scholarly journals Combining Geostatistics and Remote Sensing Data to Improve Spatiotemporal Analysis of Precipitation

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3132
Author(s):  
Emmanouil A. Varouchakis ◽  
Anna Kamińska-Chuchmała ◽  
Grzegorz Kowalik ◽  
Katerina Spanoudaki ◽  
Manuel Graña
Keyword(s):  
Remote Sensing ◽  
Wide Spectrum ◽  
Rapid Development ◽  
Remote Sensing Data ◽  
Spatiotemporal Analysis ◽  
Space Time ◽  
Attractive Alternative ◽  
Kriging Method ◽  
Sensing Data ◽  
Crete Island

The wide availability of satellite data from many distributors in different domains of science has provided the opportunity for the development of new and improved methodologies to aid the analysis of environmental problems and to support more reliable estimations and forecasts. Moreover, the rapid development of specialized technologies in satellite instruments provides the opportunity to obtain a wide spectrum of various measurements. The purpose of this research is to use publicly available remote sensing product data computed from geostationary, polar and near-polar satellites and radar to improve space–time modeling and prediction of precipitation on Crete island in Greece. The proposed space–time kriging method carries out the fusion of remote sensing data with data from ground stations that monitor precipitation during the hydrological period 2009/10–2017/18. Precipitation observations are useful for water resources, flood and drought management studies. However, monitoring stations are usually sparse in regions with complex terrain, are clustered in valleys, and often have missing data. Satellite precipitation data are an attractive alternative to observations. The fusion of the datasets in terms of the space–time residual kriging method exploits the auxiliary satellite information and aids in the accurate and reliable estimation of precipitation rates at ungauged locations. In addition, it represents an alternative option for the improved modeling of precipitation variations in space and time. The obtained results were compared with the outcomes of similar works in the study area.

Urbanization in India – Spatiotemporal analysis using remote sensing data

2009 ◽  
Vol 33 (3) ◽  
pp. 179-188 ◽  
Author(s):  
H. Taubenböck ◽  
M. Wegmann ◽  
A. Roth ◽  
H. Mehl ◽  
S. Dech
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Spatiotemporal Analysis ◽  
Sensing Data

scholarly journals Application of Deep Learning Algorithm to Build an Automated Cloud Segmentation Model Based on Open Data Cube Framework

2020 ◽  
Vol 36 (4) ◽  
Author(s):  
Pham Vu Dong ◽  
Bui Quang Thanh ◽  
Nguyen Quoc Huy ◽  
Vo Hong Anh ◽  
Pham Van Manh
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Rapid Development ◽  
Open Data ◽  
Remote Sensing Data ◽  
Data Cube ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Sensing Data ◽  
Multi Temporal

Cloud detection is a significant task in optical remote sensing to reconstruct the contaminated cloud area from multi-temporal satellite images. Besides, the rapid development of machine learning techniques, especially deep learning algorithms, can detect clouds over a large area in optical remote sensing data. In this study, the method based on the proposed deep-learning method called ODC-Cloud, which was built on convolutional blocks and integrating with the Open Data Cube (ODC) platform. The results showed that our proposed model achieved an overall 90% accuracy in detecting cloud in Landsat 8 OLI imagery and successfully integrated with the ODC to perform multi-scale and multi-temporal analysis. This is a pioneer study in techniques of storing and analyzing big optical remote sensing data.

scholarly journals Improving the Efficiency of Remote Sensing Data Interpretation by Analyzing Neighborhood Descriptors

2018 ◽  
Vol 28 (3) ◽  
pp. 352-365
Author(s):  
Stanislav A. Yamashkin ◽  
Anatoliy A. Yamashkin
Keyword(s):  
Remote Sensing ◽  
Classification Accuracy ◽  
System Analysis ◽  
Remote Sensing Data ◽  
Automated Analysis ◽  
Data Interpretation ◽  
Space Time ◽  
The State ◽  
Survey Analysis ◽  
Sensing Data

Introduction. In evaluating the space-time structure of the Earth’s surface, the data of remote sensing of the Earth become more important. Increasing the effectiveness of space survey analysis tools is possible through studying the problem of obtaining an integrated space-time characterization of the state of lands. The purpose of this study is to improve the accuracy of the automated analysis of remote sensing data by taking into account the invariant and dynamic descriptors of the vicinity. Materials and Methods. In order to improve the accuracy of the remote sensing data classification, a computation of complex space-time characteristics of the state of the lands was conducted based on the system analysis of data characterizing the dynamic and invariant states of the territory surrounding the geophysical site. The formalization of this process includes methods for calculating a set of numerical descriptors of the neighborhood: local entropy, local range, standard deviation, color moment, histogram of hues, and color cortege. A technique for calculating a complex descriptor based on the Fisher vector is described. To approbate the solution, a plan for the experiment was drawn up and a sample of the initial data was sampled. Results. The approbation of the methodology and the algorithm developed on its basis, implemented as a set of programs, on the test polygon system showed a variation in the classification accuracy in the range of 81–89% (without regard to the neighborhood), and taking into account the neighborhood, it increases to 91–97%. It is revealed that a significant increase in the radius of the analyzed neighborhood leads to a decrease in the classification accuracy. Conclusions. The application of the developed set of programs allows for the rapid implementation of modeling of spatial systems for the purpose of thematic mapping of land use and analyzing the development of emergency situations. The developed methodology for analyzing lands with regard to the descriptors of the neighborhood makes it possible to improve the accuracy of classification.

scholarly journals END-TO-END PHYSICS-INFORMED REPRESENTATION LEARNING FOR SATELLITE OCEAN REMOTE SENSING DATA: APPLICATIONS TO SATELLITE ALTIMETRY AND SEA SURFACE CURRENTS

2021 ◽  
Vol V-3-2021 ◽  
pp. 295-302
Author(s):  
R. Fablet ◽  
M. M. Amar ◽  
Q. Febvre ◽  
M. Beauchamp ◽  
B. Chapron
Keyword(s):  
Remote Sensing ◽  
Satellite Altimetry ◽  
Remote Sensing Data ◽  
Representation Learning ◽  
Space Time ◽  
Satellite Orbits ◽  
Sensing Data ◽  
End To End ◽  
Ocean Remote Sensing ◽  
Learning Schemes

Abstract. This paper addresses physics-informed deep learning schemes for satellite ocean remote sensing data. Such observation datasets are characterized by the irregular space-time sampling of the ocean surface due to sensors’ characteristics and satellite orbits. With a focus on satellite altimetry, we show that end-to-end learning schemes based on variational formulations provide new means to explore and exploit such observation datasets. Through Observing System Simulation Experiments (OSSE) using numerical ocean simulations and real nadir and wide-swath altimeter sampling patterns, we demonstrate their relevance w.r.t. state-of-the-art and operational methods for space-time interpolation and short-term forecasting issues. We also stress and discuss how they could contribute to the design and calibration of ocean observing systems.

Multi-Stage Fusion and Multi-Source Attention Network for Multi-Modal Remote Sensing Image Segmentation

2021 ◽  
Vol 12 (6) ◽  
pp. 1-20
Author(s):  
Jiaqi Zhao ◽  
Yong Zhou ◽  
Boyu Shi ◽  
Jingsong Yang ◽  
Di Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Rapid Development ◽  
Remote Sensing Data ◽  
Semantic Segmentation ◽  
Sensor Technology ◽  
Feature Maps ◽  
Attention Network ◽  
Modal Data ◽  
Sensing Data ◽  
Multi Stage

With the rapid development of sensor technology, lots of remote sensing data have been collected. It effectively obtains good semantic segmentation performance by extracting feature maps based on multi-modal remote sensing images since extra modal data provides more information. How to make full use of multi-model remote sensing data for semantic segmentation is challenging. Toward this end, we propose a new network called Multi-Stage Fusion and Multi-Source Attention Network ((MS) 2 -Net) for multi-modal remote sensing data segmentation. The multi-stage fusion module fuses complementary information after calibrating the deviation information by filtering the noise from the multi-modal data. Besides, similar feature points are aggregated by the proposed multi-source attention for enhancing the discriminability of features with different modalities. The proposed model is evaluated on publicly available multi-modal remote sensing data sets, and results demonstrate the effectiveness of the proposed method.

Quantifying the costs of soil constraints to Australian agriculture: a case study of wheat in north-eastern Australia

Soil Research ◽  
2016 ◽  
Vol 54 (6) ◽  
pp. 700 ◽  
Author(s):  
Y. P. Dang ◽  
P. W. Moody
Keyword(s):  
Remote Sensing ◽  
Hybrid Approach ◽  
Remote Sensing Data ◽  
Spatiotemporal Analysis ◽  
Yield Potential ◽  
Wheat Grain ◽  
Sensing Data ◽  
Soil Constraints ◽  
Eastern Australia ◽  
The Cost

Soil salinity, sodicity, acidity and alkalinity, elemental toxicities, such as boron, chloride and aluminium, and compaction are important soil constraints to agricultural sustainability in many soils of Australia. There is considerable variation in the existing information on the costs of each of the soil constraints to Australian agriculture. Determination of the cost of soil constraints requires measuring the magnitude and causes of yield gap (Yg) between yield potential and actual yield. We propose a ‘hybrid approach’ consisting of determining the magnitude of Yg and the cause(s) of Yg for spatiotemporal representation of Yg that can be apportioned between management and soil constraint effects, thereby allowing a better estimate of the cost of mitigation of the constraints. The principles of this approach are demonstrated using a 2820-ha wheat-growing farm over a 10-year period to quantify the costs of the proportion of forfeited Yg due to soil constraints. Estimated Yg over the whole farm varied annually from 0.6 to 2.4Mgha–1, with an average of 1.4Mgha–1. A multiyear spatiotemporal analysis of remote sensing data identified that 44% of the farm was consistently poor performing, suggesting the potential presence of at least one soil constraint. The percentage decrease in productivity due to soil constraints varied annually from 5% to 24%, with an average estimated annual loss of wheat grain production of 182 Mg per year on 1069ha. With the 2015 season’s average wheat grain price (A$0.29kg–1), the estimated annual value of lost agricultural production due to soil constraints was estimated at A$52780 per year. For successful upscaling of the hybrid approach to regional or national scale, Australia has reliable data on the magnitude of Yg. The multiyear spatiotemporal analysis of remote sensing data would identify stable, consistently poor performing areas at a similar scale to Yg. Soil maps could then be used to identify the most-limiting soil constraints in the consistently poor performing areas. The spatial distribution of soil constraint at similar scale could be used to obtain the cost of lost production using soil constraint–grain yield models.

scholarly journals A Distributed Storage and Access Approach for Massive Remote Sensing Data in MongoDB

2019 ◽  
Vol 8 (12) ◽  
pp. 533 ◽  
Author(s):  
Shuang Wang ◽  
Guoqing Li ◽  
Xiaochuang Yao ◽  
Yi Zeng ◽  
Lushen Pang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Relational Databases ◽  
Large Scale ◽  
Distributed Storage ◽  
Rapid Development ◽  
Remote Sensing Data ◽  
Image Data ◽  
Sensing Data ◽  
Cluster Architecture ◽  
Distributed Cluster

With the rapid development of earth-observation technology, the amount of remote sensing data has increased exponentially, and traditional relational databases cannot satisfy the requirements of managing large-scale remote sensing data. To address this problem, this paper undertakes intensive research of the NoSQL (Not Only SQL) data management model, especially the MongoDB database, and proposes a new approach to managing large-scale remote sensing data. Firstly, based on the sharding technology of MongoDB, a distributed cluster architecture was designed and established for massive remote sensing data. Secondly, for the convenience in the unified management of remote sensing data, an archiving model was constructed, and remote sensing data, including structured metadata and unstructured image data, were stored in the above cluster separately, with the metadata stored in the form of a document, and image data stored with the GridFS mechanism. Finally, by designing different shard strategies and comparing MongoDB cluster with a typical relational database, several groups of experiments were conducted to verify the storage performance and access performance of the cluster. The experimental results show that the proposed method can overcome the deficiencies of traditional methods, as well as scale out the database, which is more suitable for managing massive remote sensing data and can provide technical support for the management of massive remote sensing data.

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