Google Earth Engine (GEE) for Modeling and Monitoring Hydrometeorological Events Using Remote Sensing Data DOI
Khaled Hazaymeh, Mohammad Zeitoun

Advances in environmental engineering and green technologies book series, Journal Year: 2023, Volume and Issue: unknown, P. 114 - 134

Published: Nov. 24, 2023

Google Earth Engine (GEE) has emerged as a powerful platform for modeling and monitoring extreme hydrometeorological events. In recent years, GEE been used extensively studying floods, droughts, other natural disasters. It offers comprehensive suite of tools that can help researchers practitioners better understand the complex interactions between weather, climate, water resources. By providing access to wealth satellite imagery, climate data, geospatial datasets, enables users model monitor these events with unprecedented accuracy efficiency. This book chapter explores various ways in which be events, understanding their needs, including case studies practical examples. It's worth noting this mainly focuses on using remote sensing data analysis into monitoring.

Language: Английский

A novel agricultural drought index based on multi-source remote sensing data and interpretable machine learning DOI Creative Commons
Hao Chen, Yang Ni, Xuanhua Song

et al.

Agricultural Water Management, Journal Year: 2025, Volume and Issue: 308, P. 109303 - 109303

Published: Jan. 16, 2025

Language: Английский

Citations

3
Inversion of large-scale citrus soil moisture using multi-temporal Sentinel-1 and Landsat-8 data DOI Creative Commons

Zongjun Wu,

Ningbo Cui, Wenjiang Zhang

et al.

Agricultural Water Management, Journal Year: 2024, Volume and Issue: 294, P. 108718 - 108718

Published: Feb. 15, 2024

Soil moisture is a significant variable in agricultural study and precision irrigation decision-making. It determines the soil water availability for plants, directly influencing plant growth, yield quality. Owing to variations regional microclimate, landform difference, type vegetation coverage, has strong spatial-temporal heterogeneity on large scale. Micro-wave remote sensing can be used invert based dielectric constant under different weather conditions, while optical utilizes spectral characteristics estimate physiological ecological information of vegetation. In this study, two new hybrid models (ACO-RF SSA-RF) were structured by optimizing standalone random forest (RF) ant colony optimization algorithm (ACO) sparrow search (SSA), six input combinations multi-temporal Sentinel-1 Landsat-8 data from sensors (optical, thermal radar sensors) used. The RF, ACO-RF, SSA-RF with inputs employed predict at depths (5 cm, 10 20 40 cm) large-scale drip-irrigated citrus orchard. results showed that ACO-RF outperformed RF model terms prediction accuracy depth 0–40 R2 0.800–0.921 0.504–0.798, RRMSE 7.214–16.284% 11.124–22.214%, respectively. model, had better than 0.805–0.921 0.800–0.911, 7.214–13.244% 8.274–16.284%, At 5 cm inversion microwave was higher multispectral inputs, 0.556–0.888 0.541–0.886, 9.015–19.544% 9.124–22.214%, However, 0.532–0.841 0.508–0.831, 9.124–21.021% 9.142–21.214%, multispectral, thermal, exhibited highest predicting moisture, 0.635–0.921, 7.214−18.564%, Therefore, multisource recommended This approach provide support making intelligent decisions grid land lots.

Language: Английский

Citations

11
Enhancing a machine learning model for predicting agricultural drought through feature selection techniques DOI Creative Commons

Pardis Nikdad,

Mehdi Mohammadi Ghaleni, Mahnoosh Moghaddasi

et al.

Applied Water Science, Journal Year: 2024, Volume and Issue: 14(6)

Published: May 11, 2024

Abstract This study aims to determine the crucial variables for predicting agricultural drought in various climates of Iran by employing feature selection methods. To achieve this, two databases were used, one consisting ground-based measurements and other containing six reanalysis products temperature ( T ), root zone soil moisture (SM), potential evapotranspiration (PET), precipitation P ) during 1987–2019 period. The accuracy global database data was assessed using statistical criteria both single- multi-product approaches aforementioned four variables. In addition, five different methods employed select best single condition indices (SCIs) as input support vector regression (SVR) model. superior multi-products based on time series (SMT) showed increased , PET, SM variables, with an average 47%, 41%, 42%, 52% reduction mean absolute error compared SSP. hyperarid climate regions, PET index found have high relative importance 40% 36% contributions SPEI-3 SPEI-6, respectively. suggests that plays a key role regions because very low precipitation. Additionally, results show ReliefF outperformed modeling. characteristics indicate occurrence 2017 2018 Iran, particularly arid semi-arid climates, instances duration 12 months humid climates.

Language: Английский

Citations

5
A Novel Agricultural Remote Sensing Drought Index (ARSDI) for high-resolution drought assessment in Africa using Sentinel and Landsat data DOI

Nasser A. M. Abdelrahim,

Shuanggen Jin

Environmental Monitoring and Assessment, Journal Year: 2025, Volume and Issue: 197(3)

Published: Feb. 4, 2025

Language: Английский

Citations

0
Federated transfer learning for distributed drought stage prediction DOI Creative Commons

Muhammad Owais Raza,

Aqsa Umar,

Jawad Rasheed

et al.

Discover Artificial Intelligence, Journal Year: 2025, Volume and Issue: 5(1)

Published: May 11, 2025

Language: Английский

Citations

0
Spatial analysis of remote sensing and meteorological indices in a drought event in southwestern Spain DOI Creative Commons
Elia Quirós, Laura Fragoso‐Campón

Theoretical and Applied Climatology, Journal Year: 2024, Volume and Issue: 155(5), P. 3757 - 3770

Published: Jan. 31, 2024

Abstract The effects of global warming and climate change are being felt through more extreme prolonged periods drought. Multiple meteorological indices used to measure drought, but they require hydrometeorological data; however, other measured by remote sensing quantify vegetation vigor can be correlated with the former. This study investigated correlation between both index types type season. correlations were also spatially modeled in a drought event southwestern Spain. In addition, three maps different levels detail terms categorization compared. results generally showed that grassland was most well category SPEI FAPAR, LAI, NDVI. pronounced autumn spring, which is when changes senescence growth occur. spatiotemporal analysis indicated very similar behavior for grasslands grouped an area adaptation as having high evapotranspiration forecast. Finally, forest-based forecast analysis, best explained performance again NDVI, lag up 20 days. Therefore, remotely sensed good indicators status variably explanatory traditional indicators. Moreover, complementing made it possible detect areas particularly vulnerable change.

Language: Английский

Citations

1
Deep learning-based modeling of land use/land cover changes impact on land surface temperature in Greater Amman Municipality, Jordan (1980–2030) DOI Creative Commons
Khaled F. Alkaraki, Khaled Hazaymeh,

Osama M. Al-Tarawneh

et al.

GeoJournal, Journal Year: 2024, Volume and Issue: 89(4)

Published: Aug. 5, 2024

Abstract Modeling the impacts of Land Use/Land Cover changes (LULCC) on Surface Temperature (LST) is crucial in understanding and managing urban heat islands, climate change, energy consumption, human health, ecosystem dynamics. This study aimed to model past, present, future LULCC Temperatures Greater Amman Municipality (GAM) Jordan between 1980 2030. A set maps for land cover, LST, Normalized Difference Vegetation Index (NDVI), Built-up (NDBI), topography was integrated into Cellular Automata-Artificial Neural Network (CA-ANN) Long-Short-Term Model (LSTM) models predict LULC LST The results showed an expansion areas GAM from 54.13 km 2 (6.6%) 374.1 (45.3%) 2023. However, agricultural decreased 152.13 (18.5%) 140.38 (17%) 2023, while barren lands 54.44 34.71 (4.22%) Forested declined 4.58 (0.56%) 4.35 (0.53%) Rangelands/ sparsely vegetated 557 (67.7%) 270.71 (32.9%) modeling increase average all cover types, with most significant increases evident within Rangelands/Sparsely areas. slightest forested as increased 28.42 °C 34.16 forecasts a continuous values types. These findings highlight impact surface dynamics their increasing temperature, which urges adoption more sustainable planning policies livable thermally comfortable cities.

Language: Английский

Citations

1
Sustainable Agriculture-Based Climate Change Training Models using Remote Hyperspectral Image with Machine Learning Model DOI

M. Durairaj,

Kasapaka Rubenraju,

B. Krishna

et al.

Remote Sensing in Earth Systems Sciences, Journal Year: 2024, Volume and Issue: 7(4), P. 261 - 270

Published: Aug. 23, 2024

Language: Английский

Citations

1
A Comprehensive Evaluation of Agricultural Drought Vulnerability Using Fuzzy‐AHP‐Based Composite Index Integrating Sensitivity and Adaptive Capacity DOI
Debarati Bera, Dipanwita Dutta

Hydrological Processes, Journal Year: 2024, Volume and Issue: 38(11)

Published: Nov. 1, 2024

ABSTRACT With increasing extreme weather events, ground water crisis and population expansion, crop stress production failure have emerged as critical challenges. Agricultural drought vulnerability (ADV) at local regional scales has become a global concern it is directly related to food security, hunger issues poverty. The Kangsabati river basin one of the major drought‐prone in eastern India frequently affected by reduction or because fluctuation monsoonal rainfalls, poor irrigation system harsh edaphic factors. In this context, study focuses on assessing agricultural using multi‐sensor datasets geospatial techniques. ADV been assessed through multi‐source data sets covering meteorological, agricultural, soil socio‐economic aspects powerful, systematic, flexible decision‐making fuzzy‐based analytic hierarchy process (fuzzy‐AHP) technique. index functional product two composite indices: sensitivity (SI) adaptivity index. SI derived from components like intensity index, groundwater stress, erosion, percentage cultivators, marginal workers land. Adaptive capacity depends upon human, financial, physical, infrastructural natural capital. Each was considering various factors fuzzy‐AHP methods for weightage calculation. indices revealed variation resource distribution precisely each geographically distinct zone. shows that almost 60% highly sensitive zone situated upper region characterised undulating lands. A large part entire (48%) moderately drought‐sensitive. result also significant (35%) middle vulnerable drought. contrast, lower exhibits low very levels results indicate even though some areas are moderate less sensitive, high due their limited adaptive capacity. comprehensive framework developed potential region‐specific policy implementation sustainable growth.

Language: Английский

Citations

1
Spatiotemporal Drought Assessment in Ningxia Autonomous Region: A Machine Learning and Remote Sensing Approach DOI Creative Commons

M.M. Awais,

Zakria Zaheen, Zainab Fatima

et al.

Deleted Journal, Journal Year: 2024, Volume and Issue: 4(02)

Published: May 17, 2024

Drought represents a significant disaster that directly impacts the economic and ecological welfare of any nation it afflicts. This study focused on climatic anomalies drought over Ningxia Hui autonomous region in northwest China last two decades. The employed an in-depth machine learning model, which incorporated indices, thus leading to data-informed analysis patterns. accomplished this by using MODIS satellite data products available for vegetation moisture monitoring. MOD09GA, MOD11A2, MCD43A4 streams were loaded into Google Earth Engine as factors develop time-series dataset indices. Indices are Normalized Difference Vegetation Index (NDVI), Enhanced (EVI), Land Surface Temperature (LST) measurements taken account. Data temperature, precipitation, evapotranspiration was compiled period from 2003 2023 calculated standardized indices pixel level whole Standardized Precipitation (SPI), Keetch-Byram (KBDI), Precipitation-Evapotranspiration ( results indicated SPI fell significantly year 2023, 0.7 -0.3. SPEI plummeted 0.5 -0.2 during observed time frame. KBDI also went up, through 581.33 681.091 showing deterioration aridity drying soil. conclusion focuses conditions 20 years.

Language: Английский

Citations

0