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This article presents an enhanced convolutional neural network model, AgriFloodNet, to map flood-affected agricultural lands on remote sensing images. It consists of a dual patch convolutional neural network to effectively learn the permanent and flood water features from the bi-temporal Synthetic Aperture Radar (SAR) images of Sentinel-1 and the agricultural land features from the pre-flood cloud-free multispectral images (MS) of Sentinel-2. A decision-level fusion of the processing results of multisensory images is applied to create a change map to depict flooding in agricultural lands. The proposed AgriFloodNet shows superior performance in flood mapping on the images of agricultural lands from the SEN12-FLOOD dataset with an accuracy of 98.75%. In comparison, the single-sensor solutions give accuracies of 96.88% and 91.11% for SAR and MS images, respectively. Additionally, the model has been evaluated for a new flood event in Patna, India. It is estimated that 75% of the test site was covered with agricultural lands when the flood occurred, of which 61% was affected. [ABSTRACT FROM AUTHOR]

Agricultural environment mapping is the premise of the autonomous navigation of agricultural robots. Due to the undulating terrain and chaotic environment, it is challenging to accurately map the environmental maize field using existing LOAM (LiDAR odometry and mapping) methods. This paper proposes a LOAM method based on maize stalk semantic features for 6-DOF (degrees of freedom) pose estimation and field mapping with agricultural robots operating in a dynamic environment. The piecewise plane fitting method filters the ground points for the complex farmland terrain. To eliminate the unstable factors in the environment, we introduce the semantic information of maize plants into the feature extraction. The regional growth method segments the maize stalk instances, the instances are parameterized to a line model, and the optimization method calculates the pose transformation. Finally, the mapping method corrects the drift error of the odometry and outputs the maize field map. This paper compares our method with the GICP and LOAM methods. The trajectory relative errors of our method are 0.88%, 0.96%, and 2.12%, respectively, better than other methods. At the same time, the map drawn by our method has less ghosting and clearer plant edges. The results show that our method is more robust and accurate than other methods due to the introduction of semantic information in the environment. The mapping of corn fields can be further used in precision agriculture. [ABSTRACT FROM AUTHOR]

A compact and low-weight LIDAR instrument has been developed for laser-induced fluorescence spectroscopy sensing of maize fields. Fluorescence LIDAR had to be installed on a small industrial drone so that its mass was <2 kg and power consumption was <5 W. The LIDAR instrument utilized a continuous wave diode laser (405 nm, 150 mW) for inducing fluorescence and a small spectrometer for backscattered photons acquisition. For field testing, the LIDAR instrument was installed on a quadcopter for remote sensing of plants in a maize field in three periods of the plant's life. The obtained fluorescence signal maps have demonstrated that the average chlorophyll content is rather non-uniform over the field and tends to increase through the plant vegetation cycle. Field tests proved the feasibility and perspectives of autonomous LIDAR sensing of agricultural fields from drones for the detection and location of plants under stress. [ABSTRACT FROM AUTHOR]

Copyright of SDU Journal of Engineering Sciences & Design / Mühendislik Bilimleri ve Tasarım Dergisi is the property of Journal of Engineering Sciences & Design 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 impact of Microplastic Pollution (MPs) on human health, the environment, economy, and society has been previously investigated. However, there is a lack of comprehensive understanding regarding specific areas that require urgent measures to address marine pollution. The accumulation of MPs in Mediterranean coastal environments is particularly noteworthy. This is attributed to the region's economic reliance on tourism and the decline of popular tourist destinations caused by the presence of coastal and marine waste. The objective of the present research was to conduct a strategic analysis and mapping of MPs from soil samples taken from rural areas of Cyprus. Within the framework of the present research, a general picture of the status of MP pollution in areas covering significant percentages in the domestic supply of fruits and vegetables was obtained. The survey indicated the presence of more than 70% of MPs in crops at a concentration of up to 1.5 %. As a result of this research, the need to highlight the importance of the rational use of plastics and proper management to mitigate pollution is a primary concern. The rational separation of materials for recycling, information, reuse of materials, processing, and an increase in the number of recycling bins in public places are considered urgent. Cooperation between the state, institutions and industry must be based on the protection of people and the environment. [Display omitted] • Collection of soil samples for extraction of MPs from 34 locations across cultivated plots of Cyprus. • Drying of samples according to EN 933: Part 1 methodology and particle size analysis using the guidelines of BS1377 - Part 2. • Extraction of MPs from samples using a saturated sodium chloride (NaCl) solution with a concentration of 1.2 g/ml. • Presence of MPs in more than 70% of crop samples collected, specifically in topsoil collections. • Strategic planning proposed for the reduction of MPs in agriculture soil, with most important factor the education of farmers. [ABSTRACT FROM AUTHOR]

Conservation tillage is among the most widely adopted sustainable cropland management practices to enhance soil organic carbon (SOC) storage. The overall positive effect of conservation tillage on SOC sequestration has been well-documented through paired-plot field trials, but limited knowledge is available on the spatial heterogeneity of their relationship. To address this issue, effective mapping tools are needed to capture the field-specific patterns of SOC and tillage practice. Here, we developed an integrated remote sensing approach whereby the spatiotemporal evolution of tillage practice through 2017–2021 and the field-specific SOC pattern in 2021 were individually mapped in an agricultural region (7000 km2) of northeast China. Topsoil sampling was conducted in 287 georeferenced fields for SOC measurement and the tillage practice was also surveyed in 257 fields. Sentinel-2 bare soil spectra, together with SOC-related spectral indices, were used to establish a random forest (RF) model for parcel-based SOC mapping. The shortwave infrared angle (SWIRA) index, sensitive to varying crop residue cover but robust to soil moisture disturbance, was used as a proxy to quantify the expansion of conservation tillage over time. Results show that the spectra-based RF model yielded accurate SOC prediction (R2: 0.81; RMSE: 1.72 g/kg) and the resulting map demonstrated clear inter-field variations. The SWIRA index was found effective in revealing a drastic increase in the number of conservation tillage fields during the investigation period. Summing the temporal maps of SWIRA, SWIRA sum was indeed a significant factor controlling the SOC variation at the regional scale. Partial dependence analysis further showed an increasing marginal effect of cumulative conservation tillage on SOC. From the spatial perspective, geographically weighted regression analysis identified the hotspots where SWIRA sum was most influential. Closer inspection into the hotspots indicated that areas with low SOC levels reacted more strongly to increasing SWIRA sum. This study provided a methodological framework for the concurrent mapping of SOC and tillage practice through optical remote sensing, thus facilitating a spatially explicit understanding of how SOC storage could be enhanced. • An integrated remote sensing approach was developed to map SOC and conservation tillage. • Sentinel-2 bare soil imaging of exposed croplands reflected inter-field variation in SOC. • Shortwave infrared angle index demonstrated the expansion of conservation tillage over time. • A positive but spatially heterogeneous effect of cumulative conservation tillage on SOC. • SOC-depleted soils more positively responded to conservation tillage. [ABSTRACT FROM AUTHOR]

The Italian Heritage Museum, located on the second floor of a building at the Big Fresno Fair compound in Fresno, California, showcases the Italian culture and history of Fresno County. The museum is free and ADA accessible, and visitors can request a hand-held audio device for a self-guided tour. The exhibit features touchscreen panels that cover various topics such as agriculture, arts and entertainment, food and dining, and government. The museum also includes stories of Italian American servicemen and women, as well as agricultural advancements and maps of Fresno's Little Italy. The exhibit is compact but provides plenty of interactive and user-friendly information. [Extracted from the article]

Due to our increasing understanding of the role the surrounding landscape plays in ecological processes, a detailed characterization of land cover, including both agricultural and natural habitats, is ever more important for both researchers and conservation practitioners. Unfortunately, in the United States, different types of land cover data are split across thematic datasets that emphasize agricultural or natural vegetation, but not both. To address this data gap and reduce duplicative efforts in geospatial processing, we merged two major datasets, the LANDFIRE National Vegetation Classification (NVC) and USDA-NASS Cropland Data Layer (CDL), to produce an integrated land cover map. Our workflow leveraged strengths of the NVC and the CDL to produce detailed rasters comprising both agricultural and natural land-cover classes. We generated these maps for each year from 2012-2021 for the conterminous United States, quantified agreement between input layers and accuracy of our merged product and published the complete workflow necessary to update these data. In our validation analyses, we found that approximately 5.5% of NVC agricultural pixels conflicted with the CDL, but we resolved most of these conflicts based on surrounding agricultural land, leaving only 0.6% of agricultural pixels unresolved in our merged product. These ready-to-use rasters characterizing both agricultural and natural land cover will be widely useful in environmental research and management. [ABSTRACT FROM AUTHOR]

Since the 1940s, thousands of tombs with murals from the periods of Sixteen Kingdoms in Wei and Jin Dynasties have been discovered in the west of the Yellow River. A large number of mural bricks with agricultural images were unearthed in these mural tombs. According to the statistics, there are 143 bricks, which were mainly distributed in Gaotai, Jiayuguan, Dunhuang areas, and few in Jinchang, Yumen and other places. This paper discusses the agricultural tools seen in the agricultural map, analyzing their function and shape, so as to understand the agricultural production and farming techniques in the west of the Yellow River at that time which would reflect people's daily farming life. [ABSTRACT FROM AUTHOR]

Experiences from 30 years of geochemical mapping at the (sub)continental scale in Europe using a wide range of different sample media are reviewed and discussed with a focus on the most recent GEMAS (GEochemical Mapping of Agricultural Soils) project. Comparing results from the different surveys it is possible to come to conclusions as to how geochemical surveys at the continental scale could best be designed. High analytical quality and lowest possible detection limits are key requirements. In Europe good outcomes were achieved with the <2 mm fraction of soil samples and aqua regia extraction. Focus should be on high quality of sampling and analyses and more determined parameters rather than on more samples. The sample density of 1 site/2500 km2 provides a good overview of the processes governing geochemistry at the continental scale. Results should be extensively published by the project team to allow the dataset to be known and utilized by the wider scientific community. Thematic collection: This article is part of the Continental-scale geochemical mapping collection available at: https://www.lyellcollection.org/topic/collections/continental-scale-geochemical-mapping [ABSTRACT FROM AUTHOR]

Parcel-level cropland maps are an essential data source for crop yield estimation, precision agriculture, and many other agronomy applications. Here, we proposed a rice field mapping approach that combines agricultural field boundary extraction with fine-resolution satellite images and pixel-wise cropland classification with Sentinel-1 time series SAR (Synthetic Aperture Radar) imagery. The agricultural field boundaries were delineated by image segmentation using U-net-based fully convolutional network (FCN) models. Meanwhile, a simple decision-tree classifier was developed based on rice phenology traits to extract rice pixels with time series SAR imagery. Agricultural fields were then classified as rice or non-rice by majority voting from pixel-wise classification results. The evaluation indicated that SeresNet34, as the backbone of the U-net model, had the best performance in agricultural field extraction with an IoU (Intersection over Union) of 0.801 compared to the simple U-net and ResNet-based U-net. The combination of agricultural field maps with the rice pixel detection model showed promising improvement in the accuracy and resolution of rice mapping. The produced rice field map had an IoU score of 0.953, while the User's Accuracy and Producer's Accuracy of pixel-wise rice field mapping were 0.824 and 0.816, respectively. The proposed model combination scheme merely requires a simple pixel-wise cropland classification model that incorporates the agricultural field mapping results to produce high-accuracy and high-resolution cropland maps. [ABSTRACT FROM AUTHOR]

Iraq owns agricultural resources, such as agricultural land, water, and manpower. The research aims to reveal Iraq's ability to secure the food needs of its population by exploiting this agricultural potential and reviews the obstacles that hinder agricultural development in the country. The research reached a number of conclusions, including Iraq's agricultural resources are insufficient to meet the food needs of the population in light of the prevailing productivity. The research also put forward a number of recommendations, including setting a comprehensive agricultural map for all regions of Iraq, for what should be produced, according to the available land and water, and the relative importance of each product. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Al-Frahids Arts is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) 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.)

This article reports on the profiles of the National Agricultural Research Systems (NARS) in the three Maghreb countries: Algeria, Morocco and Tunisia, through their research policies, human resources, governance, funding and scientific production. A bibliometric study was carried out for the three selected countries based on the Scopus database covering the period between 2005 and 2019. The research shows the distinctive case of Tunisian growth of scientific production compared to the other two countries; it also stresses the universities' emergence as a major research actor, thus challenging the predominance of the public mission-oriented institutions dedicated to agriculture and veterinary sciences. Finally, it indicates significant differences between the three countries concerning governance and funding, and human resources. Overall, the NARS in the three countries are still very fragile, and a need for significant changes in the research policy making is certainly required to overcome these shortcomings. [ABSTRACT FROM AUTHOR]

Simultaneous Localization and Mapping (SLAM) is a process to use multiple sensors to position an unmanned mobile vehicle without previous knowledge of the environment, and meanwhile construct a map of this environment for the further applications. Over the past three decades, SLAM has been intensively researched and widely applied in mobile robot control and unmanned vehicle navigation. SLAM technology has demonstrated a great potential in autonomously navigating the mobile robot and simultaneously reconstructing the three‐dimensional (3D) information of surrounding environment. With the vigorous driving of sensor technology and 3D reconstruction algorithms, many attempts have been conducted to propose novel systems and algorithms combined with different sensors to solve the SLAM problem. Notably, SLAM has been extended to various aspects of agriculture involved with autonomous navigation, 3D mapping, field monitoring, and intelligent spraying. This paper focuses on the recent developments and applications of SLAM, particularly in complex and unstructured agricultural environment. A detailed summary of the developments of SLAM is given from three main fundamental types: light detection and ranging SLAM, Visual SLAM, and Sensor Fusion SLAM, and we also discuss the applications and prospects of SLAM technology in agricultural mapping, agricultural navigation, and precise automatic agriculture. Particular attention has been paid to the SLAM sensors, systems, and algorithms applied in agricultural tasks. Additionally, the challenges and future trends of SLAM are reported. [ABSTRACT FROM AUTHOR]

Spraying of pesticides manually causes many side effects to individuals life. The exposure can cause many health effects. In the current study an agriculture drone is designed and fabricated for mapping an agricultural field, spraying pesticides and monitoring crop health. This UAV reduces the time and labor cost. The Agri drone is capable of mapping agricultural field of different terrains and spray pesticides for different kinds of plants as per requirementin autonomous mode via an Agri Assistant software.The UAV is tested for its range & endurance and has a flight time of about 13 minutes. The UAV is also capable of eliminating 90% of pesticides by effective spraying. Such UAVs are useful for various purposes other than agricultural spraying such as mapping and sanitization. [ABSTRACT FROM AUTHOR]

Visible soil erosion processes began to develop in the territory of Moldova around the second half of the 19th century. For a long time, their quantitative indicators of its spatial spread were not due to the lack of such cartographic data. After the early 1950s, soil mapping in agricultural units and administrative districts. Fragmented relief is one of the indispensable conditions for the development of slope processes, including erosion processes. Anthropogenic impact plays an important role in increasing the areas affected by erosion. The purpose of this paper is to examine the content and distribution of humus in eroded soils in the Republic of Moldova to assess their fertility. As a result of the evaluation of the humus losses on the three degrees of erosion, it was found that, for chernozems, they correspond approximately to the row: 20, 40 and 60%. Some trans-boundary passages are observed in carbonate chernozems - being the least resistant to erosion (28, 48 and 69%) and an insignificant variation are characteristic of typical chernozems and to a lesser extent leached (17, 39, 60 and 16, 38, 64%), but in soils with a high erodibility these figures of humus losses, almost equal. Brown soils are related to erosion on average for chernozems approximately 20, 40, 65%. We can state that, exclusively, it presents the gray soils in which the degree of erosion, weak and moderate, is distinguished by lower humus losses. If we refer to the whole, then the series of 20, 40 and 60% humus losses in the eroded soils compared to the standard, we are quantitatively characterized by their real degradation effect, quite terrible, caused by erosion. But from a practical point of view it is essential, because these figures characterize the real humus degradation of the underlying soil layer (50-100 cm), which has a significance for assessing their fertility especially for perennial crops, which develops a deep root system. [ABSTRACT FROM AUTHOR]

Brazil has established itself as one of the world leaders in food production. Different types of remote sensing mapping techniques have been undertaken to support rural planning in the country. However, due to the complex dynamics of Brazilian agriculture, especially in the Cerrado biome (tropical savanna), there is a need for more feasible crop discrimination and monitoring initiatives, which require a consistent time series of remote sensing data at medium meter and potentially up to 3 day Landsat 8 and Sentinel-2 satellite time series, minimizing the cloud cover limitations for rainfed agricultural monitoring. This paper aims to explore the potential of the Harmonized Landsat 8 Sentinel-2 (HLS) data cube to map agricultural landscapes in the Brazilian Cerrado. The HLS multispectral bands from 27 scenes with less than 10% cloud cover, from October 2020 to September 2021, encompassing one entire crop growing season, were processed by the Random Forest algorithm to produce a map with four land use/cover classes (annual crops, sugarcane, renovated sugarcane fields, cultivated pastures, and native Cerrado). We performed accuracy assessment through 10-fold cross-validation and confusion matrix analyses. The results showed a high level of overall accuracy and Kappa coefficient, both with 99%, as well as high user's and producer's accuracies of at least 99%. The HLS dataset has been continuously improved, showing very promising results for rainfed agricultural mapping and monitoring. [ABSTRACT FROM AUTHOR]

About a century ago, B was recognised as an essential element for the normal growth of plants and terrestrial organisms. Limitations for plant development have been recognised in agricultural systems, particularly in highly weathered soil. Boron is rarely analysed in whole rock or soil analysis, as it requires specific analytical techniques. It is often determined, after partial extraction (aqua regia or Ca Cl), usually on a limited number of samples. Many more questions than answers exist about the environmental behaviour of B. We present B contents in agricultural soil samples (0–10 cm) collected in 33 European countries (5.6 million km2) during the GEMAS (GEochemical Mapping of Agricultural and grazing land Soil) continental-scale project. The B content, determined by ICP-MS following hot aqua regia extraction, varies in European agricultural soil from 0.5 to 49 mg/kg (median 2.42 mg/kg, n = 2108), which is somewhat similar to total B estimates for the Upper Continental Crust (17–47 mg/kg). Its spatial distribution in agricultural soil shows a patchy pattern with low values in regions with granitic bedrock and high contents in soil formed over limestone and in volcanic areas. Boron geochemical behaviour in soil is strongly dependent on other factors such as pH, CEC, presence of organic matter, clay and secondary oxides and hydroxides. Boron geochemical mapping at the continental scale in arable soil allows investigations of plant health, i.e., the beneficial and adverse effects due to the nutritional status of boron. • B determined by ICP-MS following strong acid extraction (aqua regia) • Agricultural soil dataset of Europe (GEMAS) allows studying the large scale variations. • Distribution of B in European soils is clearly related to bedrock geology and weathering. • B dependent of factors like pH, CEC, presence of organic matter, clay and oxides-hydroxides [ABSTRACT FROM AUTHOR]

Soil water content (SWC) is significant for understanding and evaluating the conditions of soils and plants. Since traditional methods such as time domain reflectometry (TDR) and neutron probes have significant drawbacks such as limitations in spatial resolution, detection depth, efficiency, and non-destruction, ground penetrating radar (GPR) has become a potential method in SWC estimation. Many features extracted from GPR data in the time and frequency domain have been proven to be sensitive to the SWC and can further achieve the estimation of it. However, the methods based on these features are easy to be interfered with by noise and the heterogeneity in soils. This article aims to solve this problem by including more features and integrating these features for a joint estimation. Firstly, we study the relationships between SWC and seven features extracted from GPR data. Consequently, we propose to include new features, i.e. the loss tangent feature and the time-frequency features, in the SWC inversion. Secondly, we achieve the multi-feature ensemble learning based on the Adaboost R. method, which largely enhances the accuracy of SWC inversions compared to the single-feature estimations. During the numerical test, we establish the stochastic medium to model the heterogeneity in the real soil. The test verifies the effectiveness and the robustness of the proposed method. Finally, a field experiment is performed on the transition zone of no-tillage and deep-ploughing croplands. A 2-D SWC map is obtained which distinctly presents the SWC difference between the two regions. Our study provides a new approach to improve the accuracy of SWC estimation using GPR. • A multi-feature ensemble learning method is proposed to inverse soil water content. • Loss tangent is used for the inversion of soil water content. • A two-dimensional soil water content map of a cropland is obtained. • Soil water content in no-tillage and deep-ploughing zones are strongly different. [ABSTRACT FROM AUTHOR]

Accurate and timely acquisition of potato spatial distribution is crucial for growth monitoring and yield forecasting. Currently, prior knowledge-based methods are very simple and efficient without collecting reference data, but their mapping accuracy in complex cropping planting systems is unsatisfactory. Deep learning approaches have the ability to automatically learn multilevel spatial and spectral features. However, these approaches still face particular challenges in improving potato mapping accuracy due to the limitations of adaptive features and the scarcity of ground samples. This study proposed a potato mapping method integrating a multi-temporal deep learning network and prior knowledge to overcome the shortcomings of the two methods. Specifically, a novel deep learning network, spectral-spatial–temporal ensemble network (SSTEN), was developed for smallholder potato area mapping by embedding unique prior knowledge. To obtain multi-year potato mapping results, we proposed a concise and efficient temporal transfer framework that combines sample generation, SSTEN transfer learning, and agriculture statistics to produce highly accurate potato maps for sample-free years. Independent ground validation data from 2021 to 2022 suggested that the SSTEN achieved an overall accuracy (OA), F1 and Kappa of 91.65%, 92.67% and 0.82, respectively, and its average overall accuracy was superior to other methods. Potato planting areas obtained by SSTEN were highly consistent with the corresponding agricultural statistical area (R2 > 0.87). The results showed that incorporating prior knowledge into SSTEN could improve the accuracy of potato mapping. We also investigated the potential of the proposed temporal transfer method for potato mapping. Our transfer method yielded a high OA of 86.46% and an area error (AE) of 7.94%. The study potentially provides technical references for smallholder potato mapping in similar agricultural regions worldwide. [ABSTRACT FROM AUTHOR]

This document provides a summary of various topics related to air quality and climate change. It discusses the short-term impacts of air pollution, particularly in Australia, where pollution levels can spike due to factors such as bushfires, dust storms, industrial emissions, and vehicles. The document also highlights the negative effects of air pollution on fertility, including decreased chances of a successful live birth after IVF treatment. Additionally, it mentions New Zealand's involvement in the MethaneSAT satellite mission, which aims to reduce methane emissions worldwide. The document touches on the increasing levels of nitrous oxide emissions and their contribution to climate change and ozone depletion. Lastly, it mentions the downward trend in Australian snow seasons due to rising temperatures and decreasing precipitation caused by greenhouse gas emissions. The document concludes by mentioning an upcoming indoor air quality conference in Australia. [Extracted from the article]

[Display omitted] • VVP maize index was developed based on the large-leaf dominated canopies and high photosynthetic efficiency. • The VVP index can be applied across different years and regions without retraining. • The national-scale 10 m annual maize maps were developed for China and the contiguous US. • Maize area declined in 2020 due to crop planting structure adjustment in the "Sickle Bend" region of China. • Maize expansion in China after 2020 occurred mainly in regions with ever-planted. Accurate and continuous maps of maize distribution are essential for food security and sustainable agricultural development. However, there are no continuous national-scale and fine-resolution maize maps and explicit updated information on the spatiotemporal dynamics of maize for most countries. Maize mapping at the national scale is challenging due to the spectral heterogeneity caused by crop growth conditions, cropping patterns, and inter-annual variations. To this end, this study developed a novel crop index-based algorithm for national-scale maize mapping. Compared to other crops, maize is characterized by large-leaf-dominated canopies and high photosynthetic efficiency. Maize shows significant changes in chlorophyll and anthocyanin content. Therefore, a robust maize index was established by exploring the temporal Variation of the Vegetation-Pigment index (VVP) during the growing period. A simple decision rule was coded on the Google Earth Engine (GEE) platform, which was used for maize mapping based on the Sentinel-2 time series in China and the contiguous United States (US) from 2018 to 2022. The national-scale 10 m annual maize maps for China and the contiguous US were developed and in good agreement with the corresponding agricultural statistics data for many years (R2 > 0.94) and 9,412 reference points (overall accuracy of 90.09 %). Compared with simply applying the vegetation index, the VVP index took account of spectral heterogeneity caused by variations in crop growth conditions, cropping patterns, and inter-annual, and the omission error of maize was reduced by over 20 %. Moreover, the VVP index can significantly improve the spatial transferability of the Random Forest (RF) classifier. The first 10 m annual maize maps for China revealed that the planted area trend decreased and then increased from 2018 to 2022. The year 2020 was the turning point. The maize planted area consisted of 68 % single maize and 32 % double cropping with maize in 2020, with the northern boundary for double cropping with maize in the Yanshan Mountains. The maize planted area in China consistently decreased by 39,352 km2 (about 9 %) from 2018 to 2020. This is mainly due to the adjustment of the maize-planted structure in the "Sickle Bend" region of China (the "Sickle Bend" policy). However, the maize planted area gradually recovered from 2020 to 2022, primarily concentrated in regions with ever-planted. This study will provide essential information for cropping structure adjustment and related agricultural policy formulation and contribute to sustainable agricultural development by mapping maize from a national to a global scale. [ABSTRACT FROM AUTHOR]

• The ISP method allows for gathering a large number of historical training samples. • The SRVI values of the same crop planted in neighboring years range from 0.91 and 1.2. • A distinct northward expansion trend is evident for all four major crops, in Heilongjiang Province, China. Mapping agricultural information such as cropping area and type is of great significance for land use and food security and a common method to retrieve such information is satellite imagery classification. The current image classification techniques require a statistically large number of training samples to obtain representative spectral signatures, ideally collected during the satellite overpass time/date. Although this prerequisite sounds simple and easy in principle, it is extremely challenging and sometimes impossible when classifying historical satellite images, as one simply cannot go back in time to collect training datasets or ground truthing. In this paper, we introduce an iterative and subsequent proximation (ISP) method to circumvent this problem for historical image classifications for historical crop mapping. This method assumes that the same crops grown two years apart have similar spectral properties within the same growing season, with limited variation. This allows the use of the crop information classified in any given year (n) to be used as training samples for the next year (n + 1) or the previous year (n-1). Iteratively repeating n-1, n-2... process leads to the mapping of historical cropping information without a priori ground truthing data. To demonstrate the ISP feasibility, we first used the historical Landsat time-series data to map four major stable crops (rice, maize, soybean, and wheat) in Hailun County, Heilongjiang Province, China, and then further expanded the ISP application to the entire Heilongjiang Province to map cropping areas from 1982 to 2020, using the classification and regression trees (CART) algorithm. The results were validated for those years when actual cropping measurement data were available and further verified with statistical data for other years. The results indicated that the proposed ISP method was appropriate for the historical mapping of four major crops, with a mapping accuracy of approximately 80 % when validated with field data, and correlation coefficients of 0.86, 0.85, 0.91, and 0.94, respectively when compared with historical statistical cropping data. The crop mapping results showed distinct trends of northward expansion in each of the four crops in Heilongjiang Province, which agreed well with previous studies. In conclusion, the ISP method is quick, easy, and convenient for historical crop mapping, which is important in understanding the agricultural production history. [ABSTRACT FROM AUTHOR]

In an era in which conventional agriculture has come under question for its environmental and social costs, regenerative agriculture suggests that land management practices can be organized around farming and grazing practices that regenerate interdependent ecological and community processes for generations to come. However, little is known about the geographies of 'regenerative' and 'conventional' agricultural lands—what defines them, where they are, and the extent to which actual agricultural lands interweave both or are characterizable by neither. In the context of the Midwest of the United States, we develop and map an index quantifying the degrees to which the agricultural lands of counties could be said to be regenerative, conventional, or both. We complement these results by using a clustering method to partition the land into distinct agricultural regions. Both approaches rely on a set of variables characterizing land we developed through an iterative dialogue across difference among our authors, who have a range of relevant backgrounds. We map, analyze, and synthesize our results by considering local contexts beyond our variables, comparing and contrasting the resulting perspectives on the geographies of midwestern agricultural lands. Our results portray agricultural lands of considerable diversity within and between states, as well as ecological and physiographic regions. Understanding the general patterns and detailed empirical geographies that emerge suggests spatial relationships that can inform peer-to-peer exchanges among farmers, agricultural extension, civil society, and policy formation. [ABSTRACT FROM AUTHOR]

The purpose of this study is to analyse the use of agricultural land on the territory of united communities and their individual districts of Zhytomyr region as the smallest units of the administrative territorial division of Ukraine. The relevance of this study, due to current changes in national land relations, dictates the need to have information about agricultural lands, their owners, tenants and the lands status for planning agricultural production, the land-leases development, sale of land. The analysis of land use was carried out according to the following indicators: location of land plots of all owners within the community district and the whole community; their area; monetary evaluation; status of documents for each land plot, in particular the availability of lease, land cultivation presence. The initial data of the researches is the data of the state land cadastre database, and data of the real property rights register, both in general access and in need of special access. As a result of the research there were defined areas and location of lands of some landowners, who leased their lands and areas of lands not leased, also there were defined areas of lands, used illegally, without any documents on land use, defined areas of lands, used or owned by agricultural producers (farms, physical persons, legal entities). A cartographic core was created for the purpose of convenient planning of agricultural land use and land management within selected districts on the territory of the community.

Agricultural landscapes support multiple functions and are of great importance for biodiversity. Heterogeneous agricultural mosaics of cropland and grassland commonly result from variable land use practices and ecosystem service demands. Switzerland's agricultural land use is considerably spatially heterogeneous due to strong variability in conditions, especially topography and climate, thus presenting challenges to automated agricultural mapping. Nationwide knowledge of the location of cropland and grassland is necessary for effective conservation and land use planning. We mapped the distribution of cropland and permanent grassland across Switzerland. We used several indices largely derived from Sentinel-2 satellite imagery captured over multiple growing seasons and parcel-based training data derived from landholder reporting. The mapping was conducted within Google Earth Engine using a random forest classifier. The resulting map has high accuracy in lowlands as well as in mountainous areas. The map will act as a base agricultural land cover dataset for researchers and practitioners working in agricultural areas of Switzerland and interested in land cover and landscape structure. The map as well as the training data and calculation algorithms (using Google Earth Engine) are freely available for download on the EnviDat platform 10.16904/envidat.205 (Pazúr et al., 2021). [ABSTRACT FROM AUTHOR]

The purpose of this study is to analyse the use of agricultural land on the territory of united communities and their individual districts of Zhytomyr region as the smallest units of the administrative territorial division of Ukraine. The relevance of this study, due to current changes in national land relations, dictates the need to have information about agricultural lands, their owners, tenants and the lands status for planning agricultural production, the land-leases development, sale of land. The analysis of land use was carried out according to the following indicators: location of land plots of all owners within the community district and the whole community; their area; monetary evaluation; status of documents for each land plot, in particular the availability of lease, land cultivation presence. The initial data of the researches is the data of the state land cadastre database, and data of the real property rights register, both in general access and in need of special access. As a result of the research there were defined areas and location of lands of some landowners, who leased their lands and areas of lands not leased, also there were defined areas of lands, used illegally, without any documents on land use, defined areas of lands, used or owned by agricultural producers (farms, physical persons, legal entities). A cartographic core was created for the purpose of convenient planning of agricultural land use and land management within selected districts on the territory of the community. [ABSTRACT FROM AUTHOR]

• Agricultural map generation and accuracy evaluation using Sentinel-1 data exclusively. • Agricultural map generation and accuracy evaluation using Sentinel-2 data exclusively. • Investigate on combined use of SAR (Sentinel-1) & optical (Sentinel-2) data to improve classification accuracy. • Investigate on Sentinel-1 and Sentinel-2 multi temporal data influence on classification accuracy. A country's food needs mainly depend upon its agriculture resources and require reliable information related to crop health, distribution, and acreage estimation to manage and monitor resources to implement a sustainable agricultural system. Different methodologies have been used to collect this information. However, the availability of earth resource satellite data with improved spatial, spectral, and temporal resolutions, such as the European Space Agency's (ESA) Copernicus program satellites Sentinel-1 (S1) and Sentinel-2 (S2), are creating more practicability to generate crop type maps. S1 and S2, both operating in a constellation of twin satellites, carry a C-band Synthetic Aperture Radar (SAR) and Multispectral Instrument (MSI). Vertical transmit and horizontal receive (VH), and vertical transmit and vertical receive (VV) channels of S1 were used to exploit the temporal backscatter of crops present in the study area. In this research, a machine learning random forest classification algorithm is used for accurate crop type mapping through the combination of SAR (S1) and optical (S2) time-series data. Random forest classifier has produced considerable improved accuracies of crop type mapping in previous studies as it uses ensemble decision trees trained on sample data which permit the vote in favor of the most popular land use/ land cover class. The key objectives of this study are to investigate the classification accuracies for different data combinations. Three plots of data are tested (i) S1 (ii) S2 (iii) S1 & S2. A combination of SAR and optical data turn out with the best overall accuracy of 97% and a kappa coefficient of 0.97. Space-borne SAR and optical data add a new aspect of crop type mapping, which increases the classification accuracy by including valuable parameters and beating the drawbacks of each other. By comparing the results, it can be concluded that combining all-weather accessible SAR and spectrally rich optical data accomplished more accurate outcomes. It would be an imperative advance for the future endeavor to estimate crop biomass and biophysical parameters. [ABSTRACT FROM AUTHOR]

Copyright of Acta Scientiarum Polonorum. Formatio Circumiectus is the property of Wydawnictwo Uniwersytetu Rolniczego im. Hugona Kollataja w Krakowie 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.)

Based on remote sensing data, it is possible to create a real-time database of agricultural sectors of the study area, in particular, types of crops, fisheries, arable land, and other sectors of agriculture. Remote sensing techniques can also be used to help determine crop yields, parasite spread, increased damage, and soil conditions using satellite imagery and aerial photography. In agricultural mapping, a classification algorithm is required that ensures the reliability and accuracy of the data extracted from the remote sensing data. Research and experiments have shown that increasing the accuracy of classification results requires not only the selection of a perfect algorithm but also a high level of knowledge and skills in the field in which the research is conducted. The mapping of agricultural sectors, in particular the classification of crops, also requires close acquaintance with the existing types of crops in the region, their dependence on natural and climatic conditions, and their development trends.

Background: Ecosystem service mapping is an important tool for decision-making in landscape planning and natural resource management. Today, pollination service mapping is based on the Lonsdorf model (InVEST software) that determines the availability of nesting and floral resources for each land cover and estimates pollination according to the foraging range of the desired species. However, it is argued that the Lonsdorf model has significant limitations in estimating pollination in a landscape that can affect the results of this model. Results: This paper presents a free software, named PollMap, that does not have the limitations of the Lonsdorf model. PollMap estimates the pollination service according to a modified version of the Lonsdorf model and assumes that only cells within the flight range of bees are important in the pollination mapping. This software is produced for estimating and mapping crop pollination in agricultural landscapes. The main assumption of this software is that in the agricultural landscapes, which are dominated by forest and agriculture ecosystems, forest patches serve only as a nesting habitat for wild bees and the surrounding fields provide floral resources. Conclusion: The present study provided new software for mapping crop pollination in agricultural landscapes that does not have the limitations of the Lonsdorf model. We showed that the use of the Lonsdorf model for pollination mapping requires attention to the limitations of this model, and by removing these limitations, we will need new software to obtain a reliable mapping of pollination in agricultural landscapes. [ABSTRACT FROM AUTHOR]

The abundance of dry marginal agricultural land in Indonesia provides an alternative land resource for crop production. This study uses Landsat 8 and Sentinel-2A satellite imagery to map the spatial extent of such land and to interpret the results obtained to track changes in its use. Supervised classification is used for image processing and the production of land-cover maps. The classified maps produc§ed and the spatial extent of each class are compared to existing maps and statistical data. Regional development by local governments has led to the occupation of a greater area of dry marginal agricultural land, and from 2000 to 2019 about 49% of such land has been occupied and converted to productive land use. This change can be identified as an increase in plantation area and the conversion of non-irrigated or irrigated areas to pavement area. Both Landsat 8 and Sentinel-2A imagery can potentially be used to map the spatial extent of dry marginal agricultural land and the maps produced can be useful for updating incomplete statistical records. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Soil Sciences & Agricultural Engineering is the property of Egyptian National Agricultural Library (ENAL) 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.)

Mapping total soil potassium based on scanning gamma spectroscopy, paired with global positioning system (GPS) technology is described. Total potassium was determined from the 1.46 MeV gamma peak area of the 40K isotope. A calibration coefficient was defined based on measuring spectra of small reference samples (dozen of cubic decimeters) and Monte Carlo simulations (Geant4) of spectra in different sample volumes (up to several hundred cubic meters). Soil potassium distribution maps of agricultural fields were created based on measuring the potassium-40 peak areas in gamma spectra and calculating the soil potassium weight percent using defined calibration coefficient. ArcMap software was used for map creation. Results demonstrated the feasibility of using gamma spectroscopy and the described calibration method for determining total soil potassium. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Tikrit University for Humanities is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) 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.)

This paper presents an original approach for identifying and mapping agricultural landscapes that are highly representative of different biogeographical and natural settings and likely to be traditional, at the national scale. In the case study of Italy, we used national land cover data of different dates to examine composition of agricultural types and their persistence over time while we employed national stratifications into ecoregions and potential natural vegetation (PNV) to include environmental representativeness. Implementing the procedure returned 120 landscapes, which are distributed across all ecoregions and 51% of the PNV types. Most landscapes match areas with certified and traditional produce (93%) and relevant potential vegetation (86%), whereas 30% overlap acknowledged historical rural landscapes. These results show that our approach highlights the coevolutionary process between traditional crops and the underlying environmental framework and, therefore, provides an ecologically sound coarse filter for selecting and mapping traditional agricultural landscapes. [ABSTRACT FROM AUTHOR]

Among geographers, recent focus on the illicit and illegal has tended to fall into two camps. Economic geographers focus on regimes of illicitness and corresponding production of specific forms of economic space; political ecologists and land change scholars, on the other hand, scrutinised how illicit commodity flows shape land and resource use, especially in the global South. This paper offers an initial integration of these two relatively separate subfields, specifically in terms of their complementary attention to uneven development. We use the concept of "global commodity chains" to explore the ways in which the regulation of agricultural commodities shapes how they are trafficked and embed in space, with particular attention to sites of international transshipment. When a commodity is illegal, spaces of transit take on significant analytical importance. As a heuristic, we present a comparative mapping of two agricultural commodity chains linking Colombia and the USA: coffee and cocaine. Their comparison highlights how "illicitness" fundamentally transforms cocaine's spatiality, requiring risk evasion that results in characteristically enlarged transit spaces and huge differentials between producer and consumer price. We show how rents circulate in those transit spaces, socially and ecologically embedding in diffuse, fluid networks with severe consequences for people and environments. We conclude with implications for work on illicit commodities and the collateral social and environmental harms they produce. [ABSTRACT FROM AUTHOR]

An accurate paddy rice map is crucial for food security, particularly for Southeast and Northeast Asia. The MODIS satellite data is useful for mapping paddy rice at continental scales but has a problem of mixed-pixel due to coarse spatial resolution. To reduce the mixed pixels, we designed a rule-based method for mapping paddy rice by integrating time-series Sentinel-1 and MODIS data. We demonstrated the method in generating annual paddy rice maps for Southeast and Northeast Asia in 2017-2019 (AsiaRiceMap10m). The resultant paddy rice maps were compared with available agricultural statistics at subnational levels and existing rice maps for some countries. The results show that the linear coefficient of determination (R²) between our paddy rice maps and agricultural statistics ranges from 0.80 to 0.97. The paddy rice planting areas in 2017 were spatially consistent with the existing maps in Vietnam (R² = 0.93) and Northeast China (R² = 0.99). The spatial distribution of the 2017-2019 composite paddy rice map is consistent with the rice map from the International Rice Research Institute. The paddy rice planting area may be underestimated in the region where the flooding signal is not strong. The dataset is useful for water resource management, rice growth, and yield monitoring. The full product is publicly available at https://doi.org/10.17632/j34b3jsvr9.1 (Han et al., 2021a). Find small examples here (https://doi.org/10.17632/cnc3tkbwcm.1) (Han et al., 2021b). [ABSTRACT FROM AUTHOR]

Turkey has favorable agricultural conditions (i.e. fertile soils, climate and rainfall) and can grow almost any type of crop in many regions, making it one of the leading sectors of the economy. For sustainable agriculture management, all factors affecting the agricultural products should be analyzed on a spatial-temporal basis. Therefore, nowadays space technologies such as remote sensing are important tools in providing an accurate mapping of the agricultural fields with timely monitoring and higher repetition frequency and accuracy. In this study, object based classification method was applied to 2017 Sentinel 2 Level 2A satellite image in order to map crop types in the Adana, Çukurova region in Turkey. Support Vector Machine (SVM) was used as a classifier. Texture information were incorporated to spectral wavebands of Sentinel-2 image, to increase the classification accuracy. In this context, all of the textural features of Gray-Level Co-occurrence Matrix (GLCM) were tested and Entropy, Standard deviation, and Mean textural features were found to be the most suitable among them. Multi-spectral and textural features were used as an input separately and/or in combination to evaluate the potential of texture in differentiating crop types and the accuracy of output thematic maps. As a result, with the addition of textural features, it was observed that the Overall Accuracy and Kappa coefficient increased by 7% and 8%, respectively. [ABSTRACT FROM AUTHOR]

We managed to create a self-developed sensor system, which is based on the simultaneous reflectance measurements at a 660 and 940 nm wavelength. The ratio of the reflectance refers to the concentration of the soil organic carbon (SOC). This instrument has a calibration range of 1.19 to 6.05 SOC%. The SOC content of twenty-six soil samples was measured by the self-developed system and a standard spectrophotometric method and we found that the SOC estimation in the self-developed system had a good approximation and the differences ranged from -27.72% ~ + 6.99%. We found a strong correlation between the data of the reference measurements (R² = 0.73) and the values indicated by our self-developed sensor system (Reference (SOX%) =1.4857 × E (SOC%) - 0.7393). This measurement system is easy to use and displays and records the data in real time. This allows one to map an agricultural production area based on the SOC concentration using its built-in GPS unit. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Surra Man Raa is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) 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.)

China has lots of funds, materials, and human resources in cloud seeding operations to help with water shortage problems across the country. But there is little guide about when and where the operations would obtain better benefits. To fill this gap, the modified Water Utilization Level (WUL) map of China mainland was plotted with the acquirable water utilization data on sub‐watershed scales in this study. Then the agricultural drought levels map of different counties were integrated with the modified WUL Map under preset rules to generate an integrated water scarcity map. Lastly, the cloud seeding demand maps of different seasons were drawn by integrating the agricultural drought seasons map of different counties with the integrated water scarcity map and the modified WUL map. Results showed that the middle north of China should have the highest priorities for cloud seeding operations during spring and summer, and some regions at the south during winter and spring, a few scattered regions at the northwest during spring and summer and a few regions at the middle of southeast China during summer and autumn should also have high priorities of cloud seeding operations. This research can help rain enhancement operations to obtain better effects on easing water scarcity problems and deepen the cooperation of water resources department and weather modification department in China. The designed intersecting rules of different maps in this paper also might inspire other relevant researches. Key Points: The results gave a clear demand from water resources field to weather modification department in China mainlandThe middle north areas of China have the severest water scarcity problems, especially during spring and summerSome less developed regions at the south, the northwest and the southeast have high water scarcity levels in different seasons [ABSTRACT FROM AUTHOR]