Search

This research explores the application of neutrosophy to understand and address the inherent complexity in the oral transmission of knowledge in family agriculture. It leverages its ability to handle contradictions and ambiguities, facilitating a richer analysis of agricultural practices and social interactions. Orality, as the primary form of communication in these communities, is essential for the transmission of agricultural and cultural knowledge. This research highlights how orality acts as a vehicle for the preservation of collective identity and the transmission of ancestral knowledge. The study used Fuzzy Cognitive Maps (FCM) to describe the causal relationships between different variables of interest. Fieldwork was conducted between October 2023 and January 2024, using semi-structured interviews to capture the oral narrative of the farmers in Apagua. The analysis of the FCM showed that the components of cultural preservation and knowledge transmission are the most influential. This study demonstrated that neutrosophy is an effective tool for analyzing the complex dynamics of oral knowledge transmission in family agriculture. The conclusions underline the interdependence between knowledge transmission, social relationships, adaptation to changes, and cultural preservation. [ABSTRACT FROM AUTHOR]

Salinization and land degradation are significant challenges in the southern regions of Kazakhstan. These issues arise due to climate change, unequal water resource distribution, and human impact. The primary concern revolves around water resources, which are influenced by the area's trans boundary flow of major rivers. The low level of water and food security has pushed the development of new approaches based on remote sensing monitoring and geographic information systems (GIS) to provide solutions for soil salinity. The research aims to focus on utilizing high-resolution radar images. This data type is effective for cloudy weather and can be useful for continued monitoring of some areas. Machine learning methods can solve the problem of automatic mapping of agricultural land salinity in Kazakhstan's southern regions. The precise mapping of the salinity area helps prevent or decrease salinity's impact on agriculture. The experiment realized that complex models such as LightGBM do not have significant accuracy performance over simple models on a small dataset compared with Ridge regression. The results allow us to recommend an approach for further improvement with ground-based measurement data and other deep-learning methods for mapping the salinity of agricultural lands. [ABSTRACT FROM AUTHOR]

Agricultural heritage represents a unique fusion of natural and cultural elements, encompassing both tangible and intangible aspects that reflect the historical bond between humanity and the land focused on food production. Recognising its significance is vital, as it not only preserves tradition and expertise but also fosters sustainability and environmental resilience. While significant initiatives like Globally Important Agricultural Heritage Systems (GIAHS) are in place to safeguard remarkable agricultural areas with unique heritage values, frameworks for recognising the historic significance of non-outstanding agricultural areas are not sufficiently consolidated. Nonetheless, it is equally important to acknowledge the heritage value of agricultural systems that, while lacking exceptional features, succeed in preserving traditional techniques and processes, thereby conserving cultural landscapes and gastronomic identities. For this reason, this paper introduces a methodology for assessing the time depth of agricultural elements such as roads, livestock routes, agricultural plots, rural buildings and land use using historical cartography. By mapping historically significant features in farming areas, this method might be useful for recognising agricultural heritage and integrate it into spatial decision-making, ensuring the preservation of its cultural and environmental values. The method’s application is presented in a case study of Spain’s floodplains and irrigation areas near Aranjuez (Community of Madrid). [ABSTRACT FROM AUTHOR]

This study examines the research output of Indian Council of Agricultural Research (ICAR) researchers from 2010 to 2023 using scientometric tools and the Web of Science database. Initially identifying 2956 articles, subsequent application of exclusion criteria yielded 2950 relevant documents, encompassing journal articles, reviews, conference papers, and other scholarly contributions. The analysis reveals a robust scientific output characterized by recent publication dates, underscoring the timeliness of ICAR's research. Key journals such as "PLOS ONE," "Frontiers in Plant Science," and "Scientific Reports" emerge as significant platforms for disseminating ICAR's findings. The Indian Agricultural Research Institute (IARI) stands out for its substantial research output and citation impact. Collaboration is a prominent feature, with many documents being co-authored, reflecting the interdisciplinary nature of ICAR's research and facilitating knowledge exchange among researchers. The study employs Biblioshiny (Bibiliometrix) and VOSviewer software for bibliometric analysis, providing insights into growth trends, collaborative patterns, authorship trends, and institutional collaborations at both national and international levels. [ABSTRACT FROM AUTHOR]

Agricultural crop mapping has advanced over the last decades due to improved approaches and the increased availability of image datasets at various spatial and temporal resolutions. Considering the spatial and temporal dynamics of different crops during a growing season, multi-temporal classification frameworks are well-suited for mapping crops at large scales. Addressing the challenges posed by imbalanced class distribution, our approach combines the strengths of different deep learning models in an ensemble learning framework, enabling more accurate and robust classification by capitalizing on their complementary capabilities. This research aims to enhance the crop classification of maize, soybean, and wheat in Bei'an County, Northeast China, by developing a novel deep learning architecture that combines a three-dimensional convolutional neural network (3D-CNN) with a variant of convolutional recurrent neural networks (ConvRNN). The proposed method integrates multi-temporal Sentinel-1 polarimetric features with Sentinel-2 surface reflectance data for multi-source fusion and achieves an overall accuracy of 91.7%, a Kappa coefficient of 85.7%, and F1 scores of 93.7%, 92.2%, and 90.9% for maize, soybean, and wheat, respectively. Our proposed model is also compared with alternative data augmentation techniques, maintaining the highest mean F1 score (87.7%). The best performer was weakly supervised with ten per cent of ground truth data collected in Bei'an in 2017 and used to produce an annual crop map for measuring the model's generalizability. The model learning reliability of the proposed method is interpreted through the visualization of model soft outputs and saliency maps. [ABSTRACT FROM AUTHOR]

The recent deployment of new satellite-based hyperspectral imaging sensors, along with advances in classification methods, has enhanced the acquisition of information on the spatiotemporal patterns of Land Use/Land Cover (LULC).. With this context, the study presented in this paper seeks to assess the combined utilization of the Pixxel's technology demonstrator hyperspectral satellite mission with the Support Vector Machines (SVM) and Random Forest (RF) machine learning classifiers for LULC mapping in an agricultural area located in Queensland, Australia. Accuracy assessment of the resulting LULC maps has been done based on relevant classification accuracy metrics. It has been observed that SVMs outperformed RF in terms of both overall classification and urban vegetation cover mapping accuracy. An overall accuracy of 92.34% with kappa of 0.90 was reported for the SVMs' results, whereas for RF, the accuracy was 89.65% with 0.89 kappa, respectively. Both classifiers produced similar results for the classes of mango tree and sugarcane crops, whereas minor differences were observed in the depiction of water bodies and artificial surfaces. Our study findings provide insightful perspectives on the potential of Pixxel datasets for delineating the spatiotemporal distribution of LULC in agricultural landscapes, showcasing Pixxel's hyperspectral sensors' promising potential in this field. [ABSTRACT FROM AUTHOR]

THIS RESEARCH aims to create and analyze thematic soil maps for promising areas for agricultural expansion along the northwest coast of Egypt. The study area is 3151.671 km² and consists of a plateau and elevated areas merging into coastal lowlands. Remote sensing data were used with a digital elevation model to map the physiographic units and characterize land surface features. Five different physiographic entities are recognized; each mostly associated with the soil sub-great groups Typic Haplocalcids and Typic Torripsamments. Data collected through land survey and laboratory analysis were used to create thematic soil layers that were analyzed to determine land factors that limit agricultural development. The results showed that the land surface factors hinder agricultural development due to the steep slope and undulation of the land surface. Analysis of thematic soil maps showed that agricultural development in the region is mainly constrained by coarse soil texture, shallow soil depth, high CaCO3 content, and low CEC. To achieve agricultural sustainability in the region, improved drainage, organic fertilization, and crop selection are required. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) plays an essential role in agricultural mapping. It reduces costs and time and increases efficiency in agricultural management activities, which improves the food industry. Agricultural mapping is necessary for resource management and requires technologies for farming challenges. The mapping in agricultural AI applications gives efficiency in mapping and its subsequent use in decision-making. This study analyses AI's current state in agricultural mapping through bibliometric indicators and a literature review to identify methods, agricultural resources, geomatic tools, mapping types, and their applications in agricultural management. The methodology begins with a bibliographic search in Scopus and the Web of Science (WoS). Subsequently, a bibliographic data analysis and literature review establish the scientific contribution, collaboration, AI methods, and trends. The United States (USA), Spain, and Italy are countries that produce and collaborate more in this area of knowledge. Of the studies, 76% use machine learning (ML) and 24% use deep learning (DL) for agricultural mapping applications. Prevailing algorithms such as Random Forest (RF), Artificial Neural Networks (ANNs), and Support Vector Machines (SVMs) correlate mapping activities in agricultural management. In addition, AI contributes to agricultural mapping in activities associated with production, disease detection, crop classification, rural planning, forest dynamics, and irrigation system improvements. [ABSTRACT FROM AUTHOR]

One of the key priorities of the Ministry of Agriculture Jihad is the mapping of croplands to estimate crop acreage and annual yield. In recent decades, remote sensing technology has proven to be highly effective in estimating the extent of crop cultivation through the use of timely images and synchronized data with diverse spatial, temporal, and spectral resolutions, leveraging advanced machine-learning algorithms. This study presented a framework for crop mapping in Marvdasht, Fars Province, by utilizing time series of Landsat-8 satellite images and advanced machine-learning algorithms. The employed algorithms included Decision Tree (DT), Random Forest (RF), Rotation Forest (RoF), Support Vector Machine (SVM), and Dynamic Time Warping (DTW) analysis. The results indicated that the dynamic time warping and random forest methods outperformed others, achieving significantly higher accuracy (with an overall accuracy improvement of 10-12%) in generating the agricultural land-use map of the study area. Furthermore, this research demonstrated the effectiveness of Bands 2-5 of Landsat-8 satellite in confidently identifying all crops in this region using the mentioned methods. [ABSTRACT FROM AUTHOR]

Land-use maps containing crop rotational information are very important in land management and physical planning. Such maps are usually generated using multitemporal data. Although recent technology allows analysts to process multitemporal information effectively, the use of single date imagery for such purpose is more efficient. This study aimed to map detailed agricultural land-use with crop rotational information based on a single date Landsat 8 imagery and SRTM-derived terrain attributes. A landscape ecological approach assuming the influence of terrain characteristics on the existence of crop and land-use types was implemented in multisource classification using random decision forest (RDF) machine learning algorithm. The use of seven optical bands and five terrain attributes could provide a land-use map at 88.03% accuracy, compared to seven optical bands only that generate 82.45% accuracy. These results are also better than those of maximum likelihood. The most influential variables in the achieved accuracy are elevation and thermal band. [ABSTRACT FROM AUTHOR]

The development of accurate bioenergetic models, that simulate scenarios for decision making and policy planning contributions, has become a necessity in the actual climate crisis context. The objective of this work is to develop a multicriteria methodology to evaluate scenarios for assessing a region's bioenergetic potential and determining optimal location of biopower plants. This method utilizes a geographic information system that incorporates local biomass distribution, crop harvest statistics, detailed yield data, preference and exclusion maps. The approach examines the current local use of residues in the context of long-term soil preservation, employing sustainable residue removal. When it is applied to the Province of Córdoba, Argentina, specifically assessing main agricultural harvest residues, the model identifies 1.8 million tn/year of theoretical residues, primarily from corn and soybean, revalorizing only 4.1% of the available harvest agricultural residues. When employing the most commonly used approaches in the literature, which involves average crop yield values for extensive regions, our results show an underestimation of theoretical biomass up to 26%. This makes it essential to incorporate greater detail in the modeling. After optimizing five biopower plants, each with a collection radius of 60 km, it becomes possible to convert 59% of these residues into bioenergy, generating 2.3 GWh/year, covering up to 23.3% of Córdoba's annual electricity demand. The novelty of this research resides in the development of a high detailed multicriteria methodology, which facilitates the fast simulation of scenarios to identify optimal biopower plant locations, applicability to different biomass types and bioenergy forms for extensive regions. [ABSTRACT FROM AUTHOR]

This article describes a series of steps to integrate multiple modes of archaeological mapping in arid and agricultural settings. We use the coastal region of Peru as a case study and share our recent field experience at Cerro San Isidro, a multicomponent hill site located in the agriculture-intensive and mid-elevation (about 500 m asl) Moro region of the Nepeña Valley. In June and July 2022, we spent eight weeks deploying a combination of drone aerial imagery, pedestrian GPS reconnaissance, and GPR survey to map the surface and subsurface features at the site and in the adjacent agricultural fields. Our efforts suggest that the ancient settlement extended over an area of at least 50 ha, well beyond the visible surface architecture. Using a multimodal approach to confirming the partial destruction of archaeological vestiges by modern agricultural encroachment is both time-effective and noninvasive. The article offers insights from our experience, including the sequence of field operations, technical troubleshooting, and the collection and integration of datasets. We discuss the methodological potential and implications of this combination of multimodal mapping and its deployment in coastal Peru, a region that, like many others in the world, is increasingly subject to rapid agricultural expansion and other anthropogenic developments. [ABSTRACT FROM AUTHOR]

The farmland in the southwestern mountainous areas of China is mostly hilly terrain with multiple obstacles, and traditional manual spraying operations are time-consuming and laborious. The use of agricultural plant protection unmanned aerial vehicle (UAV) can reduce the problem of high manual operation costs. To solve the problem of optimizing the spraying operation path of plant protection UAVs, this study focused on the complex agricultural environment in the southwestern mountainous areas of China. First, a 2D agricultural map model with multiple obstacles was constructed using MATLAB. Second, the optimization requirements for job paths were analyzed, and a path optimization model based on the grid graph method was studied, aiming to shorten the total flight distance and reduce the number of paths. By applying the genetic algorithm, efficient optimization of the spraying path of plant protection UAV was carried out. Simulation verification showed that the optimized path significantly shortened the flight distance, accelerated convergence speed, and effectively avoided local repeated paths, thereby greatly improving the spraying efficiency of plant protection UAV. [ABSTRACT FROM AUTHOR]

Crop yield estimation plays a crucial role in agricultural production planning and risk management. Utilizing simultaneous localization and mapping (SLAM) technology for the three-dimensional reconstruction of crops allows for an intuitive understanding of their growth status and facilitates yield estimation. Therefore, this paper proposes a VINS-RGBD system incorporating a semantic segmentation module to enrich the information representation of a 3D reconstruction map. Additionally, image matching using L_SuperPoint feature points is employed to achieve higher localization accuracy and obtain better map quality. Moreover, Voxblox is proposed for storing and representing the maps, which facilitates the storage of large-scale maps. Furthermore, yield estimation is conducted using conditional filtering and RANSAC spherical fitting. The results show that the proposed system achieves an average relative error of 10.87% in yield estimation. The semantic segmentation accuracy of the system reaches 73.2% mIoU, and it can save an average of 96.91% memory for point cloud map storage. Localization accuracy tests on public datasets demonstrate that, compared to Shi–Tomasi corner points, using L_SuperPoint feature points reduces the average ATE by 1.933 and the average RPE by 0.042. Through field experiments and evaluations in a strawberry field, the proposed system demonstrates reliability in yield estimation, providing guidance and support for agricultural production planning and risk management. [ABSTRACT FROM AUTHOR]

The agricultural industry is being transformed, thanks to recent innovations in computer vision and deep learning. However, the lack of specific datasets collected in natural agricultural environments is, arguably, the main bottleneck for novel discoveries and benchmarking. The present work provides a novel dataset, Magro, and a framework to expand data collection. We present the first version of the Magro Dataset V1.0, consisting of nine ROS bags (and the corresponding raw data) containing data collected in apple and pear crops. Data were gathered, repeating a fixed trajectory on different days under different illumination and weather conditions. To support the evaluation of loop closure algorithms, the trajectories are designed to have loop closures, revisiting some places from different viewpoints. We use a Clearpath's Jackal robot equipped with stereo cameras pointing to the front and left side, a 3D LIDAR, three inertial measurement units (IMU), and wheel encoders. Additionally, we provide calibrated RTK GPS data that can be used as ground truth. Our dataset is openly available, and it will be updated to have more data and variability. Finally, we tested two existing state-of-the-art algorithms for vision and point cloud-based localization and mapping on our novel dataset to validate the dataset's usability. [ABSTRACT FROM AUTHOR]

It was observed that the difference in the maximum and minimum NDVI values at a time close to harvest (mxNDVI and mnNDVI, respectively), referred to as the haNDVI index (harvest amplitude of NDVI), correlates with agricultural soil quality and the share of sowings. The NDVI becomes saturated when the values of the Leaf Area Index (LAI) signifi cantly exceed one so spatial variation in haNDVI is mainly due to the minimum post-harvest NDVI (mnNDVI). To explain the variability of mnNDVI values three hypotheses were formulated: i) impact of crop selection, ii) fi eld size impact, and iii) impact of soil. To determine which of these hypotheses had the highest impact on the variation in the mnNDVI, the developed machine learning models of this indicator were subjected to a test removing individual explanatory variables from them. Removing a variable does not cause a signifi cant increase in model error if a variable does not contribute useful information to the model. This test showed that the mnNDVI index depends almost exclusively on the crop indicator which was the median of mnNDVI for crops, not directly from soil variables such as the agricultural quality of soil or soil moisture. According to this, the hypothesis of direct impact of soil was rejected. The explanation for the observed correlation of haNDVI with soil quality is the agricultural practice of choosing crops with low mnNDVI (cereals, rapeseed) at better soil conditions and crops with high mnNDVI (fodder crops, grassland) for worse soil conditions. [ABSTRACT FROM AUTHOR]

The Southeast Asia region has a vast expanse with diverse tropical climates, making it a prominent centre of rice cultivation, contributing to about 20 % of the world's rice production and contributes 29 % of global rice methane emissions. As a staple food for many countries, accurate and up-to-date information on the rice harvested area is crucial for addressing food security issues, predicting rice yield and methane emissions, and formulating effective government policies. This paper presents the first detailed study of rice cropping intensity and harvested areas throughout Southeast Asia. Current remote sensing products have not been able to produce up-to-date cropping intensity information due to the variability of local cultivation practices. To address this problem, we integrated Sentinel-1A and Sentinel-2A/B time series data from 2020 to 2021 and developed a local unsupervised classification with phenological labelling (LUCK-PALM) method. We implemented the system on the Google Earth Engine (GEE) cloud-based platform to produce geospatial products of rice cropping intensity and harvested area at a spatial resolution of 10 m called SEA-Rice-Ci10s. The results show that Southeast Asia's total rice growing area in 2020–2021 was 28.5 Mha, with 51 % single cropping, 47 % double cropping, and 2 % triple cropping. These were equivalent to 42.9 Mha of annual harvested area, consisting of Thailand (11.2 Mha), Indonesia (8.4 Mha), Myanmar (8.4 Mha), Vietnam (6.3 Mha), Cambodia (3.9 Mha), the Philippines (3.3 Mha), Laos (0.8 Mha), Malaysia (< 0.5 Mha), and Timor-Leste (0.01 Mha). We compared our rice maps to agricultural statistics data at the district and province levels and existing rice maps for some Southeast Asian countries. The results demonstrate that our map agreed well with countries' statistics with a linear coefficient of determination (R2) from 0.85 to 0.97. Compared to existing products, our map can resolve small paddy fields of about 0.2 ha in the hilly areas. This information will be useful in addressing food security challenges and improving estimates of methane emissions from rice cultivation. The 10 m paddy rice cropping intensity map for Southeast Asia, SEA-Rice-Ci10, is available on the GEE App (https://rudiyanto.users.earthengine.app/view/seariceci2021), the Climate TRACE platform (https://climatetrace.org/) and the Zenodo repository (https://doi.org/10.5281/zenodo.10707621) (Frisa Irawan et al., 2024). [ABSTRACT FROM AUTHOR]

In most developing countries, smallholder farms are the ultimate source of income and produce a significant portion of overall crop production for the major crops. Accurate crop distribution mapping and acreage estimation play a major role in optimizing crop production and resource allocation. In this study, we aim to develop a spatio–temporal, multi-spectral, and multi-polarimetric LULC mapping approach to assess crop distribution mapping and acreage estimation for the Oromia Region in Ethiopia. The study was conducted by integrating data from the optical and radar sensors of sentinel products. Supervised machine learning algorithms such as Support Vector Machine, Random Forest, Classification and Regression Trees, and Gradient Boost were used to classify the study area into five first-class common land use types (built-up, agriculture, vegetation, bare land, and water). Training and validation data were collected from ground and high-resolution images and split in a 70:30 ratio. The accuracy of the classification was evaluated using different metrics such as overall accuracy, kappa coefficient, figure of metric, and F-score. The results indicate that the SVM classifier demonstrates higher accuracy compared to other algorithms, with an overall accuracy for Sentinel-2-only data and the integration of optical with microwave data of 90% and 94% and a kappa value of 0.85 and 0.91, respectively. Accordingly, the integration of Sentinel-1 and Sentinel-2 data resulted in higher overall accuracy compared to the use of Sentinel-2 data alone. The findings demonstrate the remarkable potential of multi-source remotely sensed data in agricultural acreage estimation in small farm holdings. These preliminary findings highlight the potential of using multi-source active and passive remote sensing data for agricultural area mapping and acreage estimation. [ABSTRACT FROM AUTHOR]

Due to the key role surrounding landscape plays in ecological processes, a detailed characterization of land cover is critical for researchers and conservation practitioners. Unfortunately, in the United States, land cover data are split across thematic datasets that emphasize agricultural or natural vegetation, but not both. To address this gap, we merged two datasets, the LANDFIRE National Vegetation Classification (NVC) and USDA-NASS Cropland Data Layer (CDL), to produce integrated 'Spatial Products for Agriculture and Nature' (SPAN). Our workflow leveraged strengths of the NVC and the CDL to create detailed rasters comprising both agricultural and natural land-cover classes. We generated SPAN annually from 2012–2021 for the conterminous United States, quantified agreement and accuracy of SPAN, and published the complete computational workflow. In our validation analyses, we found that approximately 5.5% of NVC agricultural pixels conflicted with the CDL, but we resolved most conflicts, leaving only 0.6% of agricultural pixels unresolved in SPAN. These ready-to-use rasters characterizing both agricultural and natural land cover will be widely useful in environmental research and management. [ABSTRACT FROM AUTHOR]

Monitoring crop condition, soil properties, and mapping tillage activities can be used to assess land use, forecast crops, monitor seasonal changes, and contribute to the implementation of sustainable development policy. Agricultural maps can provide independent and objective estimates of the extent of crops in a given area or growing season, which can be used to support efforts to ensure food security in vulnerable areas. Satellite data can help detect and classify different types of soil. The evolution of satellite remote sensing technologies has transformed techniques for monitoring the Earth's surface over the last several decades. The European Space Agency (ESA) and the European Union (EU) created the Copernicus program, which resulted in the European satellites Sentinel-1B (S1B) and Sentinel-2A (S2A), which allow the collection of multi-temporal, spatial, and highly repeatable data, providing an excellent opportunity for the study of land use, land cover, and change. The goal of this study is to map the land cover of Côte d'Ivoire's West Central Soubre area (5°47'1" North, 6°35'38" West) between 2014 and 2020. The method is based on a combination of S1B and S2A imagery data, as well as three types of predictors: the biophysical indices Normalized Difference Vegetation Index "(NDVI)", Modified Normalized Difference Water Index "(MNDWI)", Normalized Difference Urbanization Index "(NDBI)", and Normalized Difference Water Index "(NDWI)", as well as spectral bands (B1, B11, B2, B3, B4, B6, B7, B8) and polarization coefficients VV. For the period 2014-2020, six land classifications have been established: Thick_Forest, Clear_Drill, Urban, Water, Palm_Oil, Bareland, and Cacao_Land. The Random Forest (RF) algorithm with 60 numberOfTrees was the primary categorization approach used in the Google Earth Engine (GEE) platform. The results show that the RF classification performed well, with outOfBagErrorEstimates of 0.0314 and 0.0498 for 2014 and 2020, respectively. The classification accuracy values for the kappa coefficients were above 95%: 96.42% in 2014 and 95.28% in 2020, with an overall accuracy of 96.97% in 2014 and 96 % in 2020. Furthermore, the User Accuracy (UA) and Producer Accuracy (PA) values for the classes were frequently above 80%, with the exception of the Bareland class in 2020, which achieved 79.20%. The backscatter coefficients of the S1B polarization variables had higher GINI significance in 2014: VH (70.80) compared to VH (50.37) in 2020; and VV (57.11) in 2014 compared to VV (46.17) in 2020. Polarization coefficients had higher values than the other spectral and biophysical variables of the three predictor variables. During the study period, the Thick_Forest (35.90% ± 1.17), Palm_Oil (57.59% ± 1.48), and Water (5.90% ± 0.47) classes experienced a regression in area, while the Clear_Drill (16.96% ± 0.80), Urban (2.32% ± 0.29), Bareland (83.54% ± 1.79), and Cacao_Land (35.14% ± 1.16) classes experienced an increase. The approach used is regarded as excellent based on the results obtained. [ABSTRACT FROM AUTHOR]

In recent years, research has been conducted in scientific institutions of the Ministry of Agriculture of the Russian Federation and the Russian Academy of Sciences on introducing new technologies for the use of aerospace information in agriculture. This article, using the example of Stavropol krai, considers the possibility of using cloud services such as Google Earth Engine (GEE) and Kaggle machine learning systems for mapping agricultural fields using deep learning methods based on remote sensing data. Median images of the Sentinel 2 space system for the 2022 growing season are used as data for the selection of training and validation samples. The total volume of the prepared training samples is 3998 images. One problem for researchers and manufacturers in the field of agriculture is a lack of centralized and verified sources of geospatial data. Deep learning methods are able to solve this problem by automating the task of digitizing the geometries of agricultural fields based on remote sensing data. One of the limitations in the widespread use of deep learning is its high demand for computing resources, which are not always available to a researcher or manufacturer in the field of agriculture. This paper describes the process of preparing the necessary data for working with a neural network, including correcting and obtaining satellite images using GEE, their standardization for training a neural network in Kaggle, and further use locally. A neural network of the U-net architecture is used as part of the study. The final classification quality is 97%. The threshold of division into classes according to the classification results is established empirically and amounts to 0.62. The proposed approach makes it possible to significantly reduce the requirements for the local use of PC computing power. All the most resource-intensive processes related to the processing of satellite images are performed in the GEE system, and the learning process is transferred to the resources of the Kaggle system. The proposed combination of cloud services and deep learning methods can contribute to a wider spread of the use of modern technologies in agricultural production and scientific research. [ABSTRACT FROM AUTHOR]

Copyright of Environnement, Risques & Santé is the property of John Libbey Eurotext Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The challenge of global food security in the face of population growth, conflict, and climate change requires a comprehensive understanding of cropped areas, irrigation practices, and the distribution of major commodity crops like maize and wheat. However, such understanding should preferably be updated at seasonal intervals for each agricultural system rather than relying on a single annual assessment. Here we present the European Space Agency-funded WorldCereal system, a global, seasonal, and reproducible crop and irrigation mapping system that addresses existing limitations in current global-scale crop and irrigation mapping. WorldCereal generates a range of global products, including temporary crop extent, seasonal maize and cereal maps, seasonal irrigation maps, seasonal active cropland maps, and model confidence layers providing insights into expected product quality. The WorldCereal product suite for the year 2021 presented here serves as a global demonstration of the dynamic open-source WorldCereal system. Validation of the products was done based on best available reference data per product. A global statistical validation for the temporary crop extent product resulted in user's and producer's accuracies of 88.5 % and 92.1 %, respectively. For crop type, a verification was performed against a newly collected street view dataset (overall agreement 82.5 %) and a limited number of publicly available in situ datasets (reaching minimum agreement of 80 %). Finally, global irrigated-area estimates were derived from available maps and statistical datasets, revealing the conservative nature of the WorldCereal irrigation product. The WorldCereal system provides a vital tool for policymakers, international organizations, and researchers to better understand global crop and irrigation patterns and to inform decision-making related to food security and sustainable agriculture. Our findings highlight the need for continued community efforts such as additional reference data collection to support further development and to push the boundaries for global agricultural mapping from space. The global products are available at 10.5281/zenodo.7875104 (Van Tricht et al., 2023). [ABSTRACT FROM AUTHOR]

In recent years, the information and communication technologies (ICT) with the agricultural sector has garnered significant attention due to its potential to transform and optimize agricultural practices. This study provides a comprehensive picture of the previous and current state of ICT in agricultural research, thereby shedding light on the existing knowledge landscape and key research themes. The study presents a mapping research approach that explores the conceptual structure of ICT in agriculture research using co-occurrence analysis drawing upon the Web of Science database with the results of 8,654 documents that have been published from 1989 to 2023. From a total of 2,930 keywords and the five most frequent keywords have been identified as: "performance", "growth", "expression", "impact", and "identification". The findings from this study contribute to the broader understanding of the ICT-agriculture research landscape and provide valuable insights for researchers, practitioners, and policymakers in this evolving field. [ABSTRACT FROM AUTHOR]

This paper will examine the social and ethical impacts of using artificial intelligence (AI) in the agricultural sector. It will identify what are some of the most prevalent challenges and impacts identified in the literature, how this correlates with those discussed in the domain of AI ethics, and are being implemented into AI ethics guidelines. This will be achieved by examining published articles and conference proceedings that focus on societal or ethical impacts of AI in the agri-food sector, through a thematic analysis of the literature. The thematic analysis will be divided based on the classifications outlined through 11 overarching principles, from an established lexicon (transparency, justice and fairness, non-maleficence, responsibility, privacy, beneficence, freedom and autonomy, trust, dignity, sustainability, and solidarity). While research on AI agriculture is still relatively new, this paper aims to map the debate and illustrate what the literature says in the context of social and ethical impacts. It aim is to analyse these impacts, based on these 11 principles. This research will contrast which impacts are not being discussed in agricultural AI and which issues are not being discussed in AI ethics guidelines, but which are discussed in relation to agricultural AI. The aim of this is to identify gaps within the agricultural literature, and gaps in AI ethics guidelines, that may need to be addressed. [ABSTRACT FROM AUTHOR]

As a powerful statistical method, meta-analysis has been applied increasingly in agricultural science with remarkable progress. However, meta-analysis research reports in the agricultural discipline still need to be systematically combed. Scientometrics is often used to quantitatively analyze research on certain themes. In this study, the literature from a 30-year period (1992–2021) was retrieved based on the Web of Science database, and a quantitative analysis was performed using the VOSviewer and CiteSpace visual analysis software packages. The objective of this study was to investigate the current application of meta-analysis in agricultural sciences, the latest research hotspots, and trends, and to identify influential authors, research institutions, countries, articles, and journal sources. Over the past 30 years, the volume of the meta-analysis literature in agriculture has increased rapidly. We identified the top three authors (Sauvant D, Kebreab E, and Huhtanen P), the top three contributing organizations (Chinese Academy of Sciences, National Institute for Agricultural Research, and Northwest A&F University), and top three productive countries (the USA, China, and France). Keyword cluster analysis shows that the meta-analysis research in agricultural sciences falls into four categories: climate change, crop yield, soil, and animal husbandry. Jeffrey (2011) is the most influential and cited research paper, with the highest utilization rate for the Journal of Dairy Science. This paper objectively evaluates the development of meta-analysis in the agricultural sciences using bibliometrics analysis, grasps the development frontier of agricultural research, and provides insights into the future of related research in the agricultural sciences. [ABSTRACT FROM AUTHOR]

Accurate and effective access to agricultural and pastoral information is of great practical importance for scientific planning in agriculture and pastoral husbandry and food security reserves. This paper utilised intra-annual monthly synthetic data from Sentinel-1 and Sentinel-2, combined with band information and index information to simultaneously obtain intra-annual phenological change information and extracted different types of information for random forest classification, thus obtaining the fine-grained agricultural and pastoral map of Jingyang District. On this basis, the effects of different data sources and the importance of different features on the classification results were further analysed. The results show that in the classification results of the Sentinel-1 data method, March feature is the most important and May feature has the least influence on the classification results. In the classification results of the Sentinel-2 data method and the combined Sentinel-1 data and Sentinel-2 data, the December feature has the greatest importance and the February feature has the least impact on the classification results. The NDVI index has the highest importance and the EVI index has the lowest importance score. The importance of band features varies in the classification results of different data sources. The accuracy of the agricultural and pastoral information in Jingyang District is 83.31% and the kappa is 0.76. Rice is the largest crop with an area of 239,830,800 m2, mainly located in the northwest and mountainous areas of Jingyang District. It is followed by green manure with an area of 60,274,000 m2, mainly in the mountainous part. This study demonstrates the feasibility of extracting agricultural and pastoral information from remote sensing images, and also provides basic data for future agricultural and pastoral planning in Jingyang District. [ABSTRACT FROM AUTHOR]

Understanding the relationship between land use and opium production is critical for monitoring the dynamics of poppy cultivation and developing an effective counter narcotics policy in Afghanistan. However, mapping agricultural land accurately and rapidly is challenging, as current methods require resource-intensive and time consuming manual image-interpretation. Deep convolutional neural nets have been shown to greatly reduce the manual effort in mapping agriculture from satellite imagery but require large amounts of densely labelled training data for model training. Here we develop a generalised model using past images and labels from different medium resolution satellite sensors for fully automatic agricultural land classification using the latest medium resolution satellite imagery. The model (FCN-8) is first trained on Disaster Monitoring Constellation (DMC) satellite images from 2007 to 2009. The effect of shape, texture and spectral features on model performance are investigated along with normalisation in order to standardise input medium resolution imagery from DMC, Landsat-5, Landsat-8, and Sentinel-2 for transfer learning between sensors and across years. Textural features make the highest contribution to overall accuracy (∼73%) while the effect of shape is minimal. The model accuracy on new images, with no additional training, is comparable to visual image interpretation (overall > 95%, user accuracy > 91%, producer accuracy > 85%, and frequency weighted intersection over union > 67%). The model is robust and was used to map agriculture from archive images (1990) and can be used in other areas with similar landscapes. The model can be updated by fine tuning using smaller, sparsely labelled datasets in the future. The generalised model was used to map the change in agricultural area in Helmand Province, showing the expansion of agricultural land into former desert areas. Training generalised deep learning models using data from both new and long-term EO programmes, with little or no requirement for fine tuning, is an exciting opportunity for automating image classification across datasets and through time that can improve our understanding of the environment. [ABSTRACT FROM AUTHOR]

The canopy chlorophyll content (CCC) provides valuable information about the crop growth status. CCC can be estimated using remote sensing techniques, such as through the red-edge-based chlorophyll index (CIRE). The empirical model between CCC and CIRE calibrated using the measured dataset lacks generality. Therefore, the semi-empirical model is a better choice, which is calibrated on the physical model simulations. However, the effect of parameter settings of physical models on semi-empirical models is not clear. This study first investigated the effects of dry matter content (LMA) and mesophyll structural coefficient (Ns) on the CCC-CIRE relationships and then evaluated CCC estimation using the CIRE-based semi-empirical model calibrated on simulated datasets with different ranges of LMA and Ns. The results showed that the relationships between CCC and CIRE were sensitive to Ns and LMA. Therefore, after considering the prior information of Ns (1.0–1.5) and LMA (20–80 g m−2) for the crop, the best estimation of CCC was obtained with an R2 of 0.82 and an RMSE of 0.36 g m−2, which were substantially better than the model without considering the prior information (R2 = 0.40 and RMSE = 0.67 g m−2). These findings improved our understanding of CCC estimation using the semi-empirical model and would facilitate the accurate mapping of CCC for agricultural management. [ABSTRACT FROM AUTHOR]

Spatial modeling is one of the important tools in the geographic information systems environment that is used to show the causal relationships between the spatial variables of various phenomena. (overlay) to get accurate results. The main idea is based on building a geographic information base (Geo database) that organizes data on the phenomenon of the study area represented by environmental factors and agricultural crops in the form of layers so that each layer represents a specific topic, the aim of which is to prepare a spatial modeling map of agricultural land from matching each of the (structure) maps geology, elevations, slope, slope direction, soil, soil productive capacity) after studying and analyzing each one separately using Model Builder in ARC GIS 10.8.2 program to find out the best suitable places for agricultural crops in Al-Hamdaniya district and compare them with the crops actually planted for the year 2021. The study showed that the agricultural crops grown in Al-Hamdaniya district are located within the very good and good areas for cultivation, while there is little or no cultivation in the areas that are not suitable for cultivation. [ABSTRACT FROM AUTHOR]

Agricultural production plays an important role in the global economy, contributing greatly to the supply of food resources and human well-being. Mapping the areas of agricultural coverage and calculating their respective average annual production values is useful for orienting food security strategies, in a context of climate change and a growing global population. Despite their importance, today no complete and updated maps exist for all types of crops. To provide this critical data lack, we have harmonized statistical datasets at a regional level of detail across Europe (EU-27) to develop a unified dataset consisting of the best available data in the context of agricultural monitoring activities carried out by the CREA. This document illustrates the methodologies adopted to create an harmonized database and an open and easy-to-use cartographic atlas of agricultural area and production, based on accessible, interoperable and reusable data available from Eurostat. The availability of detailed maps of area and crop production at a regional level can help optimize a wide range of agricultural monitoring, ranging from early warning on phytopathological conditions, to assessment of crop conditions, to production forecasts and the assessment of damage caused by extreme weather events, agricultural statistics, agricultural insurance and climate mitigation and adaptation actions. [ABSTRACT FROM AUTHOR]

Rapid and accurate agricultural parcel mapping from high-resolution remote sensing imagery is fundamental to precision agriculture for smallholder farming systems. However, due to the narrow and small-size parcels, and the significant spatio-spectral variability, the existing two-stage segmentation methods cannot extract individual parcels automatically. In this article, the end-to-end vectorization of smallholder agricultural parcel boundaries (E2EVAP) framework is proposed for extracting the vertices of each parcel boundary individually in smallholder farming regions, where the semantic-contour interaction and topological loss through hierarchical instance representation (called "channel to instance") are designed for aggregating the foreground features and jointly establishing the topological relationship between instances to alleviate the topological overlap between parcel objects. Vertex shift correction based on the deep attention corner snake module guided by the parcel boundaries is also proposed to adaptively correct the boundary vertex location shift of large-scale irregular parcels. The comprehensive experimental results obtained on the iFLYTEK public agricultural parcel dataset confirm that E2EVAP shows a superior performance (with a mask mAP of 0.335 and a boundary mAP of 0.201), compared with the pixel-based (such as Mask R-CNN, HTC, and SOLOv2) and contour-based (such as DeepSnake and E2EC) benchmark methods. We believe that E2EVAP has the potential to be widely used for accurate vectorization mapping of agricultural parcels in smallholder farming areas such as South Africa and southern China. The code of E2EVAP is at https://github.com/YangPanHZAU/E2EVAP. [ABSTRACT FROM AUTHOR]

The study of changes in grassland was conducted on the territory of individual counties of the West Pomeranian Voivodship. Based on data collected in the Valorisation of the Agricultural Production Space of Poland and spatial data (an electronic 1:5000 soil and agricultural map) the basic information on the use of agricultural land in the 70s of the 20th century. Changes in use over time were analysed based on data the Statistics Poland (Pol.: Główny Urząd Statystyczny -- GUS), General Geographic Database and The Agency for Restructuring and Modernisation of Agriculture Crop Register (Pol.: Agencja Restrukturyzacji i Modernizacji Rolnictwa -- ARiMR). The studies showed that in the West Pomeranian Voivodship, a successive decrease in the share of permanent grasslands (0.9%) and arable lands (1.4%) is observed. There is a spatial variation in the share of permanent grassland in the province's counties, with a distinction between northern and southern counties. The main direction of change in grassland area is arable land, which applies to counties with the highest share of permanent grassland (PG) and counties with a high share of the best soils and forest direction. Land quality was the main factor determining the direction of grassland changes, with the best PG converted to arable land or for investment purposes. In contrast, the weakest were converted to forestry or spontaneously wooded through abandonment. The main reasons for changes in grassland areas in the West Pomeranian Voivodship were: conversion of use to arable land, afforestation of grasslands, land use for urban planning purposes, leaving land fallow and conversion for constructing fishponds. [ABSTRACT FROM AUTHOR]

In this research, we employed the convolutional classifiers, namely Fuzzy Local Information c-Means (FLICM) and Adaptive Fuzzy Local Information c-Means (ADFLICM), to classify and map chickpea agricultural fields in the Nagaur region of Rajasthan. This classification was performed using Planet Scope and Sentinel-2 data. To streamline the processing of temporal data and reduce spectral information, we experimented with the Normalized Difference Vegetation Index (NDVI) indices. To optimize the performance of the FLICM and ADFLICM algorithms, we utilized the Mean Membership Difference (MMD) technique to determine the most suitable fuzziness value. We also estimated the area of the ground-truthed chickpea fields to compare the performance of both methods. For the FLICM and ADFLICM algorithms with Planet Scope data, the estimated areas were 8914 m2 and 11278 m2, respectively. The ADFLICM algorithm was also applied to classify Sentinel-2 data, demonstrating its effectiveness with medium-resolution data, and the estimated area for Sentinel-2 data was 10633 m2. Finally, we calculated the Root Mean Square Error (RMSE) for the ADFLICM algorithm, which was found to be 0.075194, using Planet Scope satellite data as the reference dataset and Sentinel-2 satellite data as the classified dataset. [ABSTRACT FROM AUTHOR]

Soil moisture maps are essential for hydrological, agricultural and risk assessment applications. To best meet these requirements, it is essential to develop soil moisture products at high spatial resolution, which is now made possible using the free Sentinel-1 (S1) SAR (Synthetic Aperture Radar) data. Some soil moisture retrieval techniques using S1 data relied on the use of a priori weather information in order to increase the precision of soil moisture estimates, which required access to a weather-forecasting framework. This paper presents an improved and fully autonomous solution for high-resolution soil moisture mapping in bare agricultural areas. The proposed solution derives a priori weather information directly from the original Sentinel images, thus bypassing the need for a weather forecasting framework. For soil moisture estimation, the neural network technique was implemented to ensure the optimum integration of radar information. The neural networks were trained using synthetic data generated by the modified Integral Equation Model (IEM) model and validated on real data from two study sites in France and Tunisia. The main findings showed that the use of a radar signal averaged over grids of a few km 2 in addition to radar signal at plot scale instead of a priori weather information provides good soil moisture estimations. The accuracy is even slightly better compared to the accuracy obtained using a priori weather information. [ABSTRACT FROM AUTHOR]

Agricultural intensification practices have been adopted in the Brazilian savanna (Cerrado), mainly in the transition between Cerrado and the Amazon Forest, to increase productivity while reducing pressure for new land clearing. Due to the growing demand for more sustainable practices, more accurate information on geospatial monitoring is required. Remote sensing products and artificial intelligence models for pixel-by-pixel classification have great potential. Therefore, we developed a methodological framework with spectral indices (Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Soil-Adjusted Vegetation Index (SAVI)) derived from the Harmonized Landsat Sentinel-2 (HLS) and machine learning algorithms (Random Forest (RF), Artificial Neural Networks (ANNs), and Extreme Gradient Boosting (XGBoost)) to map agricultural intensification considering three hierarchical levels, i.e., temporary crops (level 1), the number of crop cycles (level 2), and the crop types from the second season in double-crop systems (level 3) in the 2021–2022 crop growing season in the municipality of Sorriso, Mato Grosso State, Brazil. All models were statistically similar, with an overall accuracy between 85 and 99%. The NDVI was the most suitable index for discriminating cultures at all hierarchical levels. The RF-NDVI combination mapped best at level 1, while at levels 2 and 3, the best model was XGBoost-NDVI. Our results indicate the great potential of combining HLS data and machine learning to provide accurate geospatial information for decision-makers in monitoring agricultural intensification, with an aim toward the sustainable development of agriculture. [ABSTRACT FROM AUTHOR]

Vision-based localization and mapping for agricultural environment is challenging due to unstructured scene with less distinguishable landmarks and more repetitive textures, scale drift caused by wide and distant view, illumination variations and abrupt movements. To address these challenges, we propose a scale-aware stereo direct odometry with online photometric calibration for agricultural environment. We first propose a scale-aware stereo direct image alignment method to explicitly capture the scale at tracking stage. Then we optimize the scale and affine brightness parameters of stereo frames to keep the scale consistency in the sliding window. Furthermore, we incorporate online photometric calibration to resist illumination changes and exposure differences of image sequence. The proposed system achieves new state-of-the-art results on two public agricultural datasets, demonstrating its effectiveness in challenging agricultural environments. [ABSTRACT FROM AUTHOR]

Mobile laser systems have been used for close-range applications, such as agricultural management, due to the high penetrability of the laser beam enabling a unique 3D representation of the plant structure. However, some challenges remain in generating 3D mapping in agricultural environments. For instance, orange orchards are composed of trees with dense canopies which the laser scanner cannot penetrate for stem mapping. Therefore, most of the laser point clouds are pulses reflected from leaves and ground. This paper analyses approaches to detect keypoints, extract features and register the point clouds acquired with a laser scanner in an orange orchard. Three keypoints extraction methods (Uniform Sampling – US, 3D Harris and 3D Intrinsic Shape Signatures – 3D ISS), SHOT feature extraction and global registration methods (ICP and CPD) were evaluated. The results showed that improvements are still necessary for point cloud registration for orange orchards. In feature-based matching methods, an approach to filter the mismatches is needed to improve the estimation of the translation and rotation parameters. In the global registration methods, initial values of the trajectory are needed for ICP. CPD achieved good results for five sequence scans without initial values. [ABSTRACT FROM AUTHOR]

The global expansion of agricultural land is a leading driver of climate change and biodiversity loss. However, the spatial resolution of current global land change models is relatively coarse, which limits environmental impact assessments. To address this issue, we developed global maps representing the potential for conversion into agricultural land at a resolution of 10 arc-seconds (approximately 300 m at the equator). We created the maps using artificial neural network (ANN) models relating locations of recent past conversions (2007–2020) into one of three cropland categories (cropland only, mosaics with >50% crops, and mosaics with <50% crops) to various predictor variables reflecting topography, climate, soil, and accessibility. Cross-validation of the models indicated good performance with area under the curve (AUC) values of 0.88–0.93. Hindcasting of the models from 1992 to 2006 revealed a similar high performance (AUC of 0.83–0.91), indicating that our maps provide representative estimates of current agricultural conversion potential provided that the drivers underlying agricultural expansion patterns remain the same. Our maps can be used to downscale projections of global land change models to more fine-grained patterns of future agricultural expansion, which is an asset for global environmental assessments. [ABSTRACT FROM AUTHOR]

The first important step toward ending hunger is sustainable agriculture, which is a vital component of the 2030 Agenda. In this study, auxiliary variables from the 2011 Population Census are combined with data from the 2016 Community Survey to develop and apply a hierarchical Bayes (HB) small area estimation approach for estimating the local-level households engaged in agriculture. A generalized variance function was used to reduce extreme proportions and noisy survey variances. The deviance information criterion (DIC) preferred the mixed logistic model with known sampling variance over the other two models (Fay-Herriot model and mixed log-normal model). For almost all local municipalities in South Africa, the proposed HB estimates outperform survey-based estimates in terms of root mean squared error (MSE) and coefficient of variation (CV). Indeed, information on local-level agricultural households can help governments evaluate programs that support agricultural households. [ABSTRACT FROM AUTHOR]

We investigated the utility of using synchronous measurements to create nitrogen (N) emission and retention maps of agricultural areas. Total N (TN) emissions from agricultural areas in three different Danish pilot catchments (1800–3737 ha) and within sub-catchments (100–1200 ha) were determined by a source apportionment approach. Intensive daily (main gauging stations) and fortnightly (synchronous stations) monitoring of discharge, TN, and nitrate-N (NO3-N) concentrations was conducted for two years. The groundwater N retention was calculated as the difference between a model-calculated NO3-N leaching from agricultural fields and the calculated agricultural N emission. The average annual N leaching and N emission in the three catchments amounted to 68, 48, and 58 kg N/ha and 6, 30, and 40 kg N/ha, respectively. The N retention in groundwater in the three catchments, calculated based on either TN or NO3-N emissions, amounted to 26 and 44%, 44 and 57%, and 93 and 97%, respectively, with large variations within two of the main catchments. From this study, we conclude that synchronous measurements in streams provide a good opportunity for developing local N emission and N retention maps. However, NO3-N should be used when dealing with N retention calculation at the finer resolution scale of 100–300 ha catchments. [ABSTRACT FROM AUTHOR]

Agricultural production is the basic pillar of food security, but the type of agricultural crops and the quantity of their production are greatly and clearly affected by natural factors of all kinds, and man has evolved in his adaptation to natural conditions in order to serve his various activities, including agricultural activity, but his control in these conditions is limited and still The natural conditions (geological structure, topography, climate, soil, and water resources) are specific to the type and location of the various agricultural activities. The uses of agricultural land are subject to natural controls, since the farmer is in direct contact with it as long as he practices agricultural activity in the open. The current research examined the study of the most important natural factors affecting the production of agricultural crops in Qayyarah district represented by the geological structure and surface appearance represented by (slope) and (elevations) in addition to the soil and its productive capacity and linking it with the production of agricultural crops for the aforementioned region for the year 2021 using geographic information systems techniques. And then building a model for spatial fit and agricultural crop production for the purpose of knowing and identifying the best spatial fit places using spatial analysis tools for the data in Arc GIS V10.8.2 to be of sure benefit as it carries new information extracted or extracted from the spatial analysis. And then the process of matching between the agricultural crop maps with the spatial fit map that was produced. The study showed a close relationship between the natural factors in the study area and the amount of crops planted for the previously mentioned year. [ABSTRACT FROM AUTHOR]

Different initiatives for mapping land use and land cover using orbital remote sensing have been carried out with the aim of supporting agri-environmental planning in the Brazilian Savanna (Cerrado). However, the complex regional agricultural dynamics require more detail in the discrimination of agricultural uses, which requires time series of satellite images (SITS). This study aimed to evaluate how the integrated use of Landsat 8 Operational Land Imager (OLI) and Sentinel-2 Multispectral Instrument data can contribute to digital land use and land cover mapping in medium spatial resolution. The municipality of Balsas, a region of agricultural expansion in the Cerrado of Maranhão State and relevant economic and agro-environmental interest, was chosen as a study area to evaluate the performance of Random Forest classification models generated with data from Landsat 8, Sentinel-2, and the Harmonized Landsat Sentinel 2 (HLS). The MSI, HLS, and OLI models presented similar performance, with Global precision of 0.857, 0.844, and 0.832, and Kappa index of 0.819, 0.803, and 0.787, respectively. The T-Test indicated that these results are not statistically different. However, despite the models' performance, the final map created with the OLI data showed a high loss of information due to the absence of pixels in the time series. The new HLS data proved suitable for land use and coverage mapping, even on a large scale, at a resolution of 30 m, with precision and reduced processing time compared to the MSI data. These results can support decision-making in the planning and execution of new agricultural mapping of the Cerrado Biome. [ABSTRACT FROM AUTHOR]

Land use and land cover maps are basic tools for a detailed understanding of spatial organization patterns, an essential tool for agro-environmental management. This article aimed to compare and evaluate the accuracy of the digital classification of land use and land cover for the Cerrado of Maranhão, using OLI (Operational Terra Imager) images from Landsat-8 and MSI (Multispectral Instrument) from Sentinel2. The analyzed images are from 2021, which were pre-processed, segmented and classified using the Random Forest algorithm. The analyzes showed that both classifications were qualified as "very good", obtaining Kappa Index (0.706) and Global Accuracy (76%) for the OLI images and Kappa Index (0.775) and Global Accuracy (84%) for the images MSI. The results obtained can support the planning and execution of new agricultural mapping and monitoring in the Cerrado, positively influencing the decision-making of research groups. [ABSTRACT FROM AUTHOR]

Soil Particle Size Distribution (PSD) is a fundamental physical property that can affect soil hydraulic properties, soil structure characterization, and available water. Many models have been applied to define the PSD curve, but predicting the spatial distribution information of PSD has been rarely investigated. Therefore, the main objective of the current study was to predict soil texture fractions using the most accurate PSD models. First, the performance of 16 mathematical PSD models was evaluated. Then, Random Forest (RF) was used to determine the relationship between covariates (i.e., remote sensing and the digital elevation model) and georeferenced measurements of the best PSD models’ parameters. Results indicated that a PSD model may be acceptable for some particle diameters or even whole particles, but not necessarily be suitable for other particles. For example, in the estimation of sand content, the best model was Simple Lognormal, while the Fred-4p was the best model in the estimation of the clay fraction. Importantly, the Jaky model with only one parameter of P did a great job in predicting soil particle fractions. Further, the spatial distribution of clay, silt, and sand contents was accurately derived from the predicted map of P (R² for Sand = 0.86). Consequently, the current research indicated that the combination of PSD models and digital soil mapping techniques can be used to quantify the spatial distribution of the PSD curve in other similar agroclimatological regions. [ABSTRACT FROM AUTHOR]