Ground-based Hyperspectral Data Research Articles

Estimating Swiss chard foliar macro- and micronutrient concentrations under different irrigation water sources using ground-based hyperspectral data and four partial least squares (PLS)-based (PLS1, PLS2, SPLS1 and SPLS2) regression algorithms

Timely information on crop foliar nutrient content provides a measure of crop nutritional and vitality status. Growers and farm managers use such information for precision crop management such as an appropriate fertilizer application to correct for any crop nutrient deficiencies at identified hotspots. Foliar heavy nutrient content could also be a direct indicator of crops having been polluted from the surroundings, which may be a result of heavy metals absorbed from, among others, contaminated soils and waste water. In the present study, we explored the potential use of four partial least squares (PLS)-based regression algorithms for estimating foliar Swiss chard macro- and micronutrient concentrations using ground-based hyperspectral data under three treatments; i.e. rainwater+fertilizer (‘R+F’), tap water+fertilizer (‘T+F’), and treated wastewater (‘W’). Swiss chard canopy-level hyperspectral measurements under these three treatments were collected using a handheld spectroradiometer 2.5months after sowing. The reflectance spectra were normalized to their first-order derivatives. The concentrations of three Swiss chard foliar macronutrients (NPK) and three micronutrients (Zn, Cu and Fe) under the three treatments were determined. Regression models for estimating macro- and micronutrient concentrations were then derived using PLS1 and sparse PLS1 methods, while the potential simultaneous estimation of the macronutrient as well as micronutrient concentrations was explored using the PLS2 and SPLS2 regression approaches. Results showed that high variances in the macro- and micronutrient concentrations can be explained by the four regression models under the three treatments (R2train ranged between 0.73 and 0.99), except when P, Zn and Cu concentrations were estimated using the PLS2-based models under the three treatments (R2train ranged between 0.08 and 0.68) and Fe concentration using SPLSR1 under ‘W’ treatment (R2train=0.64). Our results further showed that Swiss chard foliar N (RMSE=1.67%) concentration under ‘R+F’ treatment and Fe (RMSE=7.83%) concentration under the ‘T+F’ treatment most accurately estimated macro- and micronutrients. Our study also showed that the Swiss chard foliar macronutrient concentrations were more accurately estimated compared to micronutrient concentrations and PLS2 outperformed the PLS1 based regression model. The results of the current study pave the way for developing an effective foliar nutrient estimation routine suitable for monitoring Swiss chard nutrient status under different treatments.

Remote detection of fluid-related diagenetic mineralogical variations in the Wingate Sandstone at different spatial and spectral resolutions

Well-exposed eolian units of the Jurassic system on the Colorado Plateau including the Wingate Sandstone, show prominent color variations throughout southeastern Utah due to diagenetic changes that include precipitation and/or removal of iron oxide, clay, and carbonate cement. Spatially variable characteristic diagenetic changes suggest fluid-rock interactions through the sandstone. Distinctive spectral signatures of diagenetic minerals can be used to map diagenetic mineral variability and possibly fluid-flow pathways. The main objective of this work was to identify characteristic diagenetic minerals, and map their spatial variability from regional to outcrop scale in Wingate Sandstone exposures of Lisbon Valley, Utah. Laboratory reflectance spectroscopy analysis of the samples facilitated identification of diagnostic spectral characteristics of the common diagenetic minerals and their relative abundances between altered and unaltered Wingate Sandstone. Comparison of reflectance spectroscopy with satellite, airborne, and ground-based imaging spectroscopy data provided a method for mapping and evaluating spatial variations of diagenetic minerals. The Feature-oriented Principal Component Selection method was used on Advanced Spaceborne Thermal Emission and Reflection Radiometer data so as to map common mineral groups throughout the broader Wingate Sandstone exposure in the area. The Minimum Noise Fraction and Spectral Angle Mapper methods were applied on airborne HyMap and ground-based hyperspectral imaging data to identify and map mineralogical changes. The satellite and airborne data showed that out of 25.55km2 total exposure of Wingate Sandstone in Lisbon Valley, unaltered sandstone cover 12.55km2, and altered sandstone cover 8.90km2 in the northwest flank and 5.09km2 in the southern flank of the anticline. The ground-based hyperspectral data demonstrated the ability to identify and map mineral assemblages with two-dimensional lateral continuity on near-vertical rock faces. The results showed that 39.71% of the scanned outcrop is bleached and 20.60% is unbleached while 6.33% remain unclassified, and 33.36% is masked-out as vegetation. The bleached and unbleached areas are alternating throughout the vertical face of the outcrop. The relative hematite abundance observed in the unbleached areas are somewhat symmetrical. This indicates fairly similar reaction intensities along the upper and lower reaction fronts observed in the vertical section. The distribution geometry and relative abundances of diagenetic minerals not only suggest multiple paths of fluid-flow in Wingate Sandstone but also provides some insight about relative direction of past fluid-flow.

Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley

In this study we combined selected vegetation indices (VIs) and plant height information to estimate biomass in a summer barley experiment. The VIs were calculated from ground-based hyperspectral data and unmanned aerial vehicle (UAV)-based red green blue (RGB) imaging. In addition, the plant height information was obtained from UAV-based multi-temporal crop surface models (CSMs). The test site is a summer barley experiment comprising 18 cultivars and two nitrogen treatments located in Western Germany. We calculated five VIs from hyperspectral data. The normalised ratio index (NRI)-based index GnyLi (Gnyp et al., 2014) showed the highest correlation (R2=0.83) with dry biomass. In addition, we calculated three visible band VIs: the green red vegetation index (GRVI), the modified GRVI (MGRVI) and the red green blue VI (RGBVI), where the MGRVI and the RGBVI are newly developed VI. We found that the visible band VIs have potential for biomass prediction prior to heading stage. A robust estimate for biomass was obtained from the plant height models (R2=0.80–0.82). In a cross validation test, we compared plant height, selected VIs and their combination with plant height information. Combining VIs and plant height information by using multiple linear regression or multiple non-linear regression models performed better than the VIs alone. The visible band GRVI and the newly developed RGBVI are promising but need further investigation. However, the relationship between plant height and biomass produced the most robust results. In summary, the results indicate that plant height is competitive with VIs for biomass estimation in summer barley. Moreover, visible band VIs might be a useful addition to biomass estimation. The main limitation is that the visible band VIs work for early growing stages only.

Newly Combined Spectral Indices to Improve Estimation of Total Leaf Chlorophyll Content in Cotton

The total leaf chlorophyll content (TLCC) provides valuable information about the physiological status of crops. The objectives of this study were 1) to analyze the leaf area index (LAI) and soil factors that influences the estimation of TLCC using the PROSAIL model, which is a combination of the PROSPECT leaf model and the SAIL canopy model; 2) to propose newly combined spectral indices that reduce the influence of LAI and soil factors in order to improve the TLCC estimation; and 3) to test and validate the relationship between TLCC and the newly combined spectral indices. Ground-based hyperspectral data and concurrent TLCC parameters of samples were acquired at the Shihezi University Experiment Site, Xinjiang Province, China, during the 2009 and 2010 cotton growing seasons. The results showed that the newly combined spectral indices [double-peak canopy nitrogen index I (DCNI I), the ratio of the structure insensitive pigment index to the ratio vegetation index III (SIPI/RVI III), the ratio of the plant pigment ratio to the normalized difference vegetation index (PPR/NDVI), and the modified MERIS terrestrial chlorophyll index (MMTCI)] were more sensitive to chlorophyll and more resistant to LAI than the PPR, SIPI, and MERIS terrestrial chlorophyll index alone. In this study, DCNI I proved to be the best spectral index for estimating chlorophyll content, with determination coefficients (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) and root mean square error (RMSE) values of 0.80 and 8.31μg · cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">- 2</sup> , respectively. PPR/NDVI was also strongly correlated with chlorophyll content, with corresponding R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and RMSE values of 0.79 and 9.45 μg · cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> , respectively. This study concluded that DCNI I and PPR/NDVI, in association with indices related to nitrogen, have good potential for assessing nitrogen content.

A comparison of partial least squares (PLS) and sparse PLS regressions for predicting yield of Swiss chard grown under different irrigation water sources using hyperspectral data

There is an increasing demand for fresh vegetables such as Swiss chard in cognisance of their nutritive value. Early prediction of Swiss chard yield provides a valuable knowledge base for product management decisions like pre-harvest planning, post-harvest handing, food policy, and marketing. Consequently, the objective of the present study was to investigate the use of hyperspectral data in predicting yield of Swiss chard grown under different irrigation water sources. Swiss chard ground-based hyperspectral data were collected at canopy level using a handheld spectroradiometer at 2 and 2.5months after planting. Reflectance spectra were transformed to their first-order derivative and partial least squares (PLS) and sparse PLS (SPLS) regressions (R) were used for data analysis. Results showed that 95% and 97% of Swiss chard fresh and dry yields variation, respectively could be explained. SPLSR outperformed PLSR models for predicting Swiss chard fresh and dry yields. Results further showed that models developed using data collected when the crop was 2.5months old were more accurate than models derived using a 2-month old crop data, except the SPLSR model for predicting dry yield. Fresh yield estimates could be accurately modelled (root mean square error: RMSE=23.97% of the mean, Nash–Sutcliffe efficiency: NSE=0.93). However, Swiss chard dry yield could not be reliably predicted (at minimum RMSE=35.00% of the mean and a maximum NSE of 0.60 were obtained). This study demonstrates the potential of hyperspectral data in predicting Swiss chard fresh yield using combined irrigation treatments data sets. The study offers insight to the potential of large-scale prediction and estimation of Swiss chard yield using space borne and/or airborne hyperspectral data.

Fusion of remotely sensed data from airborne and ground-based sensors to enhance detection of cotton plants

The study investigated the use of aerial multispectral imagery and ground-based hyperspectral data for the discrimination of different crop types and timely detection of cotton plants over large areas. Airborne multispectral imagery and ground-based spectral reflectance data were acquired at the same time over three large agricultural fields in Burleson Co., Texas during the 2010 growing season. The discrimination accuracy of aerial- and ground-based data was examined individually; then a multi-sensor data fusion technique was applied on both datasets in order to improve the accuracy of discrimination. The individual classification accuracy of data taken with the aerial- and ground-based sensors were 90% and 93.3%, respectively. In comparison, the accuracy of discriminating crop types with fused data was 100% in the calibration and only 3.33% misclassification in the cross-validation. These results suggest that data fusion techniques could greatly enhance our ability to detect cotton from other plants.

Early detection of canopy nitrogen deficiency in winter wheat (Triticum aestivum L.) based on hyperspectral measurement of canopy chlorophyll status

A spectroscopic method was developed to measure the nitrogen status of winter wheat (Triticum aestivum L.) canopies. Two years of field experiments, including a range of cultivars grown with differing levels of nitrogen fertilization, were conducted and ground-based hyperspectral data were collected to develop and validate an empirical model for early detection of low canopy chlorophyll content. Canopy reflectance was measured with a spectrometer, fitted with a 25° field of view fibre-optic adaptor. Canopy chlorophyll density (CCD), representing the total amount of chlorophyll present in the canopy per unit ground area, was combined according to the contribution of winter wheat leaves in different layers of the canopy and related to canopy reflectance. Combined canopy chlorophyll density (CCCD) calculated with both layers 1 and 2 and with layers 1, 2 and 3 were better related to difference vegetation index (DVI=RNIR−RRED, where RNIR and RRED were reflectance at 890 nm and 670 nm, respectively) than CCD in any individual layer. Statistical prediction models of canopy chlorophyll status in winter wheat were developed. The CCCD1+2 model demonstrated lower root mean square errors and higher modelling efficiencies than those of the CCCD1 and CCCD1+2+3 models. Chlorophyll status in the two uppermost layers of the wheat canopy could be quantified using DVI. Therefore, early detection of canopy nitrogen deficiency in winter wheat was achieved.

Discrimination of Spectrally-Close Crops Using Ground-Based Hyperspectral Data

Hyperspectral remote sensing, because of its large number of narrow bands, has shown possibility of discriminating the crops. Current study was carried out to select the optimum bands for discrimination among pulses, cole crops and ornamental plants using the ground-based Hyperspectral data in Patha village, Lalitpur district, Uttar Pradesh state and Kolkata, West Bengal state. The field observations of reflectance were taken using a 512-channel spectroradiometer with a range of 325–1075 nm. The stepwise discriminant analysis was carried out and separability measures, such as Wilks’ lambda and F-Value were used as criteria for identifying the narrow bands. The analysis showed that, the best four bands for pulse crop discrimination lie mostly in NIR and early MIR regions i.e. 750, 800, 940 and 960 nm. Within cole crops discrimination is primarily determined by the green, red and NIR bands of 550, 690, 740, 770 and 980 nm. The separability study showed the bands 420,470,480,570,730,740, 940, 950, 970, 1030 nm are useful for discriminating flowers.

Зіставлення супутникових та наземних гіперспектральних даних при визначенні позиції червоного краю спектрів відбиття

Зіставлення супутникових та наземних гіперспектральних даних при визначенні позиції червоного краю спектрів відбиття

Hybrid Segmentation – Artificial Neural Network Classification of High Resolution Hyperspectral Imagery for Site-Specific Herbicide Management in Agriculture

Site-Specific Herbicide Management (SSHM) in Precision Agriculture (PA) requires weed detection in crop fields for directed herbicide application instead of spraying entire fields. This has significant economic and environmental advantages for improved agricultural practices that are essential given forecast increases in global population and food production needs. In this study, a new hybrid segmentation - Artificial Neural Network (HS-ANN) method was compared to standard Maximum Likelihood Classification (MLC) for improving crop/weed species discrimination in SSHM/PA. Very high spatial resolution (1.25 mm) ground-based hyperspectral image data were acquired over field plots of canola, pea, and wheat crops seeded with two weed species (redroot pigweed, wild oat) in southern Alberta, Canada. The very high spatial and spectral resolution image data required development of a simple yet efficient vegetation index (Modified Chlorophyll Absorption in Reflectance Index (MCARI)) threshold segmentation to separate vegetation from soil for classification. The HSANN consistently outperformed MLC in both single date and multi-temporal classifications. Higher class accuracies were obtained with multi-temporally trained ANNs (84 to 92 percent overall), with improvements up to 31 percent over MLC. With advancements in imaging technology and computer processing speed, this HS-ANN method may constitute a viable farm option for real-time detection and mapping of weed species for SSHM in agriculture.

Canopy assessment of biochemical features by ground-based hyperspectral data for an invasive species, giant reed (Arundo donax)

This study explored the potential use of hyperspectral data in the non-destructive assessment of chlorophyll, carbon, and nitrogen content of giant reed at the canopy level. We found that pseudoabsorption and derivatives of original hyperspectral data were able to describe the relationship between spectral data and measured biochemical characteristics. Based on correlogram analyses of ground-based hyperspectral data, we found that derivatives of pseudoabsorption were the best predictors of chlorophyll, carbon, and nitrogen content of giant reed canopies. Within the visible region, spectral data significantly correlated with chlorophyll content at both 461 nm and 693 nm wavelengths. Within the near-infrared region, carbon levels correlated with hyperspectral data at five causal wavelengths: 1038 nm, 1945 nm, 1132 nm, 1525 nm, and 1704 nm. The best spectral wavelength for estimating nitrogen content was 1542 nm. Such relationships between nutrient content and spectral data were best represented by exponential functions in most situations.

Spectral and thermal sensing for nitrogen and water status in rainfed and irrigated wheat environments

Variable-rate technologies and site-specific crop nutrient management require real-time spatial information about the potential for response to in-season crop management interventions. Thermal and spectral properties of canopies can provide relevant information for non-destructive measurement of crop water and nitrogen stresses. In previous studies, foliage temperature was successfully estimated from canopy-scale (mixed foliage and soil) temperatures and the multispectral Canopy Chlorophyll Content Index (CCCI) was effective in measuring canopy-scale N status in rainfed wheat (Triticum aestivum L.) systems in Horsham, Victoria, Australia. In the present study, results showed that under irrigated wheat systems in Maricopa, Arizona, USA, the theoretical derivation of foliage temperature unmixing produced relationships similar to those in Horsham. Derivation of the CCCI led to an r2 relationship with chlorophyll a of 0.53 after Zadoks stage 43. This was later than the relationship (r2 = 0.68) developed for Horsham after Zadoks stage 33 but early enough to be used for potential mid-season N fertilizer recommendations. Additionally, ground-based hyperspectral data estimated plant N (g kg−1) in Horsham with an r2 = 0.86 but was confounded by water supply and N interactions. By combining canopy thermal and spectral properties, varying water and N status can potentially be identified eventually permitting targeted N applications to those parts of a field where N can be used most efficiently by the crop.

Studying mixed grassland ecosystems I: suitable hyperspectral vegetation indices

Hyperspectral remote sensing data with a greater number of bands and narrower bandwidths can be effectively exploited for the study of ecosystem patterns and processes. Hyperspectral remote sensing of semiarid mixed grassland faces the following two challenges, however: (i) providing a good understanding of the performance of different vegetation indices (VIs) in estimating biophysical properties of grassland with a small amount of green vegetation, a large amount of dead material on the ground, and variable soil–ground conditions; and (ii) examining the spatial characterization of hyperspectral remotely sensed data to optimize sampling procedures and address scaling issues. Using ground-based hyperspectral and biophysical data, this study has compared the predictive capability of VIs for estimation of grassland leaf area index (LAI) (this paper) and examined the spatial variation of grassland LAI (the companion paper). The results in this paper indicate that the relationships between grassland LAI and VIs are significant. The performance of the renormalized difference vegetation index (RDVI), adjusted transformed soil-adjusted vegetation index (ATSAVI), and modified chlorophyll absorption ratio index 2 (MCARI2) was slightly better than that of the other VIs in the groups of ratio-based, soil-line-related, and chlorophyll-corrected VIs, respectively. By incorporating the cellulose absorption index (CAI) as a litter factor in ATSAVI, a new VI was computed (L-ATSAVI), and it improved the LAI estimation capability in our study area by about 10%.

Studying mixed grassland ecosystems II: optimum pixel size

It was determined in a companion paper that the litter-corrected adjusted transformed soil-adjusted vegetation index (L-ATSAVI) was the best leaf area index (LAI) indicator in a mixed grassland ecosystem. To optimize the sampling procedures and address the scaling issues for the mixed grass ecosystem, this study examined the dominant scale of spatial variation in both LAI and L-ATSAVI using two methods, namely Mexican hat wavelet analysis and semivariogram analysis. The results showed that both methods can identify grassland spatial variation, and the cyclicity (the nature repetition in a dataset) of grassland LAI was about 140 m along the central transect of five parallel transects within the study area. The advantage of wavelet analysis over semivariogram analysis for spatial pattern interpretation was that it could identify the exact location of the transition. The wavelet analysis demonstrated that the cyclicity of L-ATSAVI also corresponded well with features of grassland LAI along the transect. Therefore, following the sampling theorems, a pixel size of less than 35 m will retain most of the spatial variation of grassland LAI in our study area. In terms of this optimum pixel size, the scale of ground-based hyperspectral data and LAI along the transect was simulated using a low-pass filtering procedure with a 30 m moving window. Statistical analysis indicated that scale-simulated L-ATSAVI could significantly explain more grassland LAI (r2 up to 89%) than the original 3 m resolution. This conclusion can be further applied to select the optimal pixel size of remote sensing images and detect the hierarchical characteristics in a grassland landscape.