Remote Sensing of Agricultural Greenhouses and Plastic-Mulched Farmland: An Analysis of Worldwide Research

The total area of plastic-covered crops of 3019 million hectares has been increasing steadily around the world, particularly in the form of crops maintained under plastic-covered greenhouses to control their environmental conditions and their growth, thereby increasing production. This work analyzes the worldwide research dynamics on remote sensing-based mapping of agricultural greenhouses and plastic-mulched crops throughout the 21st century. In this way, a bibliometric analysis was carried out on a total of 107 publications based on the Scopus database. Different aspects of these publications were studied, such as type of publication, characteristics, categories and journal/conference name, countries, authors, and keywords. The results showed that “articles” were the type of document mostly found, while the number of published documents has exponentially increased over the last four years, growing from only one document published in 2001 to 22 in 2019. The main Scopus categories relating to the topic analyzed were Earth and Planetary Sciences (53%), Computer Science (30%), and Agricultural and Biological Sciences (28%). The most productive journal in this field was “Remote Sensing”, with 22 documents published, while China, Italy, Spain, USA, and Turkey were the five countries with the most publications. Among the main research institutions belonging to these five most productive countries, there were eight institutions from China, four from Italy, one from Spain, two from Turkey, and one from the USA. In conclusion, the evolution of the number of publications on Remote Sensing of Agricultural Greenhouses and Plastic-Mulched Farmland found throughout the period 2000–2019 allows us to classify the subject studied as an emerging research topic that is attracting an increasing level of interest worldwide, although its relative significance is still very limited within the remote sensing discipline. However, the growing demand for information on the arrangement and spatio-temporal dynamics of this increasingly important model of intensive agriculture is likely to drive this line of research in the coming years

Regional mapping of crops under agricultural nets using Sentinel-2

Geography and Environmental Studie

Sustainable Agriculture and Advances of Remote Sensing (Volume 1)

Agriculture, as the main source of alimentation and the most important economic activity globally, is being affected by the impacts of climate change. To maintain and increase our global food system production, to reduce biodiversity loss and preserve our natural ecosystem, new practices and technologies are required. This book focuses on the latest advances in remote sensing technology and agricultural engineering leading to the sustainable agriculture practices. Earth observation data, in situ and proxy-remote sensing data are the main source of information for monitoring and analyzing agriculture activities. Particular attention is given to earth observation satellites and the Internet of Things for data collection, to multispectral and hyperspectral data analysis using machine learning and deep learning, to WebGIS and the Internet of Things for sharing and publishing the results, among others

Very High Resolution (VHR) Satellite Imagery: Processing and Applications

Recently, growing interest in the use of remote sensing imagery has appeared to provide synoptic maps of water quality parameters in coastal and inner water ecosystems;, monitoring of complex land ecosystems for biodiversity conservation; precision agriculture for the management of soils, crops, and pests; urban planning; disaster monitoring, etc. However, for these maps to achieve their full potential, it is important to engage in periodic monitoring and analysis of multi-temporal changes. In this context, very high resolution (VHR) satellite-based optical, infrared, and radar imaging instruments provide reliable information to implement spatially-based conservation actions. Moreover, they enable observations of parameters of our environment at greater broader spatial and finer temporal scales than those allowed through field observation alone. In this sense, recent very high resolution satellite technologies and image processing algorithms present the opportunity to develop quantitative techniques that have the potential to improve upon traditional techniques in terms of cost, mapping fidelity, and objectivity. Typical applications include multi-temporal classification, recognition and tracking of specific patterns, multisensor data fusion, analysis of land/marine ecosystem processes and environment monitoring, etc. This book aims to collect new developments, methodologies, and applications of very high resolution satellite data for remote sensing. The works selected provide to the research community the most recent advances on all aspects of VHR satellite remote sensing

Assessment of the X- and C-Band Polarimetric SAR Data for Plastic-Mulched Farmland Classification

We present a classification of plastic-mulched farmland (PMF) and other land cover types using full polarimetric RADARSAT-2 data and dual polarimetric (HH, VV) TerraSAR-X data, acquired from a test site in Hebei, China, where the main land covers include PMF, bare soil, winter wheat, urban areas and water. The main objectives were to evaluate the outcome of using high-resolution TerraSAR-X data for classifying PMF and other land covers and to compare classification accuracies based on different synthetic aperture radar bands and polarization parameters. Initially, different polarimetric indices were calculated, while polarimetric decomposition methods were used to obtain the polarimetric decomposition components. Using these polarimetric components as input, the random forest supervised classification algorithm was applied in the classification experiments. Our results show that in this study full-polarimetric RADARSAT-2 data produced the most accurate overall classification (94.81%), indicating that full polarization is vital to distinguishing PMF from other land cover types. Dual polarimetric data had similar levels of classification error for PMF and bare soil, yielding mapping accuracies of 53.28% and 59.48% (TerraSAR-X), and 59.56% and 57.1% (RADARSAT-2), respectively. We found that Shannon entropy made the greatest contribution to accuracy in all three experiments, suggesting that it has great potential to improve agricultural land use classifications based on remote sensing

Variation of Soil Structure in the Foot and Toe Slopes of Mt. Vukan, East-central Serbia

This paper presents the variation of soil structure along the foot and toe slopes of Mt. Vukan, East-Central Serbia. The analysis of aggregate size distribution and structure indices were conducted by means of soil units, characteristic soil horizons and elevation differences along the study area. Soils of Great Field located at different elevations were found to have significant variation in ASD and soil structure indices. Topsoil horizon of Eutric Cambisols have higher MWD after dry sieving, but at the same time it has the highest variation in MWD after wet sieving, indicating low water stability, which is opposite to the coefficient of aggregability. We share an opinion that change in MWD better depicts soils structure stability to water. The results of correlation analysis indicated that clay content is correlated more to structure indices compared with SOM content. SOM is significantly correlated with ASD and soil structure indices only in Calcomelansols, whereas the significant correlation of clay content and soil structure is more evident in Eutric Cambisols and Non-calcaric Chernozems, compared with other soil units. Soil structure variation along the lowest chain of Catena might be strong, and that it has to be analyzed from the point of view of soil unit and their corresponding soil horizons

IMPACT OF GRAZING ON SOIL ORGANIC MATTER AND PHYSICAL PROPERTIES OF A FLUVISOL IN NORTWEST SERBIA

The effects of long-term (>20 yr) grazing on the selected physical properties of a non carbonated silty-clay Fluvisols were studied in the region of the Kolubara Valley, Northwest Serbia. Two adjacent land-use types (native deciduous forest and natural pasture soils converted from forests for more than 20 years) were chosen for the study. Disturbed and undisturbed soil samples were collected from three sites at each of the two different land-use types from the depths of 0–15, 15–30 and 30–45 cm. In relation to the soil under native forest, soil organic matter content, total porosity and air-filled porosity were significantly reduced after long-term of grazing. The bulk density (0.99–1.48 g cm–3) and the saturated hydraulic conductivity (6.9.10–2–3.2.10–4 cm s–1) were significantly lower in forest compared to the adjacent pasture (ex-forest) soil (1.49–1.55 g cm–3 and 3.4.10–4–5.5.10–4 cm s–1, respectively). In addition, forest had significantly lower dry mean weight diameter (7.0–9.2 mm) and greater wet mean weight diameter (2.0–2.6 mm) for 0–45 cm depth compared with the pasture (8.8–9.4 mm and 1.8–2.3 mm, respectively). The decrease of soil organic matter content and reduction in aggregate stability under long-term grazing rendered the soil more susceptible to compaction. In conclusion, the results of this study indicate that removal of permanent vegetation in the conversion process from forest areas to pasture land may lead to loss of soil productivity and serious soil degradation. Obviously, there is a need for greater attention to developing sustainable land use practices in management of these ecosystems to prevent further degradation of pasture soils in the region