In recent years, Deep Learning Multi-Layer Perceptron (DLMLP) neural networks have shown remarkable success in addressing crop yield forecast related problems. The methodologies used so far for with remotely sensed data were focused upon vegetation indices generated from optical data. prediction of an accurate manner by developing robust machine learning models based on soil health parameters is crucial since it helps keep a track as well its impact overall yield. This study aims to utilize...

Crop yield prediction is one of the crucial components agriculture that plays an important role in decision-making process for sustainable agriculture. Remote sensing provides most efficient and cost-effective solution measurement agricultural parameters such as soil moisture level, but retrieval contents from coarse resolution datasets, especially microwave remains a challenging task. In present work, machine learning-based framework proposed to generate enhanced products, i.e., classified...

Abstract The present study aims to identify potential locations for small‐scale hydroelectric power (HEP) stations in hilly regions the purpose of generating renewable energy. A rainfall‐runoff (R‐R) model Beas River catchment was established using MIKE 11 NAM estimate available discharge. calibrated and validated over period June‐2015–May‐2018 June‐2018–May‐2020, respectively, daily observed discharge data at Pandoh Dam site. exhibited good performance with a coefficient determination ( R 2...

Principal component transformation is a standard technique for multi-dimensional data analysis. The purpose of the present article to elucidate procedure interpreting PC images. discussion focuses on logically explaining how negative/positive eigenvectors (loadings) in combination with strong reflection/absorption spectral behavior at different pixels affect DN values output It an explanatory so that fuller potential PCT applications can be realized.

Glacier, snow, and ice are the essential components of Himalayan cryosphere provide a sustainable water source for different applications. Continuous accurate monitoring glaciers allows forecasting analysis natural hazards resource management. In past literature, methodologies such as spectral unmixing, object-based detection, combination various indices commonly utilized mapping ice, glaciers. Most these methods require human intervention in feature extraction, training models, validation...

A scatterometer, as an active microwave radar sensor, measures the return of waves in form a backscatter coefficient after reflection or scattering from Earth's surface. The primary objective scatterometer is to record surface-wind vector observations over ocean for study climate, monitoring, forecasting cyclones/hurricanes, and air–sea interactions. Since its first launch 1978, many technical improvements have been made due potential all-weather global-level monitoring. has found emerging...

Abstract Understanding the intricate interplay between topography and glacier changes is vital for predicting future water resources addressing impacts of climate change. This study examines in area Uttarakhand Himalayan region, where River Ganga originates, 2000 2023 using high‐resolution satellite imagery. A manual digitization process was employed to delineate boundaries 116 glaciers years 2023. Multivariate regression analysis then conducted identify quantify controlling topographical...

Due to change in global mean temperature, most Himalayan glaciers have shown retreat, resulting an increase the number and size of glacial lakes, which may give rise lake outburst flood event. These lakes typically grow from small supraglacial (SGLs). Therefore, it is important map monitor SGLs on a regular basis. Most studies utilized medium coarse resolution images for extraction lakes. With availability high spatial data, has also become possible The literature suggests some use data...

The present study evaluates the potential of Ku-band Scatterometer Satellite-1 (SCATSAT-1) for quantification spatiotemporal variability in snow cover area (SCA) over Himalayas (Himachal Pradesh) India. SCA has been measured using dual-polarized (HH and VV) backscattered SCATSAT-1 data. Two classification approaches, i.e., Linear Mixer Model (LMM) Artificial Neural Network (ANN) model have used study. Both available backscatter coefficients sigma-naught σ0 gamma-naught γ0 considered...

Due to improper agricultural and soil management, there has been a drop in crop yield over the last few years food security become major issue. For country like India, with huge population cater, problem becomes more serious. Since inception of remote sensing scientific agriculture optical along field data used for health monitoring mapping. SAR or microwave an all-weather high temporal availability which found applications various domains. studies multiple classes sub-classes same type,...

Abstract Soil salinity has become a highly disastrous phenomenon responsible for crop failure worldwide, especially in countries with low farmer incomes and food insecurity. is often due to water accumulation fields caused by improper flood irrigation whereby plants take up the leaving salts behind. It is, however, subsurface soil that affects plant growth. This prevents further intake. There have been very few studies conducted estimation. Therefore our study aimed estimate (at 60 cm depth)...

Image-based fusion is a state-of-the-art process to extract vital information by combining the two or more images acquired from different satellite sensors. Recently launched (September 26, 2016) Indian Space Research Organization's (ISRO) Ku-band (13.5 GHz)-based Scatterometer Satellite (SCATSAT-1) as an active microwave sensor can offer day–night, all-weather monitoring services, which are not possible with optical-based visible and infrared remote sensing satellites. Therefore, of optical...

Large-scale debris cover glacier mapping can be efficiently conducted from high spatial resolution (HSR) remote sensing imagery using object-based image analysis (OBIA), which works on a group of pixels. This paper presents the spectral and capabilities OBIA to classify multiple classes multisource approach by integrating multispectral, thermal, slope information into one workflow. The novel contributions this study are effective small yet important geomorphological features, classification...