Search

Accurate and rapid estimation of leaf nitrogen content (LNC) in winter wheat using hyperspectral techniques is important for growth monitoring and accurate fertilization. Based on field experiments conducted over four years at three ecological sites with four different nitrogen (N) application treatments and four different N-efficient wheat varieties in Henan Province, the canopy spectra and LNC of winter wheat were obtained simultaneously in the main growth stages. The original spectrum was subjected to continuum removal (CR), the correlation between LNC and various vegetation indices was systematically analysed, and the optimal vegetation index estimation model for LNC was constructed. Simultaneously, discrete wavelet transform (DWT) was used to further compress and extract the CR spectrum. The model was combined with partial least squares regression (PLSR) and K-nearest neighbour (KNN) algorithms to estimate LNC in winter wheat. The results showed that the spectrum treated with CR was significantly improved in its correlation with LNC and that the model established based on normalized vegetation index NDVI (CR728, CR977) combined with the CR spectrum was better compared to existing vegetation indices. The calibration and validation coefficients of determination (R2) and root mean square error (RMSE) were 0.816 and 0.799, and 0.352% and 0.342%, respectively. DWT was used to transform the CR spectrum, and the results showed that a combination of the CR spectrum and a sym8 wavelet function with PLSR based on the approximation coefficients at decomposition level 2 predicted LNC most accurately. The coefficient of determination RC2, root mean square error RMSEC, and relative percent deviation (RPDC) of the calibration set were 0.884, 0.279%, and 2.932, respectively, and RV2, RMSEV, and RPDV of the validation set were 0.855, 0.291%, and 2.619, respectively, indicating that the model had good stability and predictive ability. Combination of CR and DWT improved the modelling accuracy of wheat LNC and showed better prediction results for multi-year and multi-point samples. The results of the study can provide a basis and reference for rapid monitoring of LNC in winter wheat. [ABSTRACT FROM AUTHOR]

Remote biomass estimation can benefit agricultural practices in several ways, especially larger areas since it does not require local measurements. The advances of the last few decades in machine learning techniques have created new possibilities for estimating aboveground biomass. A pipeline was established from image acquisition to modelling shoot biomass of two wheat cultivars used in Southern Brazil (TBIO Toruk and BRS Parrudo). A UAV was used to acquire multispectral images with high spatial resolution to calculate vegetation indices (VIs). These VIs along with machine learning approaches are used to model the measured biomass of crops in different growth phases. To correlate the wheat images with measured shoot dry biomass, the following regression models were investigated: random forest, support vector regression, and artificial neural networks. An experiment was designed and conducted at the Agriculture Experimental Station of the Federal University of Rio Grande do Sul (EEA/UFRGS) to assess wheat growth. Variability in crop growth was created for all test areas by varying nitrogen availability. To determine shoot biomass, plants were sampled at three different crop growth stages: V6 (stage of six fully developed leaves), three nodes, and flowering. Our results indicate the importance of the radiometric calibration used. Also, the features extracted from images, such as the VIs combined with machine learning models can be used in precision agriculture for predicting the spatial variability of shoot biomass. The best model for Brazilian wheat cultivars was an artificial neural network with R 2 of 0.90, RMSE of 0.83t/ha, and nRMSE of 8.95%. We also found a strong correlation between ground NDVI with image-based NDVI, with an R 2 of 0.84. [ABSTRACT FROM AUTHOR]

The unmanned aerial vehicle (UAV) image spectral information and texture feature (TF) information were fused to develop an improved model for winter wheat leaf nitrogen content (LNC) monitoring model to provide a reference for wheat nitrogen status monitoring and accurate management. The data of wheat LNC and UAV-hyperspectral imaging were simultaneously obtained at the main growth stages (jointing, booting, and filling stages) of various winter wheat varieties under various nitrogen fertilizer treatments. The correlation between the vegetation indexes (VIs) in combination of any two bands, the TFs, and LNC were systematically analyzed. Then, the optimal VIs and TFs without multicollinearity problems were screened using a variance inflation factor (VIF) to form a 'graph–spectrum' fusion index. Four machine learning methods, namely ridge regression (RR), partial least squares regression (PLSR), support vector machine regression (SVR), and random forest (RF), were used to construct respective quantitative winter wheat LNC estimation models. The results revealed that the model for estimating LNC constructed using the 'graph–spectrum' information formed by eight parameters, including the normalized vegetation index NDVI (R578, R490), the difference vegetation index DVI (R830, R778), MEA490, MEA778, VAR490, VAR578, VAR778, and HOM578 as input and the RR algorithm, performed the best. It outperformed the models developed by the implementation of VIs and TFs as input. The coefficient of determination (R2), root mean square error (RMSE), and relative percent deviation (RPD) of the calibration set were 0.84, 0.25, and 2.50, correspondingly, and those of the validation set were 0.87, 0.27, and 2.33, respectively. The model of winter wheat LNC constructed by fusing spectral and TF information considerably improved the prediction accuracy. The present research results provide a basis and reference for the application of UAV hyperspectral technology in wheat nitrogen nutrient monitoring. [ABSTRACT FROM AUTHOR]

Grain protein content (GPC), as an important agronomic indicator of nutritional value, is commonly used for optimized cultivation, classified harvesting, and quality grading. Previous studies on the spectroscopic estimation of GPC were often conducted at the canopy level with vegetation indices (VIs) derived from the visible and near-infrared (VNIR) bands, which are apart from the absorption features of protein predominately located in the shortwave infrared (SWIR) region. Since those absorption features are mostly masked by water for living plants, the causal relationships between sensitive spectral bands and protein absorption features are poorly understood. Recently, continuous wavelet spectral analysis has been commonly applied to the spectroscopic estimation of vegetation parameters in the way of extracting the optimal wavelet feature (denoted as WFλ,S, λ is the shifting factor in wavelength and S is the scaling factor in the power number of two) or the red-edge position. This study proposed the wavelet-based SWIR edge position (SWEP) for estimating the GPC in cereal crops through extending the red edge position extraction technique to laboratory SWIR reflectance spectroscopy. The performance of SWEPs and WFs was evaluated in coefficient of determination (R2), root mean squared error (RMSE) and relative RMSE (RRMSE) with experimental data over field trials. Our results demonstrated that the protein absorption features in the SWIR region could be enhanced using wavelet-based methods towards accurate estimation of the GPC across rice and wheat crops. The optimal features WF1510,4 (R2 = 0.96) and WF1610,5 (R2 = 0.93) exhibited similar performance for GPC estimation as compared to normalized difference protein indices (NDPIs) on calibration data, but the latter NDPI models had much poorer performance on independent validation data (NDPI1745,1780: R2 = 0.35, RMSE = 1.83%, RRMSE = 21.70%; NDPI2080,2160: R2 = 0.57, RMSE = 1.48%, RRMSE = 17.55%). The GPC could be estimated with high accuracies on both calibration (R2 = 0.94) and validation (R2 = 0.92, RMSE = 0.64%, RRMSE = 7.59%) using the SWEP extracted from the 1450–1650 nm range at Scale 5. Given their close predictive performance, the wavelet-based SWEP was less affected by the degradation of spectral resolution in the input data than the WF. The application of SWEPs could help us better understand the spectroscopy mechanism of GPC estimation from grain reflectance spectra. It also suggests that not just the commonly used red edges but also the insufficiently exploited SWIR edges are important in the entire spectral domain for quantifying vegetation properties. [ABSTRACT FROM AUTHOR]

Time series of multi-spectral satellite data are a valuable option for crop-area monitoring, in particular by enabling rapid, accurate, and consistent estimation of crop area. In this study, we demonstrate the use of a turn-key wheat area mapping model using growing season Landsat time-series data to improve the precision of area estimates produced from a sample of field locations at which the presence of percent wheat per sample pixel was determined. The strata used in the sampling design to obtain the current season's in-situ reference data were created from a wheat map of the previous growing season. We then used a map for the current growing season to serve as an auxiliary variable in a regression estimator of the wheat area. This approach allows for rapid production of such maps and area estimates with less demand for technical expertise in an ongoing wheat monitoring system. Our automated, turn-key classification-tree algorithm produced wheat maps with comparable overall accuracy to those achieved by traditional supervised classification using manual training data collection. Pixel counts from turn-key maps for the years 2013 to 2017 over-estimated wheat area by 0.86% in 2014 to 7.26% in 2017 when compared to the official estimate from the Punjab Crop Reporting Service. Of the several approaches to estimating wheat area were compared, stratified random and simple random sampling augmented by a regression estimator were the two that yielded the smallest standard errors. Incorporating a current season, turn-key map in a regression estimator with data from a simple random sampling design represents a methodologically straightforward, operational implementation of timely, accurate, and precise monitoring of wheat area. Given the importance of wheat production in Punjab to Pakistan's national economy, this method represents an operationally efficient and effective monitoring of inter-annual shortages or surpluses in support of grain management. An automated, turn-key winter wheat mapping algorithm enables timely area estimation in advance of harvest. [ABSTRACT FROM AUTHOR]

In recent years, the application of remote sensing techniques is gaining a growing interest and importance in agriculture. Researchers often combine data from near-infrared and red spectral bands according to their specific objectives. These types of combinations present the disadvantage of lack of sensitivity due to using a single or limited group of bands. In this work on-farm canopy spectral reflectance (CSR) data, composing of ten spectral bands (SBs) plus four spectral vegetation indices (SVIs), is considered in a joint manner to set up a methodology capable to identify genotype by environment interaction (GxE) in wheat. Spectral data are analysed over five wheat genotypes grown in five different environments. Historically breeders have recognized the potentially negative implications of GxE in selection and cultivar deployment and have focused on developing tools and resources to quantify it. We propose to perform a statistical batch processing, applying two-way analysis of variance to multiple spectral data, with genotype and environment as fixed factors. Results prove that this methodology performs well in both directions, capturing differences between genotypes within a single environment, and between environments for a single genotype, representing a step forward to converting spectral data into knowledge for the subject of GxE. [ABSTRACT FROM AUTHOR]

Water deficit is the most limiting factor for wheat production, so wheat-breeding programmes are currently focused on developing high-performance genotypes under such conditions. Carbon isotope discrimination (∆13C) in grains is a trait closely related to yield and stress tolerance. However, conventional measurement of ∆13C is expensive, limiting its widespread use for genotype selection in breeding programmes. Predicting ∆13C through remote sensing could be useful for large-scale phenotyping. A set of 384 cultivars and advanced lines of spring bread wheat (Triticum aestivum L.) was grown under contrasting water conditions during two seasons. Grain yield (GY) and the ∆13C of grains were obtained at the end of both seasons, and canopy reflectance measurements were taken at anthesis and grain filling. Hyperspectral canopy reflectance was used to estimate GY and ∆13C through Multilinear Regression Analysis (MRL) considering wavelength selection using a Genetic Algorithm (GA), spectral reflectance indices (SRIs), Partial Least Square Regression (PLSR), Support Vector Regression (SVR), Random Forest (RF) and Artificial Neural Networks (ANN). The best models of both GY and ∆13C explained 78% and 60% of data variability, respectively. Additionally, the MRL models showed higher prediction rates than SRIs and similar or slightly lower rates, in most cases, than multivariate regression models, but required only 4–9 wavelengths instead of the full hyperspectral data used to develop the regression models. The use of canopy spectral reflectance data and MRL models to predict GY and Δ13C via GA for selection of the reflectance wavelengths could be a practical tool for genotype selection in wheat breeding systems. [ABSTRACT FROM AUTHOR]

Worldwide, cereals are of great interest for national food security. The recently released free Sentinel 2 imagery is promising for cereal production systems management, both at field and regional scale. The present study aims to estimate and map the durum wheat yield in Grombalia region using the Sentinel 2 red-edge bands. First, the spatial distribution of wheat in Grombalia region was mapped applying the maximum likelihood classification of Sentinel 2 images spread out between October, 2017, and June, 2018. Then the wheat yield of eleven sampled fields was estimated based on these images through empirical regressions models. The independent variable of the regressions was the yield measured in-situ by objective method. The dependent variables were different types of vegetation indices derived from Sentinel 2 red, red-edge and near-infrared bands. The vegetation index of the best model was chosen to estimate and map the wheat yield over the entire study area. The maximum likelihood method classifies accurately the wheat in Grombalia region with wheat Kappa index (K) of 0.95, wheat user accuracy of 0.95 and wheat producer accuracy of 0.99. The area of wheat cultivated in Grombalia during the campaign 2017/2018 is about 1200 ha. On the other hand, the coefficient of determination (R2) of the tested empirical regressions models for yield estimation is between 0.55 and 0.73 and the Root Mean Square Error (RMSE) varies from 3.80 to 4.90 Qx ha−1. The best model is the one that employs B7 and B5 bands. According to this model the wheat yield of the entire study area ranges from 0.90 to 53.00 Qx ha−1 with a mean of 21.00 Qx ha−1 and a total production of 23,452 Qx. This mean is very close to the official mean recorded during the campaign 2017/2018 in Nabeul governorate, which is 22.10 Qx ha−1. [ABSTRACT FROM AUTHOR]

Winter wheat is a staple food crop for most of the world's population, and the area and spatial distribution of winter wheat are key elements in estimating crop production and ensuring food security. However, winter wheat planting areas contain substantial spatial heterogeneity with mixed pixels for coarse- and moderate-resolution satellite data, leading to large errors in crop acreage estimation. This study has developed a phenology-based approach using moderate-resolution (1 km per pixel) satellite data to estimate sub-pixel planting fractions of winter wheat. Based on unmanned aerial vehicle (UAV) observations, the unique characteristics of winter wheat with high vegetation index values at the heading stage (May) and low values at the ripening stage (June) were investigated. The differences in vegetation index between heading and ripening stages increased with the planting fraction of winter wheat, and therefore the planting fractions were estimated by comparing the NDVI differences of a given pixel with those of predetermined pure winter wheat and non-winter wheat pixels. This approach was evaluated using aerial images and agricultural statistical data in an intensive agricultural region, Shandong Province in North China. The method explained 85% and 60% of the spatial variation in municipal- and county-level statistical data, respectively. More importantly, the predetermined pure winter wheat and non-winter wheat pixels can be automatically identified using MODIS data according to their NDVI differences, which strengthens the potential to use this method at regional and global scales without any field observations as references. [ABSTRACT FROM AUTHOR]

In this paper a data base of L band backscattering simulations, based on the application of a discrete scattering model and detailed ground truth collected over Argentinean wheat fields, is tested. The simulations are directly compared against backscattering measurements collected over single fields. Then the scattering model is used to tune a semi empirical and manageable model function. Finally this function, jointly with a multi-temporal algorithm, is used for soil moisture retrieval and results are tested against an independent set of measurements. The site used for training and validation is located in the Pampas region, which will be continuously monitored in the framework of the SAOCOM (SAtélite Argentino de Observación COn Microondas) programme. In general, both the direct model and the retrieval algorithm produce consistent results. However, variation in the azimuth angle of observation produces appreciable effects, and some discrepancies are observed for radar observations collected just after a rainfall. These issues deserve further investigation with a larger amount of experimental data. [ABSTRACT FROM AUTHOR]

Hemispherical directional reflectance factor (HDRF) measurements of wheat crop acquired with field goniometer system are used to estimate parameters and evaluate anisotropic reflectance models. The HDRFs were sampled at 54 view angles (6 azimuthal planes × 9 view zenith angles) to cover the hemispheric span up to 60° zenith. The observations were made at 7–8 days' interval during different phenophases of the crop. Biophysical parameters were also measured parallel to HDRF. Kernel driven Ross-Li variants and RPV (Rehman Pinty Verstrate) models parameters are generated by fitting HDRF data. RossThin-H/LiSparse-R model was fitted best for all visible-near infrared bands in early phenophases while the RossThick-H/LiSparse-R model was best fitted for heading and flowering phases in most cases. In late grain development phases (Late milking and Dough) of the wheat, HDRFs of visible bands were best fitted with RossThin-H/LiDense-R model in most instances. The HDRF simulation of wheat by RPV models is identical to Ross-Li models. Both models are adequate to account reflectance anisotropy of wheat canopy. Phenophase specific model parameters can be used to reconstruct reflectance anisotropy of wheat for different applications. [ABSTRACT FROM AUTHOR]

The ability to identify the vulnerable regions within the crop fields assist the farmers in executing the counter plans precisely. In this regard, the unmanned aerial vehicles (UAVs) equipped with red-green-blue and multispectral sensors have added a new dimension in precision agriculture for a broad range of applications. In this study, we present the transformation of an off-the-shelf quadrotor platform into an aerial-crop-observer by adding customized payload capable of delivering high-resolution multispectral imagery. The main objective of the study was to aerially quantify the response of wheat crop under the influence of different elements critical to crop health, i.e. nitrogen (N), phosphorus, zinc, irrigation levels (I), and agro-climatological conditions. Two different experimental plots with different varieties of wheat crop were selected for the study. The vegetation indices were derived after all the necessary radiometric and geometrical corrections. The linear estimation models were developed to assess the grain yield, aboveground biomass, and leaf area index. Our results indicate that high-resolution multispectral imagery acquired through lightweight UAVs offers a standing potential for quantifying aerial observations of wheat crop under a variety of field-inputs. The study is characterized through respective soil analysis, applied field inputs, calibration of aerial image sensors, the agro-climatological observations, and derivations of vegetation indices. [ABSTRACT FROM AUTHOR]

Wheat lodging significantly reduce the grain yield and quality. Mapping wheat lodging timely and accurately can help farmers get full compensatory damages in time. Therefore, the objective of this study was to employ a low-cost unmanned aerial vehicle (UAV) carrying a Red-Green-Blue camera to discriminate lodged from non-lodged wheat effectively. Low-cost UAV is easier to be accepted by Chinese farmers most of whom manage small-scale farms. After comparing a variety of colour features as well as their texture features, this study found that the texture feature of mean ofG/B(digital number ratio of the green band to the blue band) derived from occurrence measures was the optimum discriminator of lodged and non-lodged wheat. This discriminator was still effective although the spatial spectral variations in the study area were much more complex than that in previous studies. With an unsupervised method based on the discriminator, the UAV system was able to discriminate lodged wheat from non-lodged wheat. The resultant of overall accuracy was 89.55% and Kappa coefficient was 0.76. The producer’s accuracies were 81.23% and 93.62%, whereas the user’s accuracies were 86.15% and 91.08% for lodged and non-lodged wheat, respectively. The retrieved wheat lodging distribution map showed a substantial agreement with ground reference data. [ABSTRACT FROM PUBLISHER]

The normalized difference vegetation index (NDVI) is a commonly used index for monitoring crop growth status. Previous studies have shown that the leaf area index (LAI) estimation based on NDVI is limited by saturation that occurs under conditions of relatively dense canopies (LAI > 2 m2 m–2). To reduce the saturation effect, we suggested new spectral indices through the spectral indices approach. The results suggested that the two-band normalized difference spectral index (NDSI = ((ρ940 –ρ730) /(ρ940 + ρ730))) resulted from the two-band spectral indices approach and the three-band modified normalized difference spectral index (mNDSI = ((ρ940 – 0.8 × ρ950) – ρ730) /((ρ940 – 0.8 × ρ950) + ρ730)) resulted from the three-band spectral indices approach, and they were able to mitigate saturation and improve the LAI prediction with a determination coefficient (R2) of 0.77 and 0.78, respectively. In the validation based on data from independent experiments, these new indices exhibited an accuracy with relative root mean square error (RRMSE) lower than 23.38% and bias higher than –0.40. These accuracies were significantly higher than those obtained with some existing indices with good performance in LAI estimation, such as the enhanced vegetation index (EVI) (RRMSE = 30.19%, bias = –0.34) and the modified triangular vegetation index 2 (MTVI2) (RRMSE = 29.30%, bias = –0.28), and the indices with the ability to mitigate the saturation, such as the wide dynamic range vegetation index (WDRVI) (RRMSE = 31.37%, bias = –0.54), the red-edge wide dynamic range vegetation index (red-edge WDRVI) (RRMSE = 26.34%, bias = –0.54), and the normalized difference red-edge index (NDRE) (RRMSE = 28.41%, bias = –0.56). Additionally, these new indices were more sensitive under moderate to high LAI conditions (between 2 and 8 m2 m–2). Between these two new developed spectral indices, there was no significant difference in the accuracy and sensitivity assessments. Considering the index structure and convenience in application, we demonstrated that the two-band spectral index NDSI((ρ940 –ρ730) /(ρ940 + ρ730)) is efficient in mitigating saturation and has considerable potential for estimating the LAI of canopies throughout the entire growing season of wheat (Triticum aestivum L.), whereas the three-band spectral index contributes lesser in the saturation mitigation provided the red-edge band has been contained. [ABSTRACT FROM AUTHOR]

Remote sensing of solar-induced chlorophyll-a fluorescence (SIF) is a promising method for quantifying photosynthetic activity at the Earth's surface. Some recent studies therefore aim to clarify to what extend passive SIF measurements can be used to track photosynthetic processes. The present study contributes to that research by investigating the diurnal relationship between shortterm variability of SIF and gas exchange rates in terms of stomatal conductance (gs). Quantification of SIF was performed using the Fraunhofer line discriminator (FLD) principle in the O2-A absorption band and by applying a novel approach using the second derivative of the reflectance. Experimental measurements were conducted for two crops, namely winter wheat and maize under stressed and unstressed conditions. Overall, 47 high-spectral-resolution signatures and measurements of gs were collected on three different days during the 2014 growing season. During two days of unstressed conditions, diurnal variability of gs could - to a large extent (R2 = 0.62 and 0.78) - be explained by the fraction of SIF in the target radiance flux between 655 and 665 nm (Ffraction). In an experiment with heat and water stress no diurnal relationship appeared between gs and Ffraction, which can be attributed to the occurrence of non-photochemical quenching under stress conditions. [ABSTRACT FROM AUTHOR]

Leaf area index (LAI) is one of the most important plant parameters when observing agricultural crops and a decisive factor for yield estimates. Remote-sensing data provide spectral information on large areas and allow for a detailed quantitative assessment of LAI and other plant parameters. The present study compared support vector regression (SVR), random forest regression (RFR), and partial least-squares regression (PLSR) and their achieved model qualities for the assessment of LAI from wheat reflectance spectra. In this context, the validation technique used for verifying the accuracy of an empirical–statistical regression model was very important in order to allow the spatial transferability of models to unknown data. Thus, two different validation methods, leave-one-out cross-validation (cv) and independent validation (iv), were performed to determine model accuracy. The LAI and field reflectance spectra of 124 plots were collected from four fields during two stages of plant development in 2011 and 2012. In the case of cross-validation for the separate years, as well as the entire data set, SVR provided the best results (2011:R2cv = 0.739, 2012:R2cv = 0.85, 2011 and 2012:R2cv = 0.944). Independent validation of the data set from both years led to completely different results. The accuracy of PLSR (R2iv = 0.912) and RFR (R2iv = 0.770) remained almost at the same level as that of cross-validation, while SVR showed a clear decline in model performance (R2iv = 0.769). The results indicate that regression model robustness largely depends on the applied validation approach and the data range of the LAI used for model building. [ABSTRACT FROM PUBLISHER]

Detailed information about the prediction of within-field potential in terms of yield at the field scale is an attractive goal that would allow useful applications in precision agriculture. Biophysical variables characterizing crop canopies, such as the leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), fractional ground cover (Fcover) and the concentration of chlorophyll-aand -b(Cab), can be estimated from satellite remote-sensing data through the application of a neural network inversion of a radiative transfer model, such as PROSAIL. The knowledge of the temporal and spatial variability of these variables can enhance the possibilities of estimating yield at the field scale. The aim of this study is to investigate the influence of acquisition time and spatial resolution of biophysical variables estimated from satellite data on the grain yield estimation of wheat crops. We used SPOT 4 (spatial resolution: 20 m) and SPOT 5 (spatial resolution: 10 m) images, acquired at six different dates during the wheat growing season in 2012, to obtain LAI, Fcover, FAPAR, and Cab on five fields in Maccarese (Central Italy). A preliminary survey was carried out to correlate spatially biophysical variables with the final grain yield at each acquisition date. Biophysical variables estimated at a spatial resolution of 10 m during the stem elongation stage showed the best simple and spatial correlation with yield. At this stage, all the biophysical variables showed the highest correlation values as compared to the other dates. Subsequently, we used the variables estimated from SPOT data at each growth stage to calibrate multiple linear regression (MLR) and cubist regression (CR) models for two fields, which were then validated on five independent fields. Although the CR calibration models provided better accuracy than MLR, the best validation statistics were gained from MLR models, obtaining a root mean square error (RMSE) of about 1 t ha−1for three of these fields, using remote data having a spatial resolution of 10 metres and acquired between steam elongation and booting stage. The optimal acquisition time is affected,ceteris paribus, by the agricultural management and in particular by the variety that can influence the trend of crop growth. However, the optimal growth stage for yield estimation seems to be quite similar over the study area during a growth season. The validation of models on field data collected in another growing season is mainly affected by the climate conditions. These results highlight the importance of spatial resolution and the influence of acquisition time of satellite images on the estimation of yield at the field scale by remote-sensing data. [ABSTRACT FROM AUTHOR]

A full understanding of the polarimetric characteristics of wheat fields is necessary for the development of a robust methodology for the monitoring and mapping of wheat using quad-polarimetric SAR images. In this study, the polarimetric characteristics and temporal variations of wheat were analysed using a multi-temporal RADARSAT-2 quad-polarimetric dataset from a wheat-growing region of the North China Plain. The backscattering coefficient, the Freeman–Durden decomposition, and the H/A/decomposition were evaluated as functions of the growth stage and then used for classification. With each wheat growth stage, the volume scattering component ratio increased, whereas the surface scattering component ratio generally decreased. The experimental results indicate that the Freeman–Durden decomposition parameters are sensitive to the wheat growth stage. Moreover, the proposed method for mapping wheat, which combines the backscattering coefficients, polarization decompositions, and the support vector machine (hereafter referred to as BP-SVM), is able to discriminate wheat effectively, with an accuracy of up to 92.92%. This indicates that quad-polarimetric imagery from just one date is sufficient for wheat mapping. The results of this study demonstrate that quad-polarimetric RADARSAT-2 SAR imagery has great potential for the monitoring and mapping of wheat. [ABSTRACT FROM AUTHOR]

The inflection point of spectral reflectance of crop in the red edge region (680–780 nm) is termed as the red edge position (REP), which is sensitive to crop biochemical and biophysical parameters. We propose a technique for automatic detection of four dynamic wavebands, i.e. two in the far-red and two in the near-infrared (NIR) region from hyperspectral data, for REP estimation using the linear extrapolation method. A field experiment was conducted at the SHIATS Farm, Allahabad, India, with four levels of nitrogen and irrigation treatments to assess the sensitivity of REP towards crop stress. A correlation analysis was carried out between REPs and different biophysical parameters, such as leaf area index (LAI) and chlorophyll content index (CCI), recorded in each plot at 50, 70, and 90 days after sowing of wheat crop under the field experiment. The inter-comparison among different REP extraction techniques revealed that the proposed technique, i.e. the modified linear extrapolation (MLE) method, has a better ability to distinguish different crop stress conditions. REPs extracted using the MLE technique showed high correlations with a wide range of LAI, CCI, and LAI × CCI, being comparable with results obtained using the traditional linear extrapolation and polynomial fitting techniques. The behaviour of the new techniques was found to be stable at both narrower and broader bandwidth, i.e. 2 and 10 nm. A new red-edge-based index, i.e. area under REP (AREP), was used to detect the cumulative stress over wheat crop by utilizing the REP and its rate of change information at different crop growth stages. A high coefficient of determination (R2 = 0.89) was found betweenAREPand dry grain yield (Q ha−1) up to 50 Q  ha−1of wheat crop, whereas, beyond this range the relationship was found to be diminishing. [ABSTRACT FROM AUTHOR]

In semi-arid areas, a strongly variable climate represents a major risk for food safety. An operational grain yield forecasting system, which could help decision-makers to make early assessments and plan annual imports, is thus needed. It can be challenging to monitor the crop canopy and production capacity of plants, especially cereals. In this context, the aim of the present study is to analyse the characteristics of two types of irrigated and non-irrigated cereals: barley and wheat. Through the use of a rich database, acquired over a period of two years for more than 30 test fields, and from 20 optical satellite SPOT/HRV images, two research approaches are considered. First, statistical analysis is used to characterize the vegetation’s dynamics and grain yield, based on remotely sensed (satellite) normalized difference vegetation index (NDVI) measurements. A relationship is established between the NDVI and LAI (leaf area index). Different robust relationships (exponential or linear) are established between the satellite NDVI index acquired from SPOT/HRV images, just before the time of maximum growth (April), and grain and straw, for barley and wheat vegetation covers. Following validation of the proposed empirical approaches, yield maps are produced for the studied site. The second approach is based on the application of a Simple Algorithm for Yield Estimation (SAFY) growth model, developed to simulate the dynamics of the LAI and the grain yield. An inter-comparison between ground yield measurements and SAFY model simulations reveals that yields are underestimated by this model. Finally, the combination of multi-temporal satellite measurements with the SAFY model estimations is also proposed for the purposes of yield mapping. Although the results produced by the SAFY model are found to be reasonably well correlated with those determined by satellite measurements (NDVI), the grain yields are nevertheless underestimated. [ABSTRACT FROM AUTHOR]

Weed control is commonly performed by applying selective herbicides homogeneously over entire agricultural fields. However, applying herbicide only where needed could have economical and environmental benefits. The objective of this study was to apply remote sensing to the detection of grasses and broadleaf weeds among cereal and broadleaf crops. Spectral relative reflectance values at both leaf and canopy scales were obtained by field spectroscopy for four plant categories: wheat, chickpea, grass weeds, and broadleaf weeds. Total reflectance spectra of leaf tissues for botanical genera were successfully classified by general discriminant analysis (GDA). The total canopy spectral classification by GDA for specific narrow bands was 95 ± 4.19% for wheat and 94 ± 5.13% for chickpea. The total canopy spectral classification by GDA for future Vegetation and Environmental Monitoring on a New Micro-Satellite (VENμS) bands was 77 ± 8.09% for wheat and 88 ± 6.94% for chickpea, and for the operative satellite Advanced Land Imager (ALI) bands was 78 ± 7.97% for wheat and 82 ± 8.22% for chickpea. Within the critical period for weed control, an overall classification accuracy of 87 ± 5.57% was achieved for >5% vegetation coverage in a wheat field, thereby providing potential for implementation of herbicide applications. Qualitative models based on wheat, chickpea, grass weed, and broadleaf weed spectral properties have high-quality classification and prediction potential that can be used for site-specific weed management. [ABSTRACT FROM AUTHOR]

Wheat (Triticum aestivumL.) straw is not only important for long-term soil productivity, but also as a raw material for biofuel, livestock feed, building, packing, and bedding. Inventory figures in the USA for potential straw availability are largely based on whole states and counties. Site-specific information is needed to determine where sufficient straw is available for removal within farm fields. The objective of this study was to assess the accuracy and feasibility of light detection and ranging (lidar) measurements of crop height for predicting the straw yield of wheat at site-specific field locations and apply this information to determining where excess straw is available beyond soil conservation needs. An inexpensive lidar sensor was mounted on the top of a Case International Harvester 1470 combine and aimed to point forwards at a 45° angle over the combine's reel. Lidar-measured crop height was correlated with manually measured crop height in small plots (r2 = 0.79) and was a good predictor of straw productivity across three farm fields (r2 = 0.85). Crop height was better correlated with straw yield than grain yield or grain protein concentration. Crop height predicted by lidar could then be used to estimate straw yield from a predetermined linear relationship between crop height and straw yield. A straw yield map was generated during harvest by programming the lidar sensor to compute the average of 91 readings in each scan. A map of harvestable straw was computed by subtracting the amount of straw required to maintain soil organic carbon from a map of total straw yield. Lidar is potentially useful for measuring crop biomass on a combine harvester and determining where in farm fields excess crop residue can be removed for commercial purposes. Further work with lidar is needed to resolve the issue of flying chaff. [ABSTRACT FROM AUTHOR]

A field experiment with wheat was conducted with four different nitrogen and four different water stress levels, and hyperspectral reflectances in the 350–2500 nm range were recorded at six crop phenostages for two years (2009–2010 and 2010–2011). Thirty-two hyperspectral indices were determined using the first-year reflectance data. Plant nitrogen (N) status, characterized by leaf nitrogen content (LNC) and plant nitrogen accumulation (PNA), showed the highest R 2 with the spectral indices at the booting stage. The best five predictive equations for LNC were based on the green normalized difference vegetation index (GNDVI), normalized difference chlorophyll index (NDCI), normalized difference705 (ND705) index, ratio index-1dB (RI-1dB) and Vogelman index a (VOGa). Their validation using the second-year data showed high R 2 (>0.80) and ratio of performance to deviation (RPD; >2.25) and low root mean square error (RMSE; <0.24) and relative error (<10%). For PNA, five predictive equations with simple ratio pigment index (SRPI), photochemical reflectance index (PRI), modified simple ratio705 (mSR705), modified normalized difference705 (mND705) and normalized pigment chlorophyll index (NPCI) as predicting indices yielded the best relations with high R 2 > 0.80. The corresponding RMSE and RE of these ranged from 1.39 to 1.13 and from 24.5% to 33.3%, respectively. Although the predicted values show good agreement with the observed values, the prediction of LNC is more accurate than PNA, as indicated by higher RMSE and very high RE for the latter. Hence, the plant nitrogen stress of wheat can be accurately assessed through the prediction of LNC based on the five identified reflectance indices at the booting stage. [ABSTRACT FROM AUTHOR]

The wheat grain industry is Australia's second largest agricultural export commodity. There is an increasing demand for accurate, objective and near real-time crop production information by industry. The advent of the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite platform has augmented the capability of satellite-based applications to capture reflectance over large areas at acceptable pixel scale, cost and accuracy. The use of multi-temporal MODIS-enhanced vegetation index (EVI) imagery to determine crop area was investigated in this article. Here the rigour of the harmonic analysis of time-series (HANTS) and early-season metric approaches was assessed when extrapolating over the entire Queensland (QLD) cropping region for the 2005 and 2006 seasons. Early-season crop area estimates, at least 4 months before harvest, produced high accuracy at pixel and regional scales with percent errors of -8.6% and -26% for the 2005 and 2006 seasons, respectively. In discriminating among crops at pixel and regional scale, the HANTS approach showed high accuracy. The errors for specific area estimates for wheat, barley and chickpea were 9.9%, -5.2% and 10.9% (for 2005) and -2.8%, -78% and 64% (for 2006), respectively. Area estimates of total winter crop, wheat, barley and chickpea resulted in coefficient of determination (R2) values of 0.92, 0.89, 0.82 and 0.52, when contrasted against the actual shire-scale data. A significantly high coefficient of determination (0.87) was achieved for total winter crop area estimates in August across all shires for the 2006 season. Furthermore, the HANTS approach showed high accuracy in discriminating cropping area from non-cropping area and highlighted the need for accurate and up-to-date land use maps. The extrapolability of these approaches to determine total and specific winter crop area estimates, well before flowering, showed good utility across larger areas and seasons. Hence, it is envisaged that this technology might be transferable to different regions across Australia. [ABSTRACT FROM AUTHOR]

An accurate estimate of the evapotranspiration (ET) and crop water productivity (WP) at the regional scale plays a significant role in managing water-saving irrigation in the dry region. Three sites in the Gezira scheme representing research field (Gezira Research Station, GRS), farmers' fields with participatory water management approach (Abdelhakam block) and ordinary farmers' fields (Madina block) were selected to estimate spatial ET, crop yield and WP of wheat using remote sensing data coupled with ground observations. The methodology is based on surface energy balance to estimate sensible and latent heat fluxes by combining remotely sensed data from Landsat 7 ETM+ and Moderate Resolution Imaging Spectroradiometer (MODIS) with common meteorological data. A comparison between Surface Energy Balance Algorithm for Land (SEBAL) estimated ET and ET calculated based on reference evapotranspiration (ETo)-crop coefficient (kc) approach over the growing season exhibited a good agreement. The average SEBAL-based ET of wheat for the entire season at GRS was 396 mm. SEBAL estimated kc were 0.46, 1.07 and 0.3 at the initial, mid season and late season stages, respectively, whereas the kc values determined from water depletion measurements were 0.5, 1.15 and 0.5, respectively. The average crop yield at GRS, Abdelhakam block and Madina block were 2.4, 1.9 and 1.4 t ha-1, respectively. The mean values of WP for the three locations were 0.52 (coefficient of variation, CV = 0.31), 0.50 (CV = 0.33) and 0.43 (CV = 0.45) kg m-3, respectively. Achieving a coefficient of variation (CV = 0.15), water savings at the three locations could reach 21%, 42% and 53%, respectively. [ABSTRACT FROM AUTHOR]

Wheat plants were experimentally infested with wheat stem sawflies, and hyperspectral images (reflectance range from 402.8-838.7 nm) were collected from leaves of infested and non-infested plants. Mean and variance reflectance per leaf were calculated in five of 213 spectral bands (452, 553, 657, 725, and 760 nm) and compared with vegetation indices (NDVI, SI and PRI), and standard variogram parameters (nugget, sill and range values). Mean reflectance values and their variance values and vegetation indices showed significant effects of sawfly infestation in one dataset but not in another. Based on directional variogram analyses, we showed that: (1) better separation of leaf type and infested/non-infested wheat plants was seen in variograms in longitudinal direction compared to transverse; (2) mainly spectral bands in the red edge and NIR showed consistent effect of sawfly infestation; (3) range values were not affected significantly by either sawfly infestation or leaf type; and (4) sawfly-induced stress was most likely to be detected about three weeks after infestation. Variogram analysis is one of the key standards in quantitative spatial ecology, and this study supports further research into its use in remote sensing with particular emphasis on detection of biotic stress. [ABSTRACT FROM AUTHOR]

Natural gas leakage from underground pipelines is known to affect vegetation adversely, probably by displacement of the soil oxygen needed for respiration. This causes changes in plant and canopy reflectance, which may serve as indicators of gas leakage. In this study, a covariance analysis was performed between reflectance indices of maize (Zea mays) and wheat (Triticum aestivum) canopies and oxygen concentrations in a simulated natural gas leak. Twenty-nine days after oxygen shortage occurred, the reflectance indices had the highest correlation with oxygen concentrations in the soil, for both species. The effect was consistent within species but the absolute values varied between the species. Normalization by adding a constant value to the control index of one species resulted in significant linear regression models for several indices. The indices with the highest regression coefficients were used to predict the oxygen concentration in the soil. This showed that the gas leakage caused reflectance changes up to 0.5 m from the source. As it could not be proven that oxygen shortage was the cause of the reflectance changes, further work is needed to study the side-effects of gas leakage, such as bacterial oxygen depletion, on plant growth and reflectance. [ABSTRACT FROM AUTHOR]

This letter deals with estimation of LAI for a wheat crop using physical and semi-empirical BRDF models and IRS-1D LISS-III sensor data. NDVI was computed for both the models with LAI as a free parameter. The model-computed NDVI was compared with corresponding atmospherically corrected LISS-III NDVI. The estimation of LAI was carried out on the basis of a look-up table approach and minimum root mean squared deviation between model computed and observed NDVI. The estimated LAI was validated against field measurements carried out during the months of February and March 2003, at the Central State Farm, Rajasthan, India. It was found that LAI was underestimated in both physical and semi-empirical models. Results show that inclusion of multiple scattering in physical models may not always lead to a more accurate estimation of LAI and that it may be possible to estimate LAI at early stages of crop growth using semi-empirical models. The coefficient of determination (R 2) between model estimated and measured LAI was 0.57 (standard error of estimate (SE) 0.156) and 0.63 (SE 0.187) for semi-empirical and physical models, respectively, in the single scattering approximation, for February data. The corresponding values for March data were 0.57 (SE 0.206) and 0.51 (SE 0.216), respectively. [ABSTRACT FROM AUTHOR]

The continuous increase of heavy metal ions in the environment is imposing serious problems in agricultural yield and increases human health threats through accumulation in the food chain. Various studies have shown that heavy metals influence the metabolic processes and pigment concentrations in leaves, and thus affect the laser induced fluorescence (LIF) spectra. Leaf level in vivo LIF spectra using the 488 nm and 355 nm laser lines, fluorescence induction kinetics (FIK) using the 488 nm laser line, and photosynthetic pigments of the control and nickel treated wheat seedlings were measured. The peak parameters of the blue and UV-excited spectral bands were calculated by Gaussian curve fitting. The FIK measured at both the chlorophyll (Chl) fluorescence bands was used to evaluate the fluorescence decrease ratio (R Fd). The variations in R Fd and fluorescence ratios of intensities, bandwidth and band area, with varying concentrations of nickel, were revealed as promising parameters in determining the health status of wheat seedlings. These leaf level findings can be extended at canopy level in the field using laser based light detection and ranging (LIDAR) systems. [ABSTRACT FROM AUTHOR]

This study assessed whether vegetation indices derived from broadband RapidEye™ data containing the red edge region (690-730 nm) equal those computed from narrow band data in predicting nitrogen (N) status of spring wheat (Triticum aestivum L.). Various single and combined indices were computed from in-situ spectroradiometer data and simulated RapidEye™ data. A new, combined index derived from the Modified Chlorophyll Absorption Ratio Index (MCARI) and the second Modified Triangular Vegetation Index (MTVI2) in ratio obtained the best regression relationships with chlorophyll meter values (Minolta Soil Plant Analysis Development (SPAD) 502 chlorophyll meter) and flag leaf N. For SPAD, r 2 values ranged from 0.45 to 0.69 (p<0.01) for narrow bands and from 0.35 and 0.77 (p<0.01) for broad bands. For leaf N, r 2 values ranged from 0.41 to 0.68 (p<0.01) for narrow bands and 0.37 to 0.56 (p<0.01) for broad bands. These results are sufficiently promising to suggest that MCARI/MTVI2 employing broadband RapidEye™ data is useful for predicting wheat N status. [ABSTRACT FROM AUTHOR]

This article comments on a method of within-field yield prediction from IKONOS data published in this Journal (International Journal of Remote Sensing, 25, 377-388). The authors propose what they call a new matrix approach, which solves a system of linear equations relating actual yield to expected yield in four classes defined based on spectral characteristics and the relative areas for these classes as measured by remote sensing. The authors use five observations to estimate four unknown parameters, and they propose that generally p+1 observations could be used to estimate p parameters. This article shows that this approach will lead to very unreliable predictions of class yield. The problem is shown to be a standard multiple regression problem. It is argued that sample size must be considerably larger than the number of classes in order to obtain reliable yield estimates for the classes. Due to the very nature of the regressor variables, the relative areas of the classes, multicollinearity is expected to be a severe problem. Therefore, it may be useful to reduce the number of regressor variables using standard model selection procedures. [ABSTRACT FROM AUTHOR]

The current paper investigates the potential contribution of ENVISAT wide swath (WS) images for discrimination and monitoring of crops at a regional scale. The study was based on synthetic aperture radar (SAR) images acquired throughout an entire growing season. Advanced synthetic aperture radar sensor (ASAR) images in both narrow swath (NS) and WS modes were simulated based on 15 European Remote Sensing (ERS) satellite images recorded over Belgium. Unlike ‘real’ ASAR imagery, this exercise provided a consistent data set (i.e. same incidence angle, same acquisition date, same acquisition hour) to study the impact of spatial resolution on the SAR signal information content. A quantitative approach using 787 parcels of medium field size and various data combinations assessed monitoring and discrimination capabilities for six crop types: wheat, barley, grasses, sugar beet, maize and potato. The spatial resolution impact of the ASAR sensor was discussed with respect to the field size by comparing the results obtained from NS (30 m) and WS (150 m) mode images. WS temporal profiles were able to discriminate the various crops of interest and were representative of the crop development observed in the region. Furthermore, parcel-based unsupervised classifications successfully discriminated between grass, wheat, barley and other crops of large parcels (success rate of 83%). Dedicated interpretation schemes were developed in order to discriminate between cereal crops. [ABSTRACT FROM AUTHOR]

Regional estimates of crop yield are critical for a wide range of applications, including agricultural land management and carbon cycle modelling. Remotely sensed images offer great potential in estimating crop extent and yield over large areas owing to their synoptic and repetitive coverage. Over the last few decades, the most commonly used yield–vegetation index relationship has been criticized because of its strong empirical character. Therefore, the present study was mainly focused on estimating regional wheat yield by remote sensing from the parametric Monteith's model, in an intensive agricultural region (Haryana state) in India. Discrimination and area estimates of wheat crop were achieved by spectral classification of image from AWiFS (Advanced Wide Field Sensor) on‐board the IRS‐P6 satellite. Remotely sensed estimates of the fraction of absorbed photosynthetically active radiation (fAPAR) and daily temperature were used as input to a simple model based on light‐use efficiency to estimate wheat yields at the pixel level. Major winter crops (wheat, mustard and sugarcane) were discriminated from single‐date AWiFS image with an accuracy of more than 80%. The estimates of wheat acreage from AWiFS had less than 5% relative deviation from official reports, which shows the potential of single‐date AWiFS image for estimating wheat acreage in Haryana. The physical range of yield estimates from satellites using Monteith's model was within reported yields of wheat for both methods of fAPAR, in an intensive irrigated wheat‐growing region. Comparison of satellite‐based and official estimates indicates errors in regional yields within 10% for 78% and 68% of cases with fAPAR_M1 and fAPAR_M2, respectively. However, wheat yields in general are over‐ and underestimated by the fAPAR_M1 and fAPAR_M2 methods, respectively. The validation with district level wheat yields revealed a root mean square error of 0.25 and 0.35 t ha-1 from fAPAR_M1 and fAPAR_M2, respectively, which shows the better performance of the fAPAR_M1 method for estimating regional wheat yields. Future work should address improvement in crop identification and field‐scale yield estimation by integration of high and coarse resolution satellite sensor data. [ABSTRACT FROM AUTHOR]

This paper presents two main objectives of a multi‐year study applying remote sensing to precision agriculture: (1) developing new spectral indices for wheat monitoring, and (2) producing an interpretation key for mapping vegetation features with spectral indices. Agricultural monitoring with remote sensing utilizes and maps the spectral reflection of specific vegetation features. These are the indicators of plant development and crop condition. Over the years, a number of spectral indices have been developed, but the ultimate combination of information required by the farmer, and the capability of remote sensing to map this information, has not yet been achieved. The study, which lasted three years and was performed simultaneously, collected vegetation and remote‐sensing data. The study aimed to improve the current abilities of remotely sensed agriculture monitoring. Indices were developed relating to various features of wheat. These indices map the current conditions of the crop, such as nitrogen in the leaves, and predict the yield. Evaluation of these indices, and already known indices, shows that each can be used to map different crop variables. [ABSTRACT FROM AUTHOR]

Since optical and microwave sensors respond to very different target characteristics, their role in crop monitoring can be viewed as complementary. In particular, the all-weather capability of Synthetic Aperture Radar (SAR) sensors can ensure that data gaps that often exist during monitoring with optical sensors are filled. There were three Landsat Thematic Mapper (TM) satellite images and three Envisat Advanced Synthetic Aperture Radar (ASAR) satellite images acquired from reviving stage to milking stage of winter wheat. These data were successfully used to monitor crop condition and forecast grain yield and protein content. Results from this study indicated that both multi-temporal Envisat ASAR and Landsat TM imagery could provide accurate information about crop conditions. First, bivariate correlation results based on the linear regression of crop variables against backscatter suggested that the sensitivity of ASAR C-HH backscatter image to crop or soil condition variation depends on growth stage and time of image acquisition. At the reviving stage, crop variables, such as biomass, Leaf Area Index (LAI) and plant water content (PWC), were significantly positively correlated with C-HH backscatter (r = 0.65, 0.67 and 0.70, respectively), and soil water content at 5 cm, 10 cm and 20 cm depths were correlated significantly with C-VV backscatter (r = 0.44, 0.49 and 0.46, respectively). At booting stage, only a significant and negative correlation was observed between biomass and C-HH backscatter (r = -0.44), and a saturation of the SAR signal to canopy LAI could explain the poor correlation between crop variables and C-HH backscatter. Furthermore, C-HH backscatter was correlated significantly with soil water content at booting and milking stage. Compared with ASAR backscatter data, the multi-spectral Landsat TM images were more sensitive to crop variables. Secondly, a significant and negative correlation between grain yield and ASAR C-HH & C-VV backscatter at winter wheat booting stage was observed (r = -0.73 and -0.55, respectively) and a yield prediction model with a correlation coefficient of 0.91 was built based on the Normalized Difference Water Index (NDWI) data from Landsat TM on 17 April and ASAR C-HH backscatter on 27 April. Finally, grain protein content was found to be correlated significantly with ASAR C-HH backscatter at milking stage (r = -0.61) and with Structure Insensitive Pigment Index (SIPI) data from Landsat TM at grain-filling stage (r = 0.53), and a grain protein content prediction model with a correlation coefficient of 0.75 was built based on the C-HH backscatter and SIPI data. [ABSTRACT FROM AUTHOR]

The objective of the research was to compare multispectral images of the Ikonos high resolution satellite with optical data collected with a continuously moving ground-based platform. A Cropscan, Inc. multispectral radiometer was used to automatically take a measurement every 1-1.5 m. Field images of both devices were compared in their capability for detecting nitrogen variability in a wheat field that had three seeding densities and five nitrogen application rates. Every treatment had four repetitions, resulting in 60 plots of 12 m by 16 m. Due to frequent cloud cover, only one Ikonos image could be taken during the growing season. At harvest, yield variables of the plots were collected to relate to the images of both systems. Calculated normalized difference vegetation index (NDVI) from both measurement systems was a good indicator of applied nitrogen in the field: correlation was 0.76 and 0.85 for Ikonos and the platform, respectively. NDVI as also well correlated to yield variables at harvest. The best correlation found for both systems was with grain nitrogen content (kg[N]ha-1): 0.86 and 0.91 for Ikonos and the platform, respectively. The ground-based system provided a better estimate of nitrogen in the wheat crop and was better related to dry matter content. Ikonos images were higher correlated to straw yield and biomass. When estimating yield variables with NDVI measurements, the average error percentage was lower for the platform than for Ikonos. In conclusion, both measurement devices could be used to indicate wheat nitrogen stress by measuring canopy reflectance, but measurements of the platform were more accurate. [ABSTRACT FROM AUTHOR]

The sources of variation (environment, genotype and date of measurement) of spectral reflectance indices describing biomass and its physiological status, and their potential use for providing accurate and non-destructive estimates of crop phenology and yield, were studied on canopies of several collections of durum wheat genotypes showing adaptation to different Mediterranean environments. Spectral reflectance was measured during grain filling. All spectral indices and grain yields showed significant differences between contrasting environments in terms of water availability. Photosynthetic area indices and senescence indices were good indicators, for all genotype collections, of biomass and phenology, respectively, when comparing a wetter site with a drier site. When crop development was accelerated by growing plants under high temperature, provided by a spring-sown trial under Mediterranean conditions, all spectral indices showed significant variation within a period of one week through grain filling, reflecting the changes in crop phenology and the onset of senescence. The reported changes in the values, and even the signs, of the correlation coefficients across genotypes between grain yield and some reflectance indices might reflect genotypic differences in response (by avoidance) to high temperature and drought during late grain filling. Spectral reflectance data may help to understand phenological characteristics of durum wheat canopies, such as crop duration, provided the date of measurement is well chosen. [ABSTRACT FROM AUTHOR]

Gross primary productivity (GPP) estimates derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) are converted to wheat yield and compared with observed yield for counties, climate districts and entire states for the 2001 and 2002 growing seasons in Montana and North Dakota. Analyses revealed that progressive levels of spatial aggregation generally improved the relations between estimated and observed wheat yield. However, only state level yield estimates were sufficiently accurate (? 5% deviation from observed yield). The statewide yield results were encouraging because they were derived without the use of retrospective empirical analyses, which constitutes a new opportunity for timely wheat yield estimates for large regions. Additionally, this study identifies six practical limits to estimating wheat yield using MODIS GPP. As a result we describe three suggestions for improving wheat yield estimates for scientists willing to re-compute MODIS-derived GPP estimates using regionally specific inputs. [ABSTRACT FROM AUTHOR]

In this study, the hypothesis that optimum wavebands could be selected for calculation of RDI values was tested. The canopy reflectance data from 350 nm to 2500 nm were collected at various growth stages of wheat irrigated in six amounts before stem elongation. Leaf water contents (%LWC) were insensitive to the irrigation level, while the leaf area index (LAI) and dry phytomass (DM) appeared to be sensitive to the irrigation level. The irrigation levels did not cause considerable spectral response within a specific growth stage, but spectral differences between the growth stages were evident, resulting in strong relationships between the spectral data and the biophysical parameters based on the pooled data of different stages and poor and inconsistent relationships between the spectral data and the biophysical parameters based on data within a specific growth stage. Under field conditions, the peak absorption positions of the wheat canopy were found to shift away from the theoretical wavelengths in the water absorption regions. When using the pooled data covering all the growth stages, the RDI values performed better than reflectance minimum and large differences were found between the RDI values on different selected wavebands in correlating the spectral data with the biophysical parameters. The 965–1085 nm and 1192–1282 nm spectral regions appeared to be the optimum bands for relating RDI to %LWC. [ABSTRACT FROM AUTHOR]

Canopy reflectance data selected at key growth stages of winter wheat were analysed. Regression equations between foliar total nitrogen content and other foliar biochemical concentrations, dried biomass indices, and grain quality indicators were established. The results showed that there were robust correlations between foliar total nitrogen content and other foliar biochemical concentrations, dried biomass indices, and grain quality indicators. The predictions of soluble sugar content, foliar water content, stem water content, foliar starch content, foliar dried weight, plant dried weight, and Leaf Area Index (LAI), etc. by foliar total nitrogen content were successful and feasible. The predictions of grain protein and dry gluten content by foliar total nitrogen content at anthesis were surprisingly good. The wavelength bands related to foliar total nitrogen content selected by regression equation were located at 1000-1140 nm and 1200-1300 nm. [ABSTRACT FROM AUTHOR]

This study demonstrates a unique matrix approach to determine within-field variability in wheat yields using fine spatial resolution 4m IKONOS data. The matrix approach involves solving a system of simultaneous equations based on IKONOS data and post-harvest yields available at entire field scale.This approach was compared with a regression-based modelling approach involving field-sensor measured yields and the corresponding IKONOS measured indices and wavebands. The IKONOS data explained 74-78%variability in wheat yield. This is a significant result since the finer spatial resolution leads to capturing greater spatial variability and detail in landscape relative to coarser spatial resolution data. A pixel-by-pixel mapping of wheat yield variability highlights the fine spatial detail provided by IKONOS data for precision farming applications. [ABSTRACT FROM AUTHOR]

Information on the quantity and spatial distribution of canopy physiological and biochemical components is of importance for the study of nutrient cycles, productivity, vegetation stress and, more recently, in driving ecosystem models. In this context, remote sensing can play a unique and essential role because of its ability to acquire synoptic information at different time and space scales. This paper presents parts of a two-year field and laboratory study with the new airborne hyperspectral sensor, the Airborne Visible near Infrared Imaging Spectrometer (AVIS), over a test site in the Bavarian Alpine foothills, Germany (48° 8′ N, 11° 17′ E). The 80-band AVIS was developed at the Department for Earth and Environmental Sciences of the Ludwig-Maximilians-University Munich and records the 550-1000 nm spectral range. Using this system, 18 hyperspectral datasets were collected between April and September of 1999 and 2000. Weekly measurements of several plant parameters (height, biomass, leaf chlorophyll content, leaf nitrogen content) were carried out during these time periods on three (1999) and six (2000) fields of winter wheat, whereby two different cultivars were investigated in 2000. After system correction and calibration, the hyperspectral data were atmospherically corrected and calibrated to reflectance. The resulting spectra were analysed for their chemical compounds. The statistical analysis was carried out using the Chlorophyll Absorption Integral (CAI) in comparison to established indices: Optimized Soil-Adjusted Vegetation Index (OSAVI) and hyperspectral Normalized Difference Vegetation Index (hNDVI). Both the chlorophyll and nitrogen content of the leaves showed good correlations with CAI on a field mean basis. These results as well as two-dimensional information on these parameters are presented to provide information about the spatial heterogeneity within a field. [ABSTRACT FROM AUTHOR]

The need for multi-temporal data analysis for delineation of wheat crop has been demonstrated first. It is found that Maximum Likelihood Classification (MLC) with the composite data of multi-temporal images is limited by the problem of large null set containing crop pixels. Therefore, for effective classification of multi-temporal images, two approaches are evaluated: (1) MLC with different strategies--sequential MLC (s_MLC), MLC with Principal Components (pca_MLC) and iterative MLC (i_MLC); and (2) Artificial Neural Networks (ANN) with back-propagation method. These classifiers were applied on multi-temporal Indian Remote Sensing satellite (IRS)-1B images to classify wheat crop in two areas of India, one with dominant wheat and the other with less dominant wheat cultivation. Among the three strategies of MLC, i_MLC has resulted in relatively better classification of wheat. However, the result of ANN classification is superior to that of i_MLC with respect to the correctness of labelling of wheat pixels. The performance of ANN is proved to be better, in both the situations of dominant wheat and less dominant wheat cultivation. [ABSTRACT FROM AUTHOR]

Wheat growth profile based yield models for 12 districts of Punjab State and 16 districts of Haryana State have been developed using the normalised difference vegetation index (NDVI) derived from NOAA-11 AVHRR data of the 1993-94 cropping season. Atmospheric normalisation of AVHRR data was performed prior to deriving district-level area weighted average NDVI (AWANDVI). The invariant growth profile model suggested by Badhwar was fitted and spectral emergence date, maximum vegetative vigour, peak day value of profile, growth rate and senescence rate, area under the curve, etc. were derived. These parameters were related to the reported district-level wheat yields using multiple regression analysis. A field study was also conducted using a handheld spectro-radiometer at the research station of Punjab Agricultural University (PAU), Ludhiana. From this field experimental data, wheat growth profile parameters were derived which were compared with satellite based parameters. Inversion of the models was carried out to evaluate the results by comparing the reported and predicted wheat yields. The results indicate highly significant fitting of the NDVI profile to the Badhwar model as indicated by multiple linear correlation coefficients and Fisher test. A significant relationship between district-level wheat yields and fractional area under the curve was also observed. The overall correlation of 0.82 for Punjab and Haryana states was obtained between reported yield and growth profile derived parameters. Atmospheric normalisation resulted in improvement of prediction model statistics (R increased from 0.42 to 0.86). Evaluation of the models indicated that 10 out of 16 districts of Haryana State and 9 out of 12 districts of Punjab State showed relative deviations within 10% between reported and model predicted wheat yields. [ABSTRACT FROM AUTHOR]

Wheat leaves were measured radiometrically in order to spectrally characterize the water deficiency symptoms. In this study, a FieldSpec-FR was used for measuring wheat leaf spectra. After the spectral analysis using a spectral normalizing technique, the spectral absorption feature parameters: wavelength position (nm), depth and area (relative value) were extracted from each wheat leaf spectrum. The relative water content (RWC) was measured for each wheat leaf sample. A linear regression analysis was conducted between the spectral absorption feature parameters and corresponding RWCs. The experimental results from 110 samples indicated that reflectance spectra of wheat leaves in the 1650-1850 nm region were dominated by water content. With a decrease in wheat leaf RWC, the 1650-1850 nm spectral absorption features gradually become obvious. The relative errors of predicted RWCs and the absolute error of predicted wavelength positions were calculated from 12 validation samples by established regression equations. The relative errors of predicted RWCs and the absolute error of predicted wavelength position (nm) were both low (<6% for RWCs by the depth and area and <12 nm for the wavelength position, respectively). Furthermore, we discuss the potential and limitations of spectroscopic determination of wheat RWC by using remote sensing technology. [ABSTRACT FROM AUTHOR]

Spectra of wheat measured in the field in an oil-bearing area north of Changyi, south of the Yellow River delta in eastern China, were analysed to study the relations between spectral characteristics and hydrocarbon microseepage. Along two transects over known petroleum reservoirs, ethane gas contents measured in soil show high values above the oil-water contacts and faults. Delta carbonate contents also showed high values at these contacts. Both features are indicative of hydrocarbon microseepage. The red-edge positions of wheat spectra were calculated and found to be positioned at 744.43nm in areas of microseepage and 738.26nm in areas with no microseepage; a 7nm shift to longer wavelength (red shift). Furthermore, a regression analysis of the red-edge inflection point positions versus delta carbonate and ethane shows a statistically significant correlation indicating a possible relation between microseepage and spectral properties of wheat. This research demonstrates that at a field scale remote sensing can potentially be used to detect hydrocarbon microseepages in areas of agricultural monoculture. [ABSTRACT FROM AUTHOR]