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Crop Biophysical Parameters Retrieval Using Remote Sensing Data
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Crop Biophysical Parameters Retrieval Using Remote Sensing Data

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: closed (31 October 2022) | Viewed by 16723

Special Issue Editors

Dr. Jiangang Liu

E-Mail Website
Guest Editor
Department of Potato, Institute of Vegetables and Flowers Chinese Academy of Agricultural Sciences (IVF-CAAS), Beijing 100081 China
Interests: crop model; plant phenotyping; UAV; proximal remote sensing; precision agriculture
Special Issues, Collections and Topics in MDPI journals
Dr. Bo Li

E-Mail Website
Guest Editor
Seeds Research, Syngenta, Jealott’s Hill, Warfield, Bracknell RG42 6EY, UK
Interests: computer vision; image analysis; plant phenotyping; remote sensing; machine learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Zhenjiang Zhou

E-Mail Website
Guest Editor
Department of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058 China
Interests: precision irrigation and fertilization; remote sensing; agronomy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Crop biophysical parameters are essential for exploring crop growth dynamics and determining optimized strategies for crop management. High-throughput estimation of spatial–temporal biophysical parameters can accelerate crop research or breeding efficiency and prompt intelligent agriculture development. Remote sensing can provide timely, rapid, noninvasive, and efficient access to crop biophysical parameters. Crop biophysical traits including morphological parameters, spectrum and texture characteristics, physiological traits, and response to abiotic/biotic stress under different environments have been retrieved by various remote sensing platforms and sensors (e.g., digital camera, multispectral camera, hyperspectral imager, thermal imager, and light detection and ranging (LiDAR) systems). All these technologies are achieved with the development of engineering, computer science, plant physiology, molecular research, and bioinformatics. Considering the complex environment that crops grow with, the precise and robust retrieval of biophysical information still faces challenges. At the same time, various novel methods and technologies are being developed by scientists in varying fields, especially beyond the agricultural field. Research outputs from different fields need to be gathered to gain more quantitative knowledge of structure and function of plants.

This Special Issue invites studies covering crop biophysical parameter retrieval through different remote sensing platforms and sensors coupled with diversified inversion methods. Original research articles and reviews are welcome, especially in crop biophysical parameter retrieval with multisource data integration and multiscale approaches. Research areas may include (but are not limited to) the following:

  • Crop biophysical parameter retrieval
  • High-throughput crop phenotyping
  • Crop growth monitoring
  • Crop morphological parameters
  • Crop physiological traits

We look forward to receiving your contributions.

Dr. Jiangang Liu
Dr. Bo Li
Prof. Dr. Zhenjiang Zhou
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • crop biophysical parameters
  • optical remote sensing
  • active remote sensing
  • multisource data integration
  • radiative transfer model
  • deep learning

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Related Special Issue

Published Papers (6 papers)

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Research

22 pages, 2634 KiB  
Article
Predicting Nitrogen Efficiencies in Mature Maize with Parametric Models Employing In-Season Hyperspectral Imaging
by Monica B. Olson, Melba M. Crawford and Tony J. Vyn
Remote Sens. 2022, 14(22), 5884; https://doi.org/10.3390/rs14225884 - 20 Nov 2022
Cited by 3 | Viewed by 1839
Abstract
Overuse of nitrogen (N), an essential nutrient in food production systems, can lead to health issues and environmental degradation. Two parameters related to N efficiency, N Conversion Efficiency (NCE) and N Internal Efficiency (NIE), measure the amount of total biomass or grain produced, [...] Read more.
Overuse of nitrogen (N), an essential nutrient in food production systems, can lead to health issues and environmental degradation. Two parameters related to N efficiency, N Conversion Efficiency (NCE) and N Internal Efficiency (NIE), measure the amount of total biomass or grain produced, respectively, per unit of N in the plant. Utilizing remote sensing to improve these efficiency measures may positively impact the stewardship of agricultural N use in maize (Zea mays L.) production. We investigated in-season hyperspectral imaging for prediction of end-season whole-plant N concentration (pN), NCE, and NIE, using partial least squares regression (PLSR) models. Image data were collected at two mid-season growth stages (V16/V18 and R1/R2) from manned aircraft and unmanned aerial vehicles for three site years of 5 to 9 maize hybrids grown under 3 N treatments and 2 planting densities. PLSR models resulted in accurate predictions for pN at R6 (R2 = 0.73; R2 = 0.68) and NCE at R6 (R2 = 0.71; R2 = 0.73) from both imaging times. Additionally, the PLSR models based on the R1 images, the second imaging, accurately distinguished the highest and lowest ranked hybrids for pN and NCE across N rates. Neither timepoint resulted in accurate predictions for NIE. Genotype selection efficiency for end-season pN and NCE was increased through the use of the in-season PLSR imaging models, potentially benefiting early breeding screening methods. Full article
(This article belongs to the Special Issue Crop Biophysical Parameters Retrieval Using Remote Sensing Data)
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15 pages, 4171 KiB  
Article
Detecting Wheat Heads from UAV Low-Altitude Remote Sensing Images Using Deep Learning Based on Transformer
by Jiangpeng Zhu, Guofeng Yang, Xuping Feng, Xiyao Li, Hui Fang, Jinnuo Zhang, Xiulin Bai, Mingzhu Tao and Yong He
Remote Sens. 2022, 14(20), 5141; https://doi.org/10.3390/rs14205141 - 14 Oct 2022
Cited by 21 | Viewed by 3877
Abstract
The object detection method based on deep learning convolutional neural network (CNN) significantly improves the detection performance of wheat head on wheat images obtained from the near ground. Nevertheless, for wheat head images of different stages, high density, and overlaps captured by the [...] Read more.
The object detection method based on deep learning convolutional neural network (CNN) significantly improves the detection performance of wheat head on wheat images obtained from the near ground. Nevertheless, for wheat head images of different stages, high density, and overlaps captured by the aerial-scale unmanned aerial vehicle (UAV), the existing deep learning-based object detection methods often have poor detection effects. Since the receptive field of CNN is usually small, it is not conducive to capture global features. The visual Transformer can capture the global information of an image; hence we introduce Transformer to improve the detection effect and reduce the computation of the network. Three object detection networks based on Transformer are designed and developed, including the two-stage method FR-Transformer and the one-stage methods R-Transformer and Y-Transformer. Compared with various other prevalent object detection CNN methods, our FR-Transformer method outperforms them by 88.3% for AP50 and 38.5% for AP75. The experiments represent that the FR-Transformer method can gratify requirements of rapid and precise detection of wheat heads by the UAV in the field to a certain extent. These more relevant and direct information provide a reliable reference for further estimation of wheat yield. Full article
(This article belongs to the Special Issue Crop Biophysical Parameters Retrieval Using Remote Sensing Data)
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15 pages, 1489 KiB  
Article
A New Approach for Nitrogen Status Monitoring in Potato Plants by Combining RGB Images and SPAD Measurements
by Huanbo Yang, Yaohua Hu, Zhouzhou Zheng, Yichen Qiao, Bingru Hou and Jun Chen
Remote Sens. 2022, 14(19), 4814; https://doi.org/10.3390/rs14194814 - 27 Sep 2022
Cited by 9 | Viewed by 2244
Abstract
Precise nitrogen (N) application ensures the best N status of potato plants to improve crop growth and food quality and to achieve the best N use efficiency. Four N fertilization levels (0, 2, 4 and 6 g N pot−1) were used [...] Read more.
Precise nitrogen (N) application ensures the best N status of potato plants to improve crop growth and food quality and to achieve the best N use efficiency. Four N fertilization levels (0, 2, 4 and 6 g N pot−1) were used to establish a critical N dilution curve (CNDC) of potato plants cultivated in substrates with a greenhouse environment. RGB images of potato plants were obtained, and a red–green fit index (RGFI) was calculated based on the linear relationship between R and G channels and the principle of the excess green index (EXG). The N in the substrate can meet the nutritional requirements of potato plants during the first 35 days after emergence. In order to solve the complex sampling problem of maintaining a sufficient N strip for aboveground dry biomass (DM) and crop nitrogen concentration, a reference curve method for detecting N status was proposed. RGFI and SPAD values from the economically optimum 4 g N pot−1 treatment were used to derive the reference curve. The RGFI and SPAD values from the 4 g N pot−1 treatment had high correlations and were fitted with a second-order polynomial function with an R2 value of 0.860 and an RMSE value of 2.10. The validation results show that the N concentration dilution curve constructed by RGFI and SPAD values can effectively distinguish N-limiting from non-N-limiting treatments, CNDCs constructed based on RGFI and SPAD values could be used as an effective N status monitoring tool for greenhouse potato production. Full article
(This article belongs to the Special Issue Crop Biophysical Parameters Retrieval Using Remote Sensing Data)
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22 pages, 6879 KiB  
Article
Estimating LAI for Cotton Using Multisource UAV Data and a Modified Universal Model
by Puchen Yan, Qisheng Han, Yangming Feng and Shaozhong Kang
Remote Sens. 2022, 14(17), 4272; https://doi.org/10.3390/rs14174272 - 30 Aug 2022
Cited by 19 | Viewed by 2490
Abstract
Leaf area index(LAI) is an important indicator of crop growth and water status. With the continuous development of precision agriculture, estimating LAI using an unmanned aerial vehicle (UAV) remote sensing has received extensive attention due to its low cost, high throughput and accuracy. [...] Read more.
Leaf area index(LAI) is an important indicator of crop growth and water status. With the continuous development of precision agriculture, estimating LAI using an unmanned aerial vehicle (UAV) remote sensing has received extensive attention due to its low cost, high throughput and accuracy. In this study, multispectral and light detection and ranging (LiDAR) sensors carried by a UAV were used to obtain multisource data of a cotton field. The method to accurately relate ground measured data with UAV data was built using empirical statistical regression models and machine learning algorithm models (RFR, SVR and ANN). In addition to the traditional spectral parameters, it is also feasible to estimate LAI using UAVs with LiDAR to obtain structural parameters. Machine learning models, especially the RFR model (R2 = 0.950, RMSE = 0.332), can estimate cotton LAI more accurately than empirical statistical regression models. Different plots and years of cotton datasets were used to test the model robustness and generality; although the accuracy of the machine learning model decreased overall, the estimation accuracy based on structural and multisources was still acceptable. However, selecting appropriate input parameters for different canopy opening and closing statuses can alleviate the degradation of accuracy, where input parameters select multisource parameters before canopy closure while structural parameters are selected after canopy closure. Finally, we propose a gap fraction model based on a LAImax threshold at various periods of cotton growth that can estimate cotton LAI with high accuracy, particularly when the calculation grid is 20 cm (R2 = 0.952, NRMSE = 12.6%). This method does not require much data modeling and has strong universality. It can be widely used in cotton LAI prediction in a variety of environments. Full article
(This article belongs to the Special Issue Crop Biophysical Parameters Retrieval Using Remote Sensing Data)
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11 pages, 2496 KiB  
Article
Estimation of Dry Matter and N Nutrient Status of Choy Sum by Analyzing Canopy Images and Plant Height Information
by Zhao Wang, Jiang Shi, Sashuang Sun, Lijun Zhu, Yiyin He, Rong Jin, Letan Luo, Lin Zhao, Junxiang Peng and Zhenjiang Zhou
Remote Sens. 2022, 14(16), 3964; https://doi.org/10.3390/rs14163964 - 15 Aug 2022
Cited by 1 | Viewed by 1737
Abstract
The estimation accuracy of plant dry matter by spectra- or remote sensing-based methods tends to decline when canopy coverage approaches closure; this is known as the saturation problem. This study aimed to enhance the estimation accuracy of plant dry matter and subsequently use [...] Read more.
The estimation accuracy of plant dry matter by spectra- or remote sensing-based methods tends to decline when canopy coverage approaches closure; this is known as the saturation problem. This study aimed to enhance the estimation accuracy of plant dry matter and subsequently use the critical nitrogen dilution curve (CNDC) to diagnose N in Choy Sum by analyzing the combined information of canopy imaging and plant height. A three-year experiment with different N levels (0, 25, 50, 100, 150, and 200 kg∙ha−1) was conducted on Choy Sum. Variables of canopy coverage (CC) and plant height were used to build the dry matter and N estimation model. The results showed that the yields of N0 and N25 were significantly lower than those of high-N treatments (N50, N100, N150, and N200) for all three years. The variables of CC × Height had a significant linear relationship with dry matter, with R2 values above 0.87. The good performance of the CC × Height-based model implied that the saturation problem of dry matter prediction was well-addressed. By contrast, the relationship between dry matter and CC was best fitted by an exponential function. CNDC models built based on CC × Height information could satisfactorily differentiate groups of N deficiency and N abundance treatments, implying their feasibility in diagnosing N status. N application rates of 50–100 kgN/ha are recommended as optimal for a good yield of Choy Sum production in the study region. Full article
(This article belongs to the Special Issue Crop Biophysical Parameters Retrieval Using Remote Sensing Data)
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24 pages, 5341 KiB  
Article
Hyperspectral Reflectance Proxies to Diagnose In-Field Fusarium Head Blight in Wheat with Machine Learning
by Ghulam Mustafa, Hengbiao Zheng, Imran Haider Khan, Long Tian, Haiyan Jia, Guoqiang Li, Tao Cheng, Yongchao Tian, Weixing Cao, Yan Zhu and Xia Yao
Remote Sens. 2022, 14(12), 2784; https://doi.org/10.3390/rs14122784 - 10 Jun 2022
Cited by 22 | Viewed by 3345
Abstract
Hyperspectral reflectance (HR) technology as proxy approach to diagnose fusarium head blight (FHB) in wheat crop could be a real-time and non-invasive approach for its in-field management to reduce grain damage. In-field canopy’s non-imaging HR (400–2400 nm using ground-based spectrometer system), photosynthesis rate [...] Read more.
Hyperspectral reflectance (HR) technology as proxy approach to diagnose fusarium head blight (FHB) in wheat crop could be a real-time and non-invasive approach for its in-field management to reduce grain damage. In-field canopy’s non-imaging HR (400–2400 nm using ground-based spectrometer system), photosynthesis rate (Pn) and disease severity (DS) data were simultaneously acquired from artificially inoculated wheat plots over a period of two years (2020 and 2021) in the field. Subsequently, continuous wavelet transform (CWT) was employed to select the consistent spectral bands (CSBs) and to develop the canopy-based difference indices with criterion of variable importance score using random forest—recursive feature elimination. Thereby, different machine learning algorithms were employed for FHB classification and multivariate estimation, and linear regression models to evaluate the newly developed indices against conventional vegetation indices. The results showed that inoculation reduced the Pn rate of spikes, elevated reflectance in visible and short-wave infrared regions and decreased in near infrared region at different days after inoculation (DAI). CWT analysis selected five CSBs (401, 460, 570, 786 and 840 nm) employing datasets from 2020 and 2021. These spectral bands were employed to develop wheat fusarium canopy indices (WFCI1 and WFCI2). Considering the average classification accuracy (ACA) in both years of experiments, WFCI1 manifested a maximum ACA of 75% at 5 DAI with DS of 9.73% which raised to 100% at 10 DAI with a DS of 18%. ACA mentions the averaged results of all machine learning classifiers (MLC). While in the perspective of MLC, random forest (RF) outperformed the rest of the MLC, individually, it revealed 100% classification accuracy through WFCI1 at DS 10.78% on the eight DAI. The univariate estimation of disease based on WFCI1 and WFCI2 with independent data produced R2 and root mean square error (RMSE) values of 0.80 and 14.7, and 0.81 and13.50, respectively. However, Knn regression analysis with both canopy indices (WFCI1 and WFCI2) manifested the maximum accuracy for disease estimation with RMSE of 11.61 and R2 = 0.83. Conclusively, the newly proposed HR indices show great potential as proxy approach for detecting FHB at early stage and understanding the physical state of crops in field conditions for the better management and control of plant diseases. Full article
(This article belongs to the Special Issue Crop Biophysical Parameters Retrieval Using Remote Sensing Data)
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