REVIEW OF THE APPLICATIONS OF SATELLITE REMOTE SENSING IN ORGANIC FARMING – PART II

The use of remote sensing methods for monitoring, managing, and decision support in agriculture is increasingly intensifying. With the advancement of technologies, they become more accessible, while the quality and security of the obtained data are improving. Striving to improve the quality of the environment and its preservation, expanding the areas occupied by organic farming will allow us to achieve these goals. At the same time, this type of agriculture provides healthy and safe food. For this reason, it is of great importance to start applying satellite data in organic farming as quickly as possible. In Part II of the "Review of the applications of satellite remote sensing in organic farming," we examine the various areas of satellite data application in organic farming. Five different areas of satellite data application in organic farming have been identified, including satellite remote sensing monitoring of weeds, remote sensing of crop stress and irrigation needs, yield forecasting using remote sensing methods and remote sensing monitoring of plant nutrition. From the review conducted, we found that satellite data can significantly support and facilitate the transition to organic farming, adequate fertilization, application in phytosanitary monitoring of crops, and assessment of crop stress

Review of the Applications of Satellite Remote Sensing in Organic Farming (Part I)

Organic farming is a much more sustainable farming system than conventional farming. It is part of humanity's efforts to preserve biodiversity and provides healthy and safe food to humans. Remote sensing methods are widely used in agriculture. Their use will help the transition from conventional to organic farming. They can help farmers choose the most suitable place to build an organic farm. Remote sensing methods are a very powerful tool for weed control in organic farming. They can be used to determine the level of stress that crops experience. They provide a good opportunity to forecast yields on organic farms. Remote sensing methods can optimize fertilization on organic farms. They can be used to distinguish between organic and conventional agriculture, as well as to monitor biodiversity in agricultural areas. Remote sensing methods can help organic farmers make timely and adequate decisions in managing their farms

Possibilities of forecasting the yield of organic wheat using aerospace methods

With climate change, adverse natural phenomena, such as floods and droughts, are becoming more common, which in turn are a major threat to wheat yields. Almost all regions of the planet are vulnerable to such climatic events. Remote sensing methods can help farmers by giving them up-to-date information on the condition and yield forecasting of wheat crops, thus minimizing the risk of climate change

METHODOLOGY FOR REMOTE SENSING MONITORING OF ORGANIC WHEAT CROPS

In the presented methodology for aerospace monitoring of autumn wheat crops, grown under the conditions of organic farming, the ways of applying ground and aerospace methods are discussed in detail. This includes field experiments, phenological observations, GIS and remote sensing methods and data (data from Sentinel-2 satellite and WingtraOne unmanned aerial vehicle with MicaSense RedEdge-MX multispectral camera and RGB camera) and statistical analyses. In order to achieve the aim and objectives of the study, an experiment was conducted on a organically certified production field sown with einkorn (Triticum monococum) in the period 2020-2021. The field is part of the holding of ET "Borislav Slavchev" in the village of Byala Reka, Parvomai Municipality, South-Central Bulgaria on the soil type of leached chernozem clays, with a size of 136 da

Opportunities for Remote Sensing Applications in Organic Cultivation of Cereals – a Review

In recent years, a number of studies have proven that the conventional agricultural system is not sustainable, toxic to the environment, human health, and its potential to feed humanity is limited to the next 50 years. With this in mind, as well as the increasing demand for healthy and safe foods, and the increase in the proportion of people who care about how the food they consume was produced, how much it does not harm the environment and health, farmers are starting to reorient their production into organic. Over the past 40 years, remote sensing methods and technologies have increasingly been used in agriculture. They have proved extremely useful for optimizing the working processes in the sector, as well as solving many of the problems in it. With this report, we aim to draw the scientific community's attention to the possibilities provided by remote sensing methods and technologies to solve a range of problems related to organic cultivation of cereals

Determining organic barley yields from Sentinel-2 data

Abstract: The present research was conducted during the agricultural year 2022-2023 in the land of Byala Reka village, Parvomai municipality in central southern Bulgaria. The ground data for the yield was collected from an organically certified field sown with barley, which is part of the holding of ET "Borislav Slavchev". The BBCH scale for cereal plants was used to identify the phenological stages of crop development. Data from the Sentinel-2 satellite were acquired by the EOSDA LandViewer platform. Vegetation indices were generated in the same platform as selected ones used in relevant studies in conventional agriculture. Four images were used for the phenological phases BBCH-21 initiation of budding, BBCH-30 spindle, BBCH-51 grading and BBCH-77 end of milk maturity, respectively. Pixel values were extracted using the GIS software ArcGIS Pro. At the BBCH-99 technological maturity stage, ground samples were collected to measure biological yield. A correlation analysis was performed with the obtained ground and satellite data, establishing which vegetation indices are most suitable for determining yields and in which vegetation phas

APPLICATION OF VEGETATION INDEXES GENERATED BY UAV TO DETERMINE THE YIELD OF ORGANIC EINKORN (TRITICUM MONOCOCCUM L.)

The aim of the present study is to establish a group of vegetation indices generated from data obtained from an unmanned aerial vehicle for predicting the yields of einkorn grown in organic farming conditions. The experiment was conducted during the agriculture year 2020-2021 on a certified organic field located in the municipality of Parvomai, Plovdiv region

A COMPARATIVE ANALYSIS BETWEEN TWO TYPES OF DATA PROCESSING OBTAINED THROUGH UAV FROM A ORGANICAL FIELD WITH EINKORN (TRITICUM MONOCOCCUM L.)

The aim of the present study is to determine which type of data processing obtained from an unmanned aerial vehicle (UAV) is more suitable for predicting the yields of the eikorn crop grown under organic farming conditions. The comparison is made between data obtained from the UAV at the pixel level of 7 × 7 cm and when aggregating the pixels to a pixel size of 1 × 1 m. The experiment was conducted during the agricultural year 2020–2021 on a certified organic field located in the municipality of Parvomai, Plovdiv region

Satwebmare Interactive Web-Mapping System In Support Of The Sustainable Management Of The Bulgarian Coastal Zone

The article aims to represent a general overview of the prototype web-mapping interactive system SatWebMare for the Bulgarian coastal zone. The interactive system is designed to provide through geo-portal innovative products and services for integrated coastal zone management. The web-mapping system combines geo-databases from different sources such as satellite imagery, maps, vector layers and other datasets. The content of the SatWebMare Geo-Portal is briefly outlined. The web-interface system will provide access to applications and products with an improved spatial and temporal resolution for three areas of interest - sea waves, natural hazards and geomagnetism in the Area of Interest (AOI). The web-mapping system is developing based on the free and open-source software, OGS standards and following the EU INSPIRE Directive recommendations. Once the prototype system is fully developed, it will enable to provide access to value-added products and services that are useful to ministries, agencies, local authorities and other stakeholders in support of the decision making

Past decade above-ground biomass change comparisons from four multi-temporal global maps

Above-ground biomass (AGB) is considered an essential climate variable that underpins our knowledge and information about the role of forests in mitigating climate change. The availability of satellite-based AGB and AGB change (Delta AGB) products has increased in recent years. Here we assessed the past decade net Delta AGB derived from four recent global multi-date AGB maps: ESA-CCI maps, WRI-Flux model, JPL time series, and SMOS-LVOD time series. Our assessments explore and use different reference data sources with biomass re-measurements within the past decade. The reference data comprise National Forest Inventory (NFI) plot data, local Delta AGB maps from airborne LiDAR, and selected Forest Resource Assessment country data from countries with well-developed monitoring capacities. Map to reference data comparisons were performed at levels ranging from 100 m to 25 km spatial scale. The comparisons revealed that LiDAR data compared most reasonably with the maps, while the comparisons using NFI only showed some agreements at aggregation levels <10 km. Regardless of the aggregation level, AGB losses and gains according to the map comparisons were consistently smaller than the reference data. Map-map comparisons at 25 km highlighted that the maps consistently captured AGB losses in known deforestation hotspots. The comparisons also identified several carbon sink regions consistently detected by all maps. However, disagreement between maps is still large in key forest regions such as the Amazon basin. The overall AAGB map cross-correlation between maps varied in the range 0.11-0.29 (r). Reported AAGB magnitudes were largest in the high-resolution datasets including the CCI map differencing (stock change) and Flux model (gain-loss) methods, while they were smallest according to the coarser-resolution LVOD and JPL time series products, especially for AGB gains. Our results suggest that AAGB assessed from current maps can be biased and any use of the estimates should take that into account. Currently, AAGB reference data are sparse especially in the tropics but that deficit can be alleviated by upcoming LiDAR data networks in the context of Supersites and GEO-Trees