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Remote Sensing of Agro-Ecosystems

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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: closed (15 December 2022) | Viewed by 38259

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Special Issue Editors

Dr. Christos Karydas

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Guest Editor

Ecodevelopment S.A., Filyro, P.O. Box 2420, 57010 Thessaloniki, Greece
Interests: remote sensing and GIS; precision agriculture; erosion modelling; land use mapping; environmental impact assessment; fractal analysis
Special Issues, Collections and Topics in MDPI journals

Dr. Vassilis George Aschonitis

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Guest Editor

Soil and Water Resources Insitute, Hellenic Agricultural Organization—Demeter, 57001 Thessaloniki, Greece
Interests: agronomy; agricultural soil and water management
Special Issues, Collections and Topics in MDPI journals

Dr. Lachezar Filchev

E-Mail Website
Guest Editor

Remote Sensing and GIS Department, Space Research and Technology Institute, Bulgarian Academy of Sciences (SRTI-BAS), Acad. G. Bonchev St., block.1, 1113 Sofia, Bulgaria
Interests: forestry; remote sensing

Dr. Goran Dimic

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Guest Editor

Mihajlo Pupin Institute, University of Belgrade, 11060 Belgrade, Serbia
Interests: electrical engineering; wireless sensor networks

Prof. Dr. Bin Jiang

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Guest Editor

Division of GIScience, Faculty of Engineering and Sustainable Development, University of Gävle, SE-801 76 Gävle, Sweden
Interests: city science; big data analytics; sustainable urban planing; city structure and dynamics; the livingness of space
Special Issues, Collections and Topics in MDPI journals

Dr. Panos Panagos

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Guest Editor

Land Resource Management Unit, Joint Research Centre, European Commission, Via Enrico Fermi 2749, 21027 Ispra, VA, Italy
Interests: informatics; soil databases
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Agro-ecosystems are spatially coherent units of combined agricultural activities and ecological functionalities, which—moreover—demand particular attention in relation to their protection and sustainability.

Accurate and up-to-date spatial and diachronic information is essential for integrated agro-ecosystem management and remote sensing is irreplaceable for this purpose.

Today, the vast amounts of open-source, commercial, or privately produced image data, as well as tools for mining, processing, and interface available on the cloud, open up a new era in the proficient use of remote sensing in agro-ecosystems’ research.

This Special Issue invites manuscripts on the advancements in remote sensing towards a deeper scientific understanding of agro-ecosystems’ structures, functionalities, and risks.

Studies on their complexity decomposition, on their monitoring algorithms, and on the role of complementary data from field sensors are more than welcomed.

We expect that this Special Issue will demonstrate the significant added value of remote sensing of agro-ecosystems in this new era.

Indicative topics

Crop-vegetation mapping using low-altitude image data;
Integrated crop growth monitoring based on satellite imagery;
Agro-ecosystem management using data from multiple sources;
Image-based heterogeneity detection and interpretation;
Machine learning for assessing ecological balance in agro-ecosystems;
Object-based image analysis in agro-ecosystem change detection;
Modeling agricultural sustainability with fractal geometry;
Pattern analysis in agro-ecosystems using head/tail breaks;
Use of imagery and field sensors in biological and organic agriculture;
Use of drones for best practice introduction and monitoring;
Exploitation of cloud platforms for agro-environmental protection;
Advances of hyperspectral imaging for soil properties’ mapping;
Spectral indices for crop health assessment and yield prediction;
Image-oriented fertilization for precision agriculture applications.

Dr. Christos Karydas
Dr. Vassilis George Aschonitis
Dr. Lachezar Filchev
Dr. Goran Dimic
Prof. Dr. Bin Jiang
Dr. Panos Panagos
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-vegetation mapping
crop growth monitoring
machine learning
ecological balance
object-based image analysis
agricultural sustainability
fractal geometry
pattern analysis
head/tail breaks
field sensors
drones
cloud platforms
hyperspectral imaging
soil mapping
spectral indices
crop health
yield prediction
image-oriented farming
precision agriculture

Published Papers (14 papers)

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19 pages, 5451 KiB

Open AccessArticle

Dry Matter Yield and Nitrogen Content Estimation in Grassland Using Hyperspectral Sensor

by Hitoshi Nishikawa, Jouke Oenema, Fedde Sijbrandij, Keiji Jindo, Gert-Jan Noij, Frank Hollewand, Bert Meurs, Idse Hoving, Peter van der Vlugt, Max Bouten and Corné Kempenaar

Remote Sens. 2023, 15(2), 419; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15020419 - 10 Jan 2023

Cited by 1 | Viewed by 1646

Abstract

Estimation of Dry Matter Yield (DMY) and Nitrogen Content (NC) in forage is a big concern for growers. In this study, an estimation model of DMY and NC using Visible and Near Infrared (V-NIR) spectroscopy was developed. An adequate number of grass samples (5078) of perennial ryegrass (Lolium perenne), collected from Dutch grassland in 2019 and 2020 were sensed with a hyperspectral sensor, while grass height was recorded in situ by an ultrasonic sensor mounted on a tractor. The samples were treated with Artificial Intelligence (AI) techniques. PCA based feature selection was applied first, revealing that visible green wavelength (around 500 nm) and red edge wavelength (around 700 nm) were enough to express the overall variability of the dataset. Then, Feature Importance analysis of Random Forest Regressor showed that NIR wavelengths (around 910, 960 and 990nm) were the most sensitive in DMY estimation, while red edge (around 710 nm) and visible orange wavelengths (around 610 nm) were the most related to NC estimation. Finally, SHAP (SHapley Additive exPlanations) analysis was applied to the Random Forest estimation models, resulting in the visualization of wavelength selection, thus assisting in the interpretation of the results and the intermediate processes. Overall, this method can lead to the reduction of the number of wavelengths to be measured in the field and thus, to the possible development of a low cost hyperspectral sensor for the above purposes. Full article

(This article belongs to the Special Issue Remote Sensing of Agro-Ecosystems)

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23 pages, 4729 KiB

Open AccessArticle

Representation Learning with a Variational Autoencoder for Predicting Nitrogen Requirement in Rice

by Miltiadis Iatrou, Christos Karydas, Xanthi Tseni and Spiros Mourelatos

Remote Sens. 2022, 14(23), 5978; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14235978 - 25 Nov 2022

Cited by 4 | Viewed by 1967

Abstract

The scope of this research was to provide rice growers with optimal N-rate recommendations through precision agriculture applications. To achieve this goal, a prediction rice yield model was constructed, based on soil data, remote sensing data (optical and radar), climatic data, and farming practices. The dataset was collected from a rice crop surface of 89.2 ha cultivated continuously for a 5-year period and was analyzed with machine learning (ML) systems. A variational autoencoder (VAE) for reconstructing the input data of the prediction model was applied, resulting in MAE of 0.6 tn/ha, with an average yield for the study fields and period measured at 9.6 tn/ha. VAE learns the original input data representation and transforms them in a latent feature space, so that the anomalies and the discrepancies of the data are reduced. The reconstructed data by VAE provided a more sophisticated and detailed ML model, improving our knowledge about the various correlations between soil, N management parameters, and yield. Both optical and radar imagery and the climatic data were found to be of high importance for the model, as indicated by the application of XAI (explainable artificial intelligence) techniques. The new model was applied in the 2022 rice cultivation in the study fields, resulting in an average yield increase of 4.32% compared to the 5 previous years of experimentation. Full article

(This article belongs to the Special Issue Remote Sensing of Agro-Ecosystems)

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21 pages, 4533 KiB

Open AccessArticle

Implementing Sentinel-2 Data and Machine Learning to Detect Plant Stress in Olive Groves

by Ioannis Navrozidis, Thomas Alexandridis, Dimitrios Moshou, Anne Haugommard and Anastasia Lagopodi

Remote Sens. 2022, 14(23), 5947; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14235947 - 24 Nov 2022

Cited by 4 | Viewed by 2009

Abstract

Olives are an essential crop for Greece and constitute a major economic and agricultural factor. Diseases, pests, and environmental conditions are all factors that can deteriorate the health status of olive crops by causing plant stress. Researchers can utilize remote sensing to assist their actions in detecting these sources of stress and act accordingly. In this experiment, Sentinel-2 data were used to create vegetation indices for commercial olive fields in Halkidiki, Northern Greece. Twelve machine learning algorithms were tested to determine which type would be the most efficient to detect plant stress in olive trees. In parallel, a test was conducted by testing 26 thresholds to determine how setting different thresholds for stress incidence affects model performance and which threshold constitutes the best choice for more accurate classification. The results show that among all tested classification algorithms, the quadratic discriminant analysis provided the best performance of 0.99. The stress incidence threshold used in the current case to generate the best-performing model was 6%, but the results suggest that setting customized thresholds relevant to specific cases would provide optimal results. The best-performing model was used in a one-vs.-rest multiclass classification task to determine the source of the stress between four possible classes: “healthy”, “verticillium”, “spilocaea”, and “unidentified”. The multiclass model was more accurate in detection for the “healthy” class (0.99); the “verticillium” and “unidentified” classes were less accurate (0.76); and “spilocaea” had the lowest score (0.72). Findings from this research can be used by experts as a service to enhance their decision-making and support the application of efficient strategies in the field of precision crop protection. Full article

(This article belongs to the Special Issue Remote Sensing of Agro-Ecosystems)

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24 pages, 4477 KiB

Open AccessArticle

Linking Remote Sensing with APSIM through Emulation and Bayesian Optimization to Improve Yield Prediction

by Hamze Dokoohaki, Teerath Rai, Marissa Kivi, Philip Lewis, Jose L. Gómez-Dans and Feng Yin

Remote Sens. 2022, 14(21), 5389; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14215389 - 27 Oct 2022

Cited by 4 | Viewed by 2889

Abstract

The enormous increase in the volume of Earth Observations (EOs) has provided the scientific community with unprecedented temporal, spatial, and spectral information. However, this increase in the volume of EOs has not yet resulted in proportional progress with our ability to forecast agricultural systems. This study examines the applicability of EOs obtained from Sentinel-2 and Landsat-8 for constraining the APSIM-Maize model parameters. We leveraged leaf area index (LAI) retrieved from Sentinel-2 and Landsat-8 NDVI (Normalized Difference Vegetation Index) to constrain a series of APSIM-Maize model parameters in three different Bayesian multi-criteria optimization frameworks across 13 different calibration sites in the U.S. Midwest. The novelty of the current study lies in its approach in providing a mathematical framework to directly integrate EOs into process-based models for improved parameter estimation and system representation. Thus, a time variant sensitivity analysis was performed to identify the most influential parameters driving the LAI (Leaf Area Index) estimates in APSIM-Maize model. Then surrogate models were developed using random samples taken from the parameter space using Latin hypercube sampling to emulate APSIM’s behavior in simulating NDVI and LAI at all sites. Site-level, global and hierarchical Bayesian optimization models were then developed using the site-level emulators to simultaneously constrain all parameters and estimate the site to site variability in crop parameters. For within sample predictions, site-level optimization showed the largest predictive uncertainty around LAI and crop yield, whereas the global optimization showed the most constraint predictions for these variables. The lowest RMSE within sample yield prediction was found for hierarchical optimization scheme (1423 Kg ha

^{- 1}

) while the largest RMSE was found for site-level (1494 Kg ha

^{- 1}

). In out-of-sample predictions for within the spatio-temporal extent of the training sites, global optimization showed lower RMSE (1627 Kg ha

^{- 1}

) compared to the hierarchical approach (1822 Kg ha

^{- 1}

) across 90 independent sites in the U.S. Midwest. On comparison between these two optimization schemes across another 242 independent sites outside the spatio-temporal extent of the training sites, global optimization also showed substantially lower RMSE (1554 Kg ha

^{- 1}

) as compared to the hierarchical approach (2532 Kg ha

^{- 1}

). Overall, EOs demonstrated their real use case for constraining process-based crop models and showed comparable results to model calibration exercises using only field measurements. Full article

(This article belongs to the Special Issue Remote Sensing of Agro-Ecosystems)

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20 pages, 3958 KiB

Open AccessArticle

Development of a Multi-Scale Tomato Yield Prediction Model in Azerbaijan Using Spectral Indices from Sentinel-2 Imagery

by Vasilis Psiroukis, Nicoleta Darra, Aikaterini Kasimati, Pavel Trojacek, Gunay Hasanli and Spyros Fountas

Remote Sens. 2022, 14(17), 4202; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14174202 - 26 Aug 2022

Cited by 4 | Viewed by 2467

Abstract

This paper presents the development and update of a multi-scale yield prediction model for processing tomatoes. The study was carried out under the EU-funded programme “Support to Development of a Rural Business Information System (RBIS)”, and the performance of the updated crop-specific yield prediction models and their generated predictions at regional and national levels are presented. The model was built using Sentinel-2 satellite imagery to obtain cumulative values of six (6) selected vegetation indices (VIs). The data were collected on five (5) different dates for processing tomato fields in the Khachmaz region of Azerbaijan during summer 2021 (June to August) at 10- to 13-day intervals. In addition, a targeted field sampling campaign was conducted on selected Khachmaz pilot fields towards the end of the growing season to assess the potential of Sentinel-2 data to determine yield variability in tomato fields. Finally, actual recorded yields were collected at the field level to build the yield prediction regression model and evaluate its performance at different spatial scales, ranging from single field to national level, as well as under different data availability scenarios (number of consecutive Sentinel-2 images used). The results showed a high degree of correlation between all implemented VIs and processing tomato yield, with a coefficient of determination of up to 0.89 for the NDVI, providing valuable information for future estimates of tomato production across multiple spatial scales. The developed prediction model could also be used in the agri-food sector for national yield estimates to support policy and regulatory decisions at the national level. Full article

(This article belongs to the Special Issue Remote Sensing of Agro-Ecosystems)

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28 pages, 9321 KiB

Open AccessArticle

Sentinel-2 Enables Nationwide Monitoring of Single Area Payment Scheme and Greening Agricultural Subsidies in Hungary

by László Henits, Ákos Szerletics, Dávid Szokol, Gergely Szlovák, Emese Gojdár and András Zlinszky

Remote Sens. 2022, 14(16), 3917; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14163917 - 12 Aug 2022

Cited by 1 | Viewed by 1785

Abstract

The verification and monitoring of agricultural subsidy claims requires combined evaluation of several criteria at the scale of over a million cultivation units. Sentinel-2 satellite imagery is a promising data source and paying agencies are encouraged to test their pre-operational use. Here, we present the outcome of the Hungarian agricultural subsidy monitoring pilot: our goal was to propose a solution based on open-source components and evaluate the main strengths and weaknesses for Sentinel-2 in the framework of a complex set of tasks. These include the checking of the basic cultivation of grasslands and arable land and compliance to the criteria of ecological focus areas. The processing of the satellite data was conducted based on random forest for crop classification and the detection of cultivation events was conducted based on NDVI (Normalized Differential Vegetation Index) time series analysis results. The outputs of these processes were combined in a decision tree ruleset to provide the final results. We found that crop classification provided good performance (overall accuracy 88%) for 22 vegetation classes and cultivation detection was also reliable when compared to on-screen visual interpretation. The main limitation was the size of fields, which were frequently small compared to the spatial resolution of the images: more than 4% of the parcels had to be excluded, although these represent less than 3% of the cultivated area of Hungary. Based on these results, we find that operational satellite-based monitoring is feasible for Hungary, and expect further improvements from integration with Sentinel-1 due to additional temporal resolution. Full article

(This article belongs to the Special Issue Remote Sensing of Agro-Ecosystems)

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21 pages, 6640 KiB

Open AccessArticle

Spectral Reflectance Indices as a High Throughput Selection Tool in a Sesame Breeding Scheme

by Christos Petsoulas, Eleftherios Evangelou, Alexandros Tsitouras, Vassilis Aschonitis, Anastasia Kargiotidou, Ebrahim Khah, Ourania I. Pavli and Dimitrios N. Vlachostergios

Remote Sens. 2022, 14(11), 2629; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14112629 - 31 May 2022

Cited by 1 | Viewed by 1878

Abstract

On-farm genotype screening is at the core of every breeding scheme, but it comes with a high cost and often high degree of uncertainty. Phenomics is a new approach by plant breeders, who use optical sensors for accurate germplasm phenotyping, selection and enhancement of the genetic gain. The objectives of this study were to: (1) develop a high-throughput phenotyping workflow to estimate the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Red Edge index (NDRE) at the plot-level through an active crop canopy sensor; (2) test the ability of spectral reflectance indices (SRIs) to distinguish between sesame genotypes throughout the crop growth period; and (3) identify specific stages in the sesame growth cycle that contribute to phenotyping accuracy and functionality and evaluate the efficiency of SRIs as a selection tool. A diversity panel of 24 sesame genotypes was grown at normal and late planting dates in 2020 and 2021. To determine the SRIs the Crop Circle ACS-430 active crop canopy sensor was used from the beginning of the sesame reproductive stage to the end of the ripening stage. NDVI and NDRE reached about the same high accuracy in genotype phenotyping, even under dense biomass conditions where “saturation” problems were expected. NDVI produced higher broad-sense heritability (max 0.928) and NDRE higher phenotypic and genotypic correlation with the yield (max 0.593 and 0.748, respectively). NDRE had the highest relative efficiency (61%) as an indirect selection index to yield direct selection. Both SRIs had optimal results when the monitoring took place at the end of the reproductive stage and the beginning of the ripening stage. Thus, an active canopy sensor as this study demonstrated can assist breeders to differentiate and classify sesame genotypes. Full article

(This article belongs to the Special Issue Remote Sensing of Agro-Ecosystems)

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26 pages, 10297 KiB

Open AccessArticle

Machine Learning Classification of Fused Sentinel-1 and Sentinel-2 Image Data towards Mapping Fruit Plantations in Highly Heterogenous Landscapes

by Yingisani Chabalala, Elhadi Adam and Khalid Adem Ali

Remote Sens. 2022, 14(11), 2621; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14112621 - 31 May 2022

Cited by 18 | Viewed by 4424

Abstract

Mapping smallholder fruit plantations using optical data is challenging due to morphological landscape heterogeneity and crop types having overlapping spectral signatures. Furthermore, cloud covers limit the use of optical sensing, especially in subtropical climates where they are persistent. This research assessed the effectiveness of Sentinel-1 (S1) and Sentinel-2 (S2) data for mapping fruit trees and co-existing land-use types by using support vector machine (SVM) and random forest (RF) classifiers independently. These classifiers were also applied to fused data from the two sensors. Feature ranks were extracted using the RF mean decrease accuracy (MDA) and forward variable selection (FVS) to identify optimal spectral windows to classify fruit trees. Based on RF MDA and FVS, the SVM classifier resulted in relatively high classification accuracy with overall accuracy (OA) = 0.91.6% and kappa coefficient = 0.91% when applied to the fused satellite data. Application of SVM to S1, S2, S2 selected variables and S1S2 fusion independently produced OA = 27.64, Kappa coefficient = 0.13%; OA= 87%, Kappa coefficient = 86.89%; OA = 69.33, Kappa coefficient = 69. %; OA = 87.01%, Kappa coefficient = 87%, respectively. Results also indicated that the optimal spectral bands for fruit trees mapping are green (B3) and SWIR_2 (B10) for S2, whereas for S1, the vertical-horizontal (VH) polarization band. Including the textural metrics from the VV channel improved crop discrimination and co-existing land use cover types. The fusion approach proved robust and well suited for accurate smallholder fruit plantation mapping. Full article

(This article belongs to the Special Issue Remote Sensing of Agro-Ecosystems)

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22 pages, 29605 KiB

Open AccessArticle

Soya Yield Prediction on a Within-Field Scale Using Machine Learning Models Trained on Sentinel-2 and Soil Data

by Branislav Pejak, Predrag Lugonja, Aleksandar Antić, Marko Panić, Miloš Pandžić, Emmanouil Alexakis, Philip Mavrepis, Naweiluo Zhou, Oskar Marko and Vladimir Crnojević

Remote Sens. 2022, 14(9), 2256; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14092256 - 07 May 2022

Cited by 8 | Viewed by 3375

Abstract

Agriculture is the backbone and the main sector of the industry for many countries in the world. Assessing crop yields is key to optimising on-field decisions and defining sustainable agricultural strategies. Remote sensing applications have greatly enhanced our ability to monitor and manage farming operation. The main objective of this research was to evaluate machine learning system for within-field soya yield prediction trained on Sentinel-2 multispectral images and soil parameters. Multispectral images used in the study came from ESA’s Sentinel-2 satellites. A total of 3 cloud-free Sentinel-2 multispectral images per year from specific periods of vegetation were used to obtain the time-series necessary for crop yield prediction. Yield monitor data were collected in three crop seasons (2018, 2019 and 2020) from a number of farms located in Upper Austria. The ground-truth database consisted of information about the location of the fields and crop yield monitor data on 411 ha of farmland. A novel method, namely the Polygon-Pixel Interpolation, for optimal fitting yield monitor data with satellite images is introduced. Several machine learning algorithms, such as Multiple Linear Regression, Support Vector Machine, eXtreme Gradient Boosting, Stochastic Gradient Descent and Random Forest, were compared for their performance in soya yield prediction. Among the tested machine learning algorithms, Stochastic Gradient Descent regression model performed better than the others, with a mean absolute error of 4.36 kg/pixel (0.436 t/ha) and a correlation coefficient of 0.83%. Full article

(This article belongs to the Special Issue Remote Sensing of Agro-Ecosystems)

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17 pages, 4561 KiB

Open AccessArticle

Development of Prediction Models for Estimating Key Rice Growth Variables Using Visible and NIR Images from Unmanned Aerial Systems

by Zhengchao Qiu, Fei Ma, Zhenwang Li, Xuebin Xu and Changwen Du

Remote Sens. 2022, 14(6), 1384; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14061384 - 13 Mar 2022

Cited by 6 | Viewed by 3106

Abstract

The rapid and accurate acquisition of rice growth variables using unmanned aerial system (UAS) is useful for assessing rice growth and variable fertilization in precision agriculture. In this study, rice plant height (PH), leaf area index (LAI), aboveground biomass (AGB), and nitrogen nutrient index (NNI) were obtained for different growth periods in field experiments with different nitrogen (N) treatments from 2019–2020. Known spectral indices derived from the visible and NIR images and key rice growth variables measured in the field at different growth periods were used to build a prediction model using the random forest (RF) algorithm. The results showed that the different N fertilizer applications resulted in significant differences in rice growth variables; the correlation coefficients of PH and LAI with visible-near infrared (V-NIR) images at different growth periods were larger than those with visible (V) images while the reverse was true for AGB and NNI. RF models for estimating key rice growth variables were established using V-NIR images and V images, and the results were validated with an R² value greater than 0.8 for all growth stages. The accuracy of the RF model established from V images was slightly higher than that established from V-NIR images. The RF models were further tested using V images from 2019: R² values of 0.75, 0.75, 0.72, and 0.68 and RMSE values of 11.68, 1.58, 3.74, and 0.13 were achieved for PH, LAI, AGB, and NNI, respectively, demonstrating that RGB UAS achieved the same performance as multispectral UAS for monitoring rice growth. Full article

(This article belongs to the Special Issue Remote Sensing of Agro-Ecosystems)

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13 pages, 1861 KiB

Open AccessArticle

Monitoring Rainfed Alfalfa Growth in Semiarid Agrosystems Using Sentinel-2 Imagery

by Andrés Echeverría, Alejandro Urmeneta, María González-Audícana and Esther M González

Remote Sens. 2021, 13(22), 4719; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13224719 - 22 Nov 2021

Cited by 4 | Viewed by 1870

Abstract

The aim of this study was to assess the utility of Sentinel-2 images in the monitoring of the fractional vegetation cover (FVC) of rainfed alfalfa in semiarid areas such as that of Bardenas Reales in Spain. FVC was sampled in situ using 1 m² surfaces at 172 points inside 18 alfalfa fields from late spring to early summer in 2017 and 2018. Different vegetation indices derived from a series of Sentinel-2 images were calculated and were then correlated with the FVC measurements at the pixel and parcel levels using different types of equations. The results indicate that the normalized difference vegetation index (NDVI) and FVC were highly correlated at the parcel level (R² = 0.712), whereas the correlation at the pixel level remained moderate across each of the years studied. Based on the findings, another 29 alfalfa plots (28 rainfed; 1 irrigated) were remotely monitored operationally for 3 years (2017–2019), revealing that location and weather conditions were strong determinants of alfalfa growth in Bardenas Reales. The results of this study indicate that Sentinel-2 imagery is a suitable tool for monitoring rainfed alfalfa pastures in semiarid areas, thus increasing the potential success of pasture management. Full article

(This article belongs to the Special Issue Remote Sensing of Agro-Ecosystems)

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17 pages, 4984 KiB

Open AccessArticle

Within-Field Yield Prediction in Cereal Crops Using LiDAR-Derived Topographic Attributes with Geographically Weighted Regression Models

by Riley Eyre, John Lindsay, Ahmed Laamrani and Aaron Berg

Remote Sens. 2021, 13(20), 4152; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13204152 - 16 Oct 2021

Cited by 10 | Viewed by 3133

Abstract

Accurate yield estimation and optimized agricultural management is a key goal in precision agriculture, while depending on many different production attributes, such as soil properties, fertilizer and irrigation management, the weather, and topography.The need for timely and accurate sensing of these inputs at the within field-scale has led to increased adoption of very high-resolution remote and proximal sensing technologies. With regard to topography attributes, greater attention is currently being devoted to LiDAR datasets (Light Detection and Ranging), mainly because numerous topographic variables can be derived at very high spatial resolution from these datasets. The current study uses LiDAR elevation data from agricultural land in southern Ontario, Canada to derive several topographic attributes such as slope, and topographic wetness index, which were then correlated to seven years of crop yield data. The effectiveness of each topographic derivative was independently tested using a moving-window correlation technique. Finally, the correlated derivatives were selected as explanatory variables for geographically weighted regression (GWR) models. The global coefficient of determination values (determined from an average of all the local relationships) were found to be R² = 0.80 for corn, R² = 0.73 for wheat, R² = 0.71 for soybeans and R² = 0.75 for the average of all crops. These results indicate that GWR models using topographic variables derived from LiDAR can effectively explain yield variation of several crop types on an entire-field scale. Full article

(This article belongs to the Special Issue Remote Sensing of Agro-Ecosystems)

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24 pages, 6791 KiB

Open AccessArticle

Spatiotemporal Patterns of Pasture Quality Based on NDVI Time-Series in Mediterranean Montado Ecosystem

by João Serrano, Shakib Shahidian, Luis Paixão, José Marques da Silva, Tiago Morais, Ricardo Teixeira and Tiago Domingos

Remote Sens. 2021, 13(19), 3820; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13193820 - 24 Sep 2021

Cited by 13 | Viewed by 2496

Abstract

The evolution of dryland pasture quality is closely related to the seasonal and inter-annual variability characteristic of the Mediterranean climate. This variability introduces great unpredictability in the dynamic management of animal grazing. The aim of this study is to evaluate the potential of two complementary tools (satellite images, Sentinel-2 and proximal optical sensor, OptRx) for the calculation of the normalized difference vegetation index (NDVI), to monitor in a timely manner indicators of pasture quality (moisture content, crude protein, and neutral detergent fiber). In two consecutive years (2018/2019 and 2019/2020) these tools were evaluated in six fields representative of dryland pastures in the Alentejo region, in Portugal. The results show a significant correlation between pasture quality degradation index (PQDI) and NDVI measured by remote sensing (R² = 0.82) and measured by proximal optical sensor (R² = 0.83). These technological tools can potentially make an important contribution to decision making and to the management of livestock production. The complementarity of these two approaches makes it possible to overcome the limitations of satellite images that result (i) from the interference of clouds (which occurs frequently throughout the pasture vegetative cycle) and (ii) from the interference of tree canopy, an important layer of the Montado ecosystem. This work opens perspectives to explore new solutions in the field of Precision Agriculture technologies based on spectral reflectance to respond to the challenges of economic and environmental sustainability of extensive livestock production systems. Full article

(This article belongs to the Special Issue Remote Sensing of Agro-Ecosystems)

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Unmanned Aerial Vehicle (UAV) for Detection and Prediction of Damage Caused by Potato Cyst Nematode G. pallida on Selected Potato Cultivars

by Keiji Jindo, Misghina Goitom Teklu, Koen van Boheeman, Njane Stephen Njehia, Takashi Narabu, Corne Kempenaar, Leendert P. G. Molendijk, Egbert Schepel and Thomas H. Been

Remote Sens. 2023, 15(5), 1429; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15051429 - 03 Mar 2023

Cited by 5 | Viewed by 1803

Abstract

High population densities of the potato cyst nematodes (PCN) Globodera pallida and G. rostochiensis cause substantial yield losses to potato production (Solanum tuberosum) due to the delay caused to tuber formation by the retardation of plant growth. It requires meticulous estimation of the population densities by using soil sampling and applying the right combination of nematode management to deal with the PCN problem. This study aims to assess the use of an unmanned vehicle (UAV) in detecting and estimating the effect of ranges of densities of a PCN, G. pallida, on four cultivated potato cultivars with resistance to PCN in a naturally infested potato field in The Netherlands. First, the initial population density (Pi) of G. pallida was estimated by using an intensive sampling method of collecting about 1.5 kg of soil per m² from the center of each 3 × 5 m plot. At harvest, the fresh tuber yield of the potato cultivars (Avarna, Fontane, Sarion, and Serresta) were assessed. The Seinhorst yield loss model was used to investigate the relationship between Pi and fresh tuber yield. Secondly, the spatial data of UAV with optical and thermal sensors were analyzed to find any relationship between Pi and UAV indices. By using the classical yield loss model, all four cultivars were found to be affected by Pi with a relative minimum fresh tuber yield m, which ranged from 0.26 to 0.40. The maximum fresh tuber yield varied from 49.48 to 80.36 tons (ha)⁻¹. The density at which the fresh tuber yield started to deteriorate was in the range of 0.62–2.16 eggs (g dry soil)⁻¹. A regression was observed between Pi, and all UAV indices in a similar pattern to that of the fresh tuber yield by using the Seinhorst yield loss model, except for the cultivar Avarna for the two UAV indices (NDRE and NDVI). Unlike the tolerance limit, the relative minimum values of the UAV indices—except the chlorophyll index—differ when compared among each other and when compared with that of the fresh tuber yield within the same cultivar. This indicates that all indices can be useful for detection and decision making for statutory purposes but not for estimating damage (except the chlorophyll index). Full article

(This article belongs to the Special Issue Remote Sensing of Agro-Ecosystems)

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