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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Monitoring Rice Crops in Piemonte (Italy): Towards an Operational Service Based on Free Satellite Data
摘要： Rice plays an important role in Italy and particularly in Piemonte Region (NW Italy). It heavily impacts on waters resources determining critical situations related to irrigation management. This work, stimulated by the Agriculture Department of Piemonte Region Administration, tries to point out the potentialities of freely available satellite data to describe both agronomic and water dynamics of rice during its phenological season. SAR (Synthetic Aperture Radar) measurements from Sentinel-1 mission, proved to be effective in describing water dynamics and structure variations of crop. Temporal profiles of the SAR back-scattered signal (σ0) were used to describe submersion phases and structural changes of crops. Differently, optical data from Sentinel-2 and Landsat 8 missions, were jointly used to monitor crop health and water content after plants emersion. Spectral indices (NDVI, NDWI, GRVI) time series were used for this purpose. Results, for the 2016 year, demonstrate that this integrated approach can well describe the main rice crop agronomic phases.
关键词： Landsat,time series,agronomic services,Sentinel,rice
更新于2025-09-09 09:28:46
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A Novel Automatic Method for Alfalfa Mapping Using Time Series of Landsat-8 OLI Data
摘要： Remote sensing (RS) data have been utilized increasingly for mapping various crops at local and regional scales using various techniques. However, training data collection of these methods is costly and time consuming. On the other hand, time series of RS data have provided valuable information about crop phenological patterns, which can be utilized for automatic crop mapping independent of training data. Hence, the aim of this research is to develop a new automatic method to map alfalfa by identification of specific characteristics of alfalfa based on time series of Landsat 8 OLI images in four study sites in Iran and the United States. Alfalfa fields are usually harvested periodically and two neighboring farms may not be harvested simultaneously. To address this challenge, the alfalfa spectral reflectance values in various bands were compared with those of other crops during the growing season. In the following, three assumptions were made to find suitable relationships for demonstrating alfalfa characteristics as well as separating it from other crops. The results indicated that the summation of differences between the red and NIR reflectance values of alfalfa in the time series of Landsat images is significant; and also, the average values of the NIR and red bands during the growing season are remarkably higher and lower than those of other crops, respectively. Hence, based on these findings, a new specific feature was developed to detect alfalfa with the overall accuracy of 93%, 90%, 94%, and 90% in Moghan, Qazvin, Razan, and Parker Valley, respectively.
关键词： phenology,Landsat time series,spectral feature,Alfalfa,automatic crop mapping
更新于2025-09-09 09:28:46
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Empirical cross sensor comparison of Sentinel-2A and 2B MSI, Landsat-8 OLI, and Landsat-7 ETM+ top of atmosphere spectral characteristics over the conterminous United States
摘要： Remote sensing landscape monitoring approaches frequently benefit from a dense time series of observations. To enhance these time series, data from multiple satellite systems need to be integrated. Landsat image data is a valuable 30-meter resolution source of spatial information to assess forest conditions over time. Together both operational Landsat satellites—7 and 8—provide a revisit frequency of 8 days at the equator. This moderate temporal frequency provides essential information to detect annual large area abrupt land cover changes. However, the ability to measure subtle and short lived intraseasonal changes is challenged by gaps in Landsat imagery at key points in time. The first Sentinel-2 satellite mission was launched by the European Space Agency in 2015. This moderate resolution data stream provides an opportunity to supplement the Landsat data record. The objective of this study is to assess the potential for integrating top of atmosphere Landsat and Sentinel 2 image data archived in the Google Earth Engine compute environment. In this paper we assess absolute and proportional differences in near-contemporaneous observations for six bands with comparable spectral response functions and spatial resolution between the Sentinel-2 Multi Spectral Instrument and Landsat Operational Land Imager and Enhanced Thematic Mapper Plus imagery. We assessed differences using absolute difference metrics and major axis linear regression between over 10,000 image pairs across the conterminous United States and present cross sensor transformation models. Major axis linear regression results indicate that Sentinel MSI data are as spectrally comparable to the two types of Landsat image data as the Landsat sensors are with each other. Root-mean-square deviation (RMSD) values ranging from 0.0121 to 0.0398 were obtained between MSI and Landsat spectral values, and RMSD values ranging from 0.0124 and 0.0372 were obtained between OLI and ETM+. Despite differences in their spatial, spectral, and temporal characteristics, integration of these datasets appears to be feasible through the application of bandwise linear regression corrections.
关键词： Sensor integration,ETM+,Sentinel-2,MSI,OLI,Time series,Change detection,Landsat
更新于2025-09-09 09:28:46
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Radiometric Cross-Calibration of GF-4 PMS Sensor Based on Assimilation of Landsat-8 OLI Images
摘要： Earth observation data obtained from remote sensors must undergo radiometric calibration before use in quantitative applications. However, the large view angles of the panchromatic multispectral sensor (PMS) aboard the GF-4 satellite pose challenges for cross-calibration due to the effects of atmospheric radiation transfer and the bidirectional reflectance distribution function (BRDF). To address this problem, this paper introduces a novel cross-calibration method based on data assimilation considering cross-calibration as an optimal approximation problem. The GF-4 PMS was cross-calibrated with the well-calibrated Landsat-8 Operational Land Imager (OLI) as the reference sensor. In order to correct unequal bidirectional reflection effects, an adjustment factor for the BRDF was established, making complex models unnecessary. The proposed method employed the Shuffled Complex Evolution-University of Arizona (SCE-UA) algorithm to find the optimal calibration coefficients and BRDF adjustment factor through an iterative process. The validation results revealed a surface reflectance error of <5% for the new cross-calibration coefficients. The accuracy of calibration coefficients were significantly improved when compared to the officially published coefficients as well as those derived using conventional methods. The uncertainty produced by the proposed method was less than 7%, meeting the demands for future quantitative applications and research. This method is also applicable to other sensors with large view angles.
关键词： GF-4 PMS,cross-calibration,SCE-UA,data assimilation,Landsat-8 OLI,BRDF
更新于2025-09-09 09:28:46
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Multi-Temporal Remote Sensing of Landscape Dynamics and Pattern Changes in Dire District, Southern, Ethiopia
摘要： Science and reporting information needs for monitoring dynamics in land-cover over time prompted research, and made operational, a wide variety of change detection methods utilizing multiple dates of remotely sensed data. Improper land use results in land degradation and decline in agricultural productivity. Hence, in order to get maximum benefits out of land, proper utilization of its resources is inevitable. The present study was aimed at identifying the land cover changes in the study area in the last 25 years and determines the extent and direction of change that has occurred. The study made use of Landsat TM 1986 and 2011 Remote Sensing Satellite Image for analysis to determine the extent and pattern of rangeland change. The results of the landuse/landcover change detection showed that in the last 25 years, 3 major changes were observed, grass land and open shrub land resource significantly decreased at a rate of 17.1 km2/ year and 12 km2/year respectively. On the other hand in 25 years dense bushland, open bush land, dense shrub land and cultivated land has shown increment in size at a rate of 0.23 km2/year, 13.5 km2/year, 6.3 km2/year and 0.2 km2/year, respectively within 25 years. The expansion of unpalatable woody species significantly reduced the rangeland size and availability of grasses. The consequence of the decrease in herbaceous biomass production might result in high risk of food insecurity in the area unless proper interventions are made in time.
关键词： Landuse/Landcover,Dire district,Landsat TM,GIS and remote sensing
更新于2025-09-09 09:28:46
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Using new remote sensing satellites for assessing water quality in a reservoir
摘要： Water quality monitoring could benefit from information derived from the newest generation of medium resolution Earth observation satellites. The main objective of our study was to assess the suitability of both Landsat 8 and Sentinel-2A satellites for estimating and mapping Secchi disk transparency (SDT), a common measurement of water clarity, in Cassaffousth Reservoir (Córdoba, Argentina). Ground observations and a dataset of four Landsat 8 and four Sentinel-2A images were used to create and validate models to estimate SDT in the reservoir. The selected algorithms were used to obtain graphic representations of water clarity. Slight differences were found between Landsat 8 and Sentinel-2 estimations. Thus, we demonstrated the suitability of both satellites for estimating and mapping water quality. This study highlights the importance of free and readily-available satellite datasets in monitoring water quality especially in countries where conventional monitoring programs are either lacking or unsatisfactory.
关键词： water clarity,Secchi disk depth,monitoring,Sentinel-2 MSI,remote sensing,Landsat 8 OLI
更新于2025-09-09 09:28:46
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Introduction to Special issue on Geologic Remote Sensing
摘要： Herein we provide an overview of science and technology involved in remote sensing, and outlines some practical constraints in applications to geological problems. We further summarize diagnostic spectral features of important geological material that can be detected using satellite- and air-borne remote sensing. Finally, the papers contained in the special issue are briefly introduced.
关键词： Geologic Remote Sensing,Spectral Features,Hyperspectral Remote Sensing,LANDSAT,ASTER
更新于2025-09-09 09:28:46
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Extending the SCOPE model to combine optical reflectance and soil moisture observations for remote sensing of ecosystem functioning under water stress conditions
摘要： A radiative transfer and process-based model, called Soil-Canopy-Observation of Photosynthesis and Energy fluxes (SCOPE), relates remote sensing signals with plant functioning (i.e., photosynthesis and evapotranspiration). Relying on optical remote sensing data, the SCOPE model estimates photosynthesis and evapotranspiration, but these ecosystem-level fluxes may be significantly overestimated if water availability is the primary limiting factor for vegetation. Remedying this shortcoming, additional information from extra sources is needed. In this study, we propose considering water stress in SCOPE by incorporating soil moisture data in the model, besides using satellite optical reflectance observations. A functional link between soil moisture, soil surface resistance, leaf water potential and carboxylation capacity is introduced as an extra element in SCOPE, resulting in a soil moisture integrated version of the model, SCOPE-SM. The modified model simulates additional state variables: (i) vapor pressure (ei), both in the soil pore space and leaf stomata in equilibrium with liquid water potential; (ii) the maximum carboxylation capacity (Vcmax) by a soil moisture dependent stress factor; and (iii) the soil surface resistance (rss) through approximation by a soil moisture dependent hydraulic conductivity. The new approach was evaluated at a Fluxnet site (US-Var) with dominant C3 grasses and covering a wet-to-dry episode from January to August 2004. By using the original SCOPE (version 1.61), we simulated half-hourly time steps of plant functioning via locally measured weather data and time series of Landsat (TM and ETM) imagery. Then, SCOPE-SM was similarly applied to simulate plant functioning for three cases using Landsat imagery: (i) with modeled ei; (ii) with modeled ei and Vcmax; and (iii) with modeled ei, Vcmax, and rss. The outputs of all four simulations were compared to flux tower plant functioning measurements. The results indicate a significant improvement proceeding from the first to the fourth case in which we used both Landsat optical imagery and soil moisture data through SCOPE-SM. Our results show that the combined use of optical reflectance and soil moisture observations has great potential to capture variations of photosynthesis and evapotranspiration during drought episodes. Further, we found that the information contained in soil moisture observations can describe more variations of measured evapotranspiration compared to the information contained in thermal observations.
关键词： SCOPE-SM model,Landsat,Evapotranspiration,Vegetation properties,Water stress,Remote sensing,Soil moisture,Vegetation functioning,Vapor pressure,Photosynthesis,Maximum carboxylation capacity,Soil surface resistance,Reflectance
更新于2025-09-09 09:28:46
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Spatial Algal Bloom Characterization by Landsat 8-Oli and Field Data Analysis
摘要： Water pollution is an important problem around the world as it is closely related to human and environmental health. Field campaigns are expensive, time consuming and may provide little information. Remote sensing provides synoptic spatio-temporal views and can lead to a better understanding of lake ecology. In this work an extreme algal bloom event which occurred in a reservoir is characterized by LANDSAT 8-OLI sensor and in situ sampling. Chlorophyll-a concentration and algae abundance data are measured on samples collected simultaneously with satellite pass and used to build semiempirical models. Two linear functions to calculate chlorophyll-a from satellite data are presented and compared. A linear model from band 2 (blue) and band 5 (NIR) presents the best performance with a determination coefficient equal to 0,89. In situ and satellite chlorophyll-a lead comparable trophic class assessment, hypertrophic. Both Models fail to predict chlorophyll-a concentration near river intrusion (North), where low values of reflectance are recorded.
关键词： linear regression,chlorophyll-a,phytoplankton,LANDSAT 8-OLI,eutrophication
更新于2025-09-09 09:28:46
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[IEEE 2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP) - Vancouver, BC, Canada (2018.8.29-2018.8.31)] 2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP) - A Cloud Detection Algorithm for Remote Sensing Images Using Fully Convolutional Neural Networks
摘要： This paper presents a deep-learning based framework for addressing the problem of accurate cloud detection in remote sensing images. This framework benefits from a Fully Convolutional Neural Network (FCN), which is capable of pixel-level labeling of cloud regions in a Landsat 8 image. Also, a gradient-based identification approach is proposed to identify and exclude regions of snow/ice in the ground truths of the training set. We show that using the hybrid of the two methods (threshold-based and deep-learning) improves the performance of the cloud identification process without the need to manually correct automatically generated ground truths. In average the Jaccard index and recall measure are improved by 4.36% and 3.62%, respectively.
关键词： deep-learning,Landsat 8,FCN,image segmentation,U-Net,remote sensing,CNN,Cloud detection
更新于2025-09-09 09:28:46