准确度和普适性,适用于整个生育期小麦叶片氮含量的监测。     

The alley farming index: Preliminary steps towards a more realistic model

This paper presents the Alley Farming Index (AFI), a modification of the replacement value of the intercrop (RVI) index. The RVI index is used to assist in determining the ecological and economic benefits of a polyculture system and is potentially useful in intercropping situations where only annual crops are utilized. Alley farming is a modification of the alley cropping system where food crops are planted in between, regularly pruned, widely spaced trees. Unlike the RVI index the modified equation, presented here, accommodates alley farming where perennials and the amount of tree prunings used as green manure are important parameters. The AFI is presented in two forms, one that assumes a linear relationship between the quantity of tree prunings applied as green manure and annual crop yield, and a second more generalized form which accommodates other relationships between green manure application and crop yield (e.g., logarithmic or parabolic). Although designed specifically for alley farming the modified index can also accommodate alley cropping systems.This revised version was published online in November 2005 with corrections to the Cover Date.