Portable Field Spectroradiometer Research Articles

Full-band, multi-angle, multi-scale, and temporal dynamic field spectral measurements in China

Field-measured spectra are critical for remote sensing physical modelling, retrieval of structural, biophysical, and biochemical parameters, and other practical applications. We present a library of field spectra, which includes (1) portable field spectroradiometer measurements of vegetation, soil, and snow in the full-wave band, (2) multi-angle spectra measurements of desert vegetation, chernozems, and snow with consideration of the anisotropic reflectance of land surface, (3) multi-scale spectra measurements of leaf and canopy of different vegetation cover surfaces, and (4) continuous reflectance spectra time-series data revealing vegetation growth dynamics of maize, rice, wheat, rape, grassland, and so on. To the best of our knowledge, this library is unique in simultaneously providing full-band, multi-angle, multi-scale spectral measurements of the main surface elements of China covering a large spatial extent over a 10-year period. Furthermore, the 101 by 101 satellite pixels of Landsat ETM/OLI and MODIS surface reflectance centered around the field site were extracted, providing a vital linkage between ground measurements and satellite observations. The code language used for this work is Matlab 2016a.

Unmanned Aerial Vehicle (UAV)–Based Imaging Spectroscopy for Predicting Wheat Leaf Nitrogen

Quantitative estimation of crop nitrogen is the key to site-specific management for enhanced nitrogen (N) use efficiency and a sustainable crop production system. As an alternate to the conventional approach through wet chemistry, sensor-based noninvasive, rapid, and near-real-time assessment of crop N at the field scale has been the need for precision agriculture. The present study attempts to predict leaf N of wheat crop through spectroscopy using a field portable spectroradiometer (spectral range of 400–2500 nm) on the ground in the crop field and an imaging spectrometer (spectral range of 400–1000 nm) from an unmanned aerial vehicle (UAV) with the objectives to evaluate (1) four multivariate spectral models (i.e., artificial neural network, extreme learning machine [ELM], least absolute shrinkage and selection operator, and support vector machine regression) and (2) two sets of hyperspectral data collected from two platforms and two different sensors. In the former part of the study, ELM outperforms the other methods with maximum calibration and validation R2 of 0.99 and 0.96, respectively. Furthermore, the image data set acquired from UAV gives higher performance compared to field spectral data. Also, significant bands are identified using stepwise multiple linear regression and used for modeling to generate a wheat leaf N map of the experimental field.

Developing a p-NDVI Map for Highland Kimchi Cabbage Using Spectral Information from UAVs and a Field Spectral Radiometer

Kimchi cabbage grows in South Korea and is an essential ingredient for making kimchi with the kimjang method. The technique of accurately managing and monitoring crops such as kimchi cabbage plays an important role in stabilizing consumer prices. Unmanned aerial vehicles (UAVs) are expected to be used more widely in global and local agriculture. The agricultural sites at which kimchi cabbages are cultivated are affected by various climatic, terrain, and soil conditions, requiring technologies that can accurately and quickly acquire such information. UAVs and sensors are able to provide some of these data. In this study, we set up a cultivation environment for kimchi cabbage and investigated the correlation between a UAV-attached multispectral sensor and a field-portable spectroradiometer. Reflectance measurement using a spectroradiometer was performed on 99 kimchi cabbages in a Mt. Maebong testbed. We aimed to find a method for obtaining accurate vegetation information by combining the high spatial and temporal resolution information of the UAV observation with the spectral resolution of the spectroradiometer. Spectral analysis was used to identify the difference between healthy and poorly growing cabbage and to find the wavelength that most affected the growth. The hyperspectrum of the spectroradiometer reflected the accurate vegetation characteristics and contributed greatly to the identification of vegetation indices. A method for correcting the errors that occurred in the ground and UAV monitoring and the difference arising from the application of the broadband wavelength of the UAV and the single wavelength of the spectroradiometer through correlation analysis is presented. The calibration equation method was applied to UAV spatial information and was used to create a precise normalized distribution vegetation index (p-NDVI) map. The p-NDVI map was organized into four categories for the selection of cabbages with healthy (good) growth. Our results show that (1) the merged spectral analysis method was found to be more accurate and distinct than conventional methods, and (2) methods for estimating cabbage growth status showed a higher significant correlation than the UAV-based NDVI. At the maturity stage, high accuracy (R2 = 0.7816, RMSE = 0.06) was achieved for NDVI. Although this map is the result of the limited vegetation monitoring of UAV images taken during the maturity stage, it could be of great help for managing the quality and production of cabbage. However, the efficient management of highland kimchi cabbage requires continuous research under various conditions to enable periodic and systematic monitoring using UAVs and sensors.

Identification of Water and Nitrogen Stress Indicative Spectral Bands Using Hyperspectral Remote Sensing in Maize During Post-Monsoon Season

Realization of agricultural crop condition through field survey is quite expensive, time consuming and sometimes not practical for remote locations. Optical remote sensing techniques can provide information on real condition of the crops by observing spectral reflectance at different crop growth phases and is less expensive and less time consuming. Hyperspectral remote sensing provides a unique opportunity for non-destructive, timely and accurate estimation of crop biophysical and biochemical properties. In this study, a field experiment was conducted to identify the water and nitrogen stress indicative spectral bands using ground-based hyperspectral data and to assess the predictive capability of selective bands on yield of maize under water and nitrogen stress environment. The experiment comprised of three irrigation scheduling treatments based on IW/CPE ration of 0.6, 0.8 and 1.2 and three nitrogen level treatments, i.e., 100, 200 and 300 kg of N ha−1, respectively, with three replications in a split plot design. The spectral reflectance was measured before irrigation at tasseling and dough stage of the maize crop using portable field spectroradiometer. The results of stepwise multiple linear regression indicated the highest predicting capability of spectral bands 540 nm, 780 nm and 860 nm for leaf nitrogen and 700 nm, 740 nm and 860 nm for leaf water content. The derived biophysical parameters based on spectral reflectance viz. relative leaf water content (%), leaf area index and leaf nitrogen contentment (%) at tasseling stage of maize crop accounted for 80%, 61% and 66% variation in grain yield, respectively.

Laboratory, field, mast-borne and airborne spectral reflectance measurements of boreal landscape during spring

Abstract. We publish and describe a surface spectral reflectance data record of seasonal snow (dry, wet, shadowed), forest ground (lichen, moss) and forest canopy (spruce and pine, branches) constituting the main elements of the boreal landscape. The reflectances are measured with spectro(radio)meters covering the wavelengths from visible (VIS) to short-wave infrared (SWIR) (350 to 2500 nm). In this paper, we describe the instruments used and how the spectral observations at different scales along with the concurrent in situ reference data have been collected, processed and archived. Information on the quality of the data and factors causing uncertainty are discussed. The main experimental site is located in the Sodankylä Arctic Space Centre in northern Finland (67.37∘ N, 26.63∘ E; 179 m a.s.l) and the surrounding region. The collection includes highly controlled snow and conifer branch laboratory spectral measurements, portable field spectroradiometer observations of snow and snow-free ground at different locations, and continuous mast-borne reflectance time series data of a pine forest and forest opening. In addition to the surface level spectral reflectance, data from airborne imaging spectrometer campaigns over the Sodankylä boreal forest and Saariselkä fell region at selected spectral bands are included in the collection. All measurements of the data record correspond to a typical polar-orbiting satellite observation event in the high-latitude spring season regarding their Sun or illumination source (calibrated lamp) zenith angle and close-to-nadir instrument viewing angle. For all measurement geometries, observations are given in surface reflectance quantity corresponding to the typical representation of a satellite observation quantity to facilitate their comparison with other data sources. The openly accessible spectral reflectance data at multiple scales are suitable to climate and hydrological research and remote sensing model validation and development. To facilitate easy access to the data record the four datasets described here are deposited in a permanent data repository (http://www.zenodo.org/communities/boreal_reflectances/) (Hannula et al., 2019). Each dataset of a distinct scale has its own unique DOI – laboratory: https://doi.org/10.5281/zenodo.3580078 (Hannula and Heinilä, 2018a); field: https://doi.org/10.5281/zenodo.3580825 (Heinilä et al., 2019a); mast-borne: https://doi.org/10.5281/zenodo.3580096 (Hannula and Heinilä, 2018b); and airborne: https://doi.org/10.5281/zenodo.3580451 (Heinilä, 2019a) and https://doi.org/10.5281/zenodo.3580419 (Heinilä, 2019b).

Inherent Reflectance Variability of Vegetation

With the inception of small unmanned aircraft systems (sUAS), remotely sensed images have been captured much closer to the ground, which has meant better resolution and smaller ground sample distances (GSDs). This has provided the precision agriculture community with the ability to analyze individual plants, and in certain cases, individual leaves on those plants. This has also allowed for a dramatic increase in data acquisition for agricultural analysis. Because satellite and manned aircraft remote sensing data collections had larger GSDs, self-shadowing was not seen as an issue for agricultural remote sensing. However, sUAS are able to image these shadows which can cause issues in data analysis. This paper investigates the inherent reflectance variability of vegetation by analyzing six Coneflower plants, as a surrogate for other cash crops, across different variables. These plants were measured under different forecasts (cloudy and sunny), at different times (08:00 a.m., 09:00 a.m., 10:00 a.m., 11:00 a.m. and 12:00 p.m.), and at different GSDs (2, 4 and 8 cm) using a field portable spectroradiometer (ASD Field Spec). In addition, a leafclip spectrometer was utilized to measure individual leaves on each plant in a controlled lab environment. These spectra were analyzed to determine if there was any significant difference in the health of the various plants measured. Finally, a MicaSense RedEdge-3 multispectral camera was utilized to capture images of the plants every hour to analyze the variability produced by a sensor designed for agricultural remote sensing. The RedEdge-3 was held stationary at 1.5 m above the plants while collecting all images, which produced a GSD of 0.1 cm/pixel. To produce 2, 4, and 8 cm GSD, the MicaSense RedEdge-3 would need to be at an altitude of 30.5 m, 61 m and 122 m respectively. This study did not take background effects into consideration for either the ASD or MicaSense. Results showed that GSD produced a statistically significant difference (p < 0.001) in Normalized Difference Vegetation Index (NDVI, a commonly used metric to determine vegetation health), R 2 values demonstrated a low correlation between time of day and NDVI, and a one-way ANOVA test showed no statistically significant difference in the NDVI computed from the leafclip probe (p-value of 0.018). Ultimately, it was determined that the best condition for measuring vegetation reflectance was on cloudy days near noon. Sunny days produced self-shadowing on the plants which increased the variability of the measured reflectance values (higher standard deviations in all five RedEdge-3 channels), and the shadowing of the plants decreased as time approached noon. This high reflectance variability in the coneflower plants made it difficult to accurately measure the NDVI.

Prediction of Soil Carbon Fractions Using a Field Spectroradiometer Equipped with an Illuminating Contact Probe

This research compared the accuracy of laboratory reference measurements of soil C and N fractions with soil reflectance spectra acquired using a portable field spectroradiometer with an illuminating contact probe. Soil samples were taken from eight, 1.6 ha watersheds, located in El Reno, Oklahoma on native warm season grasslands and agronomic managements with landform complexes serving as replicates within and among treatments. Soil samples were taken from 0–30-cm. Measurements included total soil organic carbon (TSOC), total soil nitrogen (TSN), residual C of acid hydrolysis (RCAH), and particulate organic matter C (POMC) and N (POMN). Soil reflectance in the 350 to 2500 nm region was correlated with individual laboratory measurements. Each reference dataset was divided into model development data (70%) and model validation data (30%). Calibrated models were applied to validation datasets. Statistical analysis revealed that prediction efficiencies of soil reflectance models were highly quantitative. Coefficients of determination (R2) were near 1 (≥0.90) and ratios of predicted values to the measured standard deviation (RPD) were >2, indicative of good predictive models. The field spectroradiometer enabled us to parameterize soil spatial variability and soil reflectance measurements, reducing the resources required to acquire edaphic measurements.

Assessing Nitrogen and water status of winter wheat using a digital camera

In this research, winter wheat Nitrogen and water status assessment by a digital camera was studied. Field experiments were conducted during growing seasons, 2012, 2013 and 2014 in Potsdam, Germany. Different treatments of N fertilization and water regimes were applied to the plant. Aboveground biomass was sampled three times during each season, and analyzed for plant nitrogen content, fresh and dry biomasses, and water content. Color images of the plant canopy were taken in the field by a digital camera. The images were then processed and various features were derived. Multivariable models using the features and two different algorithms, Partial Least Square Regression (PLSR) and Random Forest (RF) were developed. The models were built for data of single sampling dates and combination of sampling dates of each season. In addition, spectral measurements were taken by a portable field spectroradiometer and a few vegetation indices (VIs) were calculated. Linear regression models were built between the VIs and the plant parameters. According to the results obtained, PLSR presented the strongest models for both single-date and combined-dates data. R2 (corresponding RMSE) of the models for N content and water content varied in the range of 0.77–0.91 (0.25–0.14), and 0.53–0.95 (0.75–0.36), respectively, for single-date data, and 0.76–0.89 (0.24–0.15), and 0.65–0.92 (1.49–1.66), respectively, for combined-dates data. Performance of RF was better for combined-dates than single-date data. The conclusion is that a combination of digital image processing with an appropriate machine learning method has a high potential for plant growth status assessment in the field.

A robust vegetation index for remotely assessing chlorophyll content of dorsiventral leaves across several species in different seasons

BackgroundLeaf chlorophyll content (LCC) provides valuable information about plant physiology. Most of the published chlorophyll vegetation indices at the leaf level have been based on the spectral characteristics of the adaxial leaf surface, thus, they are not appropriate for estimating LCC when both the adaxial and abaxial leaf surfaces influence the spectral reflectance. We attempted to address this challenge by measuring the spectral reflectance of the adaxial and abaxial leaf surfaces of several plant species at different growth stages using a portable field spectroradiometer. The relationships between more than 30 published reflectance indices with LCC were analyzed to determine which index estimated LCC most effectively. Additionally, since the relationships determined on one set of samples might have poor predictive performances when applied to other samples, a robust wavelength region is required to render the spectral index generally applicable, regardless of the leaf surface or plant species.ResultsThe Modified Datt (MDATT) index, which is the ratio of reflectance difference defined as (Rλ3 − Rλ1)/(Rλ3 − Rλ2), exhibited the strongest correlation (R2 = 0.856, RMSE = 6.872 μg/cm2), with LCC of all the indices tested when all the leaf samples from the adaxial and abaxial surfaces were combined. The optimal wavelength regions, which were derived from the contour maps of R2 between the MDATT index and LCC for the datasets of one side or both leaf surfaces of each plant species and their intersection, indicated that the red-edge to near-infrared wavelength (723–885 nm) was optimal for λ1, while the red-edge region (697–771 nm) was optimal for λ2 and λ3. In these optimal wavelength regions, when the MDATT index was used to estimate LCC, an R2 higher than 0.8 could be obtained. The correlation of the MDATT index with LCC was the same when the positions of λ2 and λ3 were exchanged in the index.ConclusionsMDATT is proposed as an optimal index for the remote estimation of vegetation chlorophyll content across several plant species in different growth stages when reflectance from both leaf surfaces is considered. The red-edge to near-infrared wavelength (723–885 nm) for λ1, as well as the red-edge region (697–771 nm) for λ2 or λ3, are considered to be the most robust for constructing the MDATT index for estimating LCC, regardless of the leaf surface or plant species.

Hyperspectral Monitoring of Green Roof Vegetation Health State in Sub-Mediterranean Climate: Preliminary Results.

In urban and industrial environments, the constant increase of impermeable surfaces has produced drastic changes in the natural hydrological cycle. Decreasing green areas not only produce negative effects from a hydrological-hydraulic perspective, but also from an energy point of view, modifying the urban microclimate and generating, as shown in the literature, heat islands in our cities. In this context, green infrastructures may represent an environmental compensation action that can be used to re-equilibrate the hydrological and energy balance and reduce the impact of pollutant load on receiving water bodies. To ensure that a green infrastructure will work properly, vegetated areas have to be continuously monitored to verify their health state. This paper presents a ground spectroscopy monitoring survey of a green roof installed at the University of Calabria fulfilled via the acquisition and analysis of hyperspectral data. This study is part of a larger research project financed by European Structural funds aimed at understanding the influence of green roofs on rainwater management and energy consumption for air conditioning in the Mediterranean area. Reflectance values were acquired with a field-portable spectroradiometer that operates in the range of wavelengths 350–2500 nm. The survey was carried out during the time period November 2014–June 2015 and data were acquired weekly. Climatic, thermo-physical, hydrological and hydraulic quantities were acquired as well and related to spectral data. Broadband and narrowband spectral indices, related to chlorophyll content and to chlorophyll–carotenoid ratio, were computed. The two narrowband indices NDVI705 and SIPI turned out to be the most representative indices to detect the plant health status.

Simultaneous identification of spring wheat nitrogen and water status using visible and near infrared spectra and Powered Partial Least Squares Regression

The common method of estimating N demand in cereals by spectral measurements may be negatively affected by variation in other crop properties, in particular by crop water status. In this study, we tested whether it is possible to distinguish between N and water status in spring wheat, at the time of split fertilization (plant growth stage – BBCH 32), by means of spectral reflectance data obtained from a sensor with an effective spectral range of 400–950nm (i.e. without utilizing the distinct short-wave infrared (SWIR) water bands, which requires a more costly sensor). In 2012 and 2013 we ran a spring wheat field experiment in SE Norway. The experiment comprised 36 treatment plots arranged in a split-plot design, with N fertilization on major plots (either 70 or 100kgNha−1 applied at sowing) and water regime on sub-plots (either limited water supply, natural (rain-fed) water supply, or natural water supply+irrigation). Canopy reflectance was measured on all plots at BBCH 32, using a portable field spectroradiometer (tec5 AG, Germany). Immediately afterwards, aboveground wheat biomass was sampled from 0.25m2 quadrats on each plot, and analyzed for total N, fresh and dry matter. The spectral data were first pre-processed by logarithm linearization, 1st derivative filtering using the Savitzky–Golay method and mean normalization. Principal Component Analysis (PCA) performed at the treatment level (i.e. not utilizing ground truth measurements) showed that the first component in all datasets (2012, 2013 and combined) was related to the water regime. The second component in the single year datasets (or the third in the combined dataset) was related to N fertilizer. When combining the spectral information and the ground truth data by means of Powered Partial Least Squares Regression (PPLS), we were able to calibrate models (combined dataset) which fitted well with measured nitrogen at ratio of performance to deviation: RPD=2.52 (R2=0.86) and water concentration at RPD=2.35 (R2=0.84) in aboveground spring wheat biomass. The multivariate calibration method revealed several distinct spectral regions containing information related to either wheat plant N or water concentration. This method was superior to an index-based approach, with the best model performance of RPD=2.26 (R2=0.83) and RPD=1.49 (R2=0.68) for N and water concentration, respectively.

Sugar beet (Beta vulgaris L.) and thistle (Cirsium arvensis L.) discrimination based on field spectral data

Creeping thistle (Cirsium arvensis (L.) Scop.) is a perennial weed that causes yield loss in sugar beet (Beta vulgaris L.) crops. The weeds are usually mapped for site specific weed management because they tend to grow in patches. Remote sensing techniques have shown promising results in species discrimination and therefore provide potential for weed mapping. In this study we examined the feasibility of high-resolution imaging for sugar beet and thistle discrimination and proposed a protocol to select multispectral camera filters. Spectral samples from sugar beet and thistle were acquired with a field portable spectroradiometer under field conditions and Partial Least Squares Discriminant Analysis (PLS-DA) classification models were developed with 211 and 36 spectral features of 1.56 and 10 nm bandwidths, respectively. The classification rates obtained using these models were regarded as the maximum obtainable. Then, spectral responses of a multi-band camera equipped with the filter configuration proposed by the PLS-DA models were simulated. Finally, a simulation of crop-weed discrimination was made using small unmanned aerial vehicles (UAV)-based multispectral images. More than 95% of the thistles and 89% of the sugar beets were correctly classified when continuous spectral data were used with 1.56 and 10 nm bandwidths. Accuracy dropped to 93% of thistles identified and 84% of sugar beets correctly classified when only the four best bands were used. The validation based on aerial images showed that sugar beets and thistle plants could be discriminated in images if sufficient pure pixels containing leaf spectra were available, that is with spatial resolutions of 6 mm pixel−1 or finer.

Spectral detection of near-surface moisture content and water-table position in northern peatland ecosystems

Wildland fire occurrence has been increasing in peatland ecosystems during recent decades. As such, there is a need for broadly applicable tools to detect and monitor controls on combustion such as surface peat moisture and water-table position. A field portable spectroradiometer was used to measure surface reflectance of two Sphagnum moss-dominated peatland experiments, one being an experimental study utilizing a factorial experiment of vegetation and water-table manipulations and the other being a field site located in the Upper Peninsula of Michigan that is broadly representative of northern peatland ecosystems. Relationships were developed correlating spectral indices to surface moisture as well as water-table position using the surface reflectance data. Spectral convolutions, or integrations of the hyperspectral data with the relative spectral response curves of multispectral sensors, were also applied to represent spectral sensitivity of commonly used Earth observing sensors. Band ratios previously used to monitor surface moisture with these sensors were assessed. Strong relationships with surface moisture and water-table position are evident for both the narrow-band indices as well as broad-band indices. This study also revealed a dependence of spectral relationships on changes in vegetation cover, particularly ericoid shrubs, within the vegetation manipulation experiment.

Investigation of potential integration of spectroradiometer data with GIS technology: The Spectro-GIS tools

The Earth's surface consists of different ground cover types. The spectral signature of these ground cover targets is unique and can be determined in the field through quantitative measurement of radiance and reflectance response by portable spectroradiometers. In this study, a field portable spectroradiometer, the GER 1500, covering the Ultraviolet, Visible and Near-infrared wavelengths from 350 nm to 1050 nm was used to record the spectral response reading of different ground cover types. The measurements were made at the time when the Sun was at several instant positions to find out the influences and impacts on the spectroradiometer observations. These instant positions of the Sun were determined via spherical computation. The outcome from the measurements made against selected target features by the spectroradiometer is an output file containing signature plot data that was generated in .sig and/or ASCII format. The attempt of the study was to convert that spectroradiometer data into a GIS-enable format. The development of a Spectro-GIS tool was customized using Visual Basic. Net programming language that enables the tools to run independently and automate the process of the conversion and generation of spectral library of the surface targets is highlighted. The results of this study will be benefited to the earth observation community in a way of providing alternative automation of spatial data archiving as well as the data integration and fusion of the land spectral signatures.

Nonlinear Response in a Field Portable Spectroradiometer: Characterization and Effects on Output Reflectance

We report the characterization and correction of nonlinear responses of a commercial field portable spectroradiometer intended to be used to monitor vegetation physiology. Calibration of photoresponse allowed the successful correction of spectral data and the modeling of biases in reflectance at different levels of the dynamic range. Finally, the impact of nonlinearities on a spectral estimator of photosynthetic status, the photochemical reflectance index (PRI) is discussed. Significance of the biases proved that, although nonlinearity can potentially affect reflectance along most of the dynamic range of the instrument, experimental uncertainties can limit its impact. Nonlinearity biased PRI by affecting the reference band of the index and suggested unreal changes on plant physiology. Results show that nonlinearity could be a significant problem in field spectroscopy, especially in the case of spectroradiometers integrated in unattended systems to monitor vegetation responses to radiation. An automatic adjustment of integration time to reach only a certain level of the dynamic range may reduce nonlinearity effects, though may not always avoid them. We conclude that linearity characterization is necessary to understand impacts and correct potential biases.

Predict Model and Analysis of the Sandy Soil Moisture with Hyperspectral

The objective of this paper is to reveal the relationship between the soil moisture content and Hyperspectral.The paper tested 60 sandy soil samples in the south of Gurban tunggut desert with SVC field-portable spectroradiometer and established the liner regression between sandy moisture content and infrared bands which are sensitive to soil moisture content.The results showed that field test spectral of the sandy soil and logarithmic transformed refletance of the sandy soil did not show fine regression with the soil moisture content;the refletance which was continuum-removed and then first derivative regressed well with the soil moisture,the coefficient of determination(R2) was 0.855.This method provides a new idea to retrieve sandy soil moisture which is practicable and operated easily.

Optimal Vegetation Indices for Winter Wheat Growth Status Based on Multi-Spectral Reflectance

In order to select appropriate vegetation indices for winter wheat, field experiments with four levels of N-fertilizer (0, 30, 60, and 90 kg ha−1) in two repetitions were conducted for three years. Hyper-spectral reflectance data using a portable field spectroradiometer (351 to 2,500 nm) were recorded from 10 am to 2 pm under cloudless conditions at two different growth stages of winter wheat. All two-band and three-band combinations of several vegetation indices were subsequently calculated in an algorithm for determining linear regression analysis against SPAD value, protein content, and grain yield. R square matrices were used to make contour plots and 3-D scatters. Using overlaying in analysis tools of ArcMap the between first and second year results, a number of common hot spots with strong correlations were revealed. The selected hot spots were validated with the dataset of the third year to choose the best vegetation indices for crop variable estimations.

Prediction of Wheat Yield and Protein Using Remote Sensors on Plots—Part I: Assessing near Infrared Model Robustness for Year and Site Variations

Validation of reflectance-based prediction models for plant properties is often performed on just one or two years of data. Hence, we aimed to perform a more comprehensive study regarding the validation of prediction models for grain yield and protein concentration. A FieldSpec3 portable field spectroradiometer was used to measure canopy reflectance in spring wheat. Spectral reflectance data were collected from three different experimental locations in up to four different years during the period 2007–2010, so that seven unique site years were included, comprising, altogether, 976 individual plots. Several datasets had moderate to severe lodging, which had a markedly negative influence on the prediction results. To correct for this problem, a classification model for the classes “lodging” and “standing crop” was calibrated from the spectral data. The model gave a total classification accuracy of 98.3%. Prediction models for grain yield and grain protein concentration were computed by means of the recent statistical method powered partial least squares (PPLS). Models were calibrated and validated on several combinations of the spectral datasets in order to reveal spatial and temporal effects on the prediction performance. The model performance generally increased with increasing variation in the calibration data, both in time (i.e. more years included) and space (i.e. more sites included). The best model for grain yield explained 94% [root mean square error of prediction ( RMSEP) = 156g m−2] of the variance and the predictions of grain protein concentration explained 67% [ RMSEP=1.51 g dry matter (DM)100g−1] of the variance. The performance of the grain yield PPLS models was compared with that of models based on some widely used vegetation indices [normalised difference vegetation index (NDVI), modified soil adjusted vegetation index (MSAVI), red edge inflection point (REIP) and d-chl-ab]. The explained variance of the models based on vegetation indices did not exceed 55%, indicating that these models were inferior to full spectrum models. This study shows that one or two years of spectral measurement are insufficient for building fully operational models for cereal property predictions.

Characterizing the Moisture Content of Tea with Diffuse Reflectance Spectroscopy Using Wavelet Transform and Multivariate Analysis

Effects of the moisture content (MC) of tea on diffuse reflectance spectroscopy were investigated by integrated wavelet transform and multivariate analysis. A total of 738 representative samples, including fresh tea leaves, manufactured tea and partially processed tea were collected for spectral measurement in the 325–1,075 nm range with a field portable spectroradiometer. Then wavelet transform (WT) and multivariate analysis were adopted for quantitative determination of the relationship between MC and spectral data. Three feature extraction methods including WT, principal component analysis (PCA) and kernel principal component analysis (KPCA) were used to explore the internal structure of spectral data. Comparison of those three methods indicated that the variables generated by WT could efficiently discover structural information of spectral data. Calibration involving seeking the relationship between MC and spectral data was executed by using regression analysis, including partial least squares regression, multiple linear regression and least square support vector machine. Results showed that there was a significant correlation between MC and spectral data (r = 0.991, RMSEP = 0.034). Moreover, the effective wavelengths for MC measurement were detected at range of 888–1,007 nm by wavelet transform. The results indicated that the diffuse reflectance spectroscopy of tea is highly correlated with MC.

Indication of the Expression of Transgene in Rice Plant Based on Hyperspectral Remote Sensing Technique II——Growth Monitoring of Samples in the Contrast Experiment

Since the complication of monitoring and evaluating the problems about the transgenic expression and its impacts on the receptor in the transgenic crop breeding and other relevant evaluated works, the authors in the present work tried to assess the differences of spectral parameters of the transgenic rice in contrast with its parent group quantitatively and qualitatively, fulfilling the growth monitoring of the transgenic samples. The spectral parameters (spectral morphological characteristics and indices) chosen are highly related to internal or external stresses to the receipts, and thus could be applied as indicators of biophysical or biochemical processes changes of plant. By ASD portable field spectroradiometer with high-density probe, fine foliar spectra of 8 groups were obtained. By analyzing spectral angle and continuum removal, the spectral morphological differences and their locations of sample spectra were found which could be as auxiliary priori knowledge for quantitative analysis. By investigating spectral indices of the samples, the quantitative differences of spectra were revealed about foliar chlorophyll a+b and carotenoid content. In this study both the spectral differences between transgenic and parent groups and among transgenic groups were investigated. The results show that hyperspectral technique is promising and a helpful auxiliary tool in the study of monitoring the transgenic crop and other relevant researches. By this technique, quantitative and qualitative results of sample spectra could be provided as prior knowledge, as certain orientation, for laboratory professional advanced transgenic breeding study.