Temperature-Vegetation-soil Moisture-Precipitation Drought Index (TVMPDI); 21-year drought monitoring in Iran using satellite imagery within Google Earth Engine

Remote Sensing (RS) offers efficient tools for drought monitoring, especially in countries with a lack of reliable and consistent in-situ multi-temporal datasets. In this study, a novel RS- based Drought Index (RSDI) named Temperature-Vegetation-soil Moisture-Precipitation Drought Index (TVMPDI) was proposed. To the best of our knowledge, TVMPDI is the first RSDI using four different drought indicators in its formulation. TVMPDI was then validated and compared with six conventional RSDIs including VCI, TCI, VHI, TVDI, MPDI and TVMDI. To this end, precipitation and soil temperature in-situ data have been used. Different time scales of meteorological Standardized Precipitation Index (SPI) index have also been used for the validation 2 of the RSDIs. TVMPDI was highly correlated with the monthly precipitation and soil temperature in-situ data at 0.76 and 0.81 values respectively. The correlation coefficients between the RSDIs and 3-month SPI ranged from 0.07 to 0.28, identifying the TVMPDI as the most suitable index for subsequent analyses. Since the proposed TVMPDI could considerably outperform the other selected RSDIs, all spatiotemporal drought monitoring analyses in Iran were conducted by TVMPDI over the past 21 years. In this study, different products of the Moderate Resolution Imaging Spectrometer (MODIS), Tropical Rainfall Measuring Mission (TRMM), and Global Precipitation Measurement (GPM) datasets containing 15206 images were used on the Google Earth Engine (GEE) cloud computing platform. According to the results, Iran experienced the most severe drought in 2000 with a 0.715 TVMPDI value lasting for almost two years. Conversely, the TVMPDI showed a minimum value equal to 0.6781 in 2019 as the lowest annual drought level. The drought severity and trend in the 31 provinces of Iran have also been mapped. Consequently, various levels of decrease over the 21 years were found for different provinces, while Isfahan and Gilan were the only provinces showing an ascending drought trend (with a 0.004% and 0.002% trendline slope respectively). Khuzestan also faced a worrying drought prevalence that occurred in several years. In summary, this study provides updated information about drought trends in Iran using an advanced and efficient RSDI implemented in the cloud computing GEE platform. These results are beneficial for decision-makers and officials responsible for environmental sustainability, agriculture and the effects of climate change.Peer ReviewedPostprint (author's final draft

Assessing the performance of machine learning algorithms in Google Earth Engine for land use and land cover analysis: A case study of Muğla province, Türkiye

Regions with high tourism density are very sensitive to human activities. Ensuring sustainability by preserving the cultural characteristics and natural structure of these regions is of critical importance in order to transfer these assets to the future world heritage. Detecting and mapping changes in land use and land cover (LULC) using innovative methods within short time intervals are of great importance for both monitoring the regional change and making administrative planning by taking necessary measures in a timely manner. In this context, this study focuses on the creation of a 4-class LULC map of Muğla province over the Google Earth Engine (GEE) platform by utilizing three different machine learning algorithms, namely, Support Vector Machines (SVM), Random Forest (RF), and Classification and Regression Tree (CART), and on comparison of their accuracy assessments. For improved classification accuracy, as well with the Sentinel-2 and Landsat-8 satellite images, the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) are also derived and used in classification of the major land use classes, which are ‘built-up area & barren land’, ‘dense vegetation’, ‘water surface’, and ‘shrub, grassland & sparse vegetation’. Experimental results show that the most relevant algorithm is RF with 0.97 overall accuracy and 0.96 Kappa value, followed by SVM and CART algorithms, respectively. These results indicate that the RF classifier outperforms both SVM and CART classifiers in terms of accuracy. Moreover, based on the results of the RF classifier, 19% (2,429 km2) of the study region is classified as built-up area & barren land, 48% (6,135 km2) as dense vegetation, 2% (301 km2) as water surface and 30% (3,832 km2) as shrub, grassland & sparse vegetation class

Drought propagation in brazilian biomes revealed by remote sensing

Drought events have been reported in all Brazilian regions every year, evolving slowly over time and large areas, and largely impacting agriculture, hydropower production, and water supplies. In the last two decades, major drought events have occurred over the country, such as the 2010 and 2015 events in the Amazon, the 2012 event in the Pampa, and the 2014 event in the Cerrado biome. This research aimed to understand drought propagation and patterns over these biomes through joint analysis of hydrological, climatic, and vegetation indices based on remote sensing data. To understand the drought cascade propagation patterns, we assessed precipitation, evapotranspiration, soil moisture (at surface and sub-surface), terrestrial water storage, land surface temperature, enhanced vegetation index, and gross primary productivity. Similar drought patterns were observed in the 2015 Amazon and 2012 Pampa droughts, with meteorological and agricultural droughts followed by a hydrological drought, while the 2014 event in the Cerrado was more associated with a hydrological drought. Moreover, the 2015 Amazon drought showed a different pattern than that of 2010, with higher anomalies in precipitation and lower anomalies in evapotranspiration. Thus, drought propagation behaves differently in distinct Brazilian biomes. Our results highlight that terrestrial water storage anomalies were able to represent the hydrological drought patterns over the country. Our findings reveal important aspects of drought propagation using remote sensing in a heterogenous country largely affected by such events