Search results for "Sentinel-2"
showing 10 items of 47 documents
Multi-Season Phenology Mapping of Nile Delta Croplands Using Time Series of Sentinel-2 and Landsat 8 Green LAI
2022
Space-based cropland phenology monitoring substantially assists agricultural managing practices and plays an important role in crop yield predictions. Multitemporal satellite observations allow analyzing vegetation seasonal dynamics over large areas by using vegetation indices or by deriving biophysical variables. The Nile Delta represents about half of all agricultural lands of Egypt. In this region, intensifying farming systems are predominant and multi-cropping rotations schemes are increasing, requiring a high temporal and spatial resolution monitoring for capturing successive crop growth cycles. This study presents a workflow for cropland phenology characterization and mapping based on…
Assessment of workflow feature selection on forest LAI prediction with sentinel-2A MSI, landsat 7 ETM+ and Landsat 8 OLI
2020
The European Space Agency (ESA)’s Sentinel-2A (S2A) mission is providing time series that allow the characterisation of dynamic vegetation, especially when combined with the National Aeronautics and Space Administration (NASA)/United States Geological Survey (USGS) Landsat 7 (L7) and Landsat 8 (L8) missions. Hybrid retrieval workflows combining non-parametric Machine Learning Regression Algorithms (MLRAs) and vegetation Radiative Transfer Models (RTMs) were proposed as fast and accurate methods to infer biophysical parameters such as Leaf Area Index (LAI) from these data streams. However, the exact design of optimal retrieval workflows is rarely discussed. In this study, the impact of…
Using optical satellite and aerial imagery for automatic coastline mapping
2020
The continuous availability and rapid accessibility to multispectral data from satellite platforms within the Copernicus Programme represents a great opportunity for users in different fields of applications as: Agriculture, observation of coastal zones, monitoring land cover change. The aim of this paper is to identify a suitable method to map coastline using Sentinel-2 optical satellite image. The method provides the use of two indexes developed in remote sensing field for water environment: NDWI (Normalized difference water index) and MNDWI (Modified Normalized difference water index). Starting from the construction of maps of these indexes and, identifying appropriate threshold values, …
Analysis of Biophysical Variables in an Onion Crop (Allium cepa L.) with Nitrogen Fertilization by Sentinel-2 Observations
2022
The production of onions bulbs (Allium cepa L.) requires a high amount of nitrogen. Ac cording to the demand of sustainable agriculture, the information-development and communication technologies allow for improving the efficiency of nitrogen fertilization. In the south of the province of Buenos Aires, Argentina, between 8000 and 10,000 hectares per year−1 are cultivated in the districts of Villarino and Patagones. This work aimed to analyze the relationship of biophysical variables: leaf area index (LAI), canopy chlorophyll content (CCC), and canopy cover factor (fCOVER), with the nitrogen fertilization of an intermediate cycle onion crop and its effects on yield. A field trial study with …
Multiple Cost Functions and Regularization Options for Improved Retrieval of Leaf Chlorophyll Content and LAI through Inversion of the PROSAIL Model
2013
Abstract: Lookup-table (LUT)-based radiative transfer model inversion is considered a physically-sound and robust method to retrieve biophysical parameters from Earth observation data but regularization strategies are needed to mitigate the drawback of ill-posedness. We systematically evaluated various regularization options to improve leaf chlorophyll content (LCC) and leaf area index (LAI) retrievals over agricultural lands, including the role of (1) cost functions (CFs); (2) added noise; and (3) multiple solutions in LUT-based inversion. Three families of CFs were compared: information measures, M-estimates and minimum contrast methods. We have only selected CFs without additional parame…
Deep learning for agricultural land use classification from Sentinel-2
2020
[ES] En el campo de la teledetección se ha producido recientemente un incremento del uso de técnicas de aprendizaje profundo (deep learning). Estos algoritmos se utilizan con éxito principalmente en la estimación de parámetros y en la clasificación de imágenes. Sin embargo, se han realizado pocos esfuerzos encaminados a su comprensión, lo que lleva a ejecutarlos como si fueran “cajas negras”. Este trabajo pretende evaluar el rendimiento y acercarnos al entendimiento de un algoritmo de aprendizaje profundo, basado en una red recurrente bidireccional de memoria corta a largo plazo (2-BiLSTM), a través de un ejemplo de clasificación de usos de suelo agrícola de la Comunidad Valenciana dentro d…
Planktothrix rubescens in freshwater reservoirs: remote sensing potentiality for mapping cell density
2012
Planktothrix rubescens is sadly famous for producing microcystins (MCs), which are powerful hepatotoxins. During the winter 2005/06, P. rubescens has been found in the Pozzillo, Nicoletti, Prizzi and Garcia reservoirs, Sicily; in 2008 it was also detected in SS. Trinita di Delia and Castello reservoirs. Indeed, during periods of low shortwave irradiance such as winter, when light weakly penetrates water column and the water cools, P. rubescens filaments float up to the surface, forming red-colored blooms. Ancillary meteorological measurements highlighted low air temperatures between two frosts in December 2007 and February 2008, with a simultaneous reduction in the incoming total solar radi…
Chlorophyll and Suspended Solids Estimation in Portuguese Reservoirs (Aguieira and Alqueva) from Sentinel-2 Imagery
2021
Reservoirs have been subject to anthropogenic stressors, becoming increasingly degraded. The evaluation of ecological potential in reservoirs is remarkably challenging, and consistent and regular monitoring using the traditional in situ methods defined in the WFD is often time- and money-consuming. Alternatively, remote sensing offers a low-cost, high frequency, and practical complement to these methods. This paper proposes a novel approach, using a C2RCC processor to analyze Sentinel-2 imagery data to retrieve information on water quality in two reservoirs of Portugal, Aguieira and Alqueva. We evaluate the temporal and spatial evolution of Chl a and total suspended solids (TSS), between 20…
Turbidez y profundidad de disco de Secchi con Sentinel-2 en embalses con diferente estado trófico en la Comunidad Valenciana
2019
[ES] En los estudios de calidad de aguas por teledetección, uno de los principales indicadores es la transparencia o turbidez del agua. La transparencia puede ser medida in situ mediante la profundidad del disco de Secchi (SD), y la turbidez con un turbidímetro. En las últimas décadas se han utilizado diferentes relaciones entre bandas de diferentes sensores obtenidas por teledetección para la estimación de estos parámetros. En este trabajo, a partir de datos de campo obtenidos a lo largo de 2017 y 2018 en embalses de la cuenca del Júcar con gran variedad de estados tróficos, se han calibrado diferentes índices y bandas para poder estimar la transparencia a partir de imágenes Sentinel-2 (S2…
Farm-Scale Crop Yield Prediction from Multi-Temporal Data Using Deep Hybrid Neural Networks
2021
Farm-scale crop yield prediction is a natural development of sustainable agriculture, producing a rich amount of food without depleting and polluting environmental resources. Recent studies on crop yield production are limited to regional-scale predictions. The regional-scale crop yield predictions usually face challenges in capturing local yield variations based on farm management decisions and the condition of the field. For this research, we identified the need to create a large and reusable farm-scale crop yield production dataset, which could provide precise farm-scale ground-truth prediction targets. Therefore, we utilise multi-temporal data, such as Sentinel-2 satellite images, weath…