Assessing the Potential of Sentinel-2 Derived Vegetation Indices to Retrieve Phenological Stages of Mango in Ghana

Torgbor, Benjamin Adjah; Rahman, Muhammad Moshiur; Robson, Andrew; Brinkhoff, James; Khan, Azeem

doi:10.3390/horticulturae8010011

Cited by 8 publications

(3 citation statements)

References 61 publications

(83 reference statements)

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“…It is in the management strategies via ICT that humans have the greatest potential for a positive impact on food security. For example, the advancement of remote sensing technology allows for the detection of substantial differences caused by pest and disease invasions in FV, as well as the possible consequences of seasonal weather variations to avoid food loss (Lin et al ., 2021; Torgbor et al ., 2021). In selling season, hyperspectral imaging sensors can be used for monitoring the freshness of the FV and prices can be decided depending on the freshness scores.…”

Section: Results and Analysismentioning

confidence: 99%

Implementation of information and communication technologies in fruit and vegetable supply chain: a systematic literature review

Yuvaraj

Basu

Abdulrahman

et al. 2023

IMDS

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PurposeInformation and communication technology (ICT) implementation has demonstrated usefulness in supply chain coordination and efficiency optimization in various industries and sectors. This study investigates the extent of ICT deployment in fruits and vegetable supply chains (FVSC) from “farm-to-fork” to ensure food security.Design/methodology/approachThis paper employs a systematic literature review (SLR) methodology and identified a total of 99 journal articles ranging from 2001 to April 2023 for analysis. The reviewed articles have been classified based on the framework proposed from the perspective of food security. Bibliometric and content analysis is carried out with the final list of articles to extract useful insights.FindingsThe findings reveal that ICT implementation in FVSC is a relatively new research area; researchers have started investigating several aspects of ICT in FVSC through varied research methodologies. Experimental research aimed at addressing food safety and condition monitoring of fruits and vegetables (FV) has started to gain traction while theory building is yet to gain traction in the literature reviewed. Findings indicate further research is required on technologies like blockchain (BCT), artificial intelligence (AI) and machine learning (ML), especially on key objectives such as food security, and the triple-bottom-line approach of sustainability. It also indicates that implementing relevant ICTs in FVSC can help delay, if not avert, the food crisis predicted by Malthusian theory.Research limitations/implicationsThis study used only well-established databases to ensure quality of the studies examined. There is a possibility of missing out on articles from other sources not considered. As a result, future SLR studies may employ additional databases, such as Springer Link, Taylor and Francis, Emerald Insight and Google Scholar. Other methodologies such as expert interviews and extra empirical methodologies may also be employed to give a more balanced picture and insights into ICTs implementation in FVSC.Practical implicationsThis study offers a summative detail of the status of ICT implementation in FVSC and can serve as a reference guide for stakeholders in developing strategies for efficient FVSC management. This research work highlights the impact of ICT implementation in FVSC on the four pillars of food security which include improved availability, accessibility, utilization and stability.Originality/valueThis study focuses on ICT implementation for food security in FVSC. The SLR highlights the gaps and proffers potential solutions that enhance global efforts on food security through ICT-enabled reduction in food waste and food loss in FVSC.

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Section: Results and Analysismentioning

confidence: 99%

Implementation of information and communication technologies in fruit and vegetable supply chain: a systematic literature review

Yuvaraj

Basu

Abdulrahman

et al. 2023

IMDS

View full text Add to dashboard Cite

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“…Bands showing a GSD = 20 m were oversampled to 10 m (nearest neighbor). NDVI, EVI, and GNDVI are spectral indices widely used for estimating crop biomass [42], assessing plant health status [43,44], and conducting phenological analyses [45,46]. Conversely, NDWI 1-2-3 and NDMI are primarily related to water content and can be used for Ψstem prediction [47][48][49].…”

Section: Satellite Datamentioning

confidence: 99%

Estimation of Intercepted Solar Radiation and Stem Water Potential in a Table Grape Vineyard Covered by Plastic Film Using Sentinel-2 Data: A Comparison of OLS-, MLR-, and ML-Based Methods

Farbo,

Trombetta,

de Palma

et al. 2024

Plants

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In the framework of precision viticulture, satellite data have been demonstrated to significantly support many tasks. Specifically, they enable the rapid, large-scale estimation of some viticultural parameters like vine stem water potential (Ψstem) and intercepted solar radiation (ISR) that traditionally require time-consuming ground surveys. The practice of covering table grape vineyards with plastic films introduces an additional challenge for estimation, potentially affecting vine spectral responses and, consequently, the accuracy of estimations from satellites. This study aimed to address these challenges with a special focus on the exploitation of Sentinel-2 Level 2A and meteorological data to monitor a plastic-covered vineyard in Southern Italy. Estimates of Ψstem and ISR were obtained using different algorithms, namely, Ordinary Least Square (OLS), Multivariate Linear Regression (MLR), and machine learning (ML) techniques, which rely on Random Forest Regression, Support Vector Regression, and Partial Least Squares. The results proved that, despite the potential spectral interference from the plastic coverings, ISR and Ψstem can be locally estimated with a satisfying accuracy. In particular, (i) the OLS regression-based approach showed a good performance in providing accurate ISR estimates using the near-infrared spectral bands (RMSE < 8%), and (ii) the MLR and ML algorithms could estimate both the ISR and vine water status with a higher accuracy (RMSE < 7 for ISR and RMSE < 0.14 MPa for Ψstem). These results encourage the adoption of medium–high resolution multispectral satellite imagery for deriving satisfying estimates of key crop parameters even in anomalous situations like the ones where plastic films cover the monitored vineyard, thus marking a significant advancement in precision viticulture.

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“…For instance, Schwieder et al [9] estimated the phenological metrics from Landsat ETM+ and Operational Land Imager (OLI) Enhanced Vegetation Index (EVI) values and used Random Forest algorithm in the Brazilian Savanna. Torgbor et al [10] tested the hypothesis that phenological stages of mango can be retrieved from Sentinel-2 derived time series vegetation indices data in Ghana. Singh et al [11] extracted phenological metrics from the Landsat data using NDVI time series to develop decision rules based on phenological metrics for sugarcane crop classification, in North India.…”

Section: Introductionmentioning

confidence: 99%

Vegetation phenology from Sentinel 2 data: a GIS open source application

Duarte

Scomparim²,

Teodoro³

2022

Earth Resources and Environmental Remote Sensing/Gis Applications XIII

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The vegetation phenology is defined as the seasonal timing of recurring biological events, the causes of their timing, their relationship to biotic and abiotic forces, and the interrelations among phases of the same or different species. Phenology is one of the most reliable indicators of vegetation dynamics. Geographical Information Systems (GIS) combined with freely available satellite imagery significantly contributes to the study of vegetation phenology. A GIS open-source application that estimates vegetation phenology metrics from time-series of Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) as proxies for phenology was already been developed. The application was based on products from MODIS sensor (MOD13 data) and considered two crops, vineyards and maize. However, this application is now obsolete in the recent version of QGIS software. This work aims to develop an improved version of the open-source application to automatically extract the phenological metrics from a set of Sentinel 2 input data. Sentinel 2 has the advantage of also being free, with higher spatial, spectral, and temporal resolution, being particularly suitable for “in-season crop forecasting”. Several limitations found in the previous version can be surpassed with the use of Sentinel 2 data. For instance, the small farms, due to the presence of multiple land cover types within a pixel, can now be monitored with much greater confidence. The application is free and open source, so it can be modified according to the user requirements and adopted to different crops and regions.

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Assessing the Potential of Sentinel-2 Derived Vegetation Indices to Retrieve Phenological Stages of Mango in Ghana

Cited by 8 publications

References 61 publications

Implementation of information and communication technologies in fruit and vegetable supply chain: a systematic literature review

Implementation of information and communication technologies in fruit and vegetable supply chain: a systematic literature review

Estimation of Intercepted Solar Radiation and Stem Water Potential in a Table Grape Vineyard Covered by Plastic Film Using Sentinel-2 Data: A Comparison of OLS-, MLR-, and ML-Based Methods

Vegetation phenology from Sentinel 2 data: a GIS open source application

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