Evaluating chlorophyll density in winter oilseed rape ( Brassica napus L.) using canopy hyperspectral red-edge parameters

Li, Lantao; Ren, Tao; Ma, Yi; Wei, Quanquan; Wang, Shanqin; Li, Xiaokun; Cong, Rihuan; Liu, Shishi; Lu, Jianwei

doi:10.1016/j.compag.2016.05.008

Cited by 68 publications

(34 citation statements)

References 73 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Existing studies utilizing hyperspectral imaging sensors are sparse compared with those utilizing non-imaging hyperspectral sensors. Field ASD hyperspectral radiometers have long been used in agricultural applications [16], such as the inversion of crop parameters [17][18][19], crop disease and pest monitoring [20][21][22], etc. Pushbroom hyperspectral sensors have been developed and deployed for agricultural applications.…”

Section: Introductionmentioning

confidence: 99%

The DOM Generation and Precise Radiometric Calibration of a UAV-Mounted Miniature Snapshot Hyperspectral Imager

Yang

Wang

et al. 2017

Remote Sensing

View full text Add to dashboard Cite

Abstract:Hyperspectral remote sensing is used in precision agriculture to remotely and quickly acquire crop phenotype information. This paper describes the generation of a digital orthophoto map (DOM) and radiometric calibration for images taken by a miniaturized snapshot hyperspectral camera mounted on a lightweight unmanned aerial vehicle (UAV). The snapshot camera is a relatively new type of hyperspectral sensor that can acquire an image cube with one spectral and two spatial dimensions at one exposure. The images acquired by the hyperspectral snapshot camera need to be mosaicked together to produce a DOM and radiometrically calibrated before analysis. However, the spatial resolution of hyperspectral cubes is too low to mosaic the images together. Furthermore, there are no systematic radiometric calibration methods or procedures for snapshot hyperspectral images acquired from low-altitude carrier platforms. In this study, we obtained hyperspectral imagery using a snapshot hyperspectral sensor mounted on a UAV. We quantitatively evaluated the radiometric response linearity (RRL) and radiometric response variation (RRV) and proposed a method to correct the RRV effect. We then introduced a method to interpolate position and orientation system (POS) information and generate a DOM with low spatial resolution and a digital elevation model (DEM) using a 3D mesh model built from panchromatic images with high spatial resolution. The relative horizontal geometric precision of the DOM was validated by comparison with a DOM generated from a digital RGB camera. A surface crop model (CSM) was produced from the DEM, and crop height for 48 sampling plots was extracted and compared with the corresponding field-measured crop height to verify the relative precision of the DEM. Finally, we applied two absolute radiometric calibration methods to the generated DOM and verified their accuracy via comparison with spectra measured with an ASD Field Spec Pro spectrometer (Analytical Spectral Devices, Boulder, CO, USA). The DOM had high relative horizontal accuracy, and compared with the digital camera-derived DOM, spatial differences were below 0.05 m (RMSE = 0.035). The determination coefficient for a regression between DEM-derived and field-measured crop height was 0.680. The radiometric precision was 5% for bands between 500 and 945 nm, and the reflectance curve in the infrared spectral region did not decrease as in previous research. The pixel and data sizes for the DOM corresponding to a field area of approximately 85 m × 34 m were small (0.67 m and approximately 13.1 megabytes, respectively), which is convenient for data transmission, preprocessing and analysis. The proposed Remote Sens. 2017, 9, 642; doi:10.3390/rs9070642 www.mdpi.com/journal/remotesensing Remote Sens. 2017, 9, 642 2 of 21 method for radiometric calibration and DOM generation from hyperspectral cubes can be used to yield hyperspectral imagery products for various applications, particularly precision agriculture.

show abstract

Section: Introductionmentioning

confidence: 99%

The DOM Generation and Precise Radiometric Calibration of a UAV-Mounted Miniature Snapshot Hyperspectral Imager

Yang

Wang

et al. 2017

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…The red edge spectrum is closely related to various physical and chemical parameters of vegetation and is an important indicator for describing plant pigment color and overall health. Other indicators include: canopy LAI estimation [80], chlorophyll density evaluation [81], and leaf element absorption [82]. Vegetation information was abundant in May, so the corresponding red edge bands and indices scored higher than in other seasons using the single-red edge method.…”

Section: Key Predictor Variables In Mfsmentioning

confidence: 99%

Feature Comparison and Optimization for 30-M Winter Wheat Mapping Based on Landsat-8 and Sentinel-2 Data Using Random Forest Algorithm

Chen

Jia

et al. 2019

Remote Sensing

View full text Add to dashboard Cite

Winter wheat cropland is one of the most important agricultural land-cover types affected by the global climate and human activity. Mapping 30-m winter wheat cropland can provide beneficial reference information that is necessary for understanding food security. To date, machine learning algorithms have become an effective tool for the rapid identification of winter wheat at regional scales. Algorithm implementation is based on constructing and selecting many features, which makes feature set optimization an important issue worthy of discussion. In this study, the accurate mapping of winter wheat at 30-m resolution was realized using Landsat-8 Operational Land Imager (OLI), Sentinel-2 Multispectral Imager (MSI) data, and a random forest algorithm. This paper also discusses the optimal combination of features suitable for cropland extraction. The results revealed that: (1) the random forest algorithm provided robust performance using multi-features (MFs), multi-feature subsets (MFSs), and multi-patterns (MPs) as input parameters. Moreover, the highest accuracy (94%) for winter wheat extraction occurred in three zones, including: pure farmland, urban mixed areas, and forest areas. (2) Spectral reflectance and the crop growth period were the most essential features for crop extraction. The MFSs combined with the three to four feature types enabled the high-precision extraction of 30-m winter wheat plots. (3) The extraction accuracy of winter wheat in three zones with multiple geographical environments was affected by certain dominant features, including spectral bands (B), spectral indices (S), and time-phase characteristics (D). Therefore, we can improve the winter wheat mapping accuracy of the three regional types by improving the spectral resolution, constructing effective spectral indices, and enriching vegetation information. The results of this paper can help effectively construct feature sets using the random forest algorithm, thus simplifying the feature construction workload and ensuring high-precision extraction results in future winter wheat mapping research.

show abstract

“…For example, absorption in the visible region (400 to 700 nm), red edge peak (680 to 760 nm), and reflectance in the near-infrared (NIR) domain (700 to 1000 nm) are primary indicators of chlorophyll absorption, chlorophyll density, intercellular leaf structure, and even the general health of a crop, and serve as a gauge for how welldeveloped, vigorous, and even dense the plant material is. [14][15][16] It is in this context that hyperspectral imaging systems and point-based spectroradiometers have proven valuable, given their ability to measure high spectral resolution from plant surfaces.…”

Section: Introductionmentioning

confidence: 99%

Yield modeling of snap bean based on hyperspectral sensing: a greenhouse study

Hassanzadeh

Aardt

Murphy

et al. 2020

J. Appl. Rem. Sens.

View full text Add to dashboard Cite

Farmers and growers typically use approaches based on the crop environment and local meteorology, many of which are labor-intensive, to predict crop yield. These approaches have found broad acceptance but lack real-time and physiological feedback for near-daily management purposes. This is true for broad-acre crops, such as snap bean, which is valued at hundreds of millions of dollars in the annual agricultural market. We aim to investigate the relationships between snap bean yield and plant spectral and biophysical information, collected using a hyperspectral spectroradiometer (400 to 2500 nm). The experiment focused on 48 single snap bean plants (cv. Huntington) in a controlled greenhouse environment during the growth period (69 days). We used applicable accuracy and precision metrics from partial least squares regression and cross-validation methods to evaluate the predictive ability of two harvest stages, namely an early-harvest and late-harvest stage, against our yield indicator (bean pod weight). Four different spectral data sets were used to investigate whether such oversampled, hyperspectral data sets could accurately and precisely model observed variability in yield, in terms of the coefficient of determination (R 2) and root-mean-square error (RMSE). The objective of our approach hinges on the philosophy that selected spectral bands from this study, i.e., those that best explain yield variability, can be downsampled from a hyperspectral system for use in a more cost-effective, operational multispectral sensor. Our results suggested the optimal period for spectral evaluation of snap bean yield is 20 to 25 or 32 days prior to harvest for the early-and late-harvest stages, respectively, with the best model performing at a low RMSE (3.02 g plant −1) and a high coefficient of determination (R 2 ¼ 0.72). An unmanned aerial systems-mounted, affordable, and wavelength-programmable multispectral imager, with bands corresponding to those identified, could provide a near real-time and reliable yield estimate prior to harvest.

show abstract

Evaluating chlorophyll density in winter oilseed rape ( Brassica napus L.) using canopy hyperspectral red-edge parameters

Cited by 68 publications

References 73 publications

The DOM Generation and Precise Radiometric Calibration of a UAV-Mounted Miniature Snapshot Hyperspectral Imager

The DOM Generation and Precise Radiometric Calibration of a UAV-Mounted Miniature Snapshot Hyperspectral Imager

Feature Comparison and Optimization for 30-M Winter Wheat Mapping Based on Landsat-8 and Sentinel-2 Data Using Random Forest Algorithm

Yield modeling of snap bean based on hyperspectral sensing: a greenhouse study

Contact Info

Product

Resources

About