Mahesh Palakuru

Vellore Institute of Technology University

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Sirisha Adamala

National Bureau of Soil Survey and Land Use Planning

D. K. Babu

Vellore Institute of Technology University

Kiran Yarrakula

Vellore Institute of Technology University

Bachina Harish Babu

Vignana Jyothi Institute of Management

Nilima Rani Chaube

Indian Space Research Organisation

Venkata Ratnam Ukkurthi

Parimala Renganayaki

Vellore Institute of Technology University

K Venkat Reddy

A. Velmurugan

ICAR - Central Island Agricultural Research Institute

Christophe Chesneau

Université de Caen Normandie

Cooperative Institutions

ICAR - Central Island Agricultural Research Institute

Vellore Institute of Technology University

Vignan's Foundation for Science, Technology & Research

Sugarcane Breeding Institute

Tamil Nadu Agricultural University

Indian Council of Agricultural Research

Institute of Engineering

Saveetha University

Koneru Lakshmaiah Education Foundation

Vignana Jyothi Institute of Management

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Identification of paddy crop phenological parameters using dual polarized SCATSAT-1 (ISRO, India) scatterometer data

Environmental Science and Pollution Research (2018)

Mahesh Palakuru Kiran Yarrakula Nilima Rani Chaube D. K. Babu Y. R. Satyaji Rao

Scatterometer

Anomaly (physics)

10.1007/s11356-018-3692-5

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Citations (20)

Investigation of Rice Crop Phenology Using C Band SENTINEL-1 SAR Data: A Case Study in Chittoor, Andhra Pradesh

Heliyon (2025)

Mahesh Palakuru D. K. Babu Sirisha Adamala Sivadass Thiruchelvam Venkata Ratnam Ukkurthi

10.1016/j.heliyon.2025.e42900

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Exploring potential of C band synthetic aperture radar imagery to investigate rice crop growth mechanism and productivity

Environmental Science and Pollution Research (2021)

Mahesh Palakuru D. K. Babu Nilima Rani Chaube

Crop Productivity

10.1007/s11356-021-13759-z

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Soil Moisture Mapping in Indian Tropical Island Ecosystem using C-band SAR Data and Neural Network Models

Research Square (Research Square) (2024)

Sirisha Adamala A. Velmurugan Swarnam Palanivel Mahesh Palakuru Subramani Thangavel

Abstract Soil moisture is one of the critical variables in identifying the stress or health of plants and its accurate information in agricultural land use is of greater importance under the threat of climate change. This study focuses on mapping the spatio-temporal dynamics of soil moisture in the Indian tropical Island ecosystem (Andaman and Nicobar Islands) using the fusion of radar (Sentinel-1A C-band synthetic aperture radar (SAR)) and optical/thermal (Sentinel-2A and Landsat 8) data. Field-based soil moisture measurements using the gravimetric method are monitored for different land uses of the study area. The study was carried out in the South Andaman district (E 92°’ to E 94°’ and latitude N 6°’ to N 14°’). A total of 60 surface soil samples (0–10 cm) were collected from four predominant land uses of the study area for 2020-22 years to represent real-time soil moisture status. Soil moisture index (SMI) is assessed based on thermal remote sensing data as Land Surface Temperature (LST) besides, normalized difference vegetation index (NDVI) from red and infrared bands data, and dielectric constants from soil textural analysis. Artificial Neural Network (ANN) based models were developed along with multiple linear regression (MLR) to retrieve the soil moisture accurately using satellite and field-derived input parameters such as backscatter coefficients (σ°: VV & VH), NDVI, SMI, and dielectric constants. The performance of modelled soil moisture is evaluated using different statistical index-based criteria concerning field-based soil moisture measurements (SMCv). It is found that positive correlation among (σ°: VV+VH) and (SMCv: %) for all land uses and high agreements (R² values) between them for barren and vegetable fields. The vegetation interferes the backscatter signal and misinterprets the soil moisture estimation solely with only SAR data. However, consideration of NDVI and SMI improves the soil moisture estimation in case of vegetation abundance land uses. The comparative results showed that ANN models surpass the MLR models in soil moisture estimation with high R² (0.67-0.99) and η (62.6-99.9) and low RMSE (0.05-2.19%) and MAE (0.03-1.74%) values without over or under estimations (R_ratio≈ 1) in South Andaman area of Andaman and Nicobar tropical ecosystem. This kind of study helps as baseline information for hydrological modelling and policy makers to plan irrigation systems, accurate management of crop quantity and quality in farms, and schedule critical irrigations accurately.

10.21203/rs.3.rs-4010135/v1

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Hydrological appraisal of nariganipalli watershed (India) using spatial information technology

Indian Journal of Ecology (2020)

Mahesh Palakuru K Venkat Reddy Sirisha Adamala Bachina Harish Babu

The basic intent of the present study is to derive the parameters required for runoff modeling using the geospatial database and estimate the runoff of Nariganipalli watershed. During the basic data preparation stage of the study, the land use map and the digital elevation model covering the study area were derived with the help of remotely sensed information. Shuttle Radar Topographic Mission data have been imported into soil and water assessment tool (SWAT) project to start watershed delineation. By providing all the inputs for the model set up, SWAT model was simulated for the period of two years (2012–13). Results of streamflow were validated with the observed data of Nariganipalli watershed. Though the rainfall in the watershed is 72.5 mm annually there is a deficiency in water in the watershed. Total requirement of water in the watershed is 93.01 ha m and available water is 68.5737 ha m. This statistics tells that there is huge difference between available water and required water. These types of models will help to reduce the water deficiency and can be utilized as a potential tool for water resource management at Nariganipalli watershed.

Time of concentration

Watershed Management

SWAT model

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Study on paddy phenomics ecosystem and yield estimation using space-borne multi sensor remote sensing data

Journal of Agrometeorology (2021)

Mahesh Palakuru Kiran Yarrakula

In the present study three phenological stages of rice namely transplanting stage, heading stage and harvesting stages were derived from MODIS EVI data. SMAP L-band was used to identify the puddling field. The performance of the estimated phenological stages from MODIS EVI and SMAP were evaluated with field data and root mean square error (RMSE) was calculated. The rice yield estimation was also performed by application of second order polynomial method. The performance of the polynomial model showed good results with the coefficient of determination of 0.74.

Phenomics

Transplanting

Paddy field

10.54386/jam.v21i2.228

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Citations (7)

Estimation of Rice crop biophysical parameters using multi-temporal sentinel-2 msi sensor data

Indian Journal of Ecology (2020)

Mahesh Palakuru D. K. Babu Parimala Renganayaki

The study focuses to efficiently estimate and mapping rice crop biophysical parameters using high temporal and spatial resolution sentinel-2 satellite imagery. Biophysical properties provide a good understanding of physics of the interaction between vegetation and solar radiation. Crop canopy properties can be collected through inversion using nadir reflectance in various bands. NDVI is sensitive to the light, angle of view, sensor after canopy peak is touched. Rice crop biophysical parameters are autonomous from viewing angle and light conditions. The major rice crop biophysical parameters are leaf area index (LAI), Chlorophyll content, fraction of photosynthetically active radiation (FAPAR), canopy water content (CWC), fraction of cover (FCover), and crop biomass respectively. Biophysical parameters are known as mirrors of plant health construction and crop yield. Regression model was developed using chlorophyll, CWC, FAPAR, LAI and FCover with coefficient of determination 0.83 to accurately predict crop biomass. It is concluded that the study is helpful to monitor overall health of rice crop and it is proved that methodology has potential to estimate and monitor biophysical parameters of rice crop. From the study it is known that sentinel-2 has the potential to estimate and map rice crop biophysical parameters precisely.

Photosynthetically active radiation

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Sugarcane Genetic Resources: Ex-situ conservation in Gene Bank and complimentary conservation strategies " In Prospects in Forestry and Agriculture"

K. Chandran M.S. Nisha Mahesh Palakuru

Ex situ conservation

Genetic Resources

Conservation Agriculture

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A comparison study of space borne Dual Polarization Difference Index (Sea Wind SCATSAT-1 Scatterometer) and NDVI (MODIS) on paddy crop growth

Mahesh Palakuru Kiran Yarrakula

Scatterometer

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Spatial mapping of rice crop evapotranspiration in godavari command area

Indian Journal of Ecology (2020)

Mahesh Palakuru Sirisha Adamala Bachina Harish Babu

The aim of this paper is determining potential rice crop evapotranspiration (ETc) using satellite data in Godavari irrigation command area of Andhra Pradesh, India. ETc was estimated from reference evapotranspiration (ETo) and crop coefficient (Kc). ETo estimated using agro-meteorological data (measured maximum and minimum air temperatures) according to FAO-24 modified Blaney-Criddle method. The Kc maps were mapped in Arc GIS software using Normalized Difference Vegetation Index (NDVI). Red (R) and Near Infra-Red (NIR) measurements of MODIS image were used to calculate NDVI. Further, the ETc map was obtained by multiplying Kc map with the ETo map. The maximum ETc computed for Rice crop was 6.04 mm/day, whereas the minimum was 2.22 mm/day for the Godavari command area.

Crop coefficient

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