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Special Issue "Within-Season Agricultural Monitoring from Remotely Sensed Data"

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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 5723

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Special Issue Editors

Dr. Ruyin Cao

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School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: algorithm development; time-series remote sensing; vegetation phenology
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Tao Cheng

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National Engineering & Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China
Interests: reflectance spectroscopy; quantitative remote sensing of vegetation; crop growth monitoring; crop mapping; smart farming
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Prof. Dr. Ran Meng

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College of Resources and Environment, Huazhong Agricultural University, Wuhan, China
Interests: plant stress; multiscale remote sensing; vegetation dynamics; smart agriculture
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Dr. Andrej Halabuk

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Institute of Landscape Ecology, Slovak Academy of Sciences, Bratislava, Slovakia
Interests: earth observation; applied remote sensing for agricultural and landscape studies

Dr. Clement E. Akumu

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Department of Agricultural and Environmental Sciences, College of Agriculture, Tennessee State University, 3500 John A. Merritt Blvd, Nashville, TN 37209, USA
Interests: remote sensing; data fusion; mapping and monitoring; land cover/land use change; greenhouse gases and carbon stock accounting; precision agriculture; natural resource management

Special Issue Information

Dear Colleagues,

Remote sensing data have been successfully used to investigate various agricultural activities, such as crop type mapping, crop phenology detection, soil moisture assessment, and crop growth monitoring. From a practical point of view, agricultural management requires timely and accurate crop and soil information provided by remote sensing data within the crop-growing season (within-season). For example, it is preferable to acquire the spatial distribution of crop types in an earlier manner, which benefits timely crop management, protection, and yield forecast. However, current agricultural monitoring from remotely sensed data is often conducted after the crop-growing season. Within-season agricultural monitoring is still impeded by limitations in remote sensing data quality, monitoring algorithms, and computing platforms.

In this context, a Special Issue entitled “Within-Season Agricultural Monitoring from Remotely Sensed Data” is being planned in Remote Sensing journal. We welcome all research or review articles on agricultural monitoring as long as they focus on work carried out during the crop-growing season. In addition, methodology papers on processing within-season remote sensing data (e.g., time-series data) are also welcome. This issue has a broad range of topics, including crop monitoring (e.g., crop type classification, crop phenology detection, crop phenotyping, crop yield prediction) and agricultural condition investigations (e.g., agricultural drought, biotic/abiotic stresses). It should be noted that remotely sensed data from satellites, drones, or field instruments should be among the main data sources.

We look forward to receiving your contributions.

Dr. Ruyin Cao
Prof. Dr. Tao Cheng
Prof. Dr. Ran Meng
Dr. Andrej Halabuk
Dr. Clement E. Akumu
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

agricultural remote sensing
crop types
crop phenotype
crop yield
in-season
phenotyping
plant stress
precision agriculture
time-series data
within-season

Published Papers (4 papers)

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Research

Open AccessArticle

Visible and Near-Infrared Hyperspectral Diurnal Variation Calibration for Corn Phenotyping Using Remote Sensing

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Remote Sens. 2023, 15(12), 3057; https://doi.org/10.3390/rs15123057 - 11 Jun 2023

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Abstract

Remote sensing coupled with hyperspectral technology has become increasingly popular to investigate plant traits, showcasing its advantages in studying plant growth, health, and productivity. The quality of the collected hyperspectral images is crucial for subsequent data analysis and plant phenotyping studies. However, diurnal variations in spectral characteristics introduce more data variance in canopy reflectance spectra, raising the cost of subsequent analyses and compromising the performance of trait estimation models. In this study, a fixed gantry platform in a cornfield was used to capture visible and near-infrared (VNIR) hyperspectral images of corn canopies at consecutive time intervals. By applying reference board calibration and locally weighted scatterplot smoothing to minimize the effects of ambient light and daily growth, diurnal spectral changes across all involved VNIR wavelengths were investigated. Several distinct diurnal patterns were observed to have close connections with the plants’ physiological effects. Diurnal calibration models were established at every wavelength by employing the least squares polynomial algorithm, with the highest coefficient of determination reaching 0.84. Moreover, by employing diurnal calibration in canopy spectra processing, the reduction in spectral variance brought about by varying imaging time was evidently exhibited. This study not only reveals the diurnal spectral variation pattern at VNIR bands but also offers a reliable, straightforward, and low-cost approach to improve the quality of remote sensing data and reduce the inherent variance brought about via the different imaging times ensuring that comparable spectral analysis can be performed under relatively fair conditions. Full article

(This article belongs to the Special Issue Within-Season Agricultural Monitoring from Remotely Sensed Data)

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Open AccessCommunication

Analysis of Relationship between Grain Yield and NDVI from MODIS in the Fez-Meknes Region, Morocco

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Remote Sens. 2023, 15(11), 2707; https://doi.org/10.3390/rs15112707 - 23 May 2023

Cited by 1 | Viewed by 1705

Abstract

Exploring the relationship between cereal yield and the remotely sensed normalized difference vegetation index (NDVI) is of great importance to decision-makers and agricultural stakeholders. In this study, an approach based on the Pearson correlation coefficient and linear regression is carried out to reveal the relationship between cereal yield and Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI data in the Fez-Meknes region of Morocco. The results obtained show strong correlations reaching 0.70 to 0.89 between the NDVI and grain yield. The linear regression model explains 58 to 79% of the variability in yield in regional provinces marked by the importance of cereal cultivation, and 51 to 53% in the mountainous provinces with less agricultural land devoted to major cereals. The regression slopes indicate that a 0.1 increase in the NDVI results in an expected increase in grain yield of 4.9 to 8.7 quintals (q) per ha, with an average of 6.8 q/ha throughout the Fez-Meknes region. The RMSE ranges from 2.12 to 4.96 q/ha. These results are promising in terms of early yield forecasting based on MODIS-NDVI data, and consequently, in terms of grain import planning, especially since the national grain production does not cover the demand. Such remote sensing data are therefore essential for administrations that are in charge of food security decisions. Full article

(This article belongs to the Special Issue Within-Season Agricultural Monitoring from Remotely Sensed Data)

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Open AccessArticle

Fresh Yield Estimation of Spring Tea via Spectral Differences in UAV Hyperspectral Images from Unpicked and Picked Canopies

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Remote Sens. 2023, 15(4), 1100; https://doi.org/10.3390/rs15041100 - 17 Feb 2023

Cited by 1 | Viewed by 1256

Abstract

At present, spring tea yield is mainly estimated through a manual sampling survey. Obtaining yield information is time consuming and laborious for the whole spring tea industry, especially at the regional scale. Remote sensing yield estimation is a popular method used in large-scale grain crop fields, and few studies on the estimation of spring tea yield from remote sensing data have been reported. This is a similar spectrum of fresh tea yield components to that of the tea tree canopy. In this study, two types of unmanned aerial vehicle (UAV) hyperspectral images from the unpicked and picked Anji white tea tree canopies are collected, and research on the estimation of the spring tea fresh yield is performed using the differences identified in the single and combined chlorophyll spectral indices (CSIs) or leaf area spectral indices (LASIs) while also considering the changes in the green coverage of the tea tree canopy by way of a linear or piecewise linear function. The results are as follows: (1) in the linear model with a single index variable (LMSV), the accuracy of spring tea fresh yield models based on the selected CSIs was better than that based on the selected LASIs as a whole, in which the model based on the curvature index (CUR) was the best with regard to the accuracy metrics; (2) compared to the LMSVs, the accuracy performance of the piecewise linear model with the same index variables (PLMSVs) was obviously improved, with an encouraging root mean square error (RMSE) and validation determination coefficient (VR²); and (3) in the piecewise model with the combined index variables (PLMCVs), its evaluation metrics are also improved, in which the best performance of them was the CUR&CUR model with a RMSE (124.602 g) and VR² (0.625). It showed that the use of PLMSVs or PLMCVs for fresh tea yield estimation could reduce the vegetation index saturation of the tea tree canopy. These results show that the spectral difference discovered through hyperspectral remote sensing can provide the potential capability of estimating the fresh yield of spring tea on a large scale. Full article

(This article belongs to the Special Issue Within-Season Agricultural Monitoring from Remotely Sensed Data)

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Open AccessArticle

Investigating the Potential of Crop Discrimination in Early Growing Stage of Change Analysis in Remote Sensing Crop Profiles

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Remote Sens. 2023, 15(3), 853; https://doi.org/10.3390/rs15030853 - 03 Feb 2023

Cited by 4 | Viewed by 1480

Abstract

Currently, remote sensing crop identification is mostly based on all available images acquired throughout crop growth. However, the available image and data resources in the early growth stage are limited, which makes early crop identification challenging. Different crop types have different phenological characteristics and seasonal rhythm characteristics, and their growth rates are different at different times. Therefore, making full use of crop growth characteristics to augment crop growth difference information at different times is key to early crop identification. In this study, we first calculated the differential features between different periods as new features based on images acquired during the early growth stage. Secondly, multi-temporal difference features of each period were constructed by combination, then a feature optimization method was used to obtain the optimal feature set of all possible combinations in different periods and the early key identification characteristics of different crops, as well as their stage change characteristics, were explored. Finally, the performance of classification and regression tree (Cart), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and Support Vector Machine (SVM) classifiers in recognizing crops in different periods were analyzed. The results show that: (1) There were key differences between different crops, with rice changing significantly in period F, corn changing significantly in periods E, M, L, and H, and soybean changing significantly in periods E, M, N, and H. (2) For the early identification of rice, the land surface water index (LSWI), simple ratio index (SR), B11, and normalized difference tillage index (NDTI) contributed most, while B11, normalized difference red-edge3 (NDRE3), LSWI, the green vegetation index (VIgreen), red-edge spectral index (RESI), and normalized difference red-edge2 (NDRE2) contributed greatly to corn and soybean identification. (3) Rice could be identified as early as 13 May, with PA and UA as high as 95%. Corn and soybeans were identified as early as 7 July, with PA and UA as high as 97% and 94%, respectively. (4) With the addition of more temporal features, recognition accuracy increased. The GBDT and RF performed best in identifying the three crops in the early stage. This study demonstrates the feasibility of using crop growth difference information for early crop recognition, which can provide a new idea for early crop recognition. Full article

(This article belongs to the Special Issue Within-Season Agricultural Monitoring from Remotely Sensed Data)

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