scholarly journals Land Cover Mapping for Agricultural Water Management of Rice-based Irrigation Systems in Myanmar Using GIS and Remote Sensing

2007 ◽  
Vol 12 (2) ◽  
pp. 1-15
Author(s):  
Aung Than Oo ◽  
Sanga-Ngoie Kazadi ◽  
Kaoru Fukuyama
Keyword(s):  
Water Management ◽  
Remote Sensing ◽  
Land Cover ◽  
Land Cover Mapping ◽  
Agricultural Water ◽  
Irrigation Systems ◽  
Agricultural Water Management ◽  
Gis And Remote Sensing

scholarly journals REMOTE SENSING FOR AGRICULTURAL WATER MANAGEMENT (RS4AWM) EDUCATIONAL COURSE WITHIN POST-GRADUATE PROGRAM FOR WATER PROFESSIONALS

2018 ◽  
Vol XLII-5 ◽  
pp. 21-27
Author(s):  
P. Karimi ◽  
S. Pareeth ◽  
C. D. Fraiture
Keyword(s):  
Water Management ◽  
Remote Sensing ◽  
Environmental Science ◽  
Graduate Program ◽  
Water Security ◽  
Climate Science ◽  
Future Generation ◽  
Agricultural Water ◽  
Agricultural Water Management ◽  
Post Graduate

<p><strong>Abstract.</strong> Geospatial technology has become a core subject in many of the graduate and post-graduate educational curriculum. Last two decades saw substantial development in the field of geospatial science including earth observation and remote sensing and these technologies are widely being used in applications related to land and water resources monitoring, agricultural water management, hydrology, climate science, ecology, environmental science, civil and planning etc. Among these geospatial technologies for agricultural water management is extremely valuable because food and water security are among the biggest challenges that many countries are facing. This is widely recognized in the United Nations Sustainable Development Goals (SDGs) 2 and 6. Reliable information at local and regional scales are the building block for identifying effective and sustainable coping strategies. In this context, developing the capacity of the local experts in using these technologies to support informed decision making is important. RS4AWM course aims at contributing toward this goal by training future generation of water and agriculture professional who will be equipped to use geospatial tools and data in addressing future food and water challenges at different scales. In this manuscript, we explain the evolution and structure of this course and how it is designed to cater the water professionals globally.</p>

scholarly journals Sustainable Agricultural Water Management in Pinios River Basin Using Remote Sensing and Hydrologic Modeling

2016 ◽  
Vol 162 ◽  
pp. 277-283 ◽  
Author(s):  
Alexander Psomas ◽  
Vasiliki Dagalaki ◽  
Yiannis Panagopoulos ◽  
Dimitra Konsta ◽  
Maria Mimikou
Keyword(s):  
Water Management ◽  
Remote Sensing ◽  
River Basin ◽  
Hydrologic Modeling ◽  
Agricultural Water ◽  
Agricultural Water Management ◽  
Pinios River

High-resolution soil moisture mapping through the use of Cosmic-Ray Neutron Sensor and Sentinel-1 data for temperate and semi-arid environments

2021 ◽  
Author(s):  
Hami Said ◽  
Modou Mbaye ◽  
Lee Kheng Heng ◽  
Emil Fulajtar ◽  
Georg Weltin ◽  
...  
Keyword(s):  
Water Management ◽  
Remote Sensing ◽  
Soil Moisture ◽  
Regional Scale ◽  
Cosmic Ray ◽  
Microwave Remote Sensing ◽  
Nuclear Technology ◽  
Agricultural Water ◽  
Agricultural Water Management ◽  
Semi Arid

&lt;p&gt;Global climate change has a major impact on the availability of water in agriculture. Sustainable agricultural productivity to ensure food security requires good agricultural water management.&lt;/p&gt;&lt;p&gt;Soil moisture is one of the important variables in irrigation management, and there are many different techniques for estimating it at different scales, from point to landscape scales.&lt;/p&gt;&lt;p&gt;Cosmic-Ray Neutron Sensor (CRNS) technology has the capability to estimate field-scale soil moisture (SM) in large areas of up to 20 to 30 ha and has demonstrated its ability to support agricultural water management and hydrology studies. However, measurement of soil moisture on a global or regional scale can only be achieved from satellite remote sensing.&lt;/p&gt;&lt;p&gt;Recently, active microwave remote sensing Synthetic Aperture Radar (SAR) imaging from Sentinel-1 shows great potential for high spatial resolution soil moisture monitoring and can be the basis for producing soil moisture maps. However, these maps can be only used after calibration. Such calibration can be done through traditional, point soil moisture sampling or measurement, which is time-consuming and costly. CRNS technology can be used for calibration and validation remote sensing imagery predictions at field and area-wide level.&lt;/p&gt;&lt;p&gt;In this study a conversion model to retrieve soil moisture from Sentinel-1 (SAR) was developed using the VV (vertical-vertical) polarization, which is highly sensitive to soil moisture, and then calibrated and validated using CRNS data from temperate (Austria) and semi-arid (Kuwait) Environments. This study is a major step in the monitoring of soil moisture at high spatial and temporal resolution by combining remote sensing and the CRNS based nuclear technology. The preliminary results show the great potential of using nuclear technology such as CRNS for remote sensing calibration of Sentinel-1 (SAR).&lt;/p&gt;

scholarly journals Toward a Yearly Country-Scale CORINE Land-Cover Map without Using Images: A Map Translation Approach

2021 ◽  
Vol 13 (6) ◽  
pp. 1060
Author(s):  
Luc Baudoux ◽  
Jordi Inglada ◽  
Clément Mallet
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Time Frame ◽  
Superior Performance ◽  
Land Cover Mapping ◽  
Visual Interpretation ◽  
Remote Sensing Images ◽  
Corine Land Cover ◽  
Land Cover Map ◽  
By Products

CORINE Land-Cover (CLC) and its by-products are considered as a reference baseline for land-cover mapping over Europe and subsequent applications. CLC is currently tediously produced each six years from both the visual interpretation and the automatic analysis of a large amount of remote sensing images. Observing that various European countries regularly produce in parallel their own land-cover country-scaled maps with their own specifications, we propose to directly infer CORINE Land-Cover from an existing map, therefore steadily decreasing the updating time-frame. No additional remote sensing image is required. In this paper, we focus more specifically on translating a country-scale remote sensed map, OSO (France), into CORINE Land Cover, in a supervised way. OSO and CLC not only differ in nomenclature but also in spatial resolution. We jointly harmonize both dimensions using a contextual and asymmetrical Convolution Neural Network with positional encoding. We show for various use cases that our method achieves a superior performance than the traditional semantic-based translation approach, achieving an 81% accuracy over all of France, close to the targeted 85% accuracy of CLC.

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