Chlorophyll Absorption Reflectance Research Articles

Non-destructive estimation of rice plant nitrogen status with Crop Circle multispectral active canopy sensor

Crop Circle is an active multispectral canopy sensor developed to support precision crop management. The Crop Circle ACS-470 model is user configurable, with a choice of six wavebands covering blue, green, red, red edge and near infrared spectral regions. The objectives of this study were to determine how well nitrogen (N) status of rice (Oryza sativa L.) can be estimated with the Crop Circle ACS-470 active sensor using green, red edge and near infrared (NIR) bands at key growth stages and identify important vegetation indices for estimating rice N status indicators. Six field experiments involving different N rates and two varieties were conducted in Sanjiang Plain in Heilongjiang Province, China during 2011 and 2012. Crop sensor data and plant samples were also collected from five farmers’ fields to further evaluate the sensor and selected vegetation indices. The results of the study indicated that among 43 different vegetation indices evaluated, modified chlorophyll absorption reflectance index 1 (MCARI1) had consistent correlations with rice aboveground biomass (R2=0.79) and plant N uptake (R2=0.83) across growth stages. Four red edge-based indices, red edge soil adjusted vegetation index (RESAVI), modified RESAVI (MRESAVI), red edge difference vegetation index (REDVI) and red edge re-normalized difference vegetation index (RERDVI), performed equally well for estimating N nutrition index (NNI) across growth stages (R2=0.76). For rice plant N concentration, the highest R2 was 0.33, and none of the indices performed satisfactorily with validation using farmers’ field data. We conclude that the Crop Circle ACS-470 active canopy sensor allows users the flexibility to select suitable bands and calculate different vegetation indices and has a great potential for in-season non-destructive estimation of rice biomass, plant N uptake and NNI.

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.

Assessment of vineyard water status variability by thermal and multispectral imagery using an unmanned aerial vehicle (UAV)

The goal of this study was to assess the water status variability of a commercial rain-fed Tempranillo vineyard (Vitis vinifera L.) by thermal and multispectral imagery using an unmanned aerial vehicle (UAV). The relationships between aerial temperatures or indices derived from the imagery and leaf stomatal conductance (g s) and stem water potential (Ψstem) were determined. Aerial temperature was significantly correlated with g s (R 2 = 0.68, p < 0.01) and Ψstem (R 2 = 0.50, p < 0.05). Furthermore, the thermal indices derived from aerial imagery were also strongly correlated with Ψstem and g s. Moreover, different spectral indices were related to vineyard water status, although NDVI (normalized difference vegetation index) and TCARI/OSAVI (ratio between transformed chlorophyll absorption in reflectance and optimized soil-adjusted vegetation index) showed the highest coefficient of determination with Ψstem (R 2 = 0.68, p < 0.05) and g s (R 2 = 0.84, p < 0.05), respectively. While the relationship with thermal imagery and water status parameters could be considered as a short-term response, NDVI and TCARI/OSAVI indices were probably reflecting the result of cumulative water deficits, hence a long-term response. In conclusion, thermal and multispectral imagery using an UAV allowed assessing and mapping spatial variability of water status within the vineyard.

A comparison of TerraSAR-X Quadpol backscattering with RapidEye multispectral vegetation indices over rice fields in the Mekong Delta, Vietnam

Satellite-based multispectral imagery and/or synthetic aperture radar (SAR) data have been widely used for vegetation characterization, plant physiological parameter estimation, crop monitoring or even yield prediction. However, the potential use of satellite-based X-band SAR data for these purposes is not fully understood. A new generation of X-band radar satellite sensors offers high spatial resolution images with different polarizations and, therefore, constitutes a valuable information source. In this study, we utilized a TerraSAR-X satellite scene recorded during a short experimental phase when the sensor was running in full polarimetric ‘Quadpol’ mode. The radar backscatter signals were compared with a RapidEye reference data set to investigate the potential relationship of TerraSAR-X backscatter signals to multispectral vegetation indices and to quantify the benefits of TerraSAR-X Quadpol data over standard dual- or single-polarization modes. The satellite scenes used cover parts of the Mekong Delta, the rice bowl of Vietnam, one of the major rice exporters in the world and one of the regions most vulnerable to climate change. The use of radar imagery is especially advantageous over optical data in tropical regions because the availability of cloudless optical data sets may be limited to only a few days per year. We found no significant correlations between radar backscatter and optical vegetation indices in pixel-based comparisons. VV and cross-polarized images showed significant correlations with combined spectral indices, the modified chlorophyll absorption ratio index/second modified triangular vegetation index (MCARI/MTVI2) and transformed chlorophyll absorption in reflectance index/optimized soil-adjusted vegetation index (TCARI/OSAVI), when compared on an object basis. No correlations between radar backscattering at any polarization and the normalized difference vegetation index (NDVI) were observed.

Comparing narrow and broad-band vegetation indices to estimate leaf chlorophyll content in planophile crop canopies

A comparison of the sensitivity of several broad- and narrow-band vegetation indices (VIs) to leaf chlorophyll content in planophile crop canopies is addressed by the analysis of a large synthetic dataset. Broad-band indices included classical slope-based VIs (i.e. NDVI—normalized difference VI and SR—simple ratio) and some indices incorporating green reflectance (i.e. Green NDVI, NIR/green ratio and the newly proposed CVI—chlorophyll vegetation index), whereas narrow-band indices included those specifically proposed to estimate leaf chlorophyll at the canopy scale (i.e. MCARI—modified chlorophyll absorption reflectance index, TCARI—transformed CARI, TCARI/OSAVI ratio—TCARI/optimized soil adjusted VI and REIP—red edge inflection position). Synthetic data were obtained from the coupled PROSPECT + SAILH leaf and canopy reflectance models in the direct mode. In addition to traditional regression-based statistics (coefficient of determination and root mean square error, RMSE), changes in sensitivity of a VI over the range of chlorophyll content were analyzed using a sensitivity function. The broad-band chlorophyll vegetation index outperformed the other VIs considered as a leaf chlorophyll estimator at the canopy scale, with the exception of the TCARI/OSAVI ratio for some soil conditions.

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.

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.

Leaf nitrogen estimation in potato based on spectral data and on simulated bands of the VENμS satellite

Relationships between leaf spectral reflectance at 400-900 nm and nitrogen levels in potato petioles and leaves were studied. Five nitrogen (N) fertilizer treatments were applied to build up levels of nitrogen variation in potato fields in Israel in spring 2006 and 2007. Reflectance of leaves was measured in the field over a spectral range of 400- 900 nm. The leaves were sampled and analyzed for petiole NO3-N and leaf percentage N (leaf-%N). Prediction models of leaf nitrogen content were developed based on an optical index named transformed chlorophyll absorption reflectance index (TCARI) and on partial least squares regression (PLSR). Prediction models were also developed based on simu- lated bands of the future VENlS satellite (Vegetation and Environment monitoring on a New Micro-Satellite). Leaf spectral reflectance correlated better with leaf-%N than with petiole NO3-N. The TCARI provided strong correlations with leaf-%N, but only at the tuber-bulking stage. The PLSR analysis resulted in a stronger correlation than TCARI with leaf-%N. An R 2 of 0.95 (p \ 0.01) and overall accuracy of 80.5% (Kappa = 74%) were determined for both vegetative and tuber-bulking periods. The simulated VENlS bands

Thermal and Narrowband Multispectral Remote Sensing for Vegetation Monitoring From an Unmanned Aerial Vehicle

Two critical limitations for using current satellite sensors in real-time crop management are the lack of imagery with optimum spatial and spectral resolutions and an unfavorable revisit time for most crop stress-detection applications. Alternatives based on manned airborne platforms are lacking due to their high operational costs. A fundamental requirement for providing <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">useful</i> remote sensing products in agriculture is the capacity to combine high spatial resolution and quick turnaround times. Remote sensing sensors placed on unmanned aerial vehicles (UAVs) could fill this gap, providing low-cost approaches to meet the critical requirements of spatial, spectral, and temporal resolutions. This paper demonstrates the ability to generate quantitative remote sensing products by means of a helicopter-based UAV equipped with inexpensive thermal and narrowband multispectral imaging sensors. During summer of 2007, the platform was flown over agricultural fields, obtaining thermal imagery in the 7.5-13-mum region (40-cm resolution) and narrowband multispectral imagery in the 400-800-nm spectral region (20-cm resolution). Surface reflectance and temperature imagery were obtained, after atmospheric corrections with MODTRAN. Biophysical parameters were estimated using vegetation indices, namely, normalized difference vegetation index, transformed chlorophyll absorption in reflectance index/optimized soil-adjusted vegetation index, and photochemical reflectance index (PRI), coupled with SAILH and FLIGHT models. As a result, the image products of leaf area index, chlorophyll content ( <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ab</sub> ), and water stress detection from PRI index and canopy temperature were produced and successfully validated. This paper demonstrates that results obtained with a low-cost UAV system for agricultural applications yielded comparable estimations, if not better, than those obtained by traditional manned airborne sensors.

出穂から開花期における地上及び航空機ハイパースペクトルデータを用いた小麦の葉身クロロフィル濃度の推定とその評価

In order to validate the availability of rbNDVI (red and blue Normalized Difference Vegetation Index) for chlorophyll estimation, we conducted estimation of leaf chlorophyll concentration at heading to anthesis stage which highly correlates with grain protein concentration using ground-based and aerial hyperspectral data at agriculture fields in Gifu prefecture. In the ground measurement, rbNDVI showed high correlation (γ2=0.731) with leaf chlorophyll concentration compared to widely used NDVI (Normalized Difference Vegetation Index; γ2=0.231) and TCARI/OSAVI (Transformed Chlorophyll Absorption in Reflectance Index/Optimized Soil-Adjusted Vegetation Index; γ2=0.333), and the estimate equation is able to applied in the different study site (Root Mean Square Error: RMSE=2.54μg cm-2) . In the aerial scale, rbNDVI also showed good result in chlorophyll estimation (2006: γ2=0.427, 2007: γ2=0.447, RMSE=4.18μg cm-2) using atmospheric correction of 6S code and reflectance matching method. According to these results, the spatial distribution map of chlorophyll concentration was created. The spatial distribution map of chlorophyll concentration using rbNDVI may provide the useful information for additional fertilization aim to the improvement of the grain protein concentration over the large area.

Narrowband Vegetation Indexes and Detection of Disease Damage in Soybeans

A portable narrowband spectroradiometer was used to detect sclerotinia stem rot infection, caused by the fungus Sclerotinia sclerotiorum in soybeans. Increasing levels of fungal inoculum were used to cause a gradient of disease infection in the field. Canopy reflectance measured in narrowband R/sub 675/-R/sub 685/ and broadband R/sub 635/-R/sub 685/ could estimate 86% of the variation in soybean plants damage measured by a count of early dead plants. Plant damage was also associated with the chlorophyll absorption in reflectance and the normalized pigment chlorophyll vegetation indexes, showing a loss of chlorophyll pigment compared to healthy plants. A new field approach is suggested for the investigation of plant damage with narrowband spectroradiometry.

Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture

Recent studies have demonstrated the usefulness of optical indices from hyperspectral remote sensing in the assessment of vegetation biophysical variables both in forestry and agriculture. Those indices are, however, the combined response to variations of several vegetation and environmental properties, such as Leaf Area Index (LAI), leaf chlorophyll content, canopy shadows, and background soil reflectance. Of particular significance to precision agriculture is chlorophyll content, an indicator of photosynthesis activity, which is related to the nitrogen concentration in green vegetation and serves as a measure of the crop response to nitrogen application. This paper presents a combined modeling and indices-based approach to predicting the crop chlorophyll content from remote sensing data while minimizing LAI (vegetation parameter) influence and underlying soil (background) effects. This combined method has been developed first using simulated data and followed by evaluation in terms of quantitative predictive capability using real hyperspectral airborne data. Simulations consisted of leaf and canopy reflectance modeling with PROSPECT and SAILH radiative transfer models. In this modeling study, we developed an index that integrates advantages of indices minimizing soil background effects and indices that are sensitive to chlorophyll concentration. Simulated data have shown that the proposed index Transformed Chlorophyll Absorption in Reflectance Index/Optimized Soil-Adjusted Vegetation Index (TCARI/OSAVI) is both very sensitive to chlorophyll content variations and very resistant to the variations of LAI and solar zenith angle. It was therefore possible to generate a predictive equation to estimate leaf chlorophyll content from the combined optical index derived from above-canopy reflectance. This relationship was evaluated by application to hyperspectral CASI imagery collected over corn crops in three experimental farms from Ontario and Quebec, Canada. The results presented here are from the L'Acadie, Quebec, Agriculture and Agri-Food Canada research site. Images of predicted leaf chlorophyll content were generated. Evaluation showed chlorophyll variability over crop plots with various levels of nitrogen, and revealed an excellent agreement with ground truth, with a correlation of r 2=.81 between estimated and field measured chlorophyll content data.