271 Results Found

Artificial intelligence (AI)-based multispectral remote sensing has been the best supporting tool using limited resources to enhance the lithological mapping abilities with accuracy, supported by ground truthing through traditional mapping techniques...

Accurate estimations of actual evapotranspiration (ETa) are essential to various environmental issues. Artificial intelligence-based models are a promising alternative to the most common direct ETa estimation techniques and indirect methods by remote...

The study on the spatial distribution of forest soil nutrients is important not only as a reference for understanding the factors affecting soil variability, but also for the rational use of soil resources and the establishment of a virtuous cycle of...

Mangroves, as unique coastal wetlands with numerous benefits, are endangered mainly due to the coupled effects of anthropogenic activities and climate change. Therefore, acquiring reliable and up-to-date information about these ecosystems is vital fo...

The power capacity of solar photovoltaics (PVs) in Korea has grown dramatically in recent years, and an accurate estimation of solar resources is crucial for the efficient management of these solar PV systems. Since the number of solar irradiance mea...

Measuring total nitrogen (TN) and total phosphorus (TP) is important in managing heavy polluted urban waters in China. This study uses high spatial resolution IKONOS imagery with four multispectral bands, which roughly correspond to Landsat/TM bands...

Knowing the expected crop yield in the current growing season provides valuable information for farmers, policy makers, and food processing plants. One of the main benefits of using reliable forecasting tools is generating more income from grown crop...

We performed a comparative analysis of the prediction accuracy of machine learning methods and ordinary Kriging (OK) hybrid methods for forest volume models based on multi-source remote sensing data combined with ground survey data. Taking Larix olge...

Remote sensing technologies can accurately capture environmental characteristics, and together with environmental modeling approaches, help to predict climate-sensitive infectious disease outbreaks. Brucellosis remains rampant worldwide in both domes...

Climate change mitigation relies heavily on understanding carbon storage dynamics in terrestrial ecosystems. This study examines the relationship between carbon storage (kg/m2) and various climatic variables, including precipitation, temperature, hum...

Until now, few studies have used the mainstreaming models to simulate the land use changes in the cities of rapid urbanizing regions. Therefore, we aimed to develop a methodology to simulate the land use changes in rapid urbanizing regions that could...

The Agronomy Editorial Office retracts the article entitled “Estimating the Crop Acreage of Menthol Mint Crop from Remote Sensing Satellite Imagery Using ANN” [...]

Leaf area index (LAI) is an important indicator of plant growth and yield that can be monitored by remote sensing. Several models were constructed using datasets derived from SRS and STR sampling methods to determine the optimal model for soybean (mu...

Land use and land cover (LULC) in Northern Bangladesh have undergone substantial transformations due to both anthropogenic and natural drivers. This study examines historical LULC changes (1990–2022) and projects future trends for 2030 and 2054...

Wetlands provide vital ecological and socioeconomic services but face escalating pressures worldwide. This study undertakes an integrated spatiotemporal assessment of the multifaceted vulnerabilities shaping Khinjhir Lake, an ecologically significant...

Understanding future landscape risk pattern change (FLRPC) scenarios will help people manage and utilize natural resources. In this study, we have selected a variety of landscape and anthropogenic factors as risk parameters for FLRPC assessment. Land...

Land use and land cover change (LULCC) is among the most apparent natural landscape processes impacted by anthropogenic activities, particularly in fast-growing regions. In India, at present, due to the impacts of anthropogenic climate change, supple...

Remote monitoring sensor systems play a significant role in the evaluation and minimization of natural disasters and risk. This article presents a sustainable and real-time early warning system of sensors employed in flash flood prediction by using a...

Machine learning (ML) models have been widely used in the modeling, design and prediction in energy systems. During the past two decades, there has been a dramatic increase in the advancement and application of various types of ML models for energy s...

Soil erosion is one of the main causes of soil degradation among others (salinization, compaction, reduction of organic matter, and non-point source pollution) and is a serious threat in the Mediterranean region. A number of soil properties, such as...

Suitable route determination for linear engineering structures is a fundamental problem in engineering geology. Rapid evaluation of alternative routes is essential, and novel approaches are indispensable. This study aims to integrate various InSAR (I...

Several satellite precipitation estimates are becoming available globally, offering new possibilities for modeling water resources, especially in regions where data are scarce. This work provides the first validation of four satellite precipitation p...

Stratospheric ozone shields life on Earth from harmful ultraviolet radiation and plays a crucial role in climate formation, while tropospheric ozone is a pollutant and greenhouse gas. Satellite methods based on measurements of outgoing thermal radiat...

Quaternary catchment X22J boasts ecological biodiversity, making ecotourism one of the thriving industries in the catchment. However, recent population growth and the migration from rural areas to urban areas have increased urbanisation. Therefore, t...

As an essential data-driven model, machine learning can simulate runoff based on meteorological data at the watershed level. It has been widely used in the simulation of hydrological runoff. Considering the impact of snow cover on runoff in high-alti...

The estimation of spatially distributed crop water use or evapotranspiration (ET) can be achieved using the energy balance for land surface algorithm and multispectral imagery obtained from remote sensing sensors mounted on air- or space-borne platfo...

Soil heat flux (G) is an important component for the closure of the surface energy balance (SEB) and the estimation of evapotranspiration (ET) by remote sensing algorithms. Over the last decades, efforts have been focused on parameterizing empirical...

Earth observation and monitoring of soil quality, long term changes of soil characteristics and deterioration processes such as degradation or desertification are among the most important objectives of remote sensing. The georeferenciation of such in...

Air quality degradation has triggered a large-scale public health crisis globally. Existing machine learning techniques have been used to attempt the remote sensing estimates of PM2.5. However, many machine learning models ignore the spatial non-stat...

Soil texture and particle size fractions (PSFs) are a critical characteristic of soil that influences most physical, chemical, and biological properties of soil; furthermore, reliable spatial predictions of PSFs are crucial for agro-ecological modeli...

A fast and accurate radiative transfer model is the prerequisite in the field of atmospheric remote sensing for limb atmospheric inversion to tackle the drawback of slow calculation speed of traditional atmospheric radiative transfer models. This pap...

Tillage information is crucial for environmental modeling as it directly affects evapotranspiration, infiltration, runoff, carbon sequestration, and soil losses due to wind and water erosion from agricultural fields. However, collecting this informat...

Grasslands are biomes of significant fiscal, social and environmental value. Grassland or rangeland management often monitors and manages grassland productivity. Productivity is determined by various biophysical parameters, one such being grass above...

Microplastic pollution in marine environments represents a significant ecological threat due to its persistence and harmful effects on biodiversity and human health. In Colombia, coastal ecosystems (particularly in La Guajira) have exhibited increasi...

The optical complexity of urban waters makes the remote retrieval of chlorophyll-a (Chl-a) concentration a challenging task. In this study, Chl-a concentration was retrieved using reflectance data of Landsat OLI images. Chl-a concentration in the Hai...

Conventional remote sensing image retrieval (RSIR) systems perform single-label retrieval with a single label to represent the most dominant semantic content for an image. Improved spatial resolution dramatically boosts the remote sensing image scene...

The size of phytoplankton not only influences its physiology, metabolic rates and marine food web, but also serves as an indicator of phytoplankton functional roles in ecological and biogeochemical processes. Therefore, some algorithms have been deve...

Mapping smallholder irrigated agriculture in sub-Saharan Africa using remote sensing techniques is challenging due to its small and scattered areas and heterogenous cropping practices. A study was conducted to examine the impact of sample size and co...

Remote sensing sensors-based image processing techniques have been widely applied in non-destructive quality inspection systems of agricultural crops. Image processing and analysis were performed with computer vision and external grading systems by g...

Focusing on water resources assessment in ungauged or sparse gauged areas, a comparative evaluation of areal precipitation was conducted by remote sensing data, limited gauged data, and a fusion of gauged data and remote sensing data based on machine...

The uncertainty from the under-estimation and over-estimation of forest aboveground biomass (AGB) is an urgent problem in optical remote sensing estimation. In order to more accurately estimate the AGB of Pinus densata forests in Shangri-La City, we...

Aerial imaging technologies have been widely applied in agricultural plant remote sensing. However, an as yet unexplored challenge with field imaging is that the environmental conditions, such as sun angle, cloud coverage, temperature, and so on, can...

Protection of water environments is an important part of overall environmental protection; hence, many people devote their efforts to monitoring and improving water quality. In this study, a self-adapting selection method of multiple artificial neura...

Hyperspectral image sensing can be used to effectively detect the distribution of harmful cyanobacteria. To accomplish this, physical- and/or model-based simulations have been conducted to perform an atmospheric correction (AC) and an estimation of p...

Recently, due to the acceleration of global warming, an accurate understanding and management of forest carbon stocks, such as forest aboveground biomass, has become very important. The vertical structure of the forest, which is the internal structur...

The retrieval of chlorophyll-a (Chl-a) concentrations relies on empirical or analytical analyses, which generally experience difficulties from the diversity of inland waters in statistical analyses and the complexity of radiative transfer equations i...

The monitoring of air pollutant concentration within cities is crucial for environment management and public health policies in order to promote sustainable cities. In this study, we present an approach to estimate the concentration of particulate ma...

Many crop production management decisions can be informed using data from high-resolution aerial images that provide information about crop health as influenced by soil fertility and moisture. Surface soil moisture is a key component of soil water ba...

Sown Biodiverse Pastures (SBP) are the basis of a high-yield grazing system tailored for Mediterranean ecosystems and widely implemented in Southern Portugal. The application of precision farming methods in SBP requires cost-effective monitoring usin...

Sufficient historical flood inventory data (FID) are crucial for accurately predicting flood susceptibility using supervised machine learning models. However, historical FID are insufficient in many regions. Remote sensing provides a promising opport...