Satellite-based data fusion crop type classification and mapping in Rio Grande do Sul, Brazil

Pott, Luan Pierre; Amado, Telmo Jorge Carneiro; Schwalbert, Raíssa; Corassa, Geomar Mateus; Ciampitti, Ignacio A.

doi:10.1016/j.isprsjprs.2021.04.015

Cited by 61 publications

(30 citation statements)

References 56 publications

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“…Second, there are a lack of databases that properly cover the entire variability associated to a given application. Although this is a problem for any type of technique [ 48 ], the effects of data gaps become more evident when machine learning techniques are applied. Third, machine learning techniques (deep learning in particular) require large amounts of computational power for model training.…”

Section: Discussionmentioning

confidence: 99%

“…A large portion of the articles employing satellite images aimed to either compensate for data gaps present in a primary data source by fusing it with another source of data (for example, combining optical and SAR images) [ 6 , 45 , 47 , 48 , 49 , 51 , 105 , 106 ], or increase the spatial resolution of the relatively coarse images collected by satellites with high revisit frequencies [ 42 , 43 , 44 , 55 , 57 , 58 , 107 , 108 , 109 , 110 ]. In the latter, the fused results usually inherit the details of the high spatial resolution images and the temporal revisit the frequencies of their counterparts, although the quality of the fused data usually do not match that obtained through actual missions, especially when surface changes are rapid and subtle [ 72 ].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Aerial data (collected using UAVs) is used mostly for detection of certain objects (e.g., certain plant species and fruits) [ 38 ] and for estimation of agricultural variables (e.g., soil moisture and nitrogen content) [ 39 , 40 , 41 ]. Satellite data are used for mapping variables as diverse as soil moisture [ 42 , 43 , 44 ], crop type [ 45 , 46 , 47 , 48 , 49 , 50 ], crop phenological states [ 51 , 52 ], evapotranspiration [ 40 , 53 , 54 , 55 , 56 , 57 , 58 ], nitrogen status [ 59 , 60 , 61 , 62 ], biomass [ 63 , 64 ], among others. While most data fusion approaches only use data in the same scale, a few studies have applied data originating from different scales [ 10 , 26 , 28 , 31 , 38 , 40 , 51 , 52 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 ].…”

Section: Introductionmentioning

confidence: 99%

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Data Fusion in Agriculture: Resolving Ambiguities and Closing Data Gaps

Barbedo

2022

Sensors

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Acquiring useful data from agricultural areas has always been somewhat of a challenge, as these are often expansive, remote, and vulnerable to weather events. Despite these challenges, as technologies evolve and prices drop, a surge of new data are being collected. Although a wealth of data are being collected at different scales (i.e., proximal, aerial, satellite, ancillary data), this has been geographically unequal, causing certain areas to be virtually devoid of useful data to help face their specific challenges. However, even in areas with available resources and good infrastructure, data and knowledge gaps are still prevalent, because agricultural environments are mostly uncontrolled and there are vast numbers of factors that need to be taken into account and properly measured for a full characterization of a given area. As a result, data from a single sensor type are frequently unable to provide unambiguous answers, even with very effective algorithms, and even if the problem at hand is well defined and limited in scope. Fusing the information contained in different sensors and in data from different types is one possible solution that has been explored for some decades. The idea behind data fusion involves exploring complementarities and synergies of different kinds of data in order to extract more reliable and useful information about the areas being analyzed. While some success has been achieved, there are still many challenges that prevent a more widespread adoption of this type of approach. This is particularly true for the highly complex environments found in agricultural areas. In this article, we provide a comprehensive overview on the data fusion applied to agricultural problems; we present the main successes, highlight the main challenges that remain, and suggest possible directions for future research.

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Section: Discussionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Data Fusion in Agriculture: Resolving Ambiguities and Closing Data Gaps

Barbedo

2022

Sensors

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“…The same happens with the different pressures over the agricultural land associated with, for example, the urbanization and ever-growing population [32]. In any case, the farming sector and the associated dimensions are determinants for sustainable food security [33], including animal welfare [34], and the new technologies open new opportunities [35], including for monitoring [36] and assessments [37]. The population growth is particularly worrying in countries such as India, for example [38], but other specific contexts also deserve special attention [39], including those from Africa [40].…”

Section: Literature Surveymentioning

confidence: 99%

Food Security and Sustainability: Discussing the Four Pillars to Encompass Other Dimensions

Guiné

Pato

Costa

et al. 2021

Foods

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The unadjusted intake of food constitutes a real challenge for the several sustainability dimensions. In this perspective, the main objectives of this research are to characterise the current contexts of food security, its relationship with sustainability, and identify proposals and actions that may support the design of more adjusted policies in the future. In addition, it is intended to assess if the food security pillars properly address the sustainability goals and if the evolution of undernutrition is accompanied by sustainable frameworks. In this way, statistical information from the FAOSTAT database was considered for the several dimensions of food security over the period 2000–2020. These data were analysed through factor-cluster approaches and panel data methodologies, namely those related to quantile regressions. As main insights, we may refer that undernutrition is more impacted by the availability of food and nutrients and political stability than by the level of GDP—Gross Domestic Product (except for the extreme cases). This means that the level of development is not the primary explanation for the problems of nutrition. The main focus of the national and international policies must be to improve the agrifood supply chains and to support political stability, in order to mitigate undernutrition worldwide and ensure a global access to sustainable and healthy diets. In addition, it is suggested to rethink the four pillars of food security (availability, access, utilisation and stability), in order to encompass other dimensions, such as climate change.

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“…Landsat-8 and Sentinel 1 images were combined in the research by Inglada et al [ 10 ], leading to an improvement in the classification of land crop results. Pott et al [ 11 ] revealed that using Sentinel 1 and 2 images and Shuttle Radar Topographic Mission (SRTM) elevation models improved the overall accuracy of classification for land crops. The combination of SAR and optic images in Pott, Amado, Schwalbert, Corassa, and Ciampitti [ 11 ] was utilized to increase the accuracy of crop type identification using RF.…”

Section: Introductionmentioning

confidence: 99%

An Innovative Fusion-Based Scenario for Improving Land Crop Mapping Accuracy

Fathololoumi

Firozjaei

Biswas

2022

Sensors

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The accuracy of land crop maps obtained from satellite images depends on the type of feature selection algorithm and classifier. Each of these algorithms have different efficiency in different conditions; therefore, developing a suitable strategy for combining the capabilities of different algorithms in preparing a land crop map with higher accuracy can be very useful. The objective of this study was to develop a fusion-based framework for improving land crop mapping accuracy. First, the features were retrieved using the Sentinel 1, Sentinel 2, and Landsat-8 imagery. Then, training data and various feature selection algorithms including recursive feature elimination (RFE), random forest (RF), and Boruta were used for optimal feature selection. Various classifiers, including artificial neural network (ANN), support vector machine (SVM), and RF, were implemented to create maps of land crops relying on optimal features and training data. After that, in order to increase the result accuracy, maps of land crops derived from several scenarios were fused using a fusion-based voting strategy at the level of decision, and new maps of land crops and classification uncertainty maps were prepared. Subsequently, the performance of different scenarios was evaluated and compared. Among the feature selection algorithms, RF accuracy was higher than RFE and Boruta. Moreover, the efficiency of RF was higher than SVM and ANN. The overall accuracy of the voting scenario was higher than all other scenarios. The finding of this research demonstrated that combining the features’ capabilities extracted from sensors in different spectral ranges, different feature selection algorithms, and classifiers improved the land crop classification accuracy.

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Satellite-based data fusion crop type classification and mapping in Rio Grande do Sul, Brazil

Cited by 61 publications

References 56 publications

Data Fusion in Agriculture: Resolving Ambiguities and Closing Data Gaps

Data Fusion in Agriculture: Resolving Ambiguities and Closing Data Gaps

Food Security and Sustainability: Discussing the Four Pillars to Encompass Other Dimensions

An Innovative Fusion-Based Scenario for Improving Land Crop Mapping Accuracy

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