Transformed Chlorophyll Absorption In Reflectance Index Research Articles

Inversion of Wheat Leaf Area Index by Multivariate Red-Edge Spectral Vegetation Index

Leaf area index (LAI) is an important parameter that determines the growth status of winter wheat and impacts the ecological and physical processes of plants in ecosystems. The problem of spectral saturation of winter wheat LAI at the booting stage was easily caused by the inversion of the univariate red-edge spectral vegetation index constructed by the red-edge band. In this paper, a new method that the univariate red-edge spectral vegetation index constructed in the red-edge band is used to invert the spectral saturation of the winter wheat LAI. The multivariable red-edge spectral vegetation index is used to invert the winter wheat LAI. This method can effectively delay the phenomenon of spectral saturation and improve the inversion precision. In this study, the Sentinel-2 data were used to invert the winter wheat LAI. An univariate and multivariate red-edge spectral vegetation index regression model was constructed based on the Red-edge Normalized Difference Spectral Indices 1 (NDSI1), Red-edge Normalized Difference Spectral Indices 2 (NDSI2), Red-edge Normalized Difference Spectral Indices 3 (NDSI3), Modified Chlorophyll Absorption Ratio Index (MCARI), MERIS Terrestrial Chlorophyll Index (MTCI), Transformed Chlorophyll Absorption in Reflectance Index (TCARI), and Transformed Chlorophyll Absorption in Reflectance Index/the optimized soil adjusted vegetation index (TCARI/OSAVI). Based on the correlation coefficient, the coefficient of determination (R2), the root mean square error (RMSE) and noise equivalent value (NE), the best model was selected and verified to generate an inverted map. The results showed that the multivariable red-edge spectral vegetation index of NDSI1 + NDSI2 + NDSI3 + TCARI/OSAVI + MCARI + MTCI + TCARI was the best model for inverting the winter wheat LAI. The R2, the RMSE and the NE values were all satisfied the requirements of the inversion precision (R2 = 0.8372/0.8818, RMSE = 0.2518/0.1985, NE = 5/5). In summary, this method can be used to judge the growth of winter wheat and provide an accurate basis for monitoring crop growth.

Dry season forage assessment across senegalese rangelands using earth observation data

Strengthening of feed security in the Sahel is urgently needed given the climate change and growing human population. A prerequisite to this is sustainable use of rangeland forage resources for livestock. Many studies have focused on the assessment of rangeland resources during the rainy season, while only a few have focused on the dry season which is the longest and most demanding period for livestock in Sahelian rangelands. The objective of this study is to develop remote sensing-based models for estimating dry season forage vegetation mass. To that end, 29 vegetation indices calculated from each of the MODIS-MCD43A4 (500 m), Landsat-8 (30 m), and Sentinel-2 (10 m) satellite products were used and tested against in situ data collected during three field-measurement campaigns in 2021 at eleven monitoring sites across Senegalese rangelands. Four statistical models were tested, namely, random forest, gradient boosting machines, and simple linear and multiple linear regressions. The two main vegetation mass variables modeled from remote sensing imagery were the standing herbaceous and litter dry mass (BH) and total forage dry mass (BT) with a dry mass of woody plant leaves added to BH. Overall, Sentinel-2 data provided the best performance for the assessment of BH with multiple linear regression (R2 = 0.74; RMSE = 378 kg DM/ha) using NDI5 (Normalized Difference Index5), GRCI (Green Residue Cover Index), SRI (Simple Ratio Index), TCARI (Transformed Chlorophyll Absorption in Reflectance Index), and DFI (Dead Fuel Index) indices. For BT, the best model was also obtained from Sentinel-2 data, including RVI3 (Ratio Vegetation Index3) (R2 = 0.78; RMSE = 496 kg DM/ha). Results showed the suitability of combining the red, green, blue, NIR, SWIR1, and SWIR2 bands in monitoring forage availability during the dry season. Our study revealed that the spectral richness of the optical sensor systems Sentinel-2, Landsat-8, and MODIS-MCD43A4 allowed for accurate assessments of dry-season forage mass of semi-arid rangelands. Adding to this, the high spatial and temporal resolution of Sentinel-2 satellite imagery makes this a promising data source for timely monitoring. These findings can support the monitoring of the animal feed balance in Sahelian countries and contribute to enhancing the resilience of pastoralism toward feed shortage through early warning systems.

Accuracy of narrow-band spectral indices estimation by wide-band remote sensing data

Narrow-band spectral indices are quite informative and important in various applications of remote sensing – to assess the condition of vegetation, soils, water bodies and other land surface formations. However, direct measurement of narrow-band spectral indices requires hyperspectral imaging. But most of modern multispectral aerospace imaging systems are wide-band. Accordingly, it is not possible to calculate the narrow-band index directly from wide-band remote sensing data. This paper discusses approaches to the narrow-band spectral indices restoration by wide-band remote sensing data using statistical models of interrelations of narrow- and wide-band indices itself, of source wide-band and narrow-band signals in close spectral bands, as well as of land surface reflectance quasi-continuous spectra translation from wide bands to narrow ones.The experimental accuracy estimation of narrow-band spectral indices restoration by wide-band multispectral satellite image is performed. Three most complicated narrow-band spectral indices, which covering a range of spectrum from visible to short-wave infrared, were considered, namely – the transformed chlorophyll absorption in reflectance index (TCARI), the optimized soil-adjusted vegetation index (OSAVI) and the normalized difference nitrogen index (NDNI). All three mentioned methods for narrow-band spectral indices restoration are analyzed. The worst result is demonstrated for regression-restored signals in spectral bands, and the best result is for the spectra translation method. Therefore, the method on the basis of spectra translation is recommended for practical implementation.

Influence of flight hours on spectral behavior of rice (Oryza Sativa L.) and soybean (Glycine Max L.) cultivars in images obtained by remotely piloted aircraft system

The research aimed to evaluate the spectral behavior of remotely piloted aircraft system (RPAS) images taken of agricultural crops during the day to answer the following hypotheses: (1) there are no significant differences between spectral reflectance information obtained through RPAS images and data from spectroradiometry; (2) the survey time can be defined as a limiting factor in obtaining accurate reflectance data; and (3) it is possible to characterize homogeneous observation windows throughout the day. With regular intervals of 1 h, 12 surveys were made in rice and soybean cultivars, using a Sequoia® camera, embedded in RPAS, and the FieldSpec®3 spectroradiometer, to validate the image spectra. Normalized Difference Vegetation Index (NDVI), Optimized Soil Adjusted Vegetation Index (OSAVI), and Transformed Chlorophyll Absorption Reflectance Index (TCARI) derived from aerial images were subjected to Kruskal–Wallis and hierarchical cluster tests to define homogeneous observation times. The Sequoia® system made it possible to obtain reflectance data that met the validation criteria, even operating under varying interaction factors during the day, so the flight schedule did not result in loss of information about the vegetation. The hierarchical cluster analysis allowed the detection of temporal patterns of NDVI, OSAVI, and TCARI throughout the day. For rice and soybeans, three more homogeneous observation windows were characterized. In terms of temporal pattern of NDVI, OSAVI, and TCARI, the Kruskal–Wallis analysis was quite sensitive to the inherent variation of the crops throughout the day, so a hypothesis of homogeneous monitoring windows input throughout the day could not be proven.

Narrow-band reflectance indices for mapping the combined effects of water and nitrogen stress in field grown tomato crops

This study assessed the use of reflectance indices for detecting the combined effects of water and nitrogen stress in tomatoes (Solanum lycopersicum L.). Spectral reflectance data were acquired from tomato plants, subjected to three water and three nitrogen treatments. Irrigation water was applied in amounts of 100, 70, and 30% of full replenishment of root zone soil water to field capacity. Nitrogen application was 100, 70, and 30% of crop nutrient requirement. The treatments were replicated five times in a randomised complete block design. Plant stress indicators, including leaf temperature (Tc), relative water content (RWC), yield, and leaf chlorophyll content (LCC) were measured at the same time of leaf reflectance data, during the growing season. Reflectance indices including Normalised Difference Vegetation Index (NDVI), Renormalised Difference Vegetation Index (RDVI), Optimised Soil Adjusted Vegetation Index (OSAVI), Photochemical Reflectance Index centered at 550 nm (PRI550), normalised PRI (PRInorm), Transformed Chlorophyll Absorption in Reflectance Index (TCARI), Water Index (WI), and WI/NDVI were obtained from the reflectance data. The results showed that the PRI550, PRInorm, and WI were the most sensitive indices for distinguishing crop water stress, while RDVI, PRInorm, and TCARI had the best correlation with nitrogen stress indicators. PRInorm was the most sensitive index for detecting the combined effect of water and nitrogen stress. This study provided more insights into the usefulness of leaf spectral features for assessing crop abiotic stress. Measuring these indices with hyperspectral sensors provides a rapid, non-destructive, and reliable approach for estimating crop stress.

Maize Crop Coefficient Estimated from UAV-Measured Multispectral Vegetation Indices.

The rapid, accurate, and real-time estimation of crop coefficients at the farm scale is one of the key prerequisites in precision agricultural water management. This study aimed to map the maize crop coefficient (Kc) with improved accuracy under different levels of deficit irrigation. The proposed method for estimating the Kc is based on multispectral images of high spatial resolution taken using an unmanned aerial vehicle (UAV). The analysis was performed on five experimental plots using Kc values measured from the daily soil water balance in Ordos, Inner Mongolia, China. To accurately estimate the Kc, the fraction of vegetation cover (fc) derived from the normalized difference vegetation index (NDVI) was used to compare with field measurements, and the stress coefficients (Ks) calculated from two vegetation index (VI) regression models were compared. The results showed that the NDVI values under different levels of deficit irrigation had no significant difference in the reproductive stage but changed significantly in the maturation stage, with a decrease of 0.09 with 72% water applied difference. The fc calculated from the NDVI had a high correlation with field measurement data, with a coefficient of determination (R2) of 0.93. The ratios of transformed chlorophyll absorption in reflectance index (TCARI) to renormalized difference vegetation index (RDVI) and TCARI to soil-adjusted vegetation index (SAVI) were used, respectively, to establish two types of Ks regression models to retrieve Kc. Compared to the TCARI/SAVI model, the TCARI/RDVI model under different levels of deficit irrigation had better correlation with Kc, with R2 and root-mean-square error (RMSE) values ranging from 0.68 to 0.80 and from 0.140 to 0.232, respectively. Compared to Kc calculated from on-site measurements, the Kc values retrieved from the VI regression models established in this study had greater ability to assess the field variability of soil and crops. Overall, use of the UAV-measured multispectral vegetation index approach could improve water management at the farm scale.

UAV Multispectral Imagery Combined with the FAO-56 Dual Approach for Maize Evapotranspiration Mapping in the North China Plain

As the key principle of precision farming, variation of actual crop evapotranspiration (ET) within the field serves as the basis for crop management. Although the estimation of evapotranspiration has achieved great progress through the combination of different remote sensing data and the FAO-56 crop coefficient (Kc) method, lack of the accurate crop water stress coefficient (Ks) at different space–time scales still hinder its operational application to farmer practices. This work aims to explore the potential of multispectral images taken from unmanned aerial vehicles (UAVs) for estimating the temporal and spatial variability of Ks under the water stress condition and mapping the variability of field maize ET combined with the FAO-56 Kc model. To search for an optimal estimation method, the performance of several models was compared including models based on Ks either derived from the crop water stress index (CWSI) or calculated by the canopy temperature ratio (Tc ratio), and combined with the basal crop coefficient (Kcb) based on the normalized difference vegetation index (NDVI). Compared with the Ks derived from the Tc ratio, the CWSI-based Ks responded well to water stress and had strong applicability and convenience. The results of the comparison show that ET derived from the Ks-CWSI had a higher correlation with the modified FAO-56 method, with an R2 = 0.81, root mean square error (RMSE) = 0.95 mm/d, and d = 0.94. In contrast, ET derived from the Ks-Tc ratio had a relatively lower correlation with an R2 = 0.68 and RMSE = 1.25 mm/d. To obtain the evapotranspiration status of the whole maize field and formulate reasonable irrigation schedules, the CWSI obtained by a handheld infrared thermometer was inverted by the renormalized difference vegetation index (RDVI) and the transformed chlorophyll absorption in reflectance index (TCARI). Then, the whole map of Ks can be derived from the VIs by the relationship between CWSI and Ks and can be taken as the basic input for ET estimation at the field scale. The final ET results based on multispectral UAV interpolation measurements can well reflect the crop ET status under different irrigation levels, and greatly help to improve irrigation scheduling through more precise management of deficit irrigation.

DETECTION OF BACTERIAL WILT DISEASE (PSEUDOMONAS SOLANCEARUM) IN BRINJAL USING HYPERSPECTRAL REMOTE SENSING

Abstract. Bacterial wilt disease (pathogen: Pseudomonas solancearum) is a major problem affecting brinjal crop. Infected leaves show yellowing, loss in turgidity, drying and ultimately the entire plant collapses. The study aims to examine the potential of hyperspectral remote sensing for detection of biotic stress caused due to bacterial wilt disease and identify best spectral band widths and hyperspectral indices indicative of disease infestation. This study was conducted in a farmer’s plot at Alampur in Baruipur block, South 24 Pargana district, West Bengal. Canopy spectra (using ASD Fieldspec 2 Spectroradiometer), chlorophyll content (by Chlorophyll meter) and Leaf Area Index (LAI) (by plant canopy imager) were collected. The healthy plants had green and fully turgid leaves whereas diseased plants had lower chlorophyll content and LAI. The reduction in chlorophyll content lowered reflectance in green region and internal leaf damage in near-infrared region. A correlation analysis was carried out between reflectance at specific bandwidths and hyperspectral indices with chlorophyll content and LAI of healthy and stressed plants. Bandwidths of 528–531 nm, 550–570 nm, 710–760 nm, and single bands such as 800 nm and 920 nm and indices viz. Greenness index, Modified Chlorophyll Absorption in Reflectance Index (MCARI), Transformed Chlorophyll Absorption in Reflectance Index (TCARI), Triangular Vegetation Index (TVI), Simple Ratio Pigment Index (SRPI), Photochemical Reflectance Index (PRI 2), Lichtenthaler Indices (LIC1, LIC2), Structure Intensive Pigment Index (SIPI) etc. were found to have strong positive correlation (R2 > 0.9) with plant parameters. These specific bandwidths and indices can be helpful in biophysical parameter estimation and early detection of crop stress, crop growth and disease monitoring.

Spectral Signature for Detecting Pest Infestation of Some Cultivated Plants in the Northern West Coast of Egypt.

Early detection of pest infestation is essential for determining the optimal time for control application. Many studies indicated the effectiveness of remote sensing technology as a tool to identify plants stressed by pest infestation. In the current study, we observed using hyper spectral remotely sensed data for discrimination between healthy and pest infested plants. Three arid-land plants were selected in the current study: sweet almond, citrus lemon trees and olives. As the first step of the analysis, spectral reflectance pattern for the three plants healthy and infected was identified. The optimal waveband and wavelength/s to differentiate between healthy and infected plants were identified. Different vegetation indices: Modified Chlorophyll Absorption in Reflectance Index (MCARI), Transformed Chlorophyll Absorption in Reflectance Index (TCARI), and Normalized Pigment Chlorophyll Index (NPCI) were calculated and compared between the values of these indices under infestation stress was examined. The results showed that healthy plants give higher reflectance values in visible spectral bands than infected plants with olives, however, healthy plants showed higher reflectance than infected plants throughout the whole spectrum with the other two plants. Vegetation indices values showed less value with healthy plants with sweet almond and citrus lemon but an opposite trend was found with olives. Blue and red spectral zones were optimal to differentiate between healthy and infected sweet almond and citrus lemon trees when all spectral zones except SWIRI were effective to differentiate between healthy and infected olives.

Mapping Maize Water Stress Based on UAV Multispectral Remote Sensing

Mapping maize water stress status and monitoring its spatial variability at a farm scale are a prerequisite for precision irrigation. High-resolution multispectral images acquired from an unmanned aerial vehicle (UAV) were used to evaluate the applicability of the data in mapping water stress status of maize under different levels of deficit irrigation at the late vegetative, reproductive and maturation growth stages. Canopy temperature, field air temperature and relative humidity obtained by a handheld infrared thermometer and a portable air temperature/relative humidity meter were used to establish a crop water stress index (CWSI) empirical model under the weather conditions in Ordos, Inner Mongolia, China. Nine vegetation indices (VIs) related to crop water stress were derived from the UAV multispectral imagery and used to establish CWSI inversion models. The results showed that non-water-stressed baseline had significant difference in the reproductive and maturation stages with an increase of 2.1 °C, however, the non-transpiring baseline did not change significantly with an increase of 0.1 °C. The ratio of transformed chlorophyll absorption in reflectance index (TCARI) and renormalized difference vegetation index (RDVI), and the TCARI and soil-adjusted vegetation index (SAVI) had the best correlations with CWSI. R2 values were 0.47 and 0.50 for TCARI/RDVI and TCARI/SAVI at the reproductive and maturation stages, respectively; and 0.81 and 0.80 for TCARI/RDVI and TCARI/SAVI at the late reproductive and maturation stages, respectively. Compared to CWSI calculated by on-site measurements, CWSI values retrieved by VI-CWSI regression models established in this study had more abilities to assess the field variability of crop and soil. This study demonstrates the potentiality of using high-resolution UAV multispectral imagery to map maize water stress.

Crop reflectance measurements for nitrogen deficiency detection in a soilless tomato crop

Early detection of nitrogen deficit stress is essential to effectively and precisely manage crop production under greenhouse conditions. This article demonstrates the use of hyperspectral machine vision as a non-contact technique for detecting crop nitrogen deficit in a soilless tomato crop. Three different levels of nitrogen concentration were applied in tomato plants grown in a growth chamber under controlled environment conditions. The results demonstrate that crop reflectance increased due to nitrogen deficiency, mostly in the wavelength bands between 775 nm–850 nm and 910 nm–960 nm. Based on the reflectance measurements several reflectance indices were calculated and correlated with the tomato leaf chlorophyll or nitrogen content and with the leaf photosynthesis rate (As). The results showed that when the As and the chlorophyll content values changed more than 0.5 μmol m−2 s−1 and 2.8 μg cm−2, respectively, the photochemical reflectance index (PRI) and the transformed chlorophyll absorption in reflectance index (TCARI) values varied more than 0.05 and 2.8, respectively. In addition, the results showed that for N changes higher than 0.20%, the optimised soil adjusted vegetation index (OSAVI) and the modified soil adjusted vegetation index (MSAVI) values varied more than 0.05 and 0.25, respectively. A new spectral index (background adjustment nitrogen index – BANI) was developed and validated under experimental conditions for the estimation of tomato plant nitrogen concentration.

Hyperspectral machine vision as a tool for water stress severity assessment in soilless tomato crop

Early detection of water deficit stress is essential for efficient crop management. In this study, hyperspectral machine vision was used as a non-contact technique for detecting changes in spectral reflectance of a soilless tomato crop grown under varying irrigation regimes. Four different irrigation treatments were imposed in tomato plants grown in slabs filled with perlite. The plants were grown in a growth chamber under controlled temperature and light conditions, and crop reflectance measurements were made using a hyperspectral camera to measure the radiation reflected by the crop from 400 nm to 1000 nm. The results showed that crop reflectance increased with increasing water deficit, and the detected reflectance increase was significant during the first day of irrigation was withheld. Based on the reflectance measurements, several crop indices were calculated and correlated with substrate volumetric water content and tomato leaf chlorophyll content. The results showed that when the modified red simple ratio tndex (mrSRI) and the modified red normalized vegetation index (mrNDVI) values increased by more than 2.5% and 23% respectively, the substrate volumetric water content decreased by more than 3%. In addition, when the Transformed Chlorophyll Absorption Reflectance Index (TCARI) value increased by about 16%, the leaf chlorophyll content decreased by about 3%. These results of the present study are promising for the development of a non-contact method for estimating plant water status in tomato crops grown under controlled environment.

Assessing the Spectral Properties of Sunlit and Shaded Components in Rice Canopies with Near-Ground Imaging Spectroscopy Data.

Monitoring the components of crop canopies with remote sensing can help us understand the within-canopy variation in spectral properties and resolve the sources of uncertainties in the spectroscopic estimation of crop foliar chemistry. To date, the spectral properties of leaves and panicles in crop canopies and the shadow effects on their spectral variation remain poorly understood due to the insufficient spatial resolution of traditional spectroscopy data. To address this issue, we used a near-ground imaging spectroscopy system with high spatial and spectral resolutions to examine the spectral properties of rice leaves and panicles in sunlit and shaded portions of canopies and evaluate the effect of shadows on the relationships between spectral indices of leaves and foliar chlorophyll content. The results demonstrated that the shaded components exhibited lower reflectance amplitude but stronger absorption features than their sunlit counterparts. Specifically, the reflectance spectra of panicles had unique double-peak absorption features in the blue region. Among the examined vegetation indices (VIs), significant differences were found in the photochemical reflectance index (PRI) between leaves and panicles and further differences in the transformed chlorophyll absorption reflectance index (TCARI) between sunlit and shaded components. After an image-level separation of canopy components with these two indices, statistical analyses revealed much higher correlations between canopy chlorophyll content and both PRI and TCARI of shaded leaves than for those of sunlit leaves. In contrast, the red edge chlorophyll index (CIRed-edge) exhibited the strongest correlations with canopy chlorophyll content among all vegetation indices examined regardless of shadows on leaves. These findings represent significant advances in the understanding of rice leaf and panicle spectral properties under natural light conditions and demonstrate the significance of commonly overlooked shaded leaves in the canopy when correlated to canopy chlorophyll content.

Evaluating Leaf and Canopy Reflectance of Stressed Rice Plants to Monitor Arsenic Contamination.

Arsenic contamination is a serious problem in rice cultivated soils of many developing countries. Hence, it is critical to monitor and control arsenic uptake in rice plants to avoid adverse effects on human health. This study evaluated the feasibility of using reflectance spectroscopy to monitor arsenic in rice plants. Four arsenic levels were induced in hydroponically grown rice plants with application of 0, 5, 10 and 20 µmol·L−1 sodium arsenate. Reflectance spectra of upper fully expanded leaves were acquired over visible and infrared (NIR) wavelengths. Additionally, canopy reflectance for the four arsenic levels was simulated using SAIL (Scattering by Arbitrarily Inclined Leaves) model for various soil moisture conditions and leaf area indices (LAI). Further, sensitivity of various vegetative indices (VIs) to arsenic levels was assessed. Results suggest that plants accumulate high arsenic amounts causing plant stress and changes in reflectance characteristics. All leaf spectra based VIs related strongly with arsenic with coefficient of determination (r2) greater than 0.6 while at canopy scale, background reflectance and LAI confounded with spectral signals of arsenic affecting the VIs’ performance. Among studied VIs, combined index, transformed chlorophyll absorption reflectance index (TCARI)/optimized soil adjusted vegetation index (OSAVI) exhibited higher sensitivity to arsenic levels and better resistance to soil backgrounds and LAI followed by red edge based VIs (modified chlorophyll absorption reflectance index (MCARI) and TCARI) suggesting that these VIs could prove to be valuable aids for monitoring arsenic in rice fields.

The dynamic simulation of rice growth parameters under cadmium stress with the assimilation of multi-period spectral indices and crop model

Obtaining precise information regarding the levels of heavy metal stress in crops is vital for food security, agricultural production, and ecological protection. In this study, we realized the dynamic simulation of rice growth parameters in three experiment fields that were exposed to varying levels of soil Cd (cadmium) concentration, intending to monitor the stress-induced changes of growth parameters on time scale. To simulate the growth parameters Leaf Area Index (LAI), Weight of Storage Organs (WSO) and Total Above Ground Production (TAGP) more accurately, we imbedded a Cd stress factor fCd into the initial World Food Study (WOFOST) model. Then, as the spectral sensing technology is a potentially promising method to monitor crop stress conditions, an optimized methodology of assimilating multi-period spectral indices into the coupled WOFOST+PROSPECT+SAIL model was adopted to obtain the optimum value of stress factor; next, the dynamic simulation of growth parameters was adjusted. Particularly, based on the specific sensibility to contamination levels at different growth stages, TCARI (Transformed Chlorophyll Absorption in Reflectance Index), REP (Red Edge Position) and RH (Reflectance Height) were selected as the multi-period spectral indices, serving as the compared targets of the cost function in the process of assimilation. The growth parameters simulated by the modified WOFOST model preferably reflected the variations of rice growth status on a time scale with R2 over 94% at all of the three levels. This study indicates that the optimized methodology of assimilating multi-period spectral indices into the crop model is applicable for simulating growth parameters under Cd stress, which provides a reference for dynamically monitoring heavy metal contamination in farmland environments.

Sensitivity Analysis of Vegetation Reflectance to Biochemical and Biophysical Variables at Leaf, Canopy, and Regional Scales

The objective of this paper is to investigate the sensitivity of reflectance to the variation in biochemical and biophysical variables at leaf, canopy, and regional scales using a modeling approach. The results show that, at the leaf scale, the variations in chlorophyll a+b content, the leaf structure parameter, and the water content dominate the reflectance variance in the visible light (VIS), near infrared (NIR), and short-wave infrared (SWIR) regions, respectively. At the canopy scale, the sensitivity of reflectance to variation in the leaf structure parameter is very slight. For sparse foliage cover (leaf area index ), LAI is the most important variable to the canopy reflectance. As LAI increases, the sensitivity of reflectance to variation in LAI is reduced to a very low value. Moreover, chlorophyll a+b, dry matter, and water content control the variation of canopy reflectance in the VIS, NIR, and SWIR regions, respectively. At the regional scale, the sensitivity of reflectance to variation in vegetation variables is highly influenced by the mixed pixels. Thirty-six vegetation indices (VIs) are chosen in this paper to illustrate the scale dependence of the estimation accuracy of vegetation variables. The results show that the relationships between the VIs and the variables highly depend on the observation scale. For chlorophyll a+b content estimation, transformed chlorophyll absorption in reflectance index (TCARI), Blue Green pigment Index, leaf chlorophyll index (LCI), modified Normalized Difference (mND705), and Plant Biochemical Index at the leaf scale and canopy scale of and TCARI at the canopy scale of are highly related. The correlation between the indices and chlorophyll content in the regional scale is, however, much lower. For water content estimation, disease water stress index (DSWI), leaf water vegetation index 2 (LWVI_2), moisture stress index (MSI), normalized difference infrared index (NDII), normalized difference water index (NDWI), hyperspectral perpendicular vegetation index (RVI), SWIR water stress index (SIWSI), SR water index (SRWI), and water index (WI) are good choices at the leaf scale and canopy scale of , while at the canopy scale of and the regional scale, the correlation between the indices and water content is very low. For LAI estimation, VIs, including the Greenness Index, simple ratio (SR), Normalized Difference VI, modified soil-adjusted vegetation index (MSAVI), modified triangular vegetation index 1 (MTVI1), modified triangular vegetation index 2 (MTVI2), optimized soil-adjusted vegetation index (OSAVI), modified chlorophyll absorption ratio index 1 (MCARI1), modified chlorophyll absorption ratio index 2 (MCARI2), Enhanced VI, LAI Determining Index, renormalized difference vegetation index (RDVI), Spectral Polygon VI, Wide Dynamic Range VI, and triangular vegetation index (TVI), have high correlation with LAI at the canopy scale of while a low correlation at the canopy scale of and the regional scale.

Combining spectral and spatial information from aerial hyperspectral images for delineating homogenous management zones

Most available hyperspectral image processing algorithms analyse the data based on the spectral information exclusively and do not treat the data as an image. Methods that use spatial and spectral information mostly perform a two-step processing technique, i.e. the spatial analysis is used as a retrospective smoothing. In this paper a recently developed method, which is based on the beamlet analysis, is presented. This method, which uses spectral and spatial information simultaneously, was adjusted in this study to multi- and hyperspectral images. The results of the new methodology, called the recursive dyadic partitioning=beamlet decorated (RDP-BD), were compared to fuzzy C-means spectral classification and to spatially coherent regions classification. These methods were used to classify nitrogen levels in an experimental potato plot, which was grown under different nitrogen treatments. The image was classified by these three methods based on the combination of two spectral indexes: transformed chlorophyll absorption reflectance index (TCARI) and optimized soil-adjust vegetation index (OSAVI), the first three principal components of the image and the complete 210 bands of the hyperspectral image. Although the spatial analysis slightly improved the classification (from 71% to 78%), the most pronounced contribution was its ability to divide the experimental plot into zones that fit the treatments borders. In addition, it was shown that the principle components and the whole spectra out-performed the spectral index. These methods should assist with testing the efficiency of variable rate nitrogen fertilisation as they utilise the informative spectral data to properly classify nitrogen level in the pixel scale and delineate management zones at the field scale.

Estimation of vertical distribution of chlorophyll concentration by bi-directional canopy reflectance spectra in winter wheat

An effective technique to measure foliage chlorophyll concentration (Chl) at a large scale and within a short time could be a powerful tool to determine fertilization amount for crop management. The objective of this study was to investigate the inversion of foliage Chl vertical-layer distribution by bi-directional reflectance difference function (BRDF) data, so as to provide a theoretical basis for monitoring the growth and development of winter wheat and for providing guidance on the application of fertilizer. Remote sensing could provide a powerful tool for large-area estimation of Chl. Because of the vertical distribution of leaves in a wheat stem, Chl vertical distribution characteristics show an obvious decreasing trend from the top of the canopy to the ground surface. The ratio of transformed chlorophyll absorption reflectance index (TCARI) to optimized soil adjusted vegetation index (OSAVI) was called the canopy chlorophyll inversion index (CCII) in this study. The value of CCII at nadir, ±20 and ±30°, at nadir, ±30 and ±40°, and at nadir, ±50 and ±60° view angles were selected and assembled as bottom-layer Chl inversion index (BLCI), middle-layer Chl inversion index (MLCI), and upper-layer Chl inversion index (ULCI), respectively, for the inversion of Chl at the vertical bottom layer, middle layer, and upper layer. The root mean squared error (RMSE) between BLCI-, MLCI-, and ULCI-derived and laboratory-measured Chl were 0.7841, 0.9426, and 1.7398, respectively. The vertical foliage Chl inversion could be used to monitor the crop growth status and to guide fertilizer and irrigation management. The results suggested that vegetation indices derived from bi-directional reflectance spectra (e.g., BLCI, ULCI, and MLCI) were satisfactory for inversion of the Chl vertical distribution.

Estimating crop stresses, aboveground dry biomass and yield of corn using multi-temporal optical data combined with a radiation use efficiency model

Crop descriptors, such as leaf area index, crop cover fraction, and leaf chlorophyll content, can be successfully estimated using appropriate spectral indices from the visible and near infrared spectral regions. However, these indices do not provide estimates of dry biomass, an important indicator of crop productivity. For estimating crop aboveground dry biomass and yield, this study developed an approach to integrate crop stressors and crop descriptors derived from optical remote sensing data with the Monteith radiation use efficiency model. Multi-temporal remote sensing data were acquired by the Compact Airborne Spectrographic Imager and the Landsat-5/7 Thematic Mapper/Enhanced Thematic Mapper Plus (TM/ETM+) sensors to monitor the growth conditions of corn in the 2001 and 2006 growing seasons. The modified triangular vegetation index (MTVI2) derived from the remote sensing data was used to estimate the fraction of absorbed photosynthetically active radiation ( f APAR ). A canopy structure dynamics model was then used to simulate the seasonal variation of f APAR . Crop water stress was estimated from the near and shortwave infrared reflectance of the Landsat images for a dry period in the 2001 growing season. By estimating leaf chlorophyll content using the Transformed Chlorophyll Absorption in Reflectance Index (TCARI) in combination with the Optimized Soil Adjusted Vegetation Index (OSAVI), different levels of nitrogen content could be identified. For the two growing seasons, the aboveground dry biomass and yield were linearly related with the cumulative absorbed photosynthetically active radiation (APAR) using the Monteith radiation use efficiency model. The cumulative APAR accounted for 96% of the corn aboveground dry biomass variability and 72% of the yield variability. Biomass and yield variability were partly explained by the variations in crop water stress intensity, which was dependent on soil texture. The seasonal radiation use efficiency was stable over the 2 years and was about 3.9 g MJ − 1 , with a confidence interval of 0.6 g MJ − 1 at the 95% confidence level. The assimilation of remotely sensed data into the radiation use efficiency model performed well for monitoring dry biomass accumulation and estimating corn yields.

Spectral indices sensitively discriminating wheat genotypes of different canopy architectures

A field experiment of 18 wheat cultivars of erectophile, planophile and horizontal canopy architectures was conducted during the 2004–2005 growing seasons in Beijing (40°10.6′ N, 116°26.3′ E), China. Canopy reflectance (350–2500 nm) at different growth stages was measured and leaf area index (LAI) and leaf chlorophyll concentration (Chl) were determined at booting. The main objective of the study was to evaluate the ability of various vegetative indices (VIs) to detect canopy architectures in wheat genotypes. The chlorophyll-sensitive spectral indices, the modified chlorophyll absorption reflectance index (MCARI) and the transformed chlorophyll absorption reflectance index (TCARI), were very sensitive to canopy architectures in the wheat plants. The MCARI values were significantly (p < 0.05) larger for the horizontal genotypes than for the planophile ones, and also larger for the planophile genotypes than for the erectophile ones for the six growth stages. The TCARI had a similar power to MCARI for discriminating between different wheat canopy architectures. At booting, both MCARI and TCARI were only weakly related to Chl in the upper, middle and lower leaves. The results emphasized the difficulties of determining crop Chl from canopy reflectance. The mechanisms that cause the differences in MCARI and TCARI among the canopy architectures are discussed.