Estimation of Crop Growth Parameters Using UAV-Based Hyperspectral Remote Sensing Data

Tao, Huilin; Feng, Haikuan; Xu, Liangji; Miao, Mengke; Huang, Long; Yue, Jibo; Li, Zhenhai; Yang, Guijun; Yang, Xiaodong; Fan, Lingling

doi:10.3390/s20051296

Cited by 117 publications

(74 citation statements)

References 61 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Meanwhile, more spectral information may not be useful in some cases. Tao et al (2020) used hyperspectral sensors to study the correlation between different vegetation indices (VIs) and red-edge parameters and crop biomass. It was found that using too many spectral features as independent variables will lead to overfitting of the model, so it is necessary to use an appropriate number of spectral features that are highly related to biomass.…”

Section: Spectral Sensorsmentioning

confidence: 99%

Applications of UAS in Crop Biomass Monitoring: A Review

et al. 2021

View full text Add to dashboard Cite

Biomass is an important indicator for evaluating crops. The rapid, accurate and nondestructive monitoring of biomass is the key to smart agriculture and precision agriculture. Traditional detection methods are based on destructive measurements. Although satellite remote sensing, manned airborne equipment, and vehicle-mounted equipment can nondestructively collect measurements, they are limited by low accuracy, poor flexibility, and high cost. As nondestructive remote sensing equipment with high precision, high flexibility, and low-cost, unmanned aerial systems (UAS) have been widely used to monitor crop biomass. In this review, UAS platforms and sensors, biomass indices, and data analysis methods are presented. The improvements of UAS in monitoring crop biomass in recent years are introduced, and multisensor fusion, multi-index fusion, the consideration of features not directly related to monitoring biomass, the adoption of advanced algorithms and the use of low-cost sensors are reviewed to highlight the potential for monitoring crop biomass with UAS. Considering the progress made to solve this type of problem, we also suggest some directions for future research. Furthermore, it is expected that the challenge of UAS promotion will be overcome in the future, which is conducive to the realization of smart agriculture and precision agriculture.

show abstract

Section: Spectral Sensorsmentioning

confidence: 99%

Applications of UAS in Crop Biomass Monitoring: A Review

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Remote sensing technology makes it possible to monitor crop growth and physiological parameters rapidly and nondestructively in large areas, providing important technical support for non-destructive monitoring of crop growth [34,[36][37][38]. Many achievements have been made in crop growth monitoring by remote sensing, including empirical statistical methods and radiative transfer models [39]. The empirical statistical methods include VI models [40][41][42], principal component regression [43,44], backpropagation (BP) neural network [36], partial least squares regression (PLSR) [45,46], which are widely used for their simplicity and flexibility.…”

Section: Introductionmentioning

confidence: 99%

“…At the pre-heading stage, VIs usually shows a strong relationship with crop biomass, so most scholars built the relationships between VIs and AGB to monitor rice growth [34]. For example, Tao H et al used stepwise regression (SWR) and PLSR methods based on VIs, red-edge parameters, and their combinations to accurately estimate AGB and LAI [39]. Devia C A used seven VIs and their combinations to estimate rice biomass in the large area based on Unmanned Aerial Vehicle (UAV) with an average correlation of 0.76 [48].…”

Section: Introductionmentioning

confidence: 99%

Rice Mapping and Growth Monitoring Based on Time Series GF-6 Images and Red-Edge Bands

et al. 2021

View full text Add to dashboard Cite

Accurate rice mapping and growth monitoring are of great significance for ensuring food security and agricultural sustainable development. Remote sensing (RS), as an efficient observation technology, is expected to be useful for rice mapping and growth monitoring. Due to the fragmented distribution of paddy fields and the undulating terrain in Southern China, it is very difficult in rice mapping. Moreover, there are many crops with the same growth period as rice, resulting in low accuracy of rice mapping. We proposed a red-edge decision tree (REDT) method based on the combination of time series GF-6 images and red-edge bands to solve this problem. The red-edge integral and red-edge vegetation index integral were computed by using two red-edge bands derived from GF-6 images to construct the REDT. Meanwhile, the conventional method based on time series normalized difference vegetation index (NDVI), normalized difference water index (NDWI), enhanced vegetation index (EVI) (NNE) was employed to compare the effectiveness of rice mapping. The results indicated that the overall accuracy and Kappa coefficient of REDT ranged from 91%–94% and 0.82–0.87, improving about 7% and 0.15 compared with the NNE method. This proved that the proposed technology was able to efficiently solve the problem of rice mapping on a large scale and regions with fragmented landscapes. Additionally, two red-edge bands of GF-6 images were applied to monitor rice growth. It concluded that the two red-edge bands played different roles in rice growth monitoring. The red-edge bands of GF-6 images were superior in rice mapping and growth monitoring. Further study needs to develop more vegetation indices (VIs) related to the red-edge to make the best use of red-edge characteristics in precision agriculture.

show abstract

“…Hyperspectral remote sensing technology has the unique advantages of high spectral resolution and spectrum unity, representing a revolutionary step in the history of remote sensing technology development [1][2][3]. Based on this 3D spectral data cube, which can acquire both 2D spatial and 1D spectral information, it is possible to detect and identify targets that are difficult to detect and identify with conventional imaging techniques [4][5][6][7][8][9][10]. Spectral imaging data include 2D spatial information and 1D spectral information, whereas imaging detectors are 2D detectors and can only acquire 2D information at a single transient.…”

Section: Introductionmentioning

confidence: 99%

A High Optical Throughput Spectral Imaging Technique Using Broadband Filters

Wang

Chen

Zhang

et al. 2020

Sensors

View full text Add to dashboard Cite

To address the miniaturization of the spectral imaging system required by a mounted platform and to overcome the low luminous flux caused by current spectroscopic technology, we propose a method for the multichannel measurement of spectra using a broadband filter in this work. The broadband filter is placed in front of a lens, and the spectral absorption characteristics of the broadband filter are used to achieve the modulation of the incident spectrum of the detection target and to establish a mathematical model for the detection of the target. The spectral and spatial information of the target can be obtained by acquiring data using a push-broom method and reconstructing the spectrum using the GCV-based Tikhonov regularization algorithm. In this work, we compare the accuracy of the reconstructed spectra using the least-squares method and the Tikhonov algorithm based on the L-curve. The effect of errors in the spectral modulation function on the accuracy of the reconstructed spectra is analyzed. We also analyze the effect of the number of overdetermined equations on the accuracy of the reconstructed spectra and consider the effect of detector noise on the spectral recovery. A comparison between the known data cubes and our simulation results shows that the spectral image quality based on broadband filter reduction is better, which validates the feasibility of the method. The proposed method of combining broadband filter-based spectroscopy with a panchromatic imaging process for measurement modulation rather than spectroscopic modulation provides a new approach to spectral imaging.

show abstract

Estimation of Crop Growth Parameters Using UAV-Based Hyperspectral Remote Sensing Data

Cited by 117 publications

References 61 publications

Applications of UAS in Crop Biomass Monitoring: A Review

Applications of UAS in Crop Biomass Monitoring: A Review

Rice Mapping and Growth Monitoring Based on Time Series GF-6 Images and Red-Edge Bands

A High Optical Throughput Spectral Imaging Technique Using Broadband Filters

Contact Info

Product

Resources

About