Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art

Drought, a climate-related disaster impacting a variety of sectors, poses challenges for millions of people in South Asia. Accurate and complete drought information with a proper monitoring system is very important in revealing the complex nature of drought and its associated factors. In this regard, deep learning is a very promising approach for delineating the non-linear characteristics of drought factors. Therefore, this study aims to monitor drought by employing a deep learning approach with remote sensing data over South Asia from 2001–2016. We considered the precipitation, vegetation, and soil factors for the deep forwarded neural network (DFNN) as model input parameters. The study evaluated agricultural drought using the soil moisture deficit index (SMDI) as a response variable during three crop phenology stages. For a better comparison of deep learning model performance, we adopted two machine learning models, distributed random forest (DRF) and gradient boosting machine (GBM). Results show that the DFNN model outperformed the other two models for SMDI prediction. Furthermore, the results indicated that DFNN captured the drought pattern with high spatial variability across three penology stages. Additionally, the DFNN model showed good stability with its cross-validated data in the training phase, and the estimated SMDI had high correlation coefficient R2 ranges from 0.57~0.90, 0.52~0.94, and 0.49~0.82 during the start of the season (SOS), length of the season (LOS), and end of the season (EOS) respectively. The comparison between inter-annual variability of estimated SMDI and in-situ SPEI (standardized precipitation evapotranspiration index) showed that the estimated SMDI was almost similar to in-situ SPEI. The DFNN model provides comprehensive drought information by producing a consistent spatial distribution of SMDI which establishes the applicability of the DFNN model for drought monitoring.

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Deep Learning Estimation of Daily Ground‐Level NO 2 Concentrations from Remote Sensing Data

Journal of Geophysical Research Atmospheres ◽

10.1029/2021jd034925 ◽

2021 ◽

Author(s):

Masoud Ghahremanloo ◽

Yannic Lops ◽

Yunsoo Choi ◽

Bijan Yeganeh

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

Remote Sensing Data ◽

Ground Level ◽

Sensing Data

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Efficient land use based on remote sensing data

Problems of AgriMarket ◽

10.46666/2021-3.2708-9991.20 ◽

2021 ◽

Vol 3 ◽

pp. 180-185

Author(s):

Y. M. Kenzhegaliyev ◽

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Keyword(s):

Remote Sensing ◽

Land Use ◽

Remote Sensing Data ◽

Crop Productivity ◽

The State ◽

Agricultural Crops ◽

Earth Remote Sensing ◽

Spectral Reflectivity ◽

Sensing Data ◽

Current Location

The goal -is to explore ways of using Earth remote sensing data for efficient land use. Methods - detailed information on current location of certain types of agricultural crops in the study areas has been summarized, which opens up opportunities for the effective use of cultivated areas. It was revealed that the basis of the principle of the method under consideration is the relationship between the state and structure of vegetation types with its reflective ability. It has been determined that information on the spectral reflective property of the vegetation cover in the future can help replace more laborious methods of laboratory analysis. For classification of farmland, satellite images of medium spatial resolution with a combination of channels in natural colors were selected. Results - a method for identifying agricultural plants by classification according to the maximum likelihood algorithm was considered. The commonly used complexes of geoinformation software products with modules for special image processing allow displaying indicators in the form of raster images. It is shown that the use of Earth remote sensing data is the most relevant solution in the field of crop recognition and makes it possible to simplify the implementation of such types of work as the analysis of the intensity of land use, the assessment of the degree of pollution with weeds and determination of crop productivity. Conclusions - the research results given in the article indicate that timely information on the current location of certain types of agricultural crops in the studied territories significantly simplifies the implementation of the tasks and increases the resource potential of agricultural lands. In turn, the timing of the survey and the state of environment affect the spectral reflectivity of vegetation.

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LIGHT POLLUTION IN SLOVENIA IN 2019 WITH SPECIAL REGARD TO NATURA 2000 AREAS

Geografski pregled ◽

10.35666/23038950.2020.42.69 ◽

2020 ◽

Vol 42 ◽

pp. 69-81

Keyword(s):

Remote Sensing ◽

Protected Areas ◽

Remote Sensing Data ◽

Natura 2000 ◽

The State ◽

Light Pollution ◽

Sensing Data ◽

Special Regard ◽

Ljubljansko Barje

Light pollution in Slovenia in 2019 with special regard to Natura 2000 areas The article shows the state of light pollution in Slovenia. Remote sensing data from the Suomi satellite were analysed. Light pollution is shown by radiance expressed in nW/(sr cm2 ). In Slovenia, there are large differences in state of light polution. The most polluted areas are located in the area of larger settlements and in areas with higher levels of infrastructure. The spread of light does not stop at the borders of protected areas, so we also analyzed the state of light pollution in Natura 2000 sites in Slovenia. It turns out that the most lightpolluted areas are those that lie around larger settlements or suburbanised regions (Ljubljansko Barje, Šmarna gora, Drava).

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Experience of the application of remote sensing data in the problems of geo-ecological monitoring of the coal mining area

E3S Web of Conferences ◽

10.1051/e3sconf/20197502001 ◽

2019 ◽

Vol 75 ◽

pp. 02001

Author(s):

Olga Giniyatullina ◽

Evgeniy Schastlivtsev ◽

Vladimir Kovalev

Keyword(s):

Remote Sensing ◽

Coal Mining ◽

Remote Sensing Data ◽

Ecological Monitoring ◽

Mining Area ◽

The State ◽

Sensing Data ◽

Coal Mining Area ◽

Mining Region ◽

Dust Load

The experience of solving problems of geoecological monitoring of coal mining region with the use of remote sensing data is presented. The results of control over the boundaries of coal-mining enterprises, assessment of the degree of self-growth of dumps, monitoring of the state of vegetation near objects of coal mining and dust load of the area are shown.

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