دکتر مهدی مؤمنی شهرکی

Aerosol Optical Depth (AOD) retrieved using Cloud-Aerosol LIDAR with Orthogonal Polarization (CALIOP) and Moderate Resolution Imaging Spectroradiometer (MODIS) plays a crucial role in aerosol sensing. However, the performance of these products varies across different aerosol concentrations. This research assessed the performance of CALIOP and MODIS AOD products over a three-year period (January 2017 to June 2019) under various aerosol concentrations in the Red Sea and the Persian Gulf. The products were compared with ship-based AERONET (Aerosol Robotic Network) measurements conducted in the study area. The findings reveal that MODIS AOD products exhibit greater reliability during clear days, whereas CALIOP AOD products are more accurate in regions characterized by high aerosol concentrations. However, CALIOP's limited product resolution prevents it from providing adequate spatial-temporal coverage under heavy aerosol concentrations. To enhance the accuracy of MODIS AOD, this paper proposes a methodology based on various machine learning (ML) algorithms, including Random Forest (RF), Multi-layer Perceptron (MLP), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVR), and Bayesian network. The study explores the performance of MODIS and CALIOP AOD products during moderate and pollution days in the Persian Gulf and Red Sea regions. The ML models are ranked based on their accuracy, with the order being XGBoost > MLP > SVRrbf > RF > Bayesian > SVRlinear for the annual and two semi-annual networks. In other words, the Bayesian method demonstrates higher efficiency (R2 = 0.97, 0.91, and 0.90) in handling seasonal subsets. The proposed model's outcomes are validated using ship-based AERONET AOD data. By comparing the estimated AOD values with the ship-based AERONET AOD values and analyzing the overall correlation results, it is evident that machine learning techniques effectively provide accurate AOD estimates for moderate and pollution days. © 2023 Turkish National Committee for Air Pollution Research and Control

Innovative data clustering method improves drought prediction in heterogeneous landscapes using GEE-derived remote sensing indices

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Heydari, H., Momeni shahraki, M., Nadi, S.

Remote Sensing Applications: Society and Environment (23529385)33

Drought is a complex natural disaster characterized by unique features influenced by environmental factors, particularly at a regional scale. Remote Sensing (RS) indices have proven to be valuable in assessing drought conditions. In this study, we utilized the five most commonly used RS indices: Normalized Difference Vegetation Index (NDVI), Deviation/Anomaly of NDVI (NDVI-Dev/ANDVI), Temperature Condition Index (TCI), Vegetation Condition Index (VCI), and 2-band Enhanced Vegetation Index (EVI2). These indices were extracted over a 30-year period (1988–2017) using Google Earth Engine (GEE) for modeling the Standardized Precipitation Index (SPI) with various time-spans (1, 3, 6, 9, 12, and 24 months) as the Ground-Based (GB) drought indices. The models were trained from 1988 to 2014 and tested on data from 2015 to 2017. To enhance RS-based drought modeling over the complex and diverse study area of Iran, we proposed two different data clustering methods based on distinct environmental factors – climatic condition and land-cover type – and compared the results. The first method employed an enhanced-climate-based approach to improve upon the standard-climate-based method. This approach considered variations in the environment caused by seasonal changes in different climatic zones, which had received less attention in previous studies. Unique Support Vector Machine (SVM) models were trained using various clusters of inputs/outputs to assess the efficiency of the RS indices in predicting different drought conditions under various circumstances. Furthermore, we introduced an innovative data clustering method based on RS-derived land-cover similarity, which resulted in further improvements in drought modeling compared to agroclimatic zoning. The fusion-based drought modeling approach, using all RS indices as inputs in the enhanced-climate-based and land-cover-based methods, demonstrated an aggregated Overall Accuracy (OA) of 92.93% and 95.11% for predicting SPI-3, respectively. Notably, within the land-cover-based method, TCI exhibited the highest aggregated performance compared to other RS indices in predicting SPI-3 with an OA of approximately 95%. © 2023 Elsevier B.V.