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Special Issue "Crop Quantitative Monitoring with Remote Sensing II"

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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: 31 January 2024 | Viewed by 1368

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Special Issue Editors

Dr. Tiecheng Bai

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Guest Editor

School of Information Engineering, Tarim University, Alaer 843300, China
Interests: crop growth simulation; precision agriculture; vegetation parameter retrieval; remote sensing assimilation
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Jianxi Huang

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College of Land Science and Technology, China Agricultural University, Beijing 100081, China
Interests: crop type mapping; crop yield forecasting; data assimilation; crop growth models
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Dr. Qingling Wu

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Department of Biology, University of Oxford, OXFORD OX1 3SZ, UK
Interests: image processing; data mining; data assimilation
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Prof. Dr. Wei Su

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Guest Editor

College of Land Science and Technology, China Agricultural University, Beijing 100081, China
Interests: LiDAR application in vegetation; vegetation parameter retrieval; vegetation monitoring; hyperspectral remote sensing
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Special Issue Information

Dear Colleagues,

Crop quantitative monitoring is important to decision support in crop production management practices for sustainable agricultural development and global food security. Today, remote sensing has been extensively used to monitor agricultural fields for crop field mapping, crop phenology, crop disaster stress, real-time crop yield estimation or forecasting, and so on. Various advanced quantitative algorithms have been developed for improved crop classification (e.g., long-term and high-resolution crop maps for wheat, maize, and rice), as well as time series for crop phenology detection and critical crop parameter retrieval (e.g., leaf area index retrieval from canopy radiative transfer model), crop disaster monitoring (drought, flooding, lodging, pests, and diseases), and so on. Applications can be at the global, national, regional, farm or field level, such as county-level yield prediction under climate change and agricultural emissions, which a combination of quantitative remote sensing and crop growth models can carry out.

Dr. Tiecheng Bai
Prof. Dr. Jianxi Huang
Dr. Qingling Wu
Prof. Dr. Wei Su
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

quantitative remote sensing
time series analysis
crop growth models
data assimilation
machine learning
deep learning
climate change
crop parameter retrieval
crop growth monitoring
crop stress monitoring
crop disaster monitoring
crop phenology detection
crop type mapping
crop yield estimation or forecasting

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Crop Quantitative Monitoring with Remote Sensing in Remote Sensing (8 articles)

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Research

23 pages, 7370 KiB

Open AccessArticle

Identification of the Initial Anthesis of Soybean Varieties Based on UAV Multispectral Time-Series Images

and

Remote Sens. 2023, 15(22), 5413; https://doi.org/10.3390/rs15225413 - 18 Nov 2023

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Abstract

Accurate and high-throughput identification of the initial anthesis of soybean varieties is important for the breeding and screening of high-quality soybean cultivars in field trials. The objectives of this study were to identify the initial day of anthesis (IA_DAS) of soybean varieties based on remote sensing multispectral time-series images acquired by unmanned aerial vehicles (UAVs), and analyze the differences in the initial anthesis of the same soybean varieties between two different climatic regions, Shijiazhuang (SJZ) and Xuzhou (XZ). First, the temporal dynamics of several key crop growth indicators and spectral indices were analyzed to find an effective indicator that favors the identification of IA_DAS, including leaf area index (LAI), above-ground biomass (AGB), canopy height (CH), normalized-difference vegetation index (NDVI), red edge chlorophyll index (CIred edge), green normalized-difference vegetation index (GNDVI), enhanced vegetation index (EVI), two-band enhanced vegetation index (EVI2) and normalized-difference red-edge index (NDRE). Next, this study compared several functions, like the symmetric gauss function (SGF), asymmetric gauss function (AGF), double logistic function (DLF), and fourier function (FF), for time-series curve fitting, and then estimated the IA_DAS of soybean varieties with the first-order derivative maximal feature (FDmax) of the CIred edge phenology curves. The relative thresholds of the CIred edge curves were also used to estimate IA_DAS, in two ways: a single threshold for all of the soybean varieties, and three different relative thresholds for early, middle, and late anthesis varieties, respectively. Finally, this study presented the variations in the IA_DAS of the same soybean varieties between two different climatic regions and discussed the probable causal factors. The results showed that CIred edge was more suitable for soybean IA_DAS identification compared with the other investigated indicators because it had no saturation during the whole crop lifespan. Compared with DLF, AGF and FF, SGF provided a better fitting of the CIred edge time-series curves without overfitting problems, although the coefficient of determination (R²) and root mean square error (RMSE) were not the best. The FDmax of the SGF-fitted CIred edge curve (SGF_CIred edge) provided good estimates of the IA_DAS, with an RMSE and mean average error (MAE) of 3.79 days and 3.00 days, respectively. The SGF-fitted_CIred edge curve can be used to group the soybean varieties into early, middle and late groups. Additionally, the accuracy of the IA_DAS was improved (RMSE = 3.69 days and MAE = 3.09 days) by using three different relative thresholds (i.e., RT50, RT55, RT60) for the three flowering groups compared to when using a single threshold (RT50). In addition, it was found that the IA_DAS of the same soybean varieties varied greatly when planted in two different climatic regions due to the genotype–environment interactions. Overall, this study demonstrated that the IA_DAS of soybean varieties can be identified efficiently and accurately based on UAV remote sensing multispectral time-series data. Full article

(This article belongs to the Special Issue Crop Quantitative Monitoring with Remote Sensing II)

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18 pages, 6258 KiB

Open AccessArticle

Estimation of Winter Wheat Yield Using Multiple Temporal Vegetation Indices Derived from UAV-Based Multispectral and Hyperspectral Imagery

and

Remote Sens. 2023, 15(19), 4800; https://doi.org/10.3390/rs15194800 - 01 Oct 2023

Viewed by 686

Abstract

Winter wheat is a major food source for the inhabitants of North China. However, its yield is affected by drought stress during the growing period. Hence, it is necessary to develop drought-resistant winter wheat varieties. For breeding researchers, yield measurement, a crucial breeding indication, is costly, labor-intensive, and time-consuming. Therefore, in order to breed a drought-resistant variety of winter wheat in a short time, field plot scale crop yield estimation is essential. Unmanned aerial vehicles (UAVs) have developed into a reliable method for gathering crop canopy information in a non-destructive and time-efficient manner in recent years. This study aimed to evaluate strategies for estimating crop yield using multispectral (MS) and hyperspectral (HS) imagery derived from a UAV in single and multiple growth stages of winter wheat. To accomplish our objective, we constructed a simple linear regression model based on the single growth stages of booting, heading, flowering, filling, and maturation and a multiple regression model that combined these five growth stages to estimate winter wheat yield using 36 vegetation indices (VIs) calculated from UAV-based MS and HS imagery, respectively. After comparing these regression models, we came to the following conclusions: (1) the flowering stage of winter wheat showed the highest correlation with crop yield for both MS and HS imagery; (2) the VIs derived from the HS imagery performed better in terms of estimation accuracy than the VIs from the MS imagery; (3) the regression model that combined the information of five growth stages presented better accuracy than the one that considered the growth stages individually. The best estimation regression model for winter wheat yield in this study was the multiple linear regression model constructed by the VI of ‘

(b_{1} - b_{2}) / (b_{3} - b_{4})

’ derived from HS imagery, incorporating the five growth stages of booting, heading, flowering, filling, and maturation with r of 0.84 and RMSE of 0.69 t/ha. The corresponding central wavelengths were 782 nm, 874 nm, 762 nm, and 890 nm, respectively. Our study indicates that the multiple temporal VIs derived from UAV-based HS imagery are effective tools for breeding researchers to estimate winter wheat yield on a field plot scale. Full article

(This article belongs to the Special Issue Crop Quantitative Monitoring with Remote Sensing II)

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