Estimation methods developing with remote sensing information for energy crop biomass: A comparative review

Chao, Zhenhua; Liu, Ning; Zhang, Peidong; Ying, Tianyu; Song, Kigook

doi:10.1016/j.biombioe.2019.02.002

Cited by 64 publications

(45 citation statements)

References 182 publications

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“…The NDVI values explain the spectral properties of the plant canopy, whereas plant height is related to the vertical structure and growth rate of a plant [53,56]. Combining the physical and spectral parameters was successful in grasping spectral and structural information and improved the regression estimation of aboveground biomass [20].…”

Section: Discussionmentioning

confidence: 99%

Development and Validation of a Model to Combine NDVI and Plant Height for High-Throughput Phenotyping of Herbage Yield in a Perennial Ryegrass Breeding Program

et al. 2019

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Sensor-based phenotyping technologies may offer a non-destructive, high-throughput and efficient assessment of herbage yield (HY) to replace current inefficient phenotyping methods. This paper assesses the feasibility of combining normalised difference vegetative index (NDVI) from multispectral imaging and ultrasonic sonar estimates of plant height to estimate HY of single plants in a large perennial ryegrass breeding program. For sensor calibration, fresh HY (FHY) and dry HY (DHY) were acquired destructively, and plant height was measured at four dates each in 2017 and 2018 from a selected subset of 480 plants. Global multiple linear regression models based on K-fold and random split cross-validation methods were used to evaluate the relationship between observed vs. predicted HY. The coefficient of determination (R 2 ) = 0.67-0.68 and a root mean square error (RMSE) between 5.43-7.60 g was obtained for the validation of predicted vs. observed DHY. The mean absolute error (MAE) and mean percentage error (MPE) ranged between 3.59-5.44 g and 22-28%, respectively. For the FHY, R 2 values ranged from 0.63 to 0.70, with an RMSE between 23.50 and 33 g, MAE between 15.11 and 24.34 g and MPE between~22% and 31%. Combining NDVI and plant height is a robust method to enable high-throughput phenotyping of herbage yield in perennial ryegrass breeding programs. current methods on large numbers plants or plots is slow and expensive, making it difficult to include large numbers of individual plants or plots [5]. This makes it challenging to capture an accurate representation of the population and estimate the correct values for individual genotypes. Therefore, HY estimation requires rapid, non-destructive phenotyping methods that can also facilitate genomic tools (e.g., genomic selection, GS) to shorten the breeding time and accelerate genetic gain. However, the number of plants for GS is likely higher than the number of plants traditionally used by breeders to perform selection breeding (e.g., The DairyBio initiative has 48,000 individual plants for genomic sub-selection breeding) where phenotyping of individual plants require accurate evaluation [5]. In recent years, high-throughput phenotyping (HTP) technologies have brought new insights to evaluate phenotypic traits efficiently in large breeding programs [6][7][8][9].Previous studies have used sensor-based data sources from aerial and ground-based platforms to estimate biophysical characteristics of various vegetations, including herbage yield of forage crops [5,10,11]. The aerial-based phenotyping platforms are suitable for lightweight red-green-blue (RGB), multispectral, and hyperspectral imaging systems and have used vegetative indices to build models for herbage yield [10,12] and biomass [13-15] estimation of pasture and cereal crops respectively. Normalised difference vegetative index (NDVI) is a widely used vegetative index for estimates of biomass [16][17][18] with limitations at high-biomass and density of crop cover [19]. Small, low-cost unmanned aerial systems (U...

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Section: Discussionmentioning

confidence: 99%

Development and Validation of a Model to Combine NDVI and Plant Height for High-Throughput Phenotyping of Herbage Yield in a Perennial Ryegrass Breeding Program

et al. 2019

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“…Satellite, aerial, and ground-based platforms equipped with advanced sensors provide the potential for fast, non-destructive, and low-cost monitoring of plant growth, development, and yield in a field environment [3]. The methods currently used include the measurement of parameters such as capacitance, spectral properties, pasture height (known as sward height), and compressed sward height [4][5][6][7][8][9][10][11][12]. A wide range of optical sensors have been used to measure a range of parameters of crops and pastures, including biomass [9][10][11][12][13][14][15][16][17][18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

“…The current work describes a new proximal sensor, and satellite and airborne methods will not be discussed in detail. However, for more information, the reader is referred to review papers such as those provided in references [3,[6][7][8][9][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Ultrasonic Proximal Sensing of Pasture Biomass

Legg

Bradley²

2019

Remote Sensing

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The optimization of pasture food value, known as ‘biomass’, is crucial in the management of the farming of grazing animals and in improving food production for the future. Optical sensing methods, particularly from satellite platforms, provide relatively inexpensive and frequently updated wide-area coverage for monitoring biomass and other forage properties. However, there are also benefits from direct or proximal sensing methods for higher accuracy, more immediate results, and for continuous updates when cloud cover precludes satellite measurements. Direct measurement, by cutting and weighing the pasture, is destructive, and may not give results representative of a larger area of pasture. Proximal sensing methods may also suffer from sampling small areas, and can be generally inaccurate. A new proximal methodology is described here, in which low-frequency ultrasound is used as a sonar to obtain a measure of the vertical variation of the pasture density between the top of the pasture and the ground and to relate this to biomass. The instrument is designed to operate from a farm vehicle moving at up to 20 km h−1, thus allowing a farmer to obtain wide coverage in the normal course of farm operations. This is the only method providing detailed biomass profile information from throughout the entire pasture canopy. An essential feature is the identification of features from the ultrasonic reflectance, which can be related sensibly to biomass, thereby generating a physically-based regression model. The result is significantly improved estimation of pasture biomass, in comparison with other proximal methods. Comparing remotely sensed biomass to the biomass measured via cutting and weighing gives coefficients of determination, R2, in the range of 0.7 to 0.8 for a range of pastures and when operating the farm vehicle at speeds of up to 20 km h−1.

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“…In contrast, remote sensing was proven as a practical approach for yield estimation at the regional and global scales [11][12][13][14][15][16][17][18]. However, the accuracy of cotton yield estimation based on remote sensing still relies on how much information is used to capture the important physiological stages during cotton development [19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Cotton Yield Estimate Using Sentinel-2 Data and an Ecosystem Model over the Southern US

Mostovoy

2019

Remote Sensing

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High-resolution data with nearly global coverage from Sentinel-2 mission open a new era for crop growth monitoring and yield estimation from remote sensing. The objective of this study is to demonstrate the potential of using Sentinel-2 biophysical data combined with an ecosystem modeling approach for estimation of cotton yield in the southern United States (US). The Boreal Ecosystems Productivity Simulator (BEPS) ecosystem model was used to simulate the cotton gross primary production (GPP) over three Sentinel-2 tiles located in Mississippi, Georgia, and Texas in 2017. Leaf area index (LAI) derived from Sentinel-2 measurements and hourly meteorological data from Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) reanalysis were used to drive the ecosystem model. The simulated GPP values at 20-m grid spacing were aggregated to the county level (17 counties in total) and compared to the cotton lint yield estimates at the county level which are available from National Agricultural Statistics Service in the United States Department of Agriculture. The results of the comparison show that the BEPS-simulated cotton GPP explains 85% of variation in cotton yield. Our study suggests that the integration of Sentinel-2 LAI time series into the ecosystem model results in reliable estimates of cotton yield.

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Estimation methods developing with remote sensing information for energy crop biomass: A comparative review

Cited by 64 publications

References 182 publications

Development and Validation of a Model to Combine NDVI and Plant Height for High-Throughput Phenotyping of Herbage Yield in a Perennial Ryegrass Breeding Program

Development and Validation of a Model to Combine NDVI and Plant Height for High-Throughput Phenotyping of Herbage Yield in a Perennial Ryegrass Breeding Program

Ultrasonic Proximal Sensing of Pasture Biomass

Cotton Yield Estimate Using Sentinel-2 Data and an Ecosystem Model over the Southern US

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