Browsing by Author "Utkarsh Kumar"

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Comparative evaluation of simplified surface energy balance index-based actual ET against lysimeter data in a tropical river basin
(MDPI, 2021) Utkarsh Kumar; Rashmi; Chandranath Chatterjee; Narendra Singh Raghuwanshi
In the past decades, multispectral and multitemporal remote sensing has been popularly used for estimating actual evapotranspiration (ETc) across the globe. It has been proven to be a cost-effective tool for understanding agricultural practices in a region. Today, because of the availability of different onboard sensors on an increasing number of different satellites, land surface activity can be captured at fine spatial and time scales. In the present study, three multi-date satellite imageries were used for the evaluation of remote sensing-based estimation of actual evapotranspiration in paddy in the command area of the tropical Kangsabati river basin. A surface energy balance model, the Simplified-Surface Energy Balance Index (S-SEBI), was applied for all three dates of the Rabi season (2014–2015) for the estimation of actual evapotranspiration. The crop coefficient was calculated using the exhaustive survey data collected from the command area and adjusted to local conditions. The ETc estimated using the S-SEBI-based model was compared with the Food and Agriculture Organization Penman–Monteith (FAO-56 PM) method multiplied by the adjusted local crop coefficient and lysimeter data in the command area. The coefficient of determination (r2) was applied to examine the accuracy of the S-SEBI model with respect to lysimeter data and the FAO-56 PM-based ETc. The results showed that the S-SEBI model performed well with the lysimeter (r2 = 0.90) in comparison with FAO-56 PM, with an r2 of 0.65. In addition to this, the S-SEBI-based ET estimates correlated well with the FAO-56 PM, with r and RMSE values of 0.06 and 1.13 mm/day (initial stage), 0.85 and 0.48 mm/day (development stage), and 0.77 and 0.52 (maturity stage) for paddy, respectively. The S-SEBI-based ETc estimate varied with different stages of crop growth and successfully captured the spatial heterogeneity within the command area. In general, this study showed that the S-SEBI method has the potential to calculate spatial evapotranspiration and provide useful information for efficient water management. The results revealed the applicability and accuracy of remote sensing-based ET for managing water resources in a command area with scarce data. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Evaluation of Spatio-Temporal Evapotranspiration Using Satellite-Based Approach and Lysimeter in the Agriculture Dominated Catchment
(Springer, 2021) Utkarsh Kumar; Ankur Srivastava; Nikul Kumari; Rashmi; Bhabagrahi Sahoo; Chandranath Chatterjee; Narendra Singh Raghuwanshi
Crop coefficient (Kc) represents the actual crop growth of the crop. It plays an important role in estimating water requirements at the different growth stages of the crop. However, FAO 56 Penman–Monteith Kc method does not account for spatial heterogeneity and uncertainty for regional climatic conditions significantly. Therefore, this study aims to develop the relation between Kc and normalized difference vegetation index (NDVI) using a linear regression and back calculations. These relationships were adjusted to local conditions using information from survey data obtained during Rabi season (2014–2015). The NDVI–Kc model (r2 = 0.86) has developed using NDVI–Kc from a fine resolution Landsat 8 remote sensing data. NDVI–Kc regression equation was utilized for generating crop coefficient for different month of season. The Vegetation Index-based AET estimated was evaluated with lysimeter data for different crop growth stage across the season. The results have shown that NDVI–Kc estimated AET has been better correlated with NDVI–Kc remote sensing model. Thus, the output of this research can help to calculate actual water demand in a command area and be helpful in allocating water from less demand area toward more demand area. © 2021, Indian Society of Remote Sensing.
Evaluation of Standardized MODIS-Terra Satellite-Derived Evapotranspiration Using Genetic Algorithm for Better Field Applicability in a Tropical River Basin
(Springer, 2023) Utkarsh Kumar; Rashmi; Ankur Srivastava; Nikul Kumari; Chandranath Chatterjee; Narendra Singh Raghuwanshi
Evapotranspiration (ET) estimation at different spatial and temporal scales with a paucity of climatic parameters in a river basin is becoming a challenging task. Accurate estimation of ET is necessary for efficient water resource management and improving water efficiency at the field scale. Therefore, this study attempts to indirectly estimate actual ET from version 006 of MODIS-Terra product (MOD16A2.006), Sentinel-2A and Variable infiltration capacity (VIC-3L) model using survey information collected from a traditional paddy field in Kangsabati river basin. Further, this study is undertaken to standardize raw MODIS-Terra ET product (MOD16A2.06) using a genetic-based algorithm for better field applicability at local condition. The MODIS-standardized ET and ET estimated using different methods along with raw MODIS-Terra ET product were evaluated against observed ET estimated using globally recommended FAO-56 Penman–Monteith (PM) equation coupled with a crop coefficient. MODIS-Terra ET estimates were standardized using a genetic-based algorithm to enhance the efficacy of MODIS-Terra ET (MODIS-raw ET) for better field applicability. The result revealed that the genetic-based algorithm (MODIS-standardized ET) improved significantly with the NSE and RMSE from approximately − 0.03 to 0.86 and 13.89 to 2.56 (mm/8 day). Of various ET models Sentinel-2A ET performed best followed by MODIS-standardized ET, VIC-3L ET and MODIS-raw ET with R2 = 0.92, NSE = 0.89, RMSE = 1.89 (mm/8 day), R2 = 0.88, NSE = 0.86, RMSE = 2.47 (mm/8 day), R2 = 0.77, NSE = 0.76, RMSE = 3.02 (mm/8 day) and R2 = 0.41, NSE = − 0.03, RMSE = 7.31 (mm/8 day), respectively. The result showed that Sentinel 2A and MODIS-standardized-based ET can be used under data scarce conditions for better field applicability and water management practices. © 2023, Indian Society of Remote Sensing.
Modelling soil temperature at multiple depths in Saurashtra region (Junagadh) of Gujarat using machine learning and shapely approach
(Springer Nature, 2025) Utkarsh Kumar; Rashmi; Ankur Srivastava; H. V. Parmar; H. H. Mashru; Parthsarthi A. Pandya; G. V. Prajapati; H. D. Rank
Forecasting soil temperature (ST) at multiple depths is crucial for understanding meteorological processes, enhancing agricultural resilience, and assessing ecological and environmental risks. Data driven model represents an alternative tool to the conventional measurement of ST e.g. soil thermometer. To develop the ML model, weekly ST and relevant meteorological variables for the city of Junagadh (Saurashtra region) are collected for the period of 2010–2023. A thorough feature analysis was performed to select the most promising feature using Pearson correlation coefficient and shapely approach. The model was developed using different combinations of input parameters (M1–M7) and trained using different machine learning algorithms. This research aims to evaluate four different machine learning approaches namely, Random Forest (RF), Gradient Boosting Regression (GBR), Support Vector Regression (SVR), and Long Short-Term Memory (LSTM), to predict the soil temperature at 5 cm, 10 cm and 20 cm depth. The result of this study showed that by choosing the optimum input parameter, there is no significant impact on accuracy of model. The best performance was obtained for Model 7 f(TDB, TMax, TMin, Evapo) model at the 10-cm soil depth, as it provided the greatest correlation coefficient (r = 0.9967) and the lowest value for root mean square error (RMSE = 0.3410 °C) and percent bias (PBIAS = − 0.0115). The result showed that model performance differences are often statistically significant, especially at shallower depths (ST5, ST10), but less so at ST20. In the current study, besides evaluating the potential of four machine learning models, the interpretation of the machine learning algorithm for soil temperature prediction was explored using SHapley Additive exPlanations (SHAP). The study used an explainable artificial intelligence (XAI) approach to provide novel interpretation and insights to elucidate model formulation and relative predictor importance. © The Author(s) 2025.
Role of AM fungi in growth promotion of high-value crops
(Elsevier, 2022) Manoj Parihar; Manoj Kumar Chitara; Hanuman Ram; Asha Kumari; Gopal Tiwari; Kiran Rana; Bisweswar Gorain; Utkarsh Kumar; Jaideep Kumar Bisht; Lakshmi Kant
In order to meet the food demands of burgeoning population, innovative and efficient management practices are required for sustainable agricultural production. The high value crops (HVCs) including vegetables, horticulture, fruit and field crops such as potato, cotton, sugarcane etc. not only strengthen the financial security of farmers but also ensure their food, fiber and nutritional availability. To improve the productivity of HVCs, use of beneficial microbial symbionts such as arbuscular mycorrhizal fungi (AMF) is very promising and eco-friendly approach. The AMF form association with most of the land plants including agricultural and HVCs. They provide numerous benefits to the plants including better availability of water and nutrients, alleviate various biotic and abiotic stresses and promote plant growth. However, AMF response in improving the plant performance depends upon several other aspects such as soil environment, AMF strains, plant genotypes, agricultural management practices etc. In this regard, future research must be towards optimization of AMF plant association, suitable inoculum production and application techniques and co-inoculation of AMF with other plant growth promoting bacteria. In the present chapter we will discuss the state-of-the-art of potential of AMF to improve the production of HVCs, its application in micro-propagation program, commercialization and future advancement for sustainable production system. © 2022 Elsevier Inc. All rights reserved.
Role of plant-associated microbes in phytoremediation of heavy metal polluted soils
(CRC Press, 2021) Manoj Parihar; Amitava Rakshit; Manoj Kumar Chitara; Hanuman Singh Jatav; Vishnu D. Rajput; Ashish Kumar Singh; Kiran Rana; Surendra Singh Jatav; Mohsina Anjum; Tatiana Minkina; Utkarsh Kumar
[No abstract available]