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1.
为探索运用水稻穗光谱植被指数预测水稻产量的可行性,以2个水稻品种为材料,设置3个氮素水平,测定了3个时期水稻叶片和穗的高光谱反射(350~2 500 nm)和色素含量,并测定了水稻的产量构成组分和籽粒产量。结果表明:与典型的植物反射光谱相比,水稻穗的反射光谱具有“绿峰消失”的特征;与叶片光谱指数[归一化差值指数(normalized vegetation index,NDVI)和光化学反射指数(photochemical reflectance index,PRI)]相比,穗光谱指数对叶绿素更敏感,而且能更准确地区分氮素水平。水稻叶片NDVI和PRI预测产量的均方根误差(RSME)分别为873.4~1 125.0、723.3~889.4 kg·hm-2,而穗NDVI和PRI预测产量的RSME分别为681.7~743.1、515.0~637.8 kg·hm-2,表明水稻穗光谱指数比叶片光谱指数更适合于水稻产量预测。  相似文献   

2.
不同作物农田的土壤呼吸与高光谱的关系   总被引：1，自引：1，他引：0  
为研究种植不同作物的农田土壤呼吸与高光谱植被指数的关系,选取3种典型夏熟作物冬小麦、油菜籽、蚕豆,于2018年10月至2019年5月进行田间随机区组试验,观测土壤呼吸、土壤温度、土壤湿度的季节动态,并观测NDVI(归一化植被指数)、DVI(差值植被指数)、RVI(比值植被指数)、EVI(增强植被指数)、PRI(光化学植被指数)5种高光谱植被指数和叶绿素SPAD值。结果表明:冬小麦、油菜籽、蚕豆田土壤呼吸季节平均值分别为1.78±0.15、1.35±0.27、1.61±0.22μmol·m^-2·s-1,冬小麦田土壤呼吸显著高于油菜籽田(P<0.05),冬小麦与蚕豆田以及油菜籽与蚕豆田土壤呼吸无显著差异(P>0.05)。冬小麦田土壤呼吸残差(基于温度指数方程的模拟值与实测值的差值)与NDVI、RVI、EVI、PRI、SPAD值均存在显著(P<0.05)或极显著(P<0.01)的相关关系,蚕豆田土壤呼吸残差与NDVI、DVI、RVI、EVI、PRI均存在极显著(P<0.01)相关关系,而油菜籽田土壤呼吸残差与上述植被指数均不存在显著的相关关系,这可能与油菜籽3-4月份花期叶片退化有关。在冬小麦和蚕豆田,可分别建立基于土壤温度、NDVI、RVI、PRI、SPAD值以及土壤温度、RVI的土壤呼吸模型,而油菜籽田土壤呼吸的季节变化仅与土壤温湿度和SPAD值有关。  相似文献   

3.
A system for imaging the photochemical reflectance index (PRI) of micropropagated plantlet leaves was developed using a low-cost charge-coupled device (CCD) camera. The reflected light intensities of leaves at 530 and 570 nm were imaged using a monochrome CCD camera with respective band-pass filters. The reflection images were used to estimate the PRI values of the leaves. The relationships between the PRI estimated from images and a chlorophyll fluorescence parameter ΔF/Fm′, determined by pulse-amplitude modulation (PAM) chlorophyll fluorometer and representing PSII quantum yield, were investigated for several plants under various conditions to test the performance of the system. The PRI estimated from images decreased with a decline in the ΔF/Fm′ for strawberry leaves treated with high intensity light under artificial light condition and lettuce leaves treated with abscisic acid. These results suggest that the system can be used for non-destructive estimation of the PRI of leaves. The system was used to estimate the PRI of micropropagated potato leaves from outside the culture vessels. The time course of PSII quantum yield for the individual leaf could be demonstrated by the PRI estimated from images of potato plantlet leaves treated with high light. The findings suggest that the system has the potential for inexpensive, simple and efficient estimation of PRI for micropropagated plantlets.  相似文献   

4.
为了探讨低温处理后毛竹Phyllostachys edulis叶片反射光谱特性与色素质量分数的相互关系,筛选出能够准确监测低温胁迫下毛竹伤害程度的光谱参数。测定了低温处理后毛竹叶片色素质量分数与反射光谱的变化参数,分析叶片光谱反射率、微分光谱及特征参数与色素质量分数的相关性。结果表明:随着温度的降低,叶绿素a和类胡萝卜素质量分数呈下降趋势(P<0.01)。反射光谱参数光谱反射指数、改良红边比、色素比值指数、归一化植被指数、红边归一化指数、改良类胡萝卜素指数和光化学反射指数等均随着温度的降低而降低(P<0.01);红边面积随着胁迫加深不断减小,红边位置向短波方向移动。在绿光区和红光区,叶绿素a和类胡萝卜素质量分数与光谱反射率及微分光谱显著相关(P<0.05),且与大部分光谱参数达到极显著相关(P<0.01),说明反射光谱特征及其参数可用来估算叶片色素质量分数。图4表5参40  相似文献   

5.
The application of spectral reflectance indices (SRIs) as proxies to screen for yield potential (YP) and heat stress (HS) is emerging in crop breeding programs. Thus, a comparison of SRIs and their associations with grain yield (GY) under YP and HS conditions is important. In this study, we assessed the usefulness of 27 SRIs for indirect selection for agronomic traits by evaluating an elite spring wheat association mapping initiative (WAMI) population comprising 287 elite lines under YP and HS conditions. Genetic and phenotypic analysis identified 11 and 9 SRIs in different developmental stages as efficient indirect selection indices for yield in YP and HS conditions, respectively. We identified enhanced vegetation index (EVI) as the common SRI associated with GY under YP at booting, heading and late heading stages, whereas photochemical reflectance index (PRI) and normalized difference vegetation index (NDVI) were the common SRIs under booting and heading stages in HS. Genome-wide association study (GWAS) using 18704 single nucleotide polymorphisms (SNPs) from Illumina iSelect 90K identified 280 and 43 marker-trait associations for efficient SRIs at different developmental stages under YP and HS, respectively. Common genomic regions for multiple SRIs were identified in 14 regions in 9 chromosomes: 1B (60–62 cM), 3A (15, 85–90, 101– 105 cM), 3B (132–134 cM), 4A (47–51 cM), 4B (71– 75 cM), 5A (43–49, 56–60, 89–93 cM), 5B (124–125 cM), 6A (80–85 cM), and 6B (57–59, 71 cM). Among them, SNPs in chromosome 5A (89–93 cM) and 6A (80–85 cM) were co-located for yield and yield related traits. Overall, this study highlights the utility of SRIs as proxies for GY under YP and HS. High heritability estimates and identification of marker-trait associations indicate that SRIs are useful tools for understanding the genetic basis of agronomic and physiological traits.  相似文献   

6.
Recent studies have demonstrated the application of vegetation indices from canopy reflected spectrum for inversion of chlorophyll concentration.Some indices are both response to variations of vegetation and environmental factors.Canopy chlorophyll concentration,an indicator of photosynthesis activity,is related to nitrogen concentration in green vegetation and serves as an indicator of the crop response to soil nitrogen fertilizer application.The combination of normalized difference vegetation index (NDVI) and photochemical reflectance index (PRI) can reduce the effect of leaf area index (LAI) and soil background.The canopy chlorophyll inversion index (CCII) was proved to be sensitive to chlorophyll concentration and very resistant to the other variations.This paper introduced the ratio of TCARI/OSAVI to make accurate predictions of winter wheat chlorophyll concentration under different cultivars.It indicated that canopy chlorophyll concentration could be evaluated by some combined vegetation indices.  相似文献   

7.
Handheld chlorophyll sensors and remote sensing are two nondestructive approaches for estimating plant nitrogen (N) status, which are now commercially available. In this paper we address three questions on the application of these technologies in perennial fruit trees: (1) can individual leaf meter measurements be used to predict N status for surrounding trees?, (2) are narrow band indices more sensitive than the normalized difference vegetation index (NDVI) to differences in plant N?, and (3) is NDVI from satellite remote sensing correlated to leaf level vegetation indices? We evaluated data from a N rate trial conducted in a commercial Fuji apple orchard (Malus domestica Borkh. cv. ‘Fuji’). SPAD and CM1000 handheld chlorophyll meters and reflectance measurements using a portable spectrometer were made on individual leaves three or four times during each growing season. The reflectance measurements were used to determine NDVI and three narrow band vegetation indices. Satellite imagery from the Quickbird sensor was acquired two or three times during each growing season and used to generate NDVI for individual trees. The leaf meter measurements and vegetation indices were compared with the N application rate and plant N status measured as total leaf tissue N.We evaluated how well single leaf meter measurements predict N status for surrounding trees by calculating the differences between actual and estimated N applications from individual measurements. On average, a sample of 12 leaves (from the same treatment and same measurement date) resulted in an estimation error of 30 kg ha−1 for either the SPAD or the CM1000 sensor, representing almost half of the range in N treatment rates. To evaluate any improvement in prediction of applied N using narrow band indices, we used analysis of variance (ANOVA) to compare three narrow band indices with the leaf meters and NDVI measured at leaf and canopy levels. Two narrow band indices, red edge vegetation stress index (RVSI) and modified chlorophyll absorption in reflectance index (MCARI) had higher F-values (31 and 41, respectively) than did NDVI from leaf level measurements (26), from satellite NDVI (6), or the CM1000 chlorophyll meter (12). The ANOVA results support improvements in leaf sensors using index values other than NDVI. We found that NDVI from satellite imagery acquired close to the leaf level measurement dates were positively correlated to the chlorophyll sensors and vegetation indices. When the data was averaged to the experiment plot level (twelve leaves total), the correlation coefficients between the satellite NDVI and the other sensors ranged from 0.68 for NDVI from leaf level reflectance to 0.84 with the CM1000 chlorophyll meter. Given the level of correlations, remote sensing might be a useful tool to extrapolate handheld measurements spatially throughout an orchard.  相似文献   

8.
This study assessed the capability of several xanthophyll, chlorophyll and structure-sensitive spectral indices to detect water stress in a commercial farm consisting of five fruit tree crop species with contrasting phenology and canopy architecture. Plots irrigated and non-irrigated for eight days of each species were used to promote a range of plant water status. Multi-spectral and thermal images were acquired from an unmanned aerial system while concomitant measurements of stomatal conductance (gs), stem water potential (Ψs) and photosynthesis were taken. The Normalized Difference Vegetation Index (NDVI), red-edge ratio (R700/R670), Transformed Chlorophyll Absorption in Reflectance Index normalized by the Optimized Soil Adjusted Vegetation Index (TCARI/OSAVI), the Photochemical Reflectance Index using reflectance at 530 (PRI) and 515 nm [PRI(570–515)] and the normalized PRI (PRInorm) were obtained from the narrow-band multi-spectral images and the relationship with the in-field measurements explored. Results showed that within the Prunus species, Ψs yielded the best correlations with PRI and PRI(570–515) (r2 = 0.53) in almond trees, with TCARI/OSAVI (r2 = 0.88) in apricot trees and with PRInorm, R700/R670 and NDVI (r2 from 0.72 to 0.88) in peach trees. Weak or no correlations were found for the Citrus species due to the low level of water stress reached by the trees. Results from the sensitivity analysis pointed out the canopy temperature (Tc) and PRI(570–515) as the first and second most sensitive indicators to the imposed water conditions in all the crops with the exception of apricot trees, in which Ψs was the most sensitive indicator at midday. PRInorm was the least sensitive index among all the water stress indicators studied. When all the crops were analyzed together, PRI(570–515) and NDVI were the indices that better correlations yielded with Crop Water Stress Index, gs and, particularly, Ψs (r2 = 0.61 and 0.65, respectively). This work demonstrated the feasibility of using narrow-band multispectral-derived indices to retrieve water status for a variety of crop species with contrasting phenology and canopy architecture.  相似文献   

9.
The aim of this study was to evaluate the accuracy of the spectro-optical, photochemical reflectance index (PRI) for quantifying the disease index (DI) of yellow rust (Biotroph Puccinia striiformis) in wheat (Triticum aestivum L.), and its applicability in the detection of the disease using hyperspectral imagery. Over two successive seasons, canopy reflectance spectra and disease index (DI) were measured five times during the growth of wheat plants (3 varieties) infected with varying amounts of yellow rust. Airborne hyperspectral images of the field site were also acquired in the second season. The PRI exhibited a significant, negative, linear, relationship with DI in the first season (r 2 = 0.91, n = 64), which was insensitive to both variety and stage of crop development from Zadoks stage 3–9. Application of the PRI regression equation to measured spectral data in the second season yielded a coefficient of determination of r 2 = 0.97 (n = 80). Application of the same PRI regression equation to airborne hyperspectral imagery in the second season also yielded a coefficient of determination of DI of r 2 = 0.91 (n = 120). The results show clearly the potential of PRI for quantifying yellow rust levels in winter wheat, and as the basis for developing a proximal, or airborne/spaceborne imaging sensor of yellow rust in fields of winter wheat.  相似文献   

10.
 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。  相似文献   

11.
封育条件下草地光谱反射特征及地上生物量估测   总被引：1，自引：0，他引：1  
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。  相似文献   

12.
不同海拔的长白山岳桦叶片反射光谱研究   总被引：1，自引：1，他引：0  
通过对长白山不同海拔下岳桦叶片反射光谱的研究,探讨岳桦叶片对高山环境的适应机制.结果表明:不同海拔间岳桦叶片光谱反射率及光谱指数有较大差异.在1750～2000m海拔梯度内,叶绿素归一化指数(chlNDI)显示;岳桦叶片叶绿素相对含量先升高再降低;光化学反射指数(PRI)显示岳桦叶片光合有效辐射利用效率在2000m处最...  相似文献   

13.
Recently reported testing of active, optical crop sensors in low-level aircraft have demonstrated a new class of airborne sensing system that can be deployed under any ambient illumination conditions, including at night. A second-generation, high-powered, light-emitting diode system has been assembled and tested over a 80 ha field of wheat (Triticum aestevum) by mapping the normalised difference vegetation index (NDVI) at altitudes ranging from 15 to 45 m above the canopy; significantly higher altitudes than existing systems. Comparisons with a detailed on-ground NDVI survey indicated the aerial sensor values were highly correlated to the on-ground sensor (0.79 < R2 < 0.85), with close to unity slope and zero offset. The maximum average deviation between aerial and on-ground NDVI values was 0.04. Sample calculations involving two exemplar algorithms, one for estimating monoculture pasture biomass and the other for estimating wheat yield, indicate such deviations to have no significant effect on prediction accuracy. The subsequent NDVI maps proved to be invariant to sensor height over the 15-45 m altitude range meaning this new sensor configuration can be deployed over undulating crops and pastures and in areas with nearby obstacles such as trees and buildings.  相似文献   

14.
Spectral reflectance in the near-infrared （NIR） shoulder （750-900 nm） region is affected by internal leaf structure, but it has rarely been investigated. In this study, a dehydration treatment and three paraquat herbicide applications were conducted to explore how spectral reflectance and shape in the NIR shoulder region responded to various stresses. A new spectral ratio index in the NIR shoulder region （NSRI）, defined by a simple ratio of reflectance at 890 nm to reflectance at 780 nm, was proposed for assessing leaf structure deterioration. Firstly, a wavelength-independent increase in spectral reflectance in the NIR shoulder region was observed from the mature leaves with slight dehydration. An increase in spectral slope in the NIR shoulder would be expected only when water stress developed sufficiently to cause severe leaf dehydration resulting in an alteration in cell structure. Secondly, the alteration of leaf cell structure caused by Paraquat herbicide applications resulted in a wavelength-dependent variation of spectral reflectance in the NIR shoulder region. The NSRI in the NIR shoulder region increased significantly under an herbicide application. Although the dehydration process also occurred with the herbicide injury, NSRI is more sensitive to herbicide injury than the water-related indices （water index and normalized difference water index） and normalized difference vegetation index. Finally, the sensitivity of NSRI to stripe rust in winter wheat was examined, yielding a determination coefficient of 0.61, which is more significant than normalized difference vegetation index （NDVI）, water index （WI） and normalized difference water index （NDWI）, with a determination coefficient of 0.45, 0.36 and 0.13, respectively. In this study, all experimental results demonstrated that NSRI will increase with internal leaf structure deterioration, and it is also a sensitive spectral index for herbicide injury or stripe rust in winter wheat.  相似文献   

15.
基于高光谱遥感的冬小麦叶水势估算模型   总被引：2，自引：0，他引：2  
【目的】采用高光谱技术,建立快速、无损与准确获取冬小麦叶水势的估算模型,为小麦灌溉的精确管理提供科学依据。【方法】利用不同水分处理的大田试验,于小麦主要生育期同步测定冠层光谱反射率、叶水势、土壤水分等信息,并探讨高光谱植被指数与冬小麦叶水势之间的定量关系。通过相关性分析、回归分析等方法,基于不同水分处理,构建4种植被指数与冬小麦叶水势的估算模型。【结果】不同水分处理和不同生育期的冬小麦,其冠层光谱反射率具有显著的变化特征。在可见光波段,冬小麦冠层反射率随着水分含量的增加而逐渐降低,而在近红外波段,其冠层反射率则随着土壤水分含量的增加而升高。随着小麦生育期的推进,在近红外波段,抽穗期的冠层反射率比拔节期的高,在灌浆期之后,红波段(670 nm)、蓝波段(450 nm)的反射率上升加快;4种植被指数与叶水势显著相关(P0.05),相关系数|r|均在0.711以上,四者均可用于冬小麦叶片水势的定量监测。在充分供水条件下(70%FC),植被指数OSAVI和EVI2与叶水势的相关系数|r|(分别为0.75和0.771)均低于植被指数NDVI和RVI与叶水势的相关系数|r|(分别为0.808和0.896),而在重度水分亏缺条件下(50%FC),植被指数OSAVI和EVI2与叶水势的相关系数|r|(分别为0.857和0.853)均高于植被指数NDVI和RVI与叶水势的相关系数|r|(分别为0.711和0.792);所建模型对45个未知样的预测结果与实测值相似度较高,其回归模型R~2、验证模型MRE、RMSE的范围分别为0.616—0.922、-17.50%—-12.52%、0.102—0.133。在70%FC水分处理下,基于EVI2(enhanced vegetation index)所得叶水势估算模型的R~2最高,为0.922,而在60%FC和50%FC水分处理下,由于考虑了土壤背景的影响,基于OSAVI所建模型的R~2最高,分别为0.922和0.856。【结论】4种植被指数均可用于冬小麦叶水势的定量监测。但是,在构建不同水分处理的叶水势估算模型时,应考虑土壤背景对冠层光谱的影响。研究结果可以为小麦精准灌溉管理提供技术依据,为星载数据的参数反演提供模型支持。  相似文献   

16.
Three levels of scale for determining leaf area index (LAI) were explored within an almond orchard of alternating rows of Nonpareil and Monterey varieties using hemispherical photography and mule lightbar (MLB) at ground level up to airborne and satellite imagery. We compared LAI estimates of 56 fisheye photos strategically placed in the orchard to validate 500,000 MLB point scans of a small portion of the aisles between tree rows to water and vegetation indexes of MASTER (MODIS/ASTER simulator) and Landsat 5 imagery. The high correlation of fisheye photo LAI to MLB LAI estimates establishes this new method against the measurement standard within the plant community while significantly increasing sample size. MLB LAI and MASTER vegetation indexes, such as NDWI (normalized difference water index), GMI (Gitelson–Merzlyak index) and NDVI (normalized difference vegetation index), were highly correlated (r2 = 0.90). In addition, a high correlation (r2 = 0.80) between the MLB measured LAI and selected Landsat derived vegetation indexes (VI) was found. This scaling and validation of LAI estimate expands the spatial area and frequency of determination for time series analysis of crop phenology studies.  相似文献   

17.
通过测试棉花关键生育阶段350～2 500 nm波段的冠层高光谱数据,用近红外波段760～850 nm及红光波段650～670 nm的2个范围内的波段,组成了高光谱归一化植被指数(NDVI)和800和670 nm两个波段组成修改型二次土壤调节植被指数(MSAVI2),分别与棉花叶面积指数(LAI)和地上鲜生物量进行相关分析,结果表明,棉花NDVI和MSAVI2与LAI和地上鲜生物量两个参数均以幂指数相关关系为最佳(RNDVI-LAI=0.729 1·,RMSAVI2-LAI=0.743 6·,n=81;RNDVI-鲜生物量=0.742 6·,RMSAVI2-鲜生物量=0.791 1·,n=59), MSAVI2与LAI和地上鲜生物量的相关性均高于NDVI与LAI和地上鲜生物量的相关性,说明MSAVI2较NDVI能更好的消除土壤背景对反射光谱造成的影响,能较精确的提取反映棉花生长状况的叶面积指数和生物量信息.  相似文献   

18.
【目的】筛选相关性好的植被指数构建马铃薯叶片叶绿素a、叶绿素b估测模型,为科学、无损地进行马铃薯叶片叶绿素含量估算提供技术支撑。【方法】采用便携式高光谱地物波谱仪,获取不同施氮水平下不同生育时期的马铃薯植株叶片光谱反射率,提取植被指数,测定马铃薯叶片叶绿素a、叶绿素b含量,并研究叶绿素含量与植被指数的相关性。【结果】12个植被指数与叶绿素a、叶绿素b含量相关性较好,其中修正归一化差异指数(mND_(705))、修正简单比值指数(mSR_(705))、地面叶绿素指数(MTCI)、修改叶绿素吸收反射指数(MCARI)与叶绿素a、叶绿素b含量相关性最好。基于这4个植被指数建立的估测模型中,MTCI构建的乘幂模型估测叶绿素a含量的效果最佳,mND_(705)构建的指数模型估测叶绿素b含量的效果最佳。【结论】MTCI构建的乘幂模型能较为精确地估测叶绿素a含量,mND_(705)构建的指数模型能较为精确地估测叶绿素b含量;这2种模型可用于间接监测马铃薯植株的氮营养亏缺状态。  相似文献   

19.
以东北地区典型地带的粳稻为例,利用植被指数测量仪PlantPen,同时测量了粳稻叶片植被指数NDVI和PRI,并根据粳稻生长发育进程分成了与物候一致的4个生育时期。首先利用二元定距变量相关分析的方法对NDVI和PRI进行相关性分析;然后,分别利用线性回归和Cubic曲线回归建立NDVI拟合PRI的回归模型,并对回归模型进行拟合优度检验和精度验证,同时对线性回归模型与Cubic曲线回归模型的拟合效果和检验结果进行对比分析。结果表明,粳稻叶片植被指数NDVI和PRI在各生育时期均有极显著的相关关系,在粳稻生长发育进程中,相关性越来越高;线性回归模型和Cubic曲线回归模型均能使NDVI较好地拟合PRI,在粳稻生长发育进程中,拟合效果也越来越好;Cubic曲线回归模型在粳稻4个生育期平均相应的指标值判定系数(R2)、均方根误差(RMSE)、绝对百分误差(MAPE)分别为0.8055、0.0358、0.534%,而线性回归模型的相应指标为0.7653、0.0488、1.365%。Cubic曲线回归模型的RMSE和MAPE值较小且R2较大。因此其拟合优度和检验精度均优于单纯的线性回归模型,可作为NDVI反演PRI一种参考模型。  相似文献   

20.
Remote sensing during the production season can provide visual indications of crop growth along with the geographic locations of those areas. A grid coordinate system was used to sample cotton and soybean fields to determine the relationship between spectral radiance, soil parameters, and cotton and soybean yield. During the 2 years of this study, mid- to late-season correlation coefficients between spectral radiance and yield generally ranged from 0.52 to 0.87. These correlation coefficients were obtained using the green–red ratio and a vegetation index similar to the normalized difference vegetation index (NDVI) using the green and red bands. After 102 days after planting (DAP), the ratio vegetation index (RVI), difference vegetation index (DVI), NDVI, and soil-adjusted vegetation index (SAVI) generally provided correlation coefficients from 0.54 to 0.87. Correlation coefficients for cotton plant height measurements taken 57 and 66 DAP during 2000 ranged from 0.51 to 0.76 for all bands, ratios, and indices examined, with the exception of Band 4 (720nm). The most consistent correlation coefficients for soybean yield were obtained 89–93 DAP, corresponding to peak vegetative production and early pod set, using RVI, DVI, NDVI, and SAVI. Correlation coefficients generally ranged from 0.52 to 0.86. When the topographic features and soil nutrient data were analyzed using principal component analysis (PCA), the interaction between the crop canopy, topographic features, and soil parameters captured in the imagery allowed the formation of predictive models, indicating soil factors were influencing crop growth and could be observed by the imagery. The optimum time during 1999 and 2000 for explaining the largest amount of variability for cotton growth occurred during the period from first bloom to first open boll, with R values ranging from 0.28 to 0.70. When the PCA-stepwise regression analysis was performed on the soybean fields, R 2 values were obtained ranging from 0.43 to 0.82, 15 DAP, and ranged from 0.27 to 0.78, 55–130 DAP. The use of individual bands located in the green, red, and NIR, ratios such as RVI and DVI, indices such as NDVI, and stepwise regression procedures performed on the cotton and soybean fields performed well during the cotton and soybean production season, though none of these single bands, ratios, or indices was consistent in the ability to correlate well with crop and soil characteristics over multiple dates within a production season. More research needs to be conducted to determine whether a certain image analysis method will be needed on a field-by-field basis, or whether multiple analysis procedures will need to be performed for each imagery date in order to provide reliable estimates of crop and soil characteristics.  相似文献   

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