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

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 24893

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Dr. Riccardo Dainelli

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Guest Editor

Institute of BioEconomy (IBE), National Research Council (CNR), Via Caproni 8, 50145 Florence, Italy
Interests: precision agriculture; satellite; unmanned aerial vehicles; image processing; cereals; vegetable crops; field variability; life cycle assessment
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Prof. Dr. Maria-Paz Diago

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Guest Editor

1. Department of Agriculture and Food Science, University of La Rioja, 26007 Logroño, La Rioja, Spain
2. Instituto de Ciencias de la Vid y del Vino, Finca La Grajera, Ctra. Burgos Km 6, 26007 Logroño, La Rioja, Spain
Interests: precision viticulture; non-invasive sensors; vineyard monitoring; grapevine water status; grapevine composition; vineyard spatial variability; spectroscopy; thermography; vineyard robotics; machine vision; yield estimation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Facing climate change and an increasing population, the reduced supply of agronomic inputs and the rational exploitation of natural resources should be reconciled with high production standards and farmer income. In this context, the use of remote-sensing tools (platform and sensors) such as satellites plays a fundamental role.

With the aim of introducing cutting-edge satellite applications to the scientific community, I am pleased to invite you to the Special Issue “Use of Satellite Imagery in Agriculture”. I encourage scientists to submit their papers (original research articles and reviews) regarding the use of optical and radar satellite images for precision agriculture application and sustainable development of agronomy. In particular, research areas may include (but are not limited to) the following: spatio-temporal variability of cropping systems, retrieval of soil properties, crop monitoring (e.g., phenology and stress), yield prediction, and crop recognition using artificial intelligence. Papers addressing satellite time series, data fusion, or collecting in situ data are also welcome, as well as those using a combination of remote-sensing tools where satellites represent the leading platform.

I look forward to receiving your contributions.

Dr. Riccardo Dainelli
Prof. Dr. Maria-Paz Diago
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. 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

precision agriculture
remote sensing
active and passive sensors
soil fertility and characteristics
crop recognition, status and yield
artificial intelligence
management zones
in situ data
time series
data fusion

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Related Special Issue

Use of Satellite Imagery in Agriculture—Volume II in Agronomy (1 article)

Published Papers (9 papers)

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Research

22 pages, 4436 KiB

Open AccessArticle

Mapping Cropland Intensification in Ecuador through Spectral Analysis of MODIS NDVI Time Series

by Laura Recuero, Lilian Maila, Víctor Cicuéndez, César Sáenz, Javier Litago, Lucía Tornos, Silvia Merino-de-Miguel and Alicia Palacios-Orueta

Agronomy 2023, 13(9), 2329; https://doi.org/10.3390/agronomy13092329 - 6 Sep 2023

Cited by 2 | Viewed by 1394

Abstract

Multiple cropping systems constitute an essential agricultural practice that will ensure food security within the increasing demand of basic cereals as a consequence of global population growth and climate change effects. In this regard, there is a need to develop new methodologies to adequately monitor cropland intensification. The main objective of this research was to assess cropland intensification by means of spectral analysis of MODIS NDVI time series in a high cloudiness tropical area such as Ecuador. A surface of 89,225 ha of the main staple crops in this country, which are rice and maize crops, was monitored to assess the evolution of the number of crop cycles. The 20-year period of NDVI time series was used to calculate the periodograms across four subperiods (2001–2005, 2006–2010, 2011–2015, 2016–2020). The maximum ordinate value of each periodogram was used as an indicator of the number of growing crop cycles per year identifying single-, double-, and triple-cropping systems in each subperiod. Cropland intensification was assessed by comparing the cropping system between the subperiods. Results reveal that more than half of the studied croplands experienced changes in the cropping systems, and 40% showed positive trends in terms of the number of growing crop cycles, being principally located near the main rivers where irrigation facilitates crop development during the dry season. Therefore, the area under single cropping decreased from over 60,000 ha in the first subperiod to less than 50,000 ha in the last two subperiods. The cropland surface subjected to multi-cropping practices increased during the second decade of the study period, with a double-cropping system being more widely used than growing three crops per year, reaching surfaces of 24,400 ha and 10,450 ha in the last subperiod, respectively. The robust results obtained in this research show the great potential of the periodogram approach for the discrimination of cropping systems and for mapping intensification areas in tropical regions where dealing with noisy remote sensing time series as a consequence of high cloudiness is a great challenge. Full article

(This article belongs to the Special Issue Use of Satellite Imagery in Agriculture)

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26 pages, 4824 KiB

Open AccessEditor’s ChoiceArticle

Bibliometric and Social Network Analysis on the Use of Satellite Imagery in Agriculture: An Entropy-Based Approach

by Riccardo Dainelli and Fabio Saracco

Agronomy 2023, 13(2), 576; https://doi.org/10.3390/agronomy13020576 - 17 Feb 2023

Cited by 5 | Viewed by 2282

Abstract

Satellite imagery is gaining popularity as a valuable tool to lower the impact on natural resources and increase profits for farmers. The purpose of this study is twofold: to mine the scientific literature to reveal the structure of this research domain, and to investigate to what extent scientific results can reach a wider public audience. To meet these two objectives, a Web of Science and a Twitter dataset were retrieved and analysed, respectively. For the academic literature, different performances of various countries were observed: the USA and China resulted as the leading actors, both in terms of published papers and employed researchers. Among the categorised keywords, “resolution”, “Landsat”, “yield”, “wheat” and “multispectral” are the most used. Then, analysing the semantic network of the words used in the various abstracts, the different facets of the research in satellite remote sensing were detected. The importance of retrieving meteorological parameters through remote sensing and the broad use of vegetation indexes emerged from these analyses. As emerging topics, classification tasks for land use assessment and crop recognition stand out, alongside the use of hyperspectral sensors. Regarding the interaction of academia with the public, the analysis showed that it is practically absent on Twitter: most of the activity therein stems from private companies advertising their business. This shows that there is still a communication gap between academia and actors from other societal sectors. Full article

(This article belongs to the Special Issue Use of Satellite Imagery in Agriculture)

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23 pages, 4335 KiB

Open AccessArticle

An Object-Based Weighting Approach to Spatiotemporal Fusion of High Spatial Resolution Satellite Images for Small-Scale Cropland Monitoring

by Soyeon Park, No-Wook Park and Sang-il Na

Agronomy 2022, 12(10), 2572; https://doi.org/10.3390/agronomy12102572 - 19 Oct 2022

Cited by 2 | Viewed by 1758

Abstract

Continuous crop monitoring often requires a time-series set of satellite images. Since satellite images have a trade-off in spatial and temporal resolution, spatiotemporal image fusion (STIF) has been applied to construct time-series images at a consistent scale. With the increased availability of high spatial resolution images, it is necessary to develop a new STIF model that can effectively reflect the properties of high spatial resolution satellite images for small-scale crop field monitoring. This paper proposes an advanced STIF model using a single image pair, called high spatial resolution image fusion using object-based weighting (HIFOW), for blending high spatial resolution satellite images. The four-step weighted-function approach of HIFOW includes (1) temporal relationship modeling, (2) object extraction using image segmentation, (3) weighting based on object information, and (4) residual correction to quantify temporal variability between the base and prediction dates and also represent both spectral patterns at the prediction date and spatial details of fine-scale images. The specific procedures tailored for blending fine-scale images are the extraction of object-based change and structural information and their application to weight determination. The potential of HIFOW was evaluated from the experiments on agricultural sites using Sentinel-2 and RapidEye images. HIFOW was compared with three existing STIF models, including the spatial and temporal adaptive reflectance fusion model (STARFM), flexible spatiotemporal data fusion (FSDAF), and Fit-FC. Experimental results revealed that the HIFOW prediction could restore detailed spatial patterns within crop fields and clear crop boundaries with less spectral distortion, which was not represented in the prediction results of the other three models. Consequently, HIFOW achieved the best prediction performance in terms of accuracy and structural similarity for all the spectral bands. Other than the reflectance prediction, HIFOW also yielded superior prediction performance for blending normalized difference vegetation index images. These findings indicate that HIFOW could be a potential solution for constructing high spatial resolution time-series images in small-scale croplands. Full article

(This article belongs to the Special Issue Use of Satellite Imagery in Agriculture)

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19 pages, 4110 KiB

Open AccessArticle

Application of Parallel Factor Analysis (PARAFAC) to the Regional Characterisation of Vineyard Blocks Using Remote Sensing Time Series

by Eva Lopez-Fornieles, Guilhem Brunel, Nicolas Devaux, Jean-Michel Roger, James Taylor and Bruno Tisseyre

Agronomy 2022, 12(10), 2544; https://doi.org/10.3390/agronomy12102544 - 18 Oct 2022

Cited by 4 | Viewed by 2009

Abstract

Monitoring wine-growing regions and maximising the value of production based on their region/local specificities requires accurate spatial and temporal monitoring. The increasing amount and variability of information from remote sensing data is a potential tool to assess this challenge for the grape and wine industry. This article provides a first insight into the capacity of a multiway analysis method applied to Sentinel-2 time series to assess the value of simultaneously considering spectral and temporal information to highlight site-specific canopy evolution in relation to environmental factors and management practices, which present a large diversity at this regional scale. Parallel Factor Analysis (PARAFAC) was used as an unsupervised technique to recover pure spectra and temporal signatures from multi-way spectral imagery of vineyards in the Languedoc-Roussillon region in the south of France. The model was developed using a time series of Sentinel-2 satellite imagery collected over 4978 vineyard blocks between May 2019 and August 2020. From the Sentinel-2 (spectral and temporal) signal, the PARAFAC analysis allowed the identification of spectral and temporal profiles in the form of pure components, which corresponded to vegetation and soil. The PARAFAC analysis also identified that two of the pure spectra were strongly related to characteristics and dynamics of vineyard cultivation at a regional scale. A conceptual framework was proposed in order to simultaneously consider both vegetation and soil profiles and to summarise the mass of data accordingly. This methodology allowed the computation of a concentration index that characterised how close a field was to a vegetation or a soil profile over the season. The concentration indices were validated for the vegetation and the soil over two growing seasons (2019 and 2020) with geostatistical analysis. A non-random distribution of the concentration index at the regional scale was assumed to highlight a strongly spatially organised phenomenon related to spatially organised environmental factors (soil, climate, training system, etc.). In a second step, spatial patterns of indices were subjected to the expertise of a panel of advisors of the wine industry in order to validate them in relation to vine-growing conditions. Results showed that the introduction of the PARAFAC method opened up the possibility to identify relevant spectro-temporal profiles for vine monitoring purposes. Full article

(This article belongs to the Special Issue Use of Satellite Imagery in Agriculture)

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16 pages, 4175 KiB

Open AccessArticle

Satellite-Based Frost Damage Detection in Support of Winter Cover Crops Management: A Case Study on White Mustard

by Mara Gabbrielli, Martina Corti, Marco Perfetto, Virginia Fassa and Luca Bechini

Agronomy 2022, 12(9), 2025; https://doi.org/10.3390/agronomy12092025 - 26 Aug 2022

Cited by 11 | Viewed by 2375

Abstract

Cover crops are grown in order to provide agro-ecological services and must be terminated before planting the subsequent cash crop. Winterkill termination (by frost damage) depends on the interaction between crop frost hardiness, temperatures and the development stage reached at the time of sub-zero temperature exposure. Remotely sensing intensity, timing and spatial variation of cover crop frost damage can be useful for modeling and planning purposes. Therefore, in this study Sentinel-2 vegetation indices were employed in order to detect frost damage in four white mustard (Sinapis alba L.) fields located in Northern Italy. We estimated the starting date of frost events by means of vegetation indices (EVI, NDRE, NDVI, MMSR, and CCCI); we quantified and mapped frost damage at the sub-field level, using ground-based frost damage measurements carried out during the 2021/2022 season. As to frost damage quantification, MMSR outperformed the other VIs followed by CCCI and EVI (R² > 0.55). The adopted procedure to detect starting dates of frost events was successful in most cases, with a one-day and a four-day delay in the two best cases (NDRE). Finally, maps of frost damage were consistent with its observed spatial variation. We demonstrated that it is possible to employ vegetation indices in order to detect cover crop frost damage and thus assessing cover crop winterkill termination efficiency in the field. Further research is needed, involving additional field monitoring of white mustard in more diverse conditions, and extension of the calibration, as well as validation. Full article

(This article belongs to the Special Issue Use of Satellite Imagery in Agriculture)

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18 pages, 3676 KiB

Open AccessArticle

Spatial and Temporal Evolution of Sowing and the Onset of the Rainy Season in a Region of Large Agricultural Expansion in Brazil

by Humberto Paiva Fonseca, Gabrielle Ferreira Pires and Livia Maria Brumatti

Agronomy 2022, 12(7), 1679; https://doi.org/10.3390/agronomy12071679 - 15 Jul 2022

Cited by 2 | Viewed by 2184

Abstract

In order to assist in high-yield agricultural management in multiple cropping systems, it is essential to understand the link between the rainy season onset and crops sowing dates, since it considerably affects the management, yield and output. We built crop calendars derived from remote sensing products and investigated the link between sowing dates and the onset of the rainy season in irrigated and rainfed agriculture in Western Bahia, a new and important agricultural frontier in Brazilian Cerrado. Crop sowing dates were obtained from green-up dates from 2001 to 2019. Rainy season onset dates were determined using CHIRPS daily precipitation data. Results indicate that sowing occurs from 26 October to 15 November and the rainy season starts from 17 to 27 October. Rainfed sowing dates are strongly correlated to rainy season onset and are particularly affected in years where rains are delayed. Sowing dates in irrigated pixels occur up to 25 days earlier than rainfed and are not correlated to rainy season onset. Irrigated farms are sowing earlier and in a shorter window than rainfed, with a stronger resilience in years where rains are delayed, and have adapted their sowing operation towards a more intensive agriculture and efficient water use during the rainy season. Full article

(This article belongs to the Special Issue Use of Satellite Imagery in Agriculture)

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15 pages, 2907 KiB

Open AccessArticle

Use of Sentinel-2 Derived Vegetation Indices for Estimating fPAR in Olive Groves

by Luisa Leolini, Marco Moriondo, Riccardo Rossi, Edoardo Bellini, Lorenzo Brilli, Álvaro López-Bernal, Joao A. Santos, Helder Fraga, Marco Bindi, Camilla Dibari and Sergi Costafreda-Aumedes

Agronomy 2022, 12(7), 1540; https://doi.org/10.3390/agronomy12071540 - 27 Jun 2022

Cited by 11 | Viewed by 2655

Abstract

Olive tree cultivation is currently a dominant agriculture activity in the Mediterranean basin, where the increasing impact of climate change coupled with the inefficient management of olive groves is negatively affecting olive oil production and quality in some marginal areas. In this context, satellite imagery may help to monitor crop growth under different environmental conditions, thus providing useful information for optimizing olive grove management and final production. However, the spatial resolution of freely-available satellite products is not yet adequate to estimate plant biophysical parameters in complex agroecosystems such as olive groves, where both olive trees and grass cover contribute to the vegetation indices (VIs) signal at pixel scale. The aim of this study is therefore to test a disentangling procedure to partition the VIs signal among the different components of the agroecosystem to use this information for the monitoring of olive growth processes during the season. Specifically, five VIs (GEMI, MCARI2, NDVI, OSAVI, MCARI2/OSAVI) as recorded by Sentinel-2 at a spatial resolution of 10 m over five olive groves in the Montalbano area (Tuscany, Central Italy), were tested as a proxy for olive tree intercepted radiation. The olive tree volume per pixel was initially used to linearly rescale the VIs signal into the relevant value for the grass cover and olive trees. The models, describing the relationship between rescaled VIs and observed fraction of Photosynthetically Active Radiation (fPAR), were fitted and then validated against independent datasets. While in the calibration phase, a greater robustness at predicting fPAR was obtained using NDVI (r = 0.96 and RRMSE = 9.86), the validation results demonstrating that GEMI and MCARI2/OSAVI provided the highest performances (GEMI: r = 0.89 and RRMSE = 21.71; MCARI2/OSAVI: r = 0.87 and RRMSE = 25.50), in contrast to MCARI2 that provided the lowest (r = 0.67 and RRMSE = 36.78). These results may be related to the VIs’ intrinsic features (e.g., lower sensitivity to atmosphere and background effects), which make some of these indices, compared to others, less sensitive to saturation effects by improving fPAR estimation (e.g., GEMI vs. NDVI). On this basis, this study evidenced the need to improve the current methodologies to reduce inter-row effects and select appropriate VIs for fPAR estimation, especially in complex agroecosystems where inter-row grass growth may affect remote sensed-derived VIs signal at an inadequate pixel resolution. Full article

(This article belongs to the Special Issue Use of Satellite Imagery in Agriculture)

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20 pages, 5570 KiB

Open AccessArticle

VICAL: Global Calculator to Estimate Vegetation Indices for Agricultural Areas with Landsat and Sentinel-2 Data

by Sergio Iván Jiménez-Jiménez, Mariana de Jesús Marcial-Pablo, Waldo Ojeda-Bustamante, Ernesto Sifuentes-Ibarra, Marco Antonio Inzunza-Ibarra and Ignacio Sánchez-Cohen

Agronomy 2022, 12(7), 1518; https://doi.org/10.3390/agronomy12071518 - 24 Jun 2022

Cited by 13 | Viewed by 5210

Abstract

The vegetation indices (VIs) estimated from remotely sensed data are simple and based on effective algorithms for quantitative and qualitative evaluations of the dynamics of biophysical crop variables such as vegetation cover, leaf area, vigor and development, and many others. Over the last decade, many VIs have been proposed and validated to enhance the vegetation signal by reducing the noise from effects produced either by the soil or by vegetation such as brightness, shadows, color, etc. VIs are commonly calculated from satellite images such as ones from Landsat and Sentinel-2 because of their medium resolution and free availability. However, despite the VIs being fairly simple algorithms, it can take hours to calculate them for an established agricultural area, mainly due to the pre-processing of the images (including atmospheric corrections, the detection of clouds and shadows), size and download time of the images, and the capacity of the computer equipment used. Time increases as the number of images increases. In this sense, the free to use Google Earth Engine (GEE) platform was here used to develop an application called VICAL to calculate 23 VIs map (VIs commonly used in agricultural applications) and time series of any agricultural area in the world with images (cloud-free) from Landsat and Sentinel-2 data. It was found that VICAL can calculate these 23 VIs accurately, and shows the potential of the GEE cloud-based tools using multispectral dataset to assess many spectral VIs. This tool is very beneficial for researchers with poor access to satellite data or in institutions with a lack of computational infrastructure to handle the large volumes of satellite datasets, since it is not necessary for the user writing a single line of code. The VICAL is open-access image analysis platform that can be modified to carry out more complex analysis or adapt it to a specific VI application. Full article

(This article belongs to the Special Issue Use of Satellite Imagery in Agriculture)

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15 pages, 3166 KiB

Open AccessArticle

Integrating Satellite and UAV Data to Predict Peanut Maturity upon Artificial Neural Networks

by Jarlyson Brunno Costa Souza, Samira Luns Hatum de Almeida, Mailson Freire de Oliveira, Adão Felipe dos Santos, Armando Lopes de Brito Filho, Mariana Dias Meneses and Rouverson Pereira da Silva

Agronomy 2022, 12(7), 1512; https://doi.org/10.3390/agronomy12071512 - 24 Jun 2022

Cited by 10 | Viewed by 2815

Abstract

The monitoring and determination of peanut maturity are fundamental to reducing losses during digging operation. However, the methods currently used are laborious and subjective. To solve this problem, we developed models to access peanut maturity using images from unmanned aerial vehicles (UAV) and satellites. We evaluated an area of approximately 8 hectares in which a regular grid of 30 points was determined with weekly evaluations starting at 90 days after sowing. Two Artificial Neural Networking (ANN) were used with Radial Basis Function (RBF) and Multilayer Perceptron (MLP) to predict the Peanut Maturity Index (PMI) with the spectral bands available from each sensor. Several vegetation indices were used as input to the ANN, with the data being split 80/20 for training and validation, respectively. The vegetation index, Normalized Difference Red Edge Index (NDRE), was the most precise coefficient of determination (R² = 0.88) and accurate mean absolute error (MAE = 0.06) for estimating PMI, regardless of the type of ANN used. The satellite with Normalized Difference Vegetation Index (NDVI) could also determine PMI with better accuracy (MAE = 0.05) than the NDRE. The performance evaluation indicates that the RBF and MLP networks are similar in predicting peanut maturity. We concluded that satellite and UAV images can predict the maturity index with good accuracy and precision. Full article

(This article belongs to the Special Issue Use of Satellite Imagery in Agriculture)

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