Sense Of Information Research Articles

Rice area mapping in Palakkad district of Kerala using Sentinel-2 data and Geographic information system technique

Proper calculation of rice cultivation area well before harvest is critical for projecting rice yields and developing policies to assure food security. This research looks at how Remote Sensing (RS) and Geographic Information System (GIS) can be used to map rice fields in Palakkad district of Kerala. The area was delineated using three multi-temporal cloud free Sentinel-2 data with 10 m spatial resolution, matching to crop's reproductive stage during mundakan season (September-October to December-January), 2020-21. To make classification easier, the administrative boundary of district was placed over the mosaicked image. The rice acreage estimation and land use classification of the major rice tract of Palakkad district comprising five blocks was done using Iterative Self-Organisation Data Analysis Technique (ISODATA) unsupervised classification provision in ArcGIS 10.1 software, employing False Colour Composite (FCC) including Blue (B2), Green (B3), Red (B4) and Near-infrared (B8) Bands of Sentinel-2 images. The classification accuracy was determined by locating a total of 60 validation points throughout the district, comprising 30 rice and 30 non-rice points. The total estimated area was 24742.76 ha, with an average accuracy of 88.33% and kappa coefficient 0.766 in five blocks of Palakkad district. The information generated will be helpful in assessing the anticipated production as well as the water demand of the rice fields.

Assessment and Mapping of Land Cover/Use Aided by Remote Sensing and GIS Techniques in Elnour Natural Forest, Blue Nile State, Sudan

The main aim of this study was to assess and mapping of land cover (LC) / land use (LU), using Remote Sensing (RS) and Geographical Information System (GIS) techniques in Elnor natural forest reserve, Blue Nile state, Sudan. The study also attempted to update some information in the study area such as LC and contour map by using different methods of data transformation and analysis such as Normalized Different Vegetation Index (NDVI), interpolation and supervised classification. Thematic Mapper (TM), Landsat Enhanced Thematic Mapper (ETM+) and Landsat8 images were used in LCLU changes and supported by field observation. The study area was affected by cutting the mother trees and overgrazing. Closed forests during 2000, 2010 and 2019 decreased from 36.9%, 28.5% and 18.7% respectively. Moreover, the total area of bare soil increased by about 14.4%, 47.2% and 39.7% in 2000, 2010 and 2019 respectively. The open forest (grassland) decreased from 48.7% in (2000) to 41.7% in (2019). A Digital land cover map was produced using LCCS software; depending on the fieldwork data, they were found in four Classes. Mixed Woodland Single Layer, Semi-Deciduous Shrub land with Open Herbaceous, Open Grassland, Single XII Layer and Hardpans. The study revealed that there are some indicators of land degradation during the last 20 years (2000, 2010 and 2019) and is expected to continue increasing unless serious measures are carried out to reduce it.

Evaluation of multiple bias correction methods with different satellite rainfall products in the Main Beles Watershed, Upper Blue Nile (Abbay) Basin, Ethiopia

Abstract This study investigates the utility of satellite-based rainfall products and the performance of bias correction methods in one of the sub-basins of the Upper Blue Nile Basin (Main Beles basin). Four satellite rainfall products are used: Climate Prediction Center (CPC) MORPHing technique (CMORPH), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), Tropical Rainfall Measuring Mission Multi-Satellite Precipitation Analysis (TMPA) 3B42V7 (TMPA 3B42V7), and Climate Forecast System Reanalysis (CFSR). The performance of the satellite rainfall products (SRPs) was compared using three bias correction methods (Delta, Empirical Quantile Mapping (EQM), and Quantile Mapping (QM)) on five metrological stations. Six statistical criteria were used to evaluate these methods on the period 2003–2016 at daily and monthly scales. The results showed that SRPs and bias correction methods of CMORPH_QM (r = 0.538) and TMPA_3B42V7_EQM (r = 0.95) data showed good performance, while PERSIANN_EQM (r = 0.348) and PERSIANN_Delta (r = 0.83) performed worst at daily and monthly time scales, respectively. This study assessed the importance of SRPs and bias correction methods to use in data scarce regions for water resources planning and other related sectors.

PRIVATIZATION AS A FUNDAMENTAL ELEMENT OF PUBLIC MANAGEMENT

According Art. 4(3) of the Constitution of Georgia “State government is implemented based on the principle of separation of powers”. This statute strengthens separation of powers between different branches of government and provides the system of checks and balances. The new concept of public management (NPM) provides flexibility of public governance, transfer of public tasks or state property to the private sector. This delegation of state tasks and the denationalization of property is called privatization and its idea is to unload the state, rational use of state resources and stabilize governance with less cost. Privatization as a new central institution of public governance, for the purposes of administrative law, is considered in a formal and material sense and material privatization is carried out in the form of organizational and functional privatization. The whole spectrum of privatization issues is presented in this articlefrom this perspective, considering the forms of its implementation

Continuum models of directed polymers on disordered diamond fractals in the critical case

We construct and study a family of random continuum polymer measures Mr corresponding to limiting partition function laws recently derived in a weak-coupling regime for polymer models on hierarchical graphs with marginally relevant disorder. The continuum polymers, which we refer to as directed paths, are identified with isometric embeddings of the unit interval [0,1] into a compact diamond fractal having Hausdorff dimension two, and there is a natural “uniform” probability measure, μ, over the space of directed paths, Γ. Realizations of the random path measures Mr exhibit strong localization properties in comparison to their subcritical counterparts in which the diamond fractal has dimension less than two. Whereas two paths p,q∈Γ sampled independently using the pure measure μ have only finitely many intersections with probability one, a realization of the disordered product measure Mr×Mr a.s. assigns positive weight to the set of pairs of paths (p,q) whose intersection sets are uncountable but of Hausdorff dimension zero. We give a more refined characterization of the size of these dimension-zero sets using generalized (logarithmic) Hausdorff measures. The law of the random measure Mr cannot be constructed as a subcritical Gaussian multiplicative chaos because the coupling strength to the Gaussian field would, in a formal sense, have to be infinite.

Understanding person-centered dementia care from the perspectives of frontline staff: Challenges, opportunities, and implications for countries with limited long-term care resources.

Semi-skilled or unskilled nursing care aides primarily provide direct care to residents in long-term care (LTC) facilities in China and many other countries around the world. There has been a significantly increased commitment from nurses and other formally trained professionals to provide person-centered care (PCC) for people with dementia (PwD). However, it is still unclear how nursing care aides who provide direct care to older residents with dementia understand and adopt PCC in their daily work. It is of utmost importance to understand person-centered dementia care from the perspectives of care aides, and to identify implications to facilitate and sustain their efforts in providing quality care for older residents with dementia. We adopted a descriptive qualitative approach and interviewed 40 care aides from 5 nursing homes in China. The interview methods followed the consolidated criteria for a qualitative research report. We found that the seemingly beneficial mental models used by care aides in their work can hinder them from playing a more adaptive role in tailoring their care to the needs of older residents. Infantilizing older residents with dementia and labeling them using mother wit can prevent meaningful, equal, and person-centered conversations between both parties. Care aides do not have regular formal interactions and sense making with nurses and other professionals in nursing homes. Increasing interactions and communication between care aides and health care professionals in nursing homes can lead to insight for changing the approach to in-service training to achieve better acceptance by care aides. The current study derives implications for operationalizing and embedding the principles of PCC in daily care.

Does Physio-environmental Determinism Influence the Infrastructural Development of Railway Stations?

The main aim of the article is to understand the role of physiographical aspects and attributes in the infrastructural development of railway stations. Remote Sensing and Geographical Information System techniques have been used for delineating physiographic factors and data of social interfaces like passengers’ mobility collected from the railway stations. A robust Synthetic Indicator has been used to classify railway stations. The ordinal regression model has run between physio-environmental attributes and Robust Synthetic Indicator. Due to its unique valley and ridge topography and newly developed railway infrastructure, Phenomenological evidence is collected from Tripura, North-eastern state of India. Very few studies have been done addressing the development of railway stations with physio-environmental viability. The study reveals that the physical environment is a significant determining factor, but passenger mobility has a more decisive influence on the infrastructural development of railway stations in Tripura. Infrastructure-driven materialistic possibilism has been proposed as a philosophical model of the infrastructural development of railway stations.

Detection of Physical and Chemical Parameters Using Water Indices (NDWI, MNDWI, NDMI, WRI, and AWEI) for Al-Abbasia River in Al-Najaf Al-Ashraf Governorate Using Remote Sensing and Geographic Information System (GIS) Techniques

The purpose of this study was to find out the connection between the water parameters that were examined in the laboratory and the water index acquired from the examination of the satellite image of the study area. This was accomplished by analysing the Landsat-8 satellite picture results as well as the geographic information system (GIS). The primary goal of this study is to develop a model for the chemical and physical characteristics of the Al-Abbasia River in Al-Najaf Al-Ashraf Governorate. The water parameters employed in this investigation are as follows: (PH, EC, TDS, TSS, Na, Mg, K, SO4, Cl, and NO3). To collect the samples, ten sampling locations were identified, and the satellite image was obtained on the same day. Geographical information systems (GIS) are commonly used for the process of projecting the coordination often Stations Along Al-Abbasia River in the image of the satellite (Landsat-8) to then analyze the spectral reflections of the items and then treat the data obtained after the analysis process by using (SPSS) Software to find the correlation coefficient and regression equations. Because of the high connections between water metrics and the water index, four regression models were discovered. These models can be used to predict the four water variables (EC, SO4, Cl, and NO3) at any point along the Al-Abbasia River directly from the satellite image.

An improved near-real-time precipitation retrieval for Brazil

Abstract. Observations from geostationary satellites can provide spatially continuous coverage at continental scales with high spatial and temporal resolution. Because of this, they are commonly used to complement ground-based precipitation measurements, whose coverage is often more limited. We present Hydronn, a neural-network-based, near-real-time precipitation retrieval for Brazil based on visible and infrared (Vis–IR) observations from the Advanced Baseline Imager (ABI) on the Geostationary Operational Environmental Satellite 16 (GOES-16). The retrieval, which employs a convolutional neural network to perform Bayesian precipitation retrievals, was developed with the aims of (1) leveraging the full potential of latest-generation geostationary observations and (2) providing probabilistic precipitation estimates with well-calibrated uncertainties. The retrieval is trained using more than 3 years of collocations with combined radar and radiometer retrievals from the Global Precipitation Measurement (GPM) core observatory over South America. The accuracy of instantaneous precipitation estimates is assessed using a separate year of GPM combined retrievals and compared to retrievals from passive microwave (PMW) sensors and HYDRO, the Vis–IR retrieval that is currently in operational use at the Brazilian Institute for Space Research. Using all available channels of the ABI, Hydronn achieves accuracy close to that of state-of-the-art PMW precipitation retrievals in both precipitation estimation and detection despite the lower information content of the Vis–IR observations. Hourly, daily, and monthly precipitation accumulations are evaluated against gauge measurements for June and December 2020 and compared to HYDRO, the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) Cloud Classification System (CCS), and the Integrated Multi-satellitE Retrievals for GPM (IMERG). Compared to HYDRO, Hydronn reduces the mean absolute error for hourly accumulations by 21 % (22 %) compared to HYDRO by 44 % (41 %) for the mean squared error (MSE) and increases the correlation by 138 % (312 %) for June (December) 2020. Compared to IMERG, the improvements correspond to 16 % (14 %), 12 % (12 %), and 20 % (56 %), respectively. Furthermore, we show that the probabilistic retrieval is well calibrated against gauge measurements when differences in the distributions of the training data and the gauge measurements are accounted for. Hydronn has the potential to significantly improve near-real-time precipitation retrievals over Brazil. Furthermore, our results show that precipitation retrievals based on convolutional neural networks (CNNs) that leverage the full range of available observations from latest-generation geostationary satellites can provide instantaneous precipitation estimates with accuracy close to that of state-of-the-art PMW retrievals. The high temporal resolution of the geostationary observation allows Hydronn to provide more accurate precipitation accumulations than any of the tested conventional precipitation retrievals. Hydronn thus clearly shows the potential of deep-learning-based precipitation retrievals to improve precipitation estimates from currently available satellite imagery.

Performance of New Near-Real-Time PERSIANN Product (PDIR-Now) for Atmospheric River Events over the Russian River Basin, California

Abstract Most heavy precipitation events and extreme flooding over the U.S. Pacific coast can be linked to prevalent atmospheric river (AR) conditions. Thus, reliable quantitative precipitation estimation with a rich spatiotemporal resolution is vital for water management and early warning systems of flooding and landslides over these regions. At the same time, high-quality near-real-time measurements of AR precipitation remain challenging due to the complex topographic features of land surface and meteorological conditions of the region: specifically, orographic features occlude radar measurements while infrared-based algorithms face challenges, differentiating between both cold brightband (BB) precipitation and the warmer nonbrightband (NBB) precipitation. It should be noted that the latter precipitation is characterized by greater orographic enhancement. In this study, we evaluate the performance of a recently developed near-real-time satellite precipitation algorithm: Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN) Dynamic Infrared–Rain Rate-Now (PDIR-Now). This model is primarily dependent on infrared information from geostationary satellites as input; consequently, PDIR-Now has the advantage of short data latency, 15–60-min delay between observation to precipitation product delivery. The performance of PDIR-Now is analyzed with a focus on AR-related events for cases dominated by NBB and BB precipitation over the Russian River basin. In our investigations, we utilize S-band (3-GHz) precipitation profilers with Joss/Parsivel disdrometer measurements at the Middletown and Santa Rosa stations to classify BB and NBB precipitation events. In general, our analysis shows that PDIR-Now is more skillful in retrieving precipitation rates over both BB and NBB events across the topologically complex study area as compared to PERSIANN-Cloud Classification System (CCS). Also, we discuss the performance of well-known operational near-real-time precipitation products from 2017 to 2019. Conventional categorical and volumetric categorical indices, as well as continuous statistical metrics, are used to show the differences between various high-resolution precipitation products such as Multi-Radar Multi-Sensor (MRMS).

“Forest fire emissions: A contribution to global climate change”

Forest fires are emitting substantial amounts of greenhouse gases and particulate matter into the atmosphere than assumed in state climate targets. It can play an important role in combustible environments, such as shrublands, grasslands, and forests, and contribute to climate change. Thus, forest fire, and climate change is intertwined concepts. As vegetation burns, release the carbon stored within them. This is the main reason why large-scale forest fires release atmospheric carbon dioxide (CO2) and hence, are responsible for increasing the rate of climate change to a great extent. It is extremely significant to measure the contribution of global forest fire and emissions trends of greenhouse gases. In this context, continental-scale carbon emissions assessments were primarily attempted using ground-based datasets for forest ecosystem fires. Considerable research has been published employing remote sensing data from coast to coast. While ground-based data are valuable, they have some restrictions that can be overcome by remote sensing. Ground-based fire data are primarily limited to the total burned area, with their completeness changing yearly with the location. Remote sensing can provide additional spatio-temporal fire information to improve fire emission estimates. In this paper, the factors driving forest fire, with a brief discussion on the triangular relationship between fire, land degradation, and climate change, the role of Remote Sensing and Geographic Information Systems (GIS), machine learning (ML), and a critical overview of state-of-the-art global climate change are presented.

Assessment of Sustainable World Heritage Areas in Saudi Arabia Based on Climate Change Impacts on Vulnerability Using RS and GIS

Heritage is considered a pillar of civilized cultural identity and an important income resource. Climate change is one of the main threats to cultural heritage as fragile buildings are highly vulnerable to its impacts, as a result, many world heritage sites, unfortunately, have been lost. This study used Remote Sensing (RS) and Geographic Information Systems (GIS) to develop risk maps that determine spatial environmental changes regarding climatic parameters. The study used satellite images to analyze changes over 20 years for three climatic factors: temperature, humidity, and precipitation patterns. The average rate of change for each indicator was developed by comparing each month over 20 years. Three sub-models classifying changes for the selected factors were created, while the climatology model integrated the three sub-models with equal weights to assess the most vulnerable World Heritage sites. The vulnerable sites were classified into five categories, from less risk to the riskier. The study showed that most of Saudi’s Heritage sites are in the moderate-risk area. The study achieved the sustainable development goals (SDGs), particularly SDG 11 and SDG 13. This paper supports decision-makers to preserve heritage sustainably and to create proactive plans for it.

Comprehensive Analysis of PERSIANN Products in Studying the Precipitation Variations over Luzon

This study evaluated the capability of satellite precipitation estimates from five products derived from Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (including PERSIANN, PERSIANN-CCS, PERSIANN-CDR, PERSIANN-CCS-CDR, and PDIR-Now) to represent precipitation characteristics over Luzon. The analyses focused on monthly and daily timescales from 2003–2015 and adopted surface observations from the Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) platform as the evaluation base. Among the five satellite precipitation products (SPPs), PERSIANN-CDR was observed to possess a better ability to qualitatively and quantitatively estimate spatiotemporal variations of precipitation over Luzon for the majority of the examined features with the exception of the extreme precipitation events, for which PERSIANN-CCS-CDR is superior to the other SPPs. These results highlight the usefulness of the addition of the cloud patch approach to PERSIANN-CDR to produce PERSIANN-CCS-CDR to depict the characteristics of extreme precipitation events over Luzon. A similar advantage of adopting the cloud patch approach in producing extreme precipitation estimates was also revealed from the comparison of PERSIANN, PERSIANN-CCS, and PDIR-Now. Our analyses also highlighted that all PERSIANN-series exhibit improved skills in regard to detecting precipitation characteristics over west Luzon compared to that over east Luzon. To overcome this weakness, we suggest that an adjustment in the cloud patch approach (e.g., using different cloud temperature thresholds or different brightness temperature and precipitation rate relationships) over east Luzon may be helpful.

Mapping Groundwater Potential Zones Using Remote Sensing and GIS Techniques in Sainj River Sub-Watershed, Kullu, Himachal Pradesh

Ensuring availability of drinking and irrigation water for population living in mountainous environment has been an important challenge for researchers, spatial planners and policy makers. The identification and mapping of groundwater potential zones in such type of regions may prove a better input to meet this challenge. The present study is an attempt to find out the groundwater potential zones using state-of-the-art technologies of Remote Sensing (RS) and Geographic Information System (GIS) selecting a case study area of Sainj river sub-watershed (area 81 km 2 ) in Kullu of Himachal Pradesh, India. Landsat-8 (OLI) satellite data (October 2016, spatial resolution (30 m resolution) Survey of India (SOI) Toposheet (Scale 1:50,000) and GIS software ArcGIS version 10.5 were used to prepare various thematic layers such as Geology, Soil, Geomorphology, Land Use Land Cover (LULC), Slope, Lineament, and Drainage Density. The watershed delineation and contour lines (100 m interval) generation were performed using Digital Elevation Model (DEM). The LULC map was prepared through supervised classification using Support Vector Machine (SVM) method in ENVI 5.0 software. Spatial analyst tool of ArcGIS 10.5 was used to identify groundwater potential zones using weighted overlay method. The study indicates that the spatial variations in groundwater occurrence are largely controlled by geological structures, landforms and slope in the study area. The study also reveals the importance and usefulness of RS and GIS techniques for proper groundwater resource appraisal and mapping groundwater potential zones. The five groundwater potential zones were identified and mapped in the study area viz. very poor (1.1 km2, 1.36%), poor (7.3 km 2 ,9.03%), moderate to good (67.2 km 2 ,83.17%), good (4 km2,4.95%) and very good (1.2 km2,1.49%). The weathered / fractured quartzite, limestone, slate, schist, and phyllite, among other minerals, characterize the zone with very good groundwater potential. While, the granite, car-biotite, met basalt, and granite gneiss are commonly found in the zone, which has a moderate to good (medium) groundwater potential. Low groundwater zone locations are generally underlain by granitoid, gneisses, and migmatite.

Characterizing land transformation and densification using urban sprawl metrics in the South Bengal region of India

The impacts of uncontrolled development may be catastrophic in areas experiencing fast population expansion and climatic variability, so the South Bengal Region, adjacent to Kolkata Municipal Corporation (KMC), is facing similar threats. It is essential to monitor such Land use and land cover changes (LULC) in a rapidly urbanizing country like India for long-term and efficient resource management. Remote Sensing (RS) and Geographic Information Systems (GIS) were used in this study to examine LULC dynamics in the South 24 Parganas district of the South Bengal Region during the past 30 years (1991-2021). Indices for urban sprawl monitoring, including patch size, density and growth, were determined statistically to analyze the urban typologies. It has also derived decadal growth rate and expansion efficiency metrics to assess the rate of changing peri-urban scenarios along the South 24 Parganas district. This research helps evaluate the quantitative aspects of urban expansion observed in eco-sensitive and cyclone-prone regions in the context of developing nations. It also helps to design guidelines based on the issues resulting from unsustainable urban sprawl. This study's findings depict substantial growth in population and built-up densities of immediate peri-urban areas to the core city by 70% at the expense of green and blue spaces.

Theater of Ancestral Mediation

Theater of Ancestral Mediation

Developing a Model for the Relationship Between Vegetation and Wind Power Using Remote Sensing and Geographic Information Systems Technology

Developing a Model for the Relationship Between Vegetation and Wind Power Using Remote Sensing and Geographic Information Systems Technology

Determining the Effect of Urbanization on Agricultural Lands Using Remote Sensing and Geographic Information System Techniques

Bu çalışmanın amacı, yıldan yıla tarımsal arazi varlığında önemli derecede daralma meydana gelen Türkiye’de; Konya ili Selçuklu ilçesinde, Uzaktan Algılama (UA) yöntemleri ve Coğrafi Bilgi Sistemleri (CBS) kullanılarak, kentleşmenin farklı yıllardaki alansal değişimi ve bu değişimin tarım arazileri üzerindeki etkisini belirlemeye yöneliktir. Çalışmada, Landsat 5-8 uydu görüntülerinde, CBS tabanlı olarak, NDVI ve NDBI indeksleri hesaplanmış; 2001, 2014 ve 2021 yıllarına ait, kentsel ve tarımsal alanlardaki değişimin görselleştirilmesi amacıyla dağılım haritaları hazırlanmıştır. Buna göre, 2001 yılında 79300.63 hektar olan tarımsal alanlarının, 2021 yılında 50167.97 hektara gerilediği, 2001 yılında 9265.19 hektar olan kentsel alanların 2021 yılında 15690.5 hektarlık alana yayıldığı tespit edilmiştir. Ayrıca çalışma alanına ait arazi yetenek sınıfları ve toprak dağılım haritaları oluşturularak, yerleşime uygun olmayan alanlar tespit edilmiştir. Sonuç olarak; sektörel anlamda bir tarafta kazanımlar var iken, diğer tarafta kayıplar söz konusudur. Bu nedenle kentsel alanlardaki genişlemenin, özellikle tarım alanlarının daralması anlamına geldiği, son 20 yıl içinde kentsel alanların % 3.35 oranında büyüdüğü, tarım alanlarında da % 15.18 oranında bir kayıp yaşandığı belirlenmiştir. Ayrıca alüvyal arazilerin ve tarımsal üretim yapılan arazilerin üzerinde yerleşim yerlerinin ve diğer yapay alanların yayıldığı tespit edilmiştir.

The state-legal personality of the Republic of Serbian Krajina

From its foundation until its end, the Republic of Serbian Krajina (the RSK) had been a parastate, remaining the parastate, having failed to meet any criteria of a normally functioning state, as prescribed by Montevideo Convention on the Rights and Duties of States, known as Montevideo Convention. Therefore, the RSK has never become the subject of international law, unlike the Republic of Srpska, upon signing The General Framework for Peace in Bosnia and Herzegovina or the Dayton Peace Agreement. The Republic of Serbian Krajina was a federative parastate, defined by the territorial discontinuity and it consisted of three federative units: the Serbian Region of Slavonia, Baranja and Western Sirmium Region, the Serbian Region of Western Slavonia and the Serbian Autonomous Region of Krajina. In the formal and legal sense, the Republic of Serbian Krajina was established after it got proclaimed on 19th December 1991. However, it fell apart, de facto, on 5th August 1995, due to the fact that its capital city of Knin, was liberated by the joined police and Croatian military forces, compelling the RSK representatives to sign the capitulation, de iure, on 8th August 1995. Keywords: state; parastate; nation; territory; government; a subject of the international law.

Evaluation of Satellite-Based and Reanalysis Precipitation Datasets with Gauge-Observed Data over Haraz-Gharehsoo Basin, Iran

Evaluating satellite-based products is vital for precipitation estimation for sustainable water resources management. The current study evaluates the accuracy of predicting precipitation using four remotely sensed rainfall datasets—Tropical Rainfall Measuring Mission products (TRMM-3B42V7), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Climate Data Records (PERSIANN-CDR), Cloud Classification System-Climate Data Record (PERSIANN-CCS-CDR), and National Centers for Environmental Prediction (NCEP)-Climate Forecast System Reanalysis (CFSR)—over the Haraz-Gharehsoo basin during 2008–2016. The benchmark values for the assessment are gauge-observed data gathered without missing precipitation data at nine ground-based measuring stations over the basin. The results indicate that the TRMM and CCS-CDR satellites provide more robust precipitation estimations in 75% of high-altitude stations at daily, monthly, and annual time scales. Furthermore, the comparative analysis reveals some precipitation underestimations for each satellite. The underestimation values obtained by TRMM CDR, CCS-CDR, and CFSR are 8.93 mm, 20.34 mm, 9.77 mm, and 17.23 mm annually, respectively. The results obtained are compared to previous studies conducted over other basins. It is concluded that considering the accuracy of each satellite product for estimating remotely sensed precipitation is valuable and essential for sustainable hydrological modelling.