Soil Moisture Estimation using Sentinel-1 SAR Data and Land Surface Temperature in Panchmahal District, Gujarat State

Sutariya, Sachin; ANKUR, Hirapara; Meherbanali, Momin; Tiwari, Mukesh; Singh, Vijay Kant; Kalubarme, Manik H.

doi:10.30897/ijegeo.777434

Cited by 14 publications

(10 citation statements)

References 32 publications

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“…Accordingly, two new architectures were presented, called CNN architecture (C II ) and CNN architecture (C III ), as shown in Figure 5. We can now say with high confidence that: (1) VV polarization is more sensitive than VH polarization for soil moisture retrieval; similar results have been found by [19][20][21][22][23]. (2) The combination of VV and VH polarization has resulted in improvement in the prediction of soil moisture from Sentinel-1 images, our result agree with [24][25][26].…”

Section: Comparison Between Cnn Architecture (𝐶 𝐼𝐼 ) and Cnn Architecture (𝐶 𝐼𝐼𝐼 )supporting

confidence: 86%

“…Sentinel-1 images can be used to retrieve soil moisture over wheat-covered areas [20]. VV polarization was more sensitive for soil moisture retrieval from Sentinel-1 data as compared to VH polarization [21][22][23]. Several studies have indicated that the accuracy of the soil moisture estimates improved when using both VV and VH polarization, instead of only VV or VH polarization [24][25][26].…”

Section: Introductionmentioning

confidence: 99%

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A Convolutional Neural Network Algorithm for Soil Moisture Prediction from Sentinel-1 SAR Images

2021

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Achieving the rational, optimal, and sustainable use of resources (water and soil) is vital to drink and feed 9.725 billion by 2050. Agriculture is the first source of food production, the biggest consumer of freshwater, and the natural filter of air purification. Hence, smart agriculture is a “ray of hope” in regard to food, water, and environmental security. Satellites and artificial intelligence have the potential to help agriculture flourish. This research is an essential step towards achieving smart agriculture. Prediction of soil moisture is important for determining when to irrigate and how much water to apply, to avoid problems associated with over- and under-watering. This also contributes to an increase in the number of areas being cultivated and, hence, agricultural productivity and air purification. Soil moisture measurement techniques, in situ, are point measurements, tedious, time-consuming, expensive, and labor-intensive. Therefore, we aim to provide a new approach to detect moisture content in soil without actually being in contact with it. In this paper, we propose a convolutional neural network (CNN) architecture that can predict soil moisture content over agricultural areas from Sentinel-1 images. The dual-pol (VV–VH) Sentinel-1 SAR data have being utilized (V = vertical, H = horizontal). The CNN model is composed of six convolutional layers, one max-pooling layer, one flatten layer, and one fully connected layer. The total number of Sentinel-1 images used for running CNN is 17,325 images. The best values of the performance metrics (coefficient of determination (R2=0.8664), mean absolute error (MAE=0.0144), and root mean square error (RMSE=0.0274)) have been achieved due to the use of Sigma naught VH and Sigma naught VV as input data to the CNN architecture (C). Results show that VV polarization is better than VH polarization for soil moisture retrieval, and that Sigma naught, Gamma naught, and Beta naught have the same influence on soil moisture estimation.

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Section: Comparison Between Cnn Architecture (𝐶 𝐼𝐼 ) and Cnn Architecture (𝐶 𝐼𝐼𝐼 )supporting

confidence: 86%

Section: Introductionmentioning

confidence: 99%

A Convolutional Neural Network Algorithm for Soil Moisture Prediction from Sentinel-1 SAR Images

2021

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“…In addition, Landsat 5 TM data, SPOT High Resolution Visible (HRV) data and ground survey data were combined for mapping and classification of high relief terrain (Franklin, 1991); and habitat land cover (LC) (Matthew, 1991). Otukei and Blaschke (2012) worked on comparison of Land Surface Temperature (LST) retrieved from Landsat 7 ETM+ low gain and high gain thermal infrared band data, and that of ground data (Celik et al, 2019;Sutari̇ya et al, 2021). Furthermore, Metz et al (2017) used air temperature ground data to check and improve the predicted LSTs obtained from MODIS LST data.…”

Section: Research Articlementioning

confidence: 99%

The Methodology and Results from Ground Validation of Satellite Observations at Gas Flaring Sites in Nigeria

Morakinyo

Lavender

Abbott

2021

International Journal of Environment and Geoinformatics

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This study examines the importance of ground validation to Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) observations at 2 gas flaring sites in Rivers State, Niger Delta, Nigeria. 12 Landsat imagery (3 Landsat 5 TM and 9 Landsat 7 ETM+) data acquired from 25/03/1987 to 08/03/2013 with < 5 % cloud contamination were used. Both sites are located within a single Landsat scene (Path 188, Row 057). Ground measurements and observations at both sites took place from (04/08/2012-21/09/2012) and (05/08/2019-22/09/2019). Parameters measured are coordinates of points and features, air temperature and relative humidity; and photographs of locations and features were taken. Both air temperature and relative humidity were measured at 3 different levels above the ground surface at 1 minute interval. The results show that the locational error of points and features from Landsat data and fieldwork measurements give negligible difference of 1.0 × 10-6 to 7.3 × 10-6. Also, 4 classes of land use and land cover (LULC) types retrieved from Landsat data are the same with those observed on site during ground measurements. The air temperature (AT) recorded and Land Surface Temperature (LST) retrieved from Landsat data for both sites show that the closer the distance to the flare, the higher the temperature and vice versa. Results show that the spatial variability in ground AT and derived LST from Landsat data differs within 0.8 to 6.0 K because AT is different from LST. Based on the results acquired, it can be concluded that ground validation is essential and required for maximum utilization and exploitation of remote sensing technology applications.

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“…Turan (2019) investigated regime types in soil temperature and moisture 10 years and above for 183 meteorological stations in the Eastern Black Sea Section and Central Anatolia Region. According to the Newhall simulation model, it was detected that the soil temperature regime of these regions entered the mesic regime (Sutariya et al, 2021;Röder et al, 2010). Until now, the soil temperature regime of Turkey based on both the Soil Taxonomy and the classical climatic regime approach (monthly average soil temperature change) has not been put forward with a holistic and systematic view.…”

Section: Introductionmentioning

confidence: 99%

The Character of Soil Temperature Regime over Turkey

TEKKANAT

ÖZTÜRK

2022

International Journal of Environment and Geoinformatics

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This paper presents the spatial and temporal characteristics of Turkish soil temperature regimes. Basic data consists of the monthly soil temperature averages from 153 stations at depths of 5, 10, 20, 50 and 100 cm for 44 years record length. The soil temperature regime of Turkey has been analyzed based on both classical climatic regime approach (monthly average soil temperature change) and Soil Taxonomy. As expected, The annual course of soil temperatures differs between the coastal and inland areas of Turkey and in the western and eastern half. Latitude and continentally shapes the soil temperature regime in the eastern half of Turkey while elevation shapes the soil temperature regime in the western half of Turkey. The frost is seen in the eastern half of Turkey in January and February, except for coastal areas. Moreover, the frost period goes up to 4 months (November-February) in northeastern Anatolia. Frost reaches up to 90 cm in this area. The high soil temperatures adversely affecting plant life (35 °C and above) is seen in the South-East Anatolia Region and Çukurova region in Turkey in July and August. These high temperatures are effective up to 80 cm in the region. The majority of Turkey has thermic soil or mesic soil. Considering long-term averages , there are four main soil temperature regimes in Turkey as frigid, thermic, mesic, hyperthermic. The eastern Mediterranean coastline and the southwestern Anatolian coastline are hyperthermic soil temperature regime character and the northeasts Anatolia (Ardahan surroundings) show a frigid temperature regime character. Percentages of thermic and hyperthermic soil temperature regime have been increasing in the last 10 years. The increase is probably due to the above normal average air temperatures from July to December and the breaking of air temperature records in the last 10 years.

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Soil Moisture Estimation using Sentinel-1 SAR Data and Land Surface Temperature in Panchmahal District, Gujarat State

Cited by 14 publications

References 32 publications

A Convolutional Neural Network Algorithm for Soil Moisture Prediction from Sentinel-1 SAR Images

A Convolutional Neural Network Algorithm for Soil Moisture Prediction from Sentinel-1 SAR Images

The Methodology and Results from Ground Validation of Satellite Observations at Gas Flaring Sites in Nigeria

The Character of Soil Temperature Regime over Turkey

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