Evaluating hyperspectral vegetation indices for estimating nitrogen concentration of winter wheat at different growth stages

Many hyperspectral vegetation indices (VIs) have been developed to estimate crop nitrogen (N) status at leaf and canopy levels. However, most of these indices have not been evaluated for estimating plant N concentration (PNC) of winter wheat (Triticum aestivum L.) at different growth stages using a common on-farm dataset. The objective of this study was to evaluate published VIs for estimating PNC of winter wheat in the North China Plain for different growth stages and years using data from both N experiments and farmers’ fields, and to identify alternative promising hyperspectral VIs through a thorough evaluation of all possible two band combinations in the range of 350–1075 nm. Three field experiments involving different winter wheat cultivars and 4–6 N rates were conducted with cooperative farmers from 2005 to 2007 in Shandong Province, China. Data from 69 farmers’ fields were also collected to evaluate further the published and newly identified hyperspectral VIs. The results indicated that best performing published and newly identified VIs could explain 51% (R₇₀₀/R₆₇₀) and 57% (R₄₁₈/R₄₀₅), respectively, of the variation in PNC at later growth stages (Feekes 8–10), but only 22% (modified chlorophyll absorption ratio index, MCARI) and 43% (R₇₆₃/R₇₆₁), respectively, at the early stages (Feekes 4–7). Red edge and near infrared (NIR) bands were more effective for PNC estimation at Feekes 4–7, but visible bands, especially ultraviolet, violet and blue bands, were more sensitive at Feekes 8–10. Across site-years, cultivars and growth stages, the combination of R₃₇₀ and R₄₀₀ as either simple ratio or a normalized difference index performed most consistently in both experimental (R ² = 0.58) and farmers’ fields (R ² = 0.51). We conclude that growth stage has a significant influence on the performance of different vegetation indices and the selection of sensitive wavelengths for PNC estimation, and new approaches need to be developed for monitoring N status at early growth stages.     

Using spectral distance, spectral angle and plant abundance derived from hyperspectral imagery to characterize crop yield variation

Vegetation indices (VIs) derived from remote sensing imagery are commonly used to quantify crop growth and yield variations. As hyperspectral imagery is becoming more available, the number of possible VIs that can be calculated is overwhelmingly large. The objectives of this study were to examine spectral distance, spectral angle and plant abundance (crop fractional cover estimated with spectral unmixing) derived from all the bands in hyperspectral imagery and compare them with eight widely used two-band and three-band VIs based on selected wavelengths for quantifying crop yield variability. Airborne 102-band hyperspectral images acquired at the peak development stage and yield monitor data collected from two grain sorghum fields were used. A total of 64 VI images were generated based on the eight VIs and selected wavelengths for each field in this study. Two spectral distance images, two spectral angle images and two abundance images were also created based on a pair of pure plant and soil reference spectra for each field. Correlation analysis with yield showed that the eight VIs with the selected wavelengths had r values of 0.73–0.79 for field 1 and 0.82–0.86 for field 2. Although all VIs provided similar correlations with yield, the modified soil-adjusted vegetation index (MSAVI) produced more consistent r values (0.77–0.79 for field 1 and 0.85–0.86 for field 2) among the selected bands. Spectral distance, spectral angle and plant abundance produced similar r values (0.76–0.78 for field 1 and 0.83–0.85 for field 2) to the best VIs. The results from this study suggest that either a VI (MSAVI) image based on one near-infrared band (800 or 825 nm) and one visible band (550 or 670 nm) or a plant abundance image based on a pair of pure plant and soil spectra can be used to estimate relative yield variation from a hyperspectral image.     

Calibration of a capacitance probe for measurement and mapping of dry matter yield in Mediterranean pastures

The main objective of this study was to calibrate a commercial capacitance probe for measuring pasture dry matter yields under Mediterranean conditions. The standard method of assessing pasture biomass is based on cutting all the forage within a specified area and requires great effort and expense to collect enough samples to accurately represent a pasture. The field tests were carried out in 2007, 2008 and 2009 on different dates (phenological stages), and on five dairy farms, representing typical pastures in the region (grasses; legumes; and bio-diverse flora, mixture of grasses, legumes and others species). The linear regression techniques used in 2007 to relate the weight of the herbage (direct measurements) to the meter reading of capacitance (indirect measurements) led to high regression coefficients in grasses (R² = 0.90; P < 0.01) and heterogeneous botanical composition (R² = 0.87; P < 0.001) and moderate regression coefficient in legumes species (R² = 0.48; P < 0.05). The validation of the calibration equations in 2008 and 2009 in two sites showed RSME values of 130 kg ha⁻¹ in heterogeneous botanical composition and 456 kg ha⁻¹ in legumes. The results indicated that the capacitance probe together with a GPS receiver might support site-specific management of pastures which would be useful in large areas.     

土壤游离氧化铁高光谱特征与定量反演

【目的】建立基于可见-近红外光谱的土壤游离铁精确预测模型,简单、快速、经济地预测土壤游离铁,有助于研究土壤发生和分类。【方法】采集广西壮族自治区的铁铝土、富铁土、淋溶土和雏形土等82个旱地土壤剖面的B层土壤,进行室内土壤化学分析、光谱测定,分析不同光谱变换后的光谱反射率与土壤游离铁含量的相关性。基于特征波段利用偏最小二乘回归(PLSR)和逐步多元线性回归(SMLR)法建立土壤游离铁含量光谱预测模型,通过决定系数(R2)、均方根误差(RMSE)和相对预测偏差(PRD)确定最优模型。【结果】土壤光谱曲线分别在457、800和900 nm波段附近有明显的游离铁吸收和反射峰特征;土壤游离铁含量与原始光谱反射率呈负相关;原始光谱经过微分变换后,游离铁含量与光谱反射率相关性显著提高;基于400～580和760～1 300 nm特征波段和一阶微分光谱变换的SMLR模型预测精度最高,其验证集的R2和RPD分别为0.85和2.62,RMSE为8.41 g·kg~(-1)。【结论】将可见近红外光谱技术应用于土壤游离铁含量高效快速地预测具有良好的可行性。广西旱地土壤光谱反射率与土壤游离铁含量具有高度的相关性,应用逐步多元线性回归方法可以很好地建立土壤游离铁含量反演模型。     

Predicting cover crop biomass by lightweight UAS-based RGB and NIR photography: an applied photogrammetric approach

Easy-to-capture and robust plant status indicators are important factors when implementing precision agriculture techniques on fields. In this study, aerial red, green and blue color space (RGB) photography and near-infrared (NIR) photography was performed on an experimental field site with nine different cover crops. A lightweight unmanned aerial system (UAS) served as platform, consumer cameras as sensors. Photos were photogrammetrically processed to orthophotos and digital surface models (DSMs). In a first validation step, the spatial precision of RGB orthophotos (x and y, ± 0.1 m) and DSMs (z, ± 0.1 m) was determined. Then, canopy cover (CC), plant height (PH), normalized differenced vegetation index (NDVI), red edge inflection point (REIP), and green red vegetation index (GRVI) were extracted. In a second validation step, the PHs derived from the DSMs were compared with ground truth ruler measurements. A strong linear relationship was observed (R ² = 0.80?0.84). Finally, destructive biomass samples were taken and compared with the remotely-sensed characteristics. Biomass correlated best with plant height (PH), and good approximations with linear regressions were found (R ² = 0.74 for four selected species, R ² = 0.58 for all nine species). CC and the vegetation indices (VIs) showed less significant and less strong overall correlations, but performed well for certain species. It is therefore evident that the use of DSM-based PHs provides a feasible approach to a species-independent non-destructive biomass determination, where the performance of VIs is more species-dependent.     

Canopy reflectance, thermal stress, and apparent soil electrical conductivity as predictors of within-field variability in grain yield and grain protein of malting barley

Three consecutive crops of malting barley grown during 2002–2004 on clay-loam on a Swedish farm (59°74’ N, 17°00’ E) were monitored for canopy reflectance at growth stages GS32 (second node detectable) and GS69 (anthesis complete), and the crops were sampled for above ground dry matter and nitrogen content. GPS-positioned unfertilised plots were established and used for soil sampling. At harvest, plots of 0.25 m² were cut in both fertilised and unfertilised plots, and 24 m² areas were also harvested from fertilised barley. The correlations between nine different vegetation indices (VIs) from each growth stage and yield and grain protein were tested. All indices were significantly correlated (at 5% level) with grain yield (GY), and protein when sampled at GS69 but only four when sampled at GS32. Three variables (the best-correlated vegetation index sampled at GS32; an index for accumulated elevated daily maximum temperatures for the grain filling period, and normalised apparent electrical conductivity (ECa) of the soil) were sufficient input in the final regressions. Using these three variables, it was possible to make either one multivariate (PLS) regression model or two linear multiple regression models for grain yield (GY) and grain protein, with correlation coefficients of 0.90 and 0.73 for yield and protein, respectively.     

广东省无瓣海桑和林地土壤碳储量研究

【目的】探究广东省无瓣海桑Sonneratia apetala和林地土壤的碳储量,为开展广东省红树林生物量为基础的碳汇调查与监测提供基础数据,也为开展全国红树林碳汇监测提供经验和方法。【方法】以无瓣海桑及林地0～100 cm土壤为研究对象,构建适用于广东省范围内的无瓣海桑生物量模型,对比研究10个地区的无瓣海桑与林地土壤碳储量。【结果】无瓣海桑生物量模型为W=0.033(D_2H)~(1.002),决定系数为0.952,模型拟合效果较好。广东省无瓣海桑林的总面积为1 724.12 hm~2,总碳储量为536 801.09 t,植被碳密度为50.81 t·hm~(-2),土壤碳密度为260.54 t·hm~(-2),总碳密度为311.35 t·hm~(-2),植被碳密度为总碳密度的16.32%,土壤碳密度为总碳密度的83.68%。10个地区无瓣海桑林总碳储量依次为:深圳2 790.65 t潮州3 088.34 t惠州10 479.30 t江门13 800.58 t茂名17 116.43 t湛江55 610.15 t中山58 562.90 t汕头66 498.62 t广州134 938.18 t珠海173 915.93 t。【结论】广东省无瓣海桑林碳储量主要集中于土壤层,不同地区的立地条件不同,其土壤碳储量及植被碳储量差异明显。     

Widescale testing of the Crop-meter for site-specific farming

A sensor for measuring crop biomass density has been designed and developed to meet the demands for practical use in site-specific farming. The mechanical sensor named ‘Crop-meter’ is based on the pendulum principle. The suitability and measuring stability of the Crop-meter has been confirmed under field conditions in different regions of Germany. Significant correlations were obtained between Crop-meter signals and soil electrical conductivity (R ²=0.16−0.66) and grain yield (R ²=0.42−0.57). To test the suitability of the Crop-meter for site-specific management, it was used to control variable application rates for nitrogen fertiliser, growth regulators and fungicides in real time. A small increase in yield (3.1%) as well as reduced application rates for agrochemicals (14.6% nitrogen fertilisers; 23.1% fungicides and growth regulators) were proved in large-scale trials.     

Comparative analysis of three chemometric techniques for the spectroradiometric assessment of canopy chlorophyll content in winter wheat

Hyperspectral data sets contain useful information for characterizing vegetation canopies not previously available from multi-spectral data sources. However, to make full use of the information content one has to find ways for coping with the strong multi-collinearity in the data. The redundancy directly results from the fact that only a few variables effectively control the vegetation signature. This low dimensionality strongly contrasts with the often more than 100 spectral channels provided by modern spectroradiometers and through imaging spectroscopy. With this study we evaluated three different chemometric techniques specifically designed to deal with redundant (and small) data sets. In addition, a widely used 2-band vegetation index was chosen (NDVI) as a baseline approach. A multi-site and multi-date field campaign was conducted to acquire the necessary reference observations. On small subplots the total canopy chlorophyll content was measured and the corresponding canopy signature (450-2500 nm) was recorded (n_obs = 42). Using this data set we investigated the predictive power and noise sensitivity of stepwise multiple linear regression (SMLR) and two ‘full spectrum’ methods: principal component regression (PCR) and partial least squares regression (PLSR). The NDVI was fitted to the canopy chlorophyll content using an exponential relation. For all techniques, a jackknife approach was used to obtain cross-validated statistics. The PLSR clearly outperformed all other techniques. PLSR gave a cross-validated RMSE of 51 mg m⁻² for canopy chlorophyll contents ranging between 38 and 475 mg m⁻² (0.99 ≤ LAI ≤ 8.74 m² m⁻²). The lowest accuracy was achieved using PCR (RMSE_cv = 82 mg m⁻² and ). The NDVI, even using chlorophyll optimized band settings, could not reach the accuracy of PLSR. Regarding the sensitivity to artificially created (white) noise, PCR showed some advantages, whereas SMLR was the most sensitive chemometric technique. For relatively small, highly multi-collinear data sets the use of partial least square regression is recommended. PLSR makes full use of the rich spectral information while being relatively insensitive to sensor noise. PLSR provides a regression model where the entire spectral information is taken - in a weighted form - into account. This method seems therefore much better adapted to deal with potentially confounding factors compared to any 2-band vegetation index which can only avoid the most harmful factor of variation.     

基于双波段光谱仪CGMD-302的小麦叶面积指数和叶干重监测

为探究双波段光谱仪CGMD-302在监测小麦长势上的可靠性与精准性,同时使用高光谱仪UniSpec SC与双波段光谱仪CGMD-302测试各生育时期小麦冠层信息,并定量分析了植被指数NDVI、RVI、DVI与叶面积指数和叶片干重之间的线性关系。结果表明,基于相同波段反射率计算出的高光谱仪植被指数和双波段光谱仪植被指数均能较好监测小麦群体长势。在CGMD-302监测的叶面积指数模型中,拟合方程的决定系数(R~2)均高于0.89,用以检验模型的均方根误差(RMSE)和相对误差(RE)分别小于0.792和0.225;叶片干重模型中,决定系数(R2)均高于0.85,用以检验模型的均方根误差(RMSE)和相对误差(RE)分别小于440kg/hm~2和0.239。通过分析发现,施氮270kg/hm~2既能保证产量又能兼顾品质,可作为适宜施氮量。适宜施氮量下,拔节期和孕穗期小麦适宜叶面积指数分别为:3.65±0.09和5.95±0.32;适宜叶干重分别为:(1 554±168)和(2 231±130)kg/hm~2。结合CGMD-302监测模型可推算出拔节期和孕穗期适宜冠层群体的植被指数区间并应用于冠层群体诊断。     

Comparison of passive and active canopy sensors for the estimation of vine biomass production

Recent advances in optical designs and electronic circuits have allowed the transition from passive to active proximal sensors. Instead of relying on the reflectance of natural sunlight, the active sensors measure the reflectance of modulated light from the crop and so they can operate under all lighting conditions. This study compared the potential of active and passive canopy sensors for predicting biomass production in 25–32 randomly selected positions of a Merlot vineyard. Both sensors provided estimates of the normalized difference vegetation index (NDVI) from a nadir view of the canopy at veraison that were good predictors of pruning weight. Although the red NDVI of the passive sensors explained more of the variation in biomass (R ² = 0.82), its relationship to pruning weight was nonlinear and was best described by a quadratic regression (NDVI = 0.55 + 0.50 wt−0.21 wt²). The theoretically greater linearity of the amber NDVI-biomass relationship could not be verified under conditions of high biomass. The linear correlation to stable isotope content in leaves (¹³C and ¹⁵N) provided evidence that canopy reflectance detected plant stresses as a result of water shortage and limited fertilizer N uptake. Thus, the canopy reflectance data provided by these mobile sensors can be used to improve site-specific management practices of vineyards.     

Assessment of forage mass from grassland swards by height measurement using an ultrasonic sensor

The non-destructive assessment of forage mass in legume-grass mixtures as a tool for yield mapping in precision farming applications has been investigated in two field experiments. An ultrasonic sensor was used to determine sward heights. Forage mass-height relationships were evaluated by carrying out static measurements on binary legume-grass mixtures of white clover (Trifolium repens L.), red clover (Trifolium pratense L.), and lucerne (Medicago sativa L.) with perennial rye grass (Lolium perenne L.) across a wide range of sward heights (5.0-104.2 cm) and forage mass (0.15-11.25 t ha⁻¹). Mobile measurements, hereafter referred to as “on-the-go” were conducted by mounting the ultrasonic sensor in combination with a high-precision Differential Global Positioning System (DGPS) on a vehicle. Data were recorded along experimental plots consisting of perennial rye grass and grass-clover mixtures similar to the mixtures that were used for the static experiment. The static experiment revealed a relationship between ultrasonic sward height and forage mass explaining 74.8% of the variance with a standard error (SE) of 1.05 t ha⁻¹ in a common dataset. The type of legume species, weed proportion, and growth period had a significant impact on the above mentioned relationship. Legume-specific regression functions had higher R²-values of up to 0.855 (white clover mixture). Datasets including legume-specific mixtures and pure swards of both components reached comparable R² values between 0.799 and 0.818 but exhibited higher SE values. The abundance of weeds resulted in increased ultrasonic sward heights for the same levels of forage mass. On-the-go measurements across experimental field plots yielded a sward height range of 1.4-70.4 cm. Abrupt forage mass changes at the transition from treatment plots to cut interspaces resulted in a significant deviation from stubble height within a distance of 50 cm to plot borders. When legume-specific equations derived from static measurements were applied to sward heights, forage mass was overestimated by 21.4% on average. Mean residuals from predicted forage mass ranged between 0.893 (pure grass) and 1.672 (red clover mixture) and increased significantly if the point sampling distance along the track was increased to more than 0.82 m on average across all plots. The prediction accuracy of forage mass from ultrasonic height measurements is promising; however, further modifications to the technique are necessary. One such improvement can be the use of spectral reflectance signatures in combination with the ultrasonic sensor.     

基于高光谱遥感的叶片总初级生产力GPP反演

为探究利用高光谱植被指数反演叶片总初级生产力(GPP)的模型,以湖北省武汉大学试验田油菜和小麦叶片高光谱反射率和光照强度(PARin)为数据源,利用7种植被指数与PARin的乘积分别反演2种植被叶片GPP,构建线性及非线性回归模型,并对模型进行验证。结果表明:1)从油菜生理特点出发,需要分生育期建模。在选择的7种植被指数中,花期SR构建的一次模型效果最优,建模和验模R2分别为0.80和0.82,RMSE不超过2.85g/(m~2·d);荚果期选择CIred edge和MTCI为优选模型,建模和验模R2为0.84和0.72,RMSE3.91g/(m~2·d);全时期基于红边波段的CIred edge、MTCI为优选模型,建模集R2达到0.80,RMSE3.67g/(m~2·d),验模R2达到0.65,RMSE3.92g/(m~2·d);2)小麦中NDVI模型效果最优,建模集R2=0.59,RMSE=2.80g/(m~2·d),验模R2=0.67,RMSE=3.39g/(m~2·d)。将油菜与小麦做对比,基于红边波段的植被指数CIred edge和MTCI对2种植被差异不敏感,R2为0.72~0.73,表明CIred edge和MTCI模型可以用于小麦和油菜叶片GPP的统一反演。     

采伐干扰对大兴安岭落叶松-苔草沼泽植被碳储量的影响

运用采伐干扰试验与树干解析法,对比分析了大兴安岭不同采伐强度(未采伐——对照、轻度择伐——25%、中度择伐——35%、强度择伐——50%)下落叶松-苔草沼泽的植被生物量、碳含量、碳储量、净初级生产力及年净固碳量的变化,揭示采伐干扰(5a后)对落叶松-苔草沼泽植被碳储量及固碳能力的影响规律。结果表明:①不同采伐强度样地植被生物量为(135.03±7.72)-(204.71±1.71) t/hm²,择伐使其降低了8.7%-34.0% (P<0.05),且呈现出随择伐强度增大而递减的变化规律;②择伐使群落建群种兴安落叶松和白桦(两树种各组分碳含量为(439.05±9.70)-(508.41±27.09) g/kg的树干和树叶碳含量降低了4.1%-11.7% (P<0.05),轻度和强度择伐使灌木层(444.87±5.40)-(472.52±9.44) g/kg与凋落物层(433.64±16.23)-(468.82±21.27) g/kg的碳含量降低了3.8%-5.9%和6.0%-7.5% (P<0.05),但择伐对草本层碳含量(399.34±83.65)-(419.20±23.75) g/kg无显著影响;③不同采伐强度样地植被碳储量为(61.16±0.67)-(99.61±1.47) t·C/hm²,择伐使其降低了15.5%-38.6% (P<0.05),且呈现随择伐强度增大而递减的变化规律;④不同采伐强度样地植被净初级生产力与年净固碳量在(6.48±0.28)-(11.87±0.92) t·hm^-2·a^-1和(3.52±0.21)-(6.29±0.92) t·C·hm^-2·a^-1之间,轻度和中度择伐使两者提高了69.1%-83.2%和52.0%-78.7% (P<0.05)。因此,轻度择伐和中度择伐能够提高落叶松-苔草沼泽净初级生产力与碳吸纳能力。     

Phenotyping a diversity panel of quinoa using UAV-retrieved leaf area index,SPAD-based chlorophyll and a random forest approach

Given its high nutritional value and capacity to grow in harsh environments, quinoa has significant potential to address a range of food security concerns. Monitoring the development of phenotypic traits during field trials can provide insights into the varieties best suited to specific environmental conditions and management strategies. Unmanned aerial vehicles (UAVs) provide a promising means for phenotyping and offer the potential for new insights into relative plant performance. During a field trial exploring 141 quinoa accessions, a UAV-based multispectral camera was deployed to retrieve leaf area index (LAI) and SPAD-based chlorophyll across 378 control and 378 saline-irrigated plots using a random forest regression approach based on both individual spectral bands and 25 different vegetation indices (VIs) derived from the multispectral imagery. Results show that most VIs had stronger correlation with the LAI and SPAD-based chlorophyll measurements than individual bands. VIs including the red-edge band had high importance in SPAD-based chlorophyll predictions, while VIs including the near infrared band (but not the red-edge band) improved LAI prediction models. When applied to individual treatments (i.e. control or saline), the models trained using all data (i.e. both control and saline data) achieved high mapping accuracies for LAI (R²?=?0.977–0.980, RMSE?=?0.119–0.167) and SPAD-based chlorophyll (R²?=?0.983–0.986, RMSE?=?2.535–2.861). Overall, the study demonstrated that UAV-based remote sensing is not only useful for retrieving important phenotypic traits of quinoa, but that machine learning models trained on all available measurements can provide robust predictions for abiotic stress experiments.

     

In-field hyperspectral proximal sensing for estimating quality parameters of mixed pasture

A study was conducted to explore the potential use of a hand-held (proximal) hyperspectral sensor equipped with a canopy pasture probe to assess a number of pasture quality parameters: crude protein (CP), acid detergent fibre (ADF), neutral detergent fibre (NDF), ash, dietary cation–anion difference (DCAD), lignin, lipid, metabolisable energy (ME) and organic matter digestibility (OMD) during the autumn season 2009. Partial least squares regression was used to develop a relationship between each of these pasture quality parameters and spectral reflectance acquired in the 500–2 400 nm range. Overall, satisfactory results were produced with high coefficients of determination (R ²), Nash–Sutcliffe efficiency (NSE) and ratio prediction to deviation (RPD). High accuracy (low root mean square error-RMSE values) for pasture quality parameters such as CP, ADF, NDF, ash, DCAD, lignin, ME and OMD was achieved; although lipid was poorly predicted. These results suggest that in situ canopy reflectance can be used to predict the pasture quality in a timely fashion so as to assist farmers in their decision making.     

Monitoring nitrogen,phosphorus and sulphur in hybrid rice (<Emphasis Type="Italic">Oryza sativa</Emphasis> L.) using hyperspectral remote sensing

Till date, the remote sensing research on crop nutrient monitoring has focused mainly on biomass and nitrogen (N) estimation and only a few attempts had been made to characterize and monitor macronutrients other than N. Field experiments were undertaken to study the remote detection of macronutrient status of rice using hyperspectral remote sensing. The variability in soil available N, phosphorus (P) and sulphur (S) and their content in plants were created using artificial fertility gradient design. The leaf and canopy hyperspectral reflectance was captured from variable macronutrient status vegetation. Linear correlation analysis between the spectral reflectance and plant nutrient status revealed significantly (p < 0.05) higher correlation coefficient at 670, 700, 730, 1090, 1260, 1460 nm for the nutrient under study. Published and proposed vegetation indices (VIs) were tested for canopy N, P and S prediction. The results of the investigation revealed that, published VIs (NDVI hyper and NDVI broadbands) could retrieve canopy N with higher accuracy, but not P and S. The predictability of the visible and short wave infrared based VI NRI₁₅₁₀ ((R₁₅₁₀ ? R₆₆₀)/(R₁₅₁₀ + R₆₆₀)) was the highest (r = 0.81, p < 0.01) for predicting N. Based on the outcomes of linear correlation analysis new VIs were proposed for remote detection of P and S. Proposed VI P_670_1260 ((R₁₂₆₀ ? R₆₇₀)/(R₁₂₆₀ + R₆₇₀)) retrieved canopy P status with higher prediction accuracy (r = 0.67, p < 0.01), whereas significantly higher canopy S prediction (r = 0.58, p < 0.01) was obtained using VI S_670_1090 ((R₁₀₉₀ ? R₆₇₀)/(R₁₀₉₀ + R₆₇₀)). The proposed spectral algorithms could be used for real time and site-specific N, P and S management in rice. Nutrient specific wavelengths, identified in the present investigation, could be used for developing relatively low-cost sensors of hand-held instruments to monitor N, P and S status of rice plant.     

基于环境卫星数据的黄河湿地植被生物量反演研究

回归模型拟合植被指数与生物量的定量关系是植被生物量反演的重要研究方法之一.研究在此基础上,基于环境卫星遥感数据和同步野外实地采样数据,以郑州黄河湿地自然保护区为试验区,比较MLRM(多元线性回归模型)与SCRM(一元曲线回归模型)反演植被生物量的能力,并估算研究区植被生物量,生成研究区生物量分布图.结果表明,文中所建立的MLRM在研究区具有较好的反演精度和预测能力.其模型显著性检验为极显著,相关系数为0.9791,模型拟合精度达到29.8 g/m2,其模型预测结果系统误差为49.9g/m2,均方根误差为67.2 g/m2,预测决定系数为0.8742,比传统的一元回归模型具有更高的精度和可靠性.估算研究区域2010年8月湿生植被生物量约为6.849199 t/hm2,相对误差为4.73％.     

Application of day and night digital photographs for estimating maize biophysical characteristics

In this study, an inexpensive camera-observation system called the Crop Phenology Recording System (CPRS), which consists of a standard digital color camera (RGB cam) and a modified near-infrared (NIR) digital camera (NIR cam), was applied to estimate green leaf area index (LAI), total LAI, green leaf biomass and total dry biomass of stalks and leaves of maize. The CPRS was installed for the 2009 growing season over a rainfed maize field at the University of Nebraska-Lincoln Agricultural Research and Development Center near Mead, NE, USA. The vegetation indices called Visible Atmospherically Resistant Index (VARI) and two green–red–blue (2g–r–b) were calculated from day-time RGB images taken by the standard commercially-available camera. The other vegetation index called Night-time Relative Brightness Index in NIR (NRBI_NIR) was calculated from night-time flash NIR images taken by the modified digital camera on which a NIR band-pass filter was attached. Sampling inspections were conducted to measure bio-physical parameters of maize in the same experimental field. The vegetation indices were compared with the biophysical parameters for a whole growing season. The VARI was found to accurately estimate green LAI (R² = 0.99) and green leaf biomass (R² = 0.98), as well as track seasonal changes in maize green vegetation fraction. The 2g–r–b was able to accurately estimate total LAI (R² = 0.97). The NRBI_NIR showed the highest accuracy in estimation of the total dry biomass weight of the stalks and leaves (R² = 0.99). The results show that the camera-observation system has potential for the remote assessment of maize biophysical parameters at low cost.