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Agronomy
Special Issues
Remote Sensing Applications for Agriculture and Crop Modelling-Series Ⅱ
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Remote Sensing Applications for Agriculture and Crop Modelling-Series Ⅱ

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Precision and Digital Agriculture".

Deadline for manuscript submissions: closed (21 June 2021) | Viewed by 22513

Special Issue Editor

Dr. Piero Toscano

E-Mail Website
Guest Editor
Institute of BioEconomy (IBE), National Research Council (CNR), Via Caproni 8, 50145 Florence, Italy
Interests: remote sensing; precision agriculture; crop modeling; climate services
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Considering the attention that the first edition of this SI has aroused, the quality of the published works and the need to provide and share new findings in expanding the scope of remote sensing and modelling for agricultural systems, we invite you join in us and share your high quality and original contributions with us.

Crop models and remote sensing techniques have been combined and applied in agriculture and crop estimation on local and regional scales as well as worldwide, based on the simultaneous development of crop models and remote sensing. The literature shows that many new remote sensing sensors and valuable methods have been developed for the retrieval of canopy state variables and soil properties from remote sensing data for assimilating the retrieved variables into crop models. At the same time, remote sensing has been used in a staggering number of applications for agriculture. With this 2nd Special Issue, we will also compile state-of-the-art research that specifically addresses and provides new steps in expanding the scope of remote sensing and modelling for agricultural systems: Data assimilation in mechanistic crop growth models, local to global monitoring activities (e.g., crop identification and crop surface estimation, crop forecasting, crop health analysis and assessment of crop damage), and applications of remote sensing at the farm level (e.g., crop condition assessment and stress detection, identification of pests and disease infestation, retrieval of quantity and quality crop characteristics). Model–data assimilation and model–data fusion contributions are welcomed, as are papers describing new management applications of remote sensing in agriculture.

Dr. Piero Toscano
Guest Editor

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. Agronomy is an international peer-reviewed open access monthly 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 2600 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

  • remote sensing
  • crop model
  • assimilation, fusion, yield
  • spatio-temporal scale
  • crop biophysical variables
  • crop status
  • crop identification and crop area
  • precision agriculture

Published Papers (6 papers)

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Research

22 pages, 69522 KiB  
Article
UAV Multispectral Imaging Potential to Monitor and Predict Agronomic Characteristics of Different Forage Associations
by Javier Plaza, Marco Criado, Nilda Sánchez, Rodrigo Pérez-Sánchez, Carlos Palacios and Francisco Charfolé
Agronomy 2021, 11(9), 1697; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy11091697 - 25 Aug 2021
Cited by 6 | Viewed by 2256
Abstract
The capability of UAVs imagery to monitor and predict the evolution of several forage associations was assessed during the whole growing cycle of 2019–20. For this purpose, eight different forage associations grown in triplicate were used: vetch-barley-triticale (VBT), vetch-triticale (VT), vetch-rye (VR), vetch-oats [...] Read more.
The capability of UAVs imagery to monitor and predict the evolution of several forage associations was assessed during the whole growing cycle of 2019–20. For this purpose, eight different forage associations grown in triplicate were used: vetch-barley-triticale (VBT), vetch-triticale (VT), vetch-rye (VR), vetch-oats (VO), pea-barley-triticale (PBT), pea-triticale (PT), pea-rye (PR) and pea-oats (PO). Six biophysical parameters were monitored through six vegetation indices on seven measurements dates distributed along the growing cycle. The experiments were carried out on the organic farm “Gallegos de Crespes” located in the municipality of Larrodrigo (Salamanca, Spain). The results obtained in the exploratory and the correlation analysis suggested that a predictive model (PLS regression) could be performed. Overall, vetch-based associations showed slightly higher values for both the field parameters and the vegetation indices than pea-based ones. Correlations were very strong and significant for each association throughout their growing cycle, suggesting that the evolution of the associations would be monitored from the spectral indices. Integrating these multispectral observations in the PLS model, the agronomic parameters of forage associations were predicted with a reliability of more than 50%. A single combination of VNIR (or even only visible) bands was able to feed the regression model, leading to a successful prediction of the agronomic parameters. Full article
(This article belongs to the Special Issue Remote Sensing Applications for Agriculture and Crop Modelling-Series Ⅱ)
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21 pages, 4774 KiB  
Article
Simplified and Advanced Sentinel-2-Based Precision Nitrogen Management of Wheat
by Francesco Saverio Santaga, Paolo Benincasa, Piero Toscano, Sara Antognelli, Emanuele Ranieri and Marco Vizzari
Agronomy 2021, 11(6), 1156; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy11061156 - 04 Jun 2021
Cited by 15 | Viewed by 2834
Abstract
This study compares simplified and advanced precision nitrogen (N) fertilization approaches for winter wheat relying on Sentinel-2 NDVI, grain yield maps, and protein content. Five N fertilization treatments were compared: (1) a standard rate, calculated by a typical N balance (Flat-N); (2) a [...] Read more.
This study compares simplified and advanced precision nitrogen (N) fertilization approaches for winter wheat relying on Sentinel-2 NDVI, grain yield maps, and protein content. Five N fertilization treatments were compared: (1) a standard rate, calculated by a typical N balance (Flat-N); (2) a variable rate calculated using a simplified linear model, adopting a proportional strategy (NDVI directly related) (Var-N-dir); (3) a variable rate calculated using a simplified linear model, adopting a compensative strategy (NDVI inversely related) (Var-N-inv); (4) a variable rate calculated using the AgroSat model (Var-N-Agrosat); and (5) a variable rate calculated applying the Agricolus model (Var-N-Agricolus). The study was carried out in four fields over two cropping seasons with a randomized blocks design. Results indicate that the weather remains the main factor influencing yield, as it typically happens in a rainfed crop. No substantial differences in crop yield were observed among the N fertilization models within each year and experimental location. However, in the more favorable season, the low-input direct model (Var-N-dir) resulted as the best choice, providing the higher NUE (nitrogen use efficiency) value. In the less favorable season, results showed a better performance of the advanced models (Var-N-Agricolus and Var-N-Agrosat), which limited yield losses and reduced intra-field variability, with relevant importance given to the increasing frequency of abnormal climate phenomena. In general, all these VRT approaches allowed reduction of the excess of fertilizers, preservation of the environment, and saving money. Full article
(This article belongs to the Special Issue Remote Sensing Applications for Agriculture and Crop Modelling-Series Ⅱ)
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15 pages, 2420 KiB  
Article
Assessment of Maize Growth and Development with High- and Medium-Resolution Remote Sensing Products
by Rocío Ballesteros, Miguel A. Moreno, Fellype Barroso, Laura González-Gómez and José F. Ortega
Agronomy 2021, 11(5), 940; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy11050940 - 10 May 2021
Cited by 6 | Viewed by 2033
Abstract
The availability of a great amount of remote sensing data for precision agriculture purposes has set the question of which resolution and indices, derived from satellites or unmanned aerial vehicles (UAVs), offer the most accurate results to characterize vegetation. This study focused on [...] Read more.
The availability of a great amount of remote sensing data for precision agriculture purposes has set the question of which resolution and indices, derived from satellites or unmanned aerial vehicles (UAVs), offer the most accurate results to characterize vegetation. This study focused on assessing, comparing, and discussing the performances and limitations of satellite and UAV-based imagery in terms of canopy development, i.e., the leaf area index (LAI), and yield, i.e., the dry aboveground biomass (DAGB), for maize. Three commercial maize fields were studied over four seasons to obtain the LAI and DAGB. The normalized difference vegetation index (NDVI) and visible atmospherically resistant index (VARI) from satellite platforms (Landsat 5TM, 7 ETM+, 8OLI, and Sentinel 2A MSI) and the VARI and green canopy cover (GCC) from UAV imagery were compared. The remote sensing predictors in addition to the growing degree days (GDD) were assessed to estimate the LAI and DAGB using multilinear regression models (MRMs). For LAI estimation, better adjustments were obtained when predictors from the UAV platform were considered. The DAGB estimation revealed similar adjustments for both platforms, although the Landsat imagery offered slightly better adjustments. The results obtained in this study demonstrate the advantage of remote sensing platforms as a useful tool to estimate essential agronomic features. Full article
(This article belongs to the Special Issue Remote Sensing Applications for Agriculture and Crop Modelling-Series Ⅱ)
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22 pages, 4181 KiB  
Article
Assimilation of Sentinel-2 Estimated LAI into a Crop Model: Influence of Timing and Frequency of Acquisitions on Simulation of Water Stress and Biomass Production of Winter Wheat
by Andreas Tewes, Carsten Montzka, Manuel Nolte, Gunther Krauss, Holger Hoffmann and Thomas Gaiser
Agronomy 2020, 10(11), 1813; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy10111813 - 18 Nov 2020
Cited by 9 | Viewed by 3022
Abstract
The Sentinel-2 (S2) Toolbox permits for the automated retrieval of leaf area index (LAI). LAI assimilation into crop simulation models could aid to improve the prediction accuracy for biomass at field level. We investigated if the combined effects of assimilation date and corresponding [...] Read more.
The Sentinel-2 (S2) Toolbox permits for the automated retrieval of leaf area index (LAI). LAI assimilation into crop simulation models could aid to improve the prediction accuracy for biomass at field level. We investigated if the combined effects of assimilation date and corresponding growth stage plus observational frequency have an impact on the crop model-based simulation of water stress and biomass production. We simulated winter wheat growth in nine fields in Germany over two years. S2 LAI estimations for each field were categorized into three phases, depending on the development stage of the crop at acquisition date (tillering, stem elongation, booting to flowering). LAI was assimilated in every possible combinational setup using the ensemble Kalman filter (EnKF). We evaluated the performance of the simulations based on the comparison of measured and simulated aboveground biomass at harvest. The results showed that the effects on water stress remained largely limited, because it mostly occurred after we stopped LAI assimilation. With regard to aboveground biomass, we found that the assimilation of only one LAI estimate from either the tillering or the booting to flowering stage resulted in simulated biomass values similar or closer to measured values than in those where more than one LAI estimate from the stem elongation phase were assimilated. LAI assimilation after the tillering phase might therefore be not necessarily required, as it may not lead to the desired improvement effect. Full article
(This article belongs to the Special Issue Remote Sensing Applications for Agriculture and Crop Modelling-Series Ⅱ)
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21 pages, 2299 KiB  
Article
Differentiating between Nitrogen and Water Deficiency in Irrigated Maize Using a UAV-Based Multi-Spectral Camera
by Taylor Becker, Taylor S. Nelsen, Michelle Leinfelder-Miles and Mark E. Lundy
Agronomy 2020, 10(11), 1671; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy10111671 - 29 Oct 2020
Cited by 10 | Viewed by 2660
Abstract
The objective of this research was to determine if canopy reflectance measured by an Unmanned Aerial Vehicle (UAV) equipped with a 5-band multi-spectral camera can differentiate between water and nitrogen (N) deficiency in irrigated maize. Crop reflectance was used to generate a Normalized [...] Read more.
The objective of this research was to determine if canopy reflectance measured by an Unmanned Aerial Vehicle (UAV) equipped with a 5-band multi-spectral camera can differentiate between water and nitrogen (N) deficiency in irrigated maize. Crop reflectance was used to generate a Normalized Difference Red Edge (NDRE), Green Leaf Index (GLI), and a Blue Reflectance Index (BRI). These indices were then used in combination to categorize N and water stressed experimental units into a Combined Index (CI) indicating water-stressed, N-stressed, or non-stressed crops. The CI generated at blister (R2) successfully identified 90% of experimental treatments to the correct group but only identified 60% of treatments when generated at the 14th leaf stage (V14). The CI methodology was subsequently applied to two independent site-years where only N deficiency gradients were imposed. The CI was not successful at separating treatments at the validation sites, incorrectly identifying water stress where there was none. Among individual indices investigated, NDRE had the strongest relationship to grain yields (r2 = 0.62, p < 0.001) but a weaker linear relationship compared to the CI (r2 = 0.68, p < 0.005) where deficit irrigation treatments were imposed. At sites where irrigation was sufficient to meet crop water demand, NDRE (r2 = 0.63, p < 0.05) had a stronger relationship to grain yield compared to the CI (r2 = 0.41, p = 0.31). This study found that, under narrow cropping system circumstances, N and irrigation-induced differences in maize productivity can be differentiated in-season by a combination of reflectance indices, but that NDRE alone provides superior information under broader contexts. Full article
(This article belongs to the Special Issue Remote Sensing Applications for Agriculture and Crop Modelling-Series Ⅱ)
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16 pages, 1828 KiB  
Article
Crop Yield Prediction through Proximal Sensing and Machine Learning Algorithms
by Farhat Abbas, Hassan Afzaal, Aitazaz A. Farooque and Skylar Tang
Agronomy 2020, 10(7), 1046; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy10071046 - 20 Jul 2020
Cited by 106 | Viewed by 8902
Abstract
Proximal sensing techniques can potentially survey soil and crop variables responsible for variations in crop yield. The full potential of these precision agriculture technologies may be exploited in combination with innovative methods of data processing such as machine learning (ML) algorithms for the [...] Read more.
Proximal sensing techniques can potentially survey soil and crop variables responsible for variations in crop yield. The full potential of these precision agriculture technologies may be exploited in combination with innovative methods of data processing such as machine learning (ML) algorithms for the extraction of useful information responsible for controlling crop yield. Four ML algorithms, namely linear regression (LR), elastic net (EN), k-nearest neighbor (k-NN), and support vector regression (SVR), were used to predict potato (Solanum tuberosum) tuber yield from data of soil and crop properties collected through proximal sensing. Six fields in Atlantic Canada including three fields in Prince Edward Island (PE) and three fields in New Brunswick (NB) were sampled, over two (2017 and 2018) growing seasons, for soil electrical conductivity, soil moisture content, soil slope, normalized-difference vegetative index (NDVI), and soil chemistry. Data were collected from 39–40 30 × 30 m2 locations in each field, four times throughout the growing season, and yield samples were collected manually at the end of the growing season. Four datasets, namely PE-2017, PE-2018, NB-2017, and NB-2018, were then formed by combing data points from three fields to represent the province data for the respective years. Modeling techniques were employed to generate yield predictions assessed with different statistical parameters. The SVR models outperformed all other models for NB-2017, NB-2018, PE-2017, and PE-2018 dataset with RMSE of 5.97, 4.62, 6.60, and 6.17 t/ha, respectively. The performance of k-NN remained poor in three out of four datasets, namely NB-2017, NB-2018, and PE-2017 with RMSE of 6.93, 5.23, and 6.91 t/ha, respectively. The study also showed that large datasets are required to generate useful results using either model. This information is needed for creating site-specific management zones for potatoes, which form a significant component for food security initiatives across the globe. Full article
(This article belongs to the Special Issue Remote Sensing Applications for Agriculture and Crop Modelling-Series Ⅱ)
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