Browsing by Author "Shetty, A."

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1-D CNN for Mineral Classification using Hyperspectral Data
(Institute of Electrical and Electronics Engineers Inc., 2023) Yadav, P.P.; Shetty, A.; Raghavendra, B.S.; Narasimhadhan, A.V.
Hyperspectral Image (HSI) is a potent remote sensing (RS) technique, capturing images over numerous narrow, contiguous spectral bands. Unlike traditional RS methods, HSI offers detailed spectral insights for each pixel, enhancing comprehension of the Earth's surface and its contents. Initially intended for mining and geology, its application has expanded across various domains. Yet, mineral identification poses challenges due to spectral signature variations and limited ground truth. Despite various advanced algorithms, including machine learning, no dedicated Deep Learning (DL) expert system exists for mineral classification in HSI. DL models require abundant training data and ground-truth, which are scarce in hyperspectral mineral data. Introducing the 1-D CNN model as a proposed method, we focus on enhancing mineral classification by increasing the available training data. The utilization of augmented training samples through the 1-D CNN model tackles the challenge of limited ground truth data, enabling accurate classification of mineral classes. Â© 2023 IEEE.
A Comparative Analysis of Forest Fire Risk Zone Mapping Methods with Expert Knowledge
(Springer, 2019) Yathish, H.; Athira, K.V.; Konkathi, K.; Umesh, U.; Shetty, A.
Despite repeated occurrences of forest fire, very less scientific studies have been reported in the Indian context especially in Kudremukh region to mitigate and suppress the fire. The objective of this article was to pool the expert knowledge on forest fire triggering factors from officials working in wildlife division in the Western Ghats of India through a questionnaire and to validate the risk zones obtained from three popular fire risk zone mapping methods namely logistic regression, multi-criteria decision analysis, and weighted overlay. Based on the earlier studies and expert knowledge, fire ignition parameters considered are elevation, slope, and aspect, proximity to roads, water bodies and area of human activities, normalized difference vegetation index (NDVI), land surface temperature (LST), and vegetation type. The regression model was based on previous fire occurrences and the other two based on expert’s opinion. The three models were validated and compared using past fire occurrence events. The logistic regression model gave 88.89% of accuracy and that of multi-criteria decision analysis with 74.6% accuracy, and that of weighted overlay method with an accuracy of 68.24% for the specific study area. The logistic regression model is useful in the presence of historical data, whereas expert knowledge is helpful for mapping risk zones using multi-criteria decision analysis and weighted overlay analysis when historical data are scarce or not available for mapping risk zones. The obtained risk maps can be used for deciding watchtower locations, installation of sensors, cameras, etc. In every forest division, it is recommended to prepare a standard questionnaire form and document their experiences on forest fire in the region under their supervision before they are getting transferred to another location. © 2019, Indian Society of Remote Sensing.
A Fully-Automated Framework for Mineral Identification on Martian Surface Using Supervised Learning Models
(Institute of Electrical and Electronics Engineers Inc., 2023) Kumari, P.; Soor, S.; Shetty, A.; Koolagudi, S.G.
The availability of various spectral libraries for CRISM (Compact Reconnaissance Imaging Spectrometer for Mars) data on NASA PDS (Planetary Data System) hugely facilitated the research on the surface mineralogy of Mars, however, building supervised learning models for mineral mapping appears to be challenging due to the lack of ground-truth/training data. In this paper, an automated framework is presented that classifies the spectra in a CRISM hyperspectral image using supervised learning models, where the required training data is produced by augmenting the mineral spectra available in the MICA (Minerals Identified in CRISM Analysis) spectral library, that keeps the key absorption signatures in the mineral spectra intact while providing adequate variability. The framework contains a pre-processing pipeline that in addition to some conventional pre-processing steps includes a new feature extraction method to capture the information of the most distinguishable absorption patterns in the spectra. The proposed framework is validated on a set of CRISM images captured from different locations on the Martian surface by using different types of supervised learning models, like random forests, support vector machines, and neural networks. An uncertainty analysis of the different steps involved in the pre-processing pipeline is provided, as well as a comparison of performances with some of the previously used methods for this purpose, which shows this framework works comparably well with a mean accuracy of around 0.8. Interactive mineral maps are also provided for the detected dominant minerals. © 2013 IEEE.
A Meaningful Reformulation of Relative Spectral Discrimination Power to Analyze Hyperspectral Data
(Institute of Electrical and Electronics Engineers Inc., 2023) Yadav, P.P.; Shetty, A.; Raghavendra, B.S.; Narasimhadhan, A.V.
Spectral matching algorithms (SMAs) discriminate and distinguish spectral signatures of earth surface features by comparing with their ground-truth spectra. Though different SMAs developed based on different theoretical strategies, choosing an effective SMA is still a challenging task. To study the performance of SMAs in distinguishing spectral signatures, few performance measure are developed and relative spectral discrimination power (RSDPW) is one such a measure. RSDPW discriminates how one spectral signature is distinct from another relative to a reference spectral signature. Classical way of measuring RSDPW do not takes into account of spectral matching between the two spectral signatures to be discriminated. Therefore, in this paper, a reformulation for RSDPW is presented to get a good idea about the spectral diversity of spectral signatures to measure RSDPW in a more meaningful manner and also to make it perspicacious. The experimental results show that the proposed reformulated RSDPW not only a meaningful way to measure it but also robust/standard enough to compare various SMAs by measuring it. Additionally, the range of RSDPW values for different levels of discrimination is demonstrated for the present study. Â© 2023 IEEE.
A QGIS Plug-in for Processing MODIS Data
(Institute of Electrical and Electronics Engineers Inc., 2019) Aishwarya Hegde, A.; Umesh, U.; Shetty, A.
In the past few decades number of Earth-observing satellites are continuously gathering information and only about 10 percent of the information is utilized by the users. With so much accessible information the researchers have not explored the datasets completely as there is absence of effective tool to process the information. MODIS data sensors have accessible data at various temporal and spatial resolutions. To productively use these datasets in open-source GIS programming like QGIS, there is a need to pre-process the dataset using a plug-in. The plug-in is built using python and PyQt interface for QGIS.The plug-in operates on MODIS Data (Terra/Aqua/Combined) computerizes and process the functionalities for MODIS products like MOD11, MOD09, MOD21. The processed datasets can be largely used in investigation of time series analysis for some earth resource application. Â© 2019 IEEE.
A Review on Emergence of a Nature-Inspired Polymer- Polydopamine in Biomedicine
(wiley, 2023) Rao, L.N.; Isloor, A.M.; Shetty, A.; Pallavi, K.C.
Biological structures have evolved throughout the millennia. Nature has been proactive with continual improvement to fine-tune the material properties resulting in optimization of the structure-function relationship. In keeping with this trend, mussels’ capacity to stick to varied moist surfaces with sufficient strength to endure powerful ocean currents has aroused curiosity and research into the role of polydopamine (PDA). It is a flexible and organic substance that exhibits individual mechanical characteristics and excellent fixation to different substrate stuff in a humidified environment. It is shown to be structurally inert and has been proved to be harmless to living systems. This makes it ideal to be used as a coating material. It has spurred the usage of the substance as molecular glue due to its perfect adherence as seen in mussels. Conformal polydopamine coatings offer sole physical and chemical properties to various substrate stuff, such as polymers, metallic things, ceramics, and many more. This has been found to help accentuate the existing properties of the coated material. PDA is a highly malleable material that may be used as a nanocomposite, a nanoparticle, and as a coating for existing materials. The additional PDA properties in biocompatibility, biodegradability, anti-microbial activity, bone regeneration, and versatility make it a promising material that can be mapped into various fields of biomedicine. In this review, we focus upon key structural aspects and related properties of PDA and how they could potentially hold as a tool for multitude biomedical and dental applications. This article will go over some recent research on polydopamine advancements in the biomedical domain. The mechanism of polymerization is first discussed followed by the various forms of polydopamine nanostructures, as well as their latest uses in biological disciplines, particularly in drug administration. The review finally is concluded by a summary of the findings. © 2023 Scrivener Publishing LLC.
A study on corrosion behavior of electrodeposited Zn-rutile TiO2 composite coatings
(2012) Kumar, M.K.P.; Venkatesha, T.V.; Pavithra, M.K.; Shetty, A.
The Zn and Zn-TiO2 composite coatings were fabricated by electrolyzing respective plating solutions of Zn and Zn-TiO2. The rutile TiO2 nanoparticles (size ?100nm) were used for the preparation of composite coatings. The corrosion behavior of the deposits was examined by electrochemical methods. The anticorrosive property of coatings was supported by measuring their corrosion potential, polarization resistance, charge transfer characteristic peak and break frequency. The surface morphology of deposits was studied by scanning electron microscopy, energydispersive X-ray diffraction spectroscopy, and X-ray diffraction techniques. The change in morphology of Zn-TiO2 composite with respect to Zn is correlated with their corrosion behavior. Copyright © Taylor & Francis Group, LLC.
A UNet Model for Accelerated Preprocessing of CRISM Hyperspectral Data for Mineral Identification on Mars
(Copernicus Publications, 2025) Kumari, P.; Soor, S.; Shetty, A.; Nair, A.M.
Accurate mineral identification on the Martian surface is critical for understanding the planetâ€™s geological history. This paper presents a UNet-based autoencoder model for efficient spectral preprocessing of CRISM MTRDR hyperspectral data, addressing the limitations of traditional methods that are computationally intensive and time-consuming. The proposed model automates key preprocessing steps, such as smoothing and continuum removal, while preserving essential mineral absorption features. Trained on augmented spectra from the MICA spectral library, the model introduces realistic variability to simulate MTRDR data conditions. By integrating this framework, preprocessing time for an 800 Ã— 800 MTRDR scene is reduced from 1.5 hours to just 5 minutes on an NVIDIA T1600 GPU. The preprocessed spectra are subsequently classified using MICAnet, a deep learning model for Martian mineral identification. Evaluation on labeled CRISM TRDR data demonstrates that the proposed approach achieves competitive accuracy while significantly enhancing preprocessing efficiency. This work highlights the potential of the UNet-based preprocessing framework to improve the speed and reliability of mineral mapping on Mars. Â© Â© 2025 Priyanka Kumari et al.
Age-based classification of arecanut crops: a case study of Channagiri, Karnataka, India
(2016) Bhojaraja, B.E.; Shetty, A.; Nagaraj, M.K.; Manju, P.
Arecanut is one of the predominant plantation crop grown in India. Yield of this crop depends upon age of the crop and there is no information on the spectral behaviour of arecanut crops across its ages. In this study popular supervised classification algorithms were utilized for age discrimination of arecanut crops using Hyperion imagery. Arecanut plantations selected for the study are located in Channagiri Taluk, Davanagere district of Karnataka state, India. Ground truth information collected involves: (i) GPS coordinates of selected plots, (ii) spectral reflectance of arecanut crops with age ranging from 1 to 50 years, using handheld spectroradiometer with 1 nm spectral resolution. These spectral measurements were made close in time to the acquisition of Hyperion imagery to build age-based spectral library. It is observed from the analysis that crops of ages below 3, 3 7, 8 15 and above 15 years were showing distinct spectral behaviour. Accordingly, crops age ranging from 1 to 50 were grouped into four classes. Classification of arecanut crops based on age groups was performed using methods like spectral angle mapper, support vector machine and minimum distance classifier, and were compared to find the most suitable method. Among the classification methods adopted, support vector machine with linear kernel function resulted in most accurate classification method with overall accuracy of 72% for within class seperability. Individual age group classification producer s accuracy varied minimum of 12.5% for 3 7 years age group and maximum of 86.25% for above 15 years age group. It may be concluded that, not only age- based arecanut crop classification is possible, but also it is possible to develop age-based spectral library for plantation crop like arecanut. 2015 Taylor & Francis.
Age-based classification of arecanut crops: a case study of Channagiri, Karnataka, India
(Taylor and Francis Ltd. michael.wagreich@univie.ac.at, 2016) Bhojaraja, B.E.; Shetty, A.; Nagaraj, M.K.; Manju, P.
Arecanut is one of the predominant plantation crop grown in India. Yield of this crop depends upon age of the crop and there is no information on the spectral behaviour of arecanut crops across its ages. In this study popular supervised classification algorithms were utilized for age discrimination of arecanut crops using Hyperion imagery. Arecanut plantations selected for the study are located in Channagiri Taluk, Davanagere district of Karnataka state, India. Ground truth information collected involves: (i) GPS coordinates of selected plots, (ii) spectral reflectance of arecanut crops with age ranging from 1 to 50 years, using handheld spectroradiometer with 1 nm spectral resolution. These spectral measurements were made close in time to the acquisition of Hyperion imagery to build age-based spectral library. It is observed from the analysis that crops of ages below 3, 3–7, 8–15 and above 15 years were showing distinct spectral behaviour. Accordingly, crops age ranging from 1 to 50 were grouped into four classes. Classification of arecanut crops based on age groups was performed using methods like spectral angle mapper, support vector machine and minimum distance classifier, and were compared to find the most suitable method. Among the classification methods adopted, support vector machine with linear kernel function resulted in most accurate classification method with overall accuracy of 72% for within class seperability. Individual age group classification producer’s accuracy varied minimum of 12.5% for 3–7 years age group and maximum of 86.25% for above 15 years age group. It may be concluded that, not only age- based arecanut crop classification is possible, but also it is possible to develop age-based spectral library for plantation crop like arecanut. © 2015 Taylor & Francis.
An exploratory analysis of rainfall: A case study on western ghats of India
(IEOM Society ieom-society@iieom.org, 2018) Rao, P.S.B.; Shetty, S.; Umesh, P.; Shetty, A.
In this study high resolution 0.250 Ã—0.250 (approximately 25KmÃ—25Km) gridded daily rainfall data is used to analyze the effect of changing climate on distribution of rainfall in different topographical zones of Western Ghats (WG) of India over the period 1901-2013. The non parametric two tailed Mann- Kendall with Hamed and Rao's method of autocorrelation and Sen's slope estimator for obtaining magnitude of change over time period is used. The rainfall trend in annual, monsoon and post-monsoon is increasing in state of Goa and Coastal region of Karnataka state and significantly decreasing in some part of Kerala and Maharashtra state. Winter season rainfall has seen a declined trend in southern part of the study area and in high elevated region of Kerala state. Even the mean rainfall over the study area is declining from 1951-1960 with disturbance in alternate sequence of flood and drought year from period 1990. The frequency of heavy rainfall events (65mm-124.4mm) are increasing in recent decades with 40-50% contribution from 2000-2013 in regions of Maharashtra state. The trend of heavy rainfall events are increasing in West Coast of India at 5% significance level with no trend in very heavy to extreme rainfall events (>124.5mm). Â© IEOM Society International.
An exploratory analysis of urbanization effects on climatic variables: a study using Google Earth Engine
(Springer Science and Business Media Deutschland GmbH, 2022) Shetty, A.; Umesh, P.; Shetty, A.
Rapid global economic expansion has resulted in a drastic increase of urbanization while impacting the Earth’s entire ecology. This study evaluates the impact of historical land-use/land-cover (LU/LC) change signatures on seasonal variation of climatic variables using a cloud platform-Google Earth Engine. Due to rapid urbanization and the noticeable spatio-temporal difference in the climate, administrative units of Dakshina Kannada district are taken for demonstration. The LU/LC of the district extracted from high-resolution images of Landsat using random forest classification, land surface temperature (LST) extracted from the thermal band of Landsat images using the mono window algorithm, evapotranspiration (ET) data extracted from MOD16A2.006 and precipitation data from CHIPRS was used. The data was extracted for the pre-monsoon and post-monsoon period 2001–2019. The district has seen a 13.67% reduction in the forest area with 18.81% increase in the built-up areas. The LST and ET has seen a progressive drift in the past two decades, with an increase of 4.07 °C in median temperature in forest areas and a decline of 2.19 mm in median ET value, which necessitates monitoring forest encroachment. The higher variation in maximum LST in built-up land (0.36∘C/year/sq.km) (near the industrial area) indicates that LU/LC change signature is the predominant driving factor and is associated with the physical characteristics of the built-up area. The ET exhibited a decreasing rate of 0.62 mm/year/sq.km of the built-up land. This study highlights the power of Google Earth Engine and free availability of satellite data in environmental protection, land-use management and sustainable development in the region. © 2021, The Author(s), under exclusive licence to Springer Nature Switzerland AG.
Analysis of variability and trends in rainfall over northern Ethiopia
(2016) Kiros, G.; Shetty, A.; Nandagiri, Lakshman
Rainfall is a key component of the hydrological cycle, and its spatiotemporal variability is essential from the both scientific and practical perspectives. This study is focused on analysis of temporal variability and trends in historical rainfall records for stations in the Geba River basin. The Geba catchment is surrounded by the Danakil basin in the east, by the Tekeze River basin in the south, and the Werie River basin in the west which is located in the northern Ethiopia regional state of Tigray between 38 38? E and 39 48? E and 13 18? N and 14 15? N. The climate over the basin is semi-arid and has large elevation differences varying from 926 to 3301 m above mean sea level. Daily rainfall data of 43 years measured at seven stations in the basin for the period of 1971 to 2013 for annual and seasonal rainfall trends have been processed and used for the analysis. The non-parametric Mann Kendall test and the Sen s slope estimator have been used to identify the existence of trends and slope magnitude in rainfall. Results revealed that although there was a mix of positive and negative trends, they were no statistically significant except at one station which showed an increasing trend in annual rainfall. Considering rainfall in different seasons, an increase in rainfall was observed in two stations in the wet season which, however, was not statistically significant. For the remaining stations, a weak decline in wet season rainfall (not statistically significant at 95 % confidence level) for four stations and absence of trend for one station were noticed. Furthermore, no statistically significant trend (positive or negative) was evident for the dry season rainfall. Results of this study may prove useful in the preparation of climate change mitigation and adaptation strategies in rainfed agricultural and water supply systems in the region. 2016, Saudi Society for Geosciences.
Analysis of variability and trends in rainfall over northern Ethiopia
(Springer Verlag service@springer.de, 2016) Kiros, G.; Shetty, A.; Nandagiri, L.
Rainfall is a key component of the hydrological cycle, and its spatiotemporal variability is essential from the both scientific and practical perspectives. This study is focused on analysis of temporal variability and trends in historical rainfall records for stations in the Geba River basin. The Geba catchment is surrounded by the Danakil basin in the east, by the Tekeze River basin in the south, and the Werie River basin in the west which is located in the northern Ethiopia regional state of Tigray between 38° 38? E and 39° 48? E and 13° 18? N and 14° 15? N. The climate over the basin is semi-arid and has large elevation differences varying from 926 to 3301 m above mean sea level. Daily rainfall data of 43 years measured at seven stations in the basin for the period of 1971 to 2013 for annual and seasonal rainfall trends have been processed and used for the analysis. The non-parametric Mann–Kendall test and the Sen’s slope estimator have been used to identify the existence of trends and slope magnitude in rainfall. Results revealed that although there was a mix of positive and negative trends, they were no statistically significant except at one station which showed an increasing trend in annual rainfall. Considering rainfall in different seasons, an increase in rainfall was observed in two stations in the wet season which, however, was not statistically significant. For the remaining stations, a weak decline in wet season rainfall (not statistically significant at 95 % confidence level) for four stations and absence of trend for one station were noticed. Furthermore, no statistically significant trend (positive or negative) was evident for the dry season rainfall. Results of this study may prove useful in the preparation of climate change mitigation and adaptation strategies in rainfed agricultural and water supply systems in the region. © 2016, Saudi Society for Geosciences.
Application of remote sensing and GIS for identification of potential ground water recharge sites in Semi-arid regions of Hard-rock terrain, in north Karnataka, South India
(Springer Science and Business Media Deutschland GmbH, 2018) Bhagwat, T.N.; Hegde, V.S.; Shetty, A.
Hydro-geomorphological characteristics, together with soil, slope, lineament density and Land use Land cover are signatures of potential ground water recharge areas, and are vital for water harvesting. In the present paper, Fifth order sub-basins in Semi-arid regions of the Varada River basin in South India is studied for selection of suitable area for recharge and prioritize the sub-basins using Indian Remote Sensing satellite (IRS) P6; Linear Imaging Self Scanning Sensor (LISS III) and ArcGIS 9.2. The Fifth order sub-basins of the Varada River spread in Hard-rock terrain and of different agro-climatic zones. The study shows that there are significant spatial variations in the fifth order basins with respect to their morphometric characteristics such as the basin area, drainage density, bifurcation ratio, and circularity ratio, constant of channel maintenance and slope of the basin. These variations reflect the differences in the hydrological process in the different Sub-basins. Based on the variations in the linear, aerial, relief as well as the slope, lineament density, and precipitation pattern rankings are assigned for each parameter with respect to ground water recharge within the Subbasins. Weighted sum overlay for precipitation, Land use, soil and Water table fluctuation are used to select the suitable areas of recharge within the sub-basins. Buffers created for lineaments and drainage networks were intersected with the suitable area of recharge for the probable tank's locations for recharge. The tank locations identified after intersection and having higher stream orders are further filtered for the identification of potential sites for ground water recharge. In the prioritized sub-basins SB-8, SB-10, SB-11 locations have been selected for recharge. © 2018, Springer International Publishing AG, part of Springer Nature.
Assessment of Burn Severity using Different Fire Indices: A Case Study of Bandipur National Park
(Institute of Electrical and Electronics Engineers Inc., 2019) Konkathi, K.; Shetty, A.
Forest fires are the significant catastrophic events which affect the landscape and vegetation in forested lands. They cause loss of biodiversity, land degradation & ecological imbalance. As the forest fires cause high damage to the habitat, it is of utmost necessity to assess the impact of fire. Burn severity mapping contributes to the evaluation of fire severity and extent of burnt areas. In this article, recently occurred forest fire (21st to 25th February 2019) in Bandipur national park was assessed using remote sensing techniques for mapping burnt area and burn severity as on-site estimations are highly impossible during forest fires. Three established fire severity indices differenced normalized burn ratio (dNBR), relativized burn ratio (RBR) and relativized dNBR (RdNBR) are assessed and compared based on number of active fire points provided by MODIS & VIIRS. The RdNBR resulted in an accuracy of 89.14% whereas RBR and dNBR produced an efficiency of 52.48% and 60.633% respectively. The burnt area under high severity was around 4099 hectares. Post-fire assessment is an essential element for finding the effects of fire on vegetation and implementing mitigation strategies. Â© 2019 IEEE.
Assessment of consumption and availability of water in the upper Omo-Gibe basin, Ethiopia
(2020) Nesru, M.; Nagaraj, M.K.; Shetty, A.
Understanding water balance components is imperative for proper policy and decision making, specifically in the upper part of the Omo-Gibe basin (UOGB) Ethiopia. The objective of this study is to explore the possibility of assessing consumption and availability of water using freely available satellite data and secondary data. Using twenty-three rain gauge stations data, a spatial average of rainfall was computed using the Thiessen polygon approach. Actual evapotranspiration (ETa) was estimated through the Surface Energy Balance System (SEBS). Input data used are, 16 clouds free Moderate Resolution Imaging Spectroradiometer (MODIS) images covering the study area for estimation of the spatial distribution of actual evapotranspiration covering the whole cropping year from the months of November 2003 to October 2004. Additionally, Priestly and Taylor s approach was used to estimate reference evapotranspiration (ET0). For the study period, the result of estimated precipitation and ETa showed that the UOGB received 41,080 mm3 of precipitation, while 24,135 mm3 become evapotranspired. The assessed outflow from the basin is 17.6% of the precipitation and demonstrated that water is a scares resource in the UOGB. 2019, Saudi Society for Geosciences.
Assessment of consumption and availability of water in the upper Omo-Gibe basin, Ethiopia
(Springer, 2020) Nesru, M.; Nagaraj, M.K.; Shetty, A.
Understanding water balance components is imperative for proper policy and decision making, specifically in the upper part of the Omo-Gibe basin (UOGB) Ethiopia. The objective of this study is to explore the possibility of assessing consumption and availability of water using freely available satellite data and secondary data. Using twenty-three rain gauge stations data, a spatial average of rainfall was computed using the Thiessen polygon approach. Actual evapotranspiration (ETa) was estimated through the Surface Energy Balance System (SEBS). Input data used are, 16 clouds free Moderate Resolution Imaging Spectroradiometer (MODIS) images covering the study area for estimation of the spatial distribution of actual evapotranspiration covering the whole cropping year from the months of November 2003 to October 2004. Additionally, Priestly and Taylor’s approach was used to estimate reference evapotranspiration (ET0). For the study period, the result of estimated precipitation and ETa showed that the UOGB received 41,080 mm3 of precipitation, while 24,135 mm3 become evapotranspired. The assessed outflow from the basin is 17.6% of the precipitation and demonstrated that water is a scares resource in the UOGB. © 2019, Saudi Society for Geosciences.
Assessment of spatial variation of soil moisture during maize growth cycle using SAR observations
(2019) Gururaj, P.; Umesh, P.; Shetty, A.
Spatial Information about Soil moisture over agricultural crops are required for efficient irrigation which in turn helps in saving water and increases crop yield. Soil moisture also useful in prediction of flooded and drought regions. However field measurement of soil moisture is not a practical approach. The main objective of the study is to track soil moisture variation all along the maize growth period in a Semi-Arid region. There are only few studies carried out on soil moisture variation considering whole maize growing period. During the crop growing period soil moisture field investigation are conducted in synchronization with Satellite pass. Sentinel-1a Synthetic Aperture RADAR (SAR) satellite, Interferometric wide swath dual polarized data with 5.405 GHz frequency and central incidence angle of 23�?� with repeat period of 12 days was used in this study. All in all during growth period 6 satellite pass scenes are acquired and processed by standard procedure using Sentinel Application Platform (SNAP) software. An attempt was made to redeem surface soil moisture for the whole maize growing crop cycle using water cloud model. The whole period of maize crop was divided into 3 parts like seedling, growing and harvesting period and soil moisture is retrieved for each period. The estimated soil moisture was validated with 30 field measured soil moisture samplings. The correlation coefficient of retrieved and actual soil moisture of seedling, growing and harvesting periods are 0.77, 0.72 and 0.6 respectively. The output of this study will be helpful in formulating strategies for irrigation water management. � 2019 SPIE.
Assessment of spatial variation of soil moisture during maize growth cycle using SAR observations
(SPIE spie@spie.org, 2019) Gururaj, P.; Umesh, U.; Shetty, A.
Spatial Information about Soil moisture over agricultural crops are required for efficient irrigation which in turn helps in saving water and increases crop yield. Soil moisture also useful in prediction of flooded and drought regions. However field measurement of soil moisture is not a practical approach. The main objective of the study is to track soil moisture variation all along the maize growth period in a Semi-Arid region. There are only few studies carried out on soil moisture variation considering whole maize growing period. During the crop growing period soil moisture field investigation are conducted in synchronization with Satellite pass. Sentinel-1a Synthetic Aperture RADAR (SAR) satellite, Interferometric wide swath dual polarized data with 5.405 GHz frequency and central incidence angle of 23Ã¯?Â° with repeat period of 12 days was used in this study. All in all during growth period 6 satellite pass scenes are acquired and processed by standard procedure using Sentinel Application Platform (SNAP) software. An attempt was made to redeem surface soil moisture for the whole maize growing crop cycle using water cloud model. The whole period of maize crop was divided into 3 parts like seedling, growing and harvesting period and soil moisture is retrieved for each period. The estimated soil moisture was validated with 30 field measured soil moisture samplings. The correlation coefficient of retrieved and actual soil moisture of seedling, growing and harvesting periods are 0.77, 0.72 and 0.6 respectively. The output of this study will be helpful in formulating strategies for irrigation water management. Â© 2019 SPIE.