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In light of recently accelerating global warming, the changes in vegetation trends are vital for the monitoring of the dynamics of both whole ecosystems and individual species. Detecting changes within the time series of specific forest ecosystems or species is very important in the context of assessing their vulnerability to climate change and other negative phenomena. Hence, the aim of this paper was to identify the trend change points and periods of greening and browning in multi-annual time series of the normalised difference vegetation index (NDVI) and enhanced vegetation index (EVI) of four main forest-forming tree species in the temperate zone: pine, spruce, oak and beech. The research was conducted over the last two decades (2002–2022), and was based on vegetation indices data derived from the Moderate Resolution Imaging Spectroradiometer (MODIS). To this end, several research approaches, including calculating the linear trends in the moving periods and BEAST algorithm, were adapted. A pattern of browning then greening then constant was detected for coniferous species, mostly pine. In turn, for broadleaved species, namely oak and beech, a pattern of greening then constant was identified, without the initial phase of browning. The main trend change points seem to be ca. 2006 and ca. 2015 for coniferous species and solely around 2015 for deciduous ones. [ABSTRACT FROM AUTHOR]

As part of the study of the ecological state of soils and vegetation in the zone of impact of the Sredneuralsky Copper Smelter (SNCS), the following tasks were solved: the content of heavy metals in the soils of the test sites laid within the zone of impact of the SNCS was determined; macrokinetic patterns of seasonal dynamics of the normalized relative vegetation index (NDVI) at the test sites in the zone of impact of the SNCS were revealed; macrokinetic patterns of vegetation response in the form of NDVI, calculated according to MODIS and Landsat 8 data, to soil pollution by a complex of heavy metals in the zone of impact of the SNCS were revealed; soil quality was ranked according to the patterns of vegetation response in the form of NDVI to soil pollution by a complex of heavy metals within the boundaries of the natural protection zone (NPZ) of the SNCS according to 2023. The intraseasonal dynamics of photosynthetically active biomass in the form of NDVI was modeled using a theoretical growth equation based on MODIS satellite data. The seasonal maximum of the vegetation index at the trial sites in 2012 fell on weeks 25‒28. In 2023, it occurred on weeks 27‒33, depending on the location of the sampling point. The rate of maximum achievement is characterized by weak intraseasonal and interannual variation. The patterns of changes in the concentration of photosynthetically active biomass in the form of maximum NDVI for the 2023 season in response to the gross content of a complex of heavy metals (Cu, Pb, Cd, and Zn) in the soil of the test sites were modeled using the theoretical equation of dose dependence. The maximum permissible level of the heavy-metal complex in the soil in the form of a geometric mean, which does not cause a decrease in the values of the vegetation index calculated according to the data of the MODIS and Landsat 8 satellites, was 101 and 106 mg kg–1, respectively. An analysis of histograms of the distribution of NDVI values within the NPZ was carried out, which showed how the true frequencies of occurrence of the values of the vegetation index correspond to the empirical boundary of the NPZ associated with the maximum dose dependence point identified by the study of the 2012. The nature of the distribution of true NDVI values for the NPZ exclusively fully supports the used zoning theory based on the analysis of dose dependence. The modern boundaries of the SNCS natural protection zone are 4‒7 km away from the center of the sanitary protection zone. [ABSTRACT FROM AUTHOR]

This study is the first to examine vegetation change on Kilimanjaro from 2000 to 2022, using the Normalised Difference Vegetation Index (NDVI) at a spatial resolution of 250 m via the Moderate Resolution Imaging Spectrometer (MODIS). There is a long-term increase in vegetation on an annual basis, but with decreases in the long/short rains at low/high elevations. Temperature significantly influences NDVI across all elevations and seasons. Precipitation effects are more variable and often delayed, especially in the forest zone. The study explores how El Nino Southern Oscillation (ENSO) events affect NDVI differently at contrasting elevations, indicating the need for further elevation-specific remote sensing research and field data. [ABSTRACT FROM AUTHOR]

The Vegetation Health Index (VHI) is a metric used to assess the health and condition of vegetation, based on satellite-derived data. It offers a comprehensive indicator of stress or vigor, commonly used in agriculture, ecology, and environmental monitoring for forecasting changes in vegetation health. Despite its advantages, there are few studies on forecasting VHI as a future projection, particularly using up-to-date and effective machine learning methods. Hence, the primary objective of this study is to forecast VHI values by utilizing remotely sensed images. To achieve this objective, the study proposes employing a combined Convolutional Neural Network (CNN) and a specific type of Recurrent Neural Network (RNN) called Long Short-Term Memory (LSTM), known as ConvLSTM. The VHI time series images are calculated based on the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) data obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Terra and Aqua satellites. In addition to the traditional image-based calculation, the study suggests using global minimum and global maximum values (global scale) of NDVI and LST time series for calculating the VHI. The results of the study showed that the ConvLSTM with a 1-layer structure generally provided better forecasts than 2-layer and 3-layer structures. The average Root Mean Square Error (RMSE) values for the 1-step, 2-step, and 3-step ahead VHI forecasts were 0.025, 0.026, and 0.026, respectively, with each step representing an 8-day forecast horizon. Moreover, the proposed global scale model using the applied ConvLSTM structures outperformed the traditional VHI calculation method. [ABSTRACT FROM AUTHOR]

The low-lying islands of the Indian Sundarbans Delta (ISD) contain a unique ecosystem with rich biodiversity, which are at the forefront of the impacts of climate change and natural disasters, such as cyclones. Therefore, in this study, we have analyzed the impact of Cyclone Yaas at the local level in the ISD. We utilized various indices derived from MODIS satellite images to analyze the local-level impacts. The results of our study revealed widespread impacts from high storm surges of 9–16 feet. In general, the daytime land surface temperatures (LSTs) were higher before compared to during the storm, due to lower insolation associated with cloudy skies. However, higher values were observed during the storm for nighttime LSTs and the vegetation indices. More specifically, at the local level, the differences were more pronounced in the vegetated and low-lying coastal areas of the islands. The results of the image analyses were also corroborated with field observations in some of the islands, which showed saltwater encroachment in agricultural lands, collapsed embankments built for protection against storm surge, and food insecurity. The results of our study highlighted the vulnerability of these islands to extreme weather events, and long-lasting impacts on the local communities. [ABSTRACT FROM AUTHOR]

Given its modern geographical and geomorphological characteristics, along with rapid socio-economic changes, the Nile Delta stands out as one of the world's most dynamic landscapes. The key drivers of the land use change in this region have been the reclamation of delta margins, changes in agricultural practices, and urban expansion. The present study aims to explore the variations in the seasonal daytime and nighttime trends of the land surface temperatures (LST) at this active agronomic system in response to the seasonal variations of vegetation cover as revealed by the normalized difference vegetation index (NDVI) during the past two decades. The data were exclusively acquired from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument for the period from January 2001 to December 2021, where geospatial and statistical analyses were accomplished to construct a LST/NDVI spatio-temporal pattern throughout the Nile Delta. Results revealed a robust negative and a significant relationship between the NDVI and the diurnal LST with high regression coefficients (R2) ranging from 0.78 to 0.97 (p value < 0.05). Maximal seasonal warming trends occurred during harvesting seasons (springs and falls), while the least warming was recorded during winters (the growing seasons). It was also observed that the nocturnal warming (0.72°C/decade) was almost as double as the corresponding value of the daytime trend (0.33°C/decade). The study recognized a seasonal climatic warming throughout the Nile Delta influenced by the human-induced land use change and agricultural practices. [ABSTRACT FROM AUTHOR]

Satellite remote sensing provides the changes information of Earth surface on large spatial scale in a long time series and has been widely used in ecology. However, the possible impact from human activities generally occurs on a smaller spatial scale and could be detected in a longer time, which requires the remote sensing data having the both higher spatial and temporal resolution. Meanwhile, the development of the spatiotemporal data fusion algorithm provides an opportunity for the requirements. In this paper, based on deep learning, we proposed a residual convolutional neural network (Res-CNN) model to improve the fusion result considerably with brand-new network architecture to fuse the NDVI retrievals from Landsat 8 and MODIS images. Experiments conducted in two different areas demonstrate improvements by comparing them with existing algorithms. The model performance was evaluated by a linear regression between predictions and observations and quantified by determination coefficients (R2), regressive ecoefficiency (slope). The two excellent models, ESTARFM and FSDAF, were compared with the new model on their performance. The results showed that the predicted NDVI had the higher exploitational on the variability in the Landsat-based NDVI with the R2 of 0.768 and 0.807 at the urban and grassland sites. The predicted NDVI was well consistent with the observations with the slope of 1.01 and 0.989, and the R-RMSE of 95.76% and 93.58% at the urban and grassland sites respectively. This study demonstrated that the Res-CNN model developed in this paper exhibits higher accuracy and stronger robustness than the traditional models. This research is full implications because it not only provides a model on the spatio-temporal data fusion, but also can provide the data of a long time series for the management and utilization of agriculture and grassland ecosystems on the regional scale. [ABSTRACT FROM AUTHOR]

Copyright of Cuadernos de Investigación Geográfica is the property of Universidad de la Rioja, Servicio de Publicaciones and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study focused on trends and driving forces of the leaf unfolding (LU) onset of oak and beech forests in the Slovak Carpathians along elevational gradients in the period 2000-2021. Particular attention was paid to improving the modelling of the LU onset using the MOD/MYD09 Moderate Resolution Imaging Spectroradiometer (MODIS) products. The LU onset was derived from the annual Normalized Difference Vegetation Index (NDVI) trajectories fitted with a double logistic function. An improved estimate of the onset was obtained by calculating 6 parameters of the logistic function and by comparing with the LU onset from phenological field observations. Between 2000 and 2021, we found a trend towards an earlier LU onset at the national level by ~0.39 day year-1 for oak stands (p = 0.13) and ~0.08 day year-1 for beech stands (p = 0.48). The analysis of trends in three elevation zones showed a difference in the LU onset of oak and beech stands as a function of elevation. For oak in 100-350 and 350-500 m zones was found a shift towards an earlier onset by ~0.41 day year-1 (p = 0.12). This corresponds to a shift of 8.6 days for the entire observation period 2000-2021. In the elevational zone above 500 m, the trend was milder, ~0.27 day year-1 (p = 0.21), i.e., 5.6 days for the entire analysed period. The shift towards an earlier onset at lower elevations and a later onset at higher elevations for beech was not statistically significant, with p-values between 0.44 and 0.51. The atypical year 2021, with the latest onset of LU during the entire observation period, fundamentally affected the significance of all trends. Nevertheless, the pixel-level analysis revealed a significant trend towards an earlier LU onset (p < 0.05) in 20.3% of oak stands. The same result was found only in 0.8% of beech stands. Strong negative correlations with R2 = 0.72 for oak and R2 = 0.81 for beech (p < 0.001) were found between the LU onset and March and April temperature deviations from the long-term normal. Temperature changes are the main driving force affecting the LU onset in the studied region.

In light of recently accelerating global warming, the changes in vegetation trends are vital for the monitoring of the dynamics of both whole ecosystems and individual species. Detecting changes within the time series of specific forest ecosystems or species is very important in the context of assessing their vulnerability to climate change and other negative phenomena. Hence, the aim of this paper was to identify the trend change points and periods of greening and browning in multi-annual time series of the normalised difference vegetation index (NDVI) and enhanced vegetation index (EVI) of four main forest-forming tree species in the temperate zone: pine, spruce, oak and beech. The research was conducted over the last two decades (2002–2022), and was based on vegetation indices data derived from the Moderate Resolution Imaging Spectroradiometer (MODIS). To this end, several research approaches, including calculating the linear trends in the moving periods and BEAST algorithm, were adapted. A pattern of browning then greening then constant was detected for coniferous species, mostly pine. In turn, for broadleaved species, namely oak and beech, a pattern of greening then constant was identified, without the initial phase of browning. The main trend change points seem to be ca. 2006 and ca. 2015 for coniferous species and solely around 2015 for deciduous ones.

To enhance the accuracy and stability of FY-4A/AGRI detection data, the MODIS, with highly accurate onboard calibration, is selected as the reference sensor for cross-radiation calibration calculations. The following are the data selection conditions: full considered time, observation geometries, field angles, cloud cover, etc. FY-4A/AGRI and Aqua-MODIS image data are selected as matching sample region locations, where the time difference between the observations for the same ground object is less than 15 min, the satellite zenith angle is less than 30°, and the field angle difference is less than 0.01. The 245 collected reflectance spectral curves are convolved with the spectral response functions of the two sensors, and the spectral band adjustment factors of the corresponding bands are calculated for spectral correction purposes. The cross-calibration coefficients for the red and near-infrared bands are calculated by linearly fitting the simulated top of the atmosphere reflectance values and digital number values from the AGRI sensor in a homogeneous area. In this paper, 16 cross-calibration calculations are performed on FY-4A/AGRI image data from August 2018 to September 2020, and the results are compared with the original calibration coefficients to test the feasibility of the proposed method. Additionally, 31 cross-calibration calculations are performed on image data from October 2020 to December 2022 to study the resulting AGRI sensor quality and performance changes. The NDVI of the FY-4A/AGRI image data was calculated before and after the cross-radiometric calibration using the maximum synthesis method. Additionally, the NDVI of the MODIS image data was compared and analyzed from three aspects: time, space, and the change trend. The results show that the spectral band adjustment factor calculated using the reflectance spectral curves of the ground objects in this paper can effectively correct for the spectral differences between the two sensors. Sixteen cross-calibration coefficients are less than 5.2% different from the original calibration coefficients, which fully proves the feasibility of the method used in this paper. All of the cross-calibration results show that the AGRI sensors have a certain degree of attenuation in the red and near-infrared bands, and the annual attenuation rates are approximately 1.37% and 2.55%, respectively. Cross-radiometric calibration has further improved the quality of the NDVI in FY-4A/AGRI imagery, enhancing the precision of its data application. [ABSTRACT FROM AUTHOR]

Copyright of Arid Zone Research / Ganhanqu Yanjiu is the property of Arid Zone Research Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The vegetation of a river basin is affected by various climate factors, such as precipitation and land surface temperature (LST). This study explores the best machine learning model for the prediction of normalized difference vegetation index (NDVI) with LST and precipitation as input parameters. The study also determines the correlation between NDVI, LST, and precipitation of the Mahanadi basin from 2003 to 2021. Monthly precipitation data was extracted from the Center for Hydrometeorology and Remote Sensing (CHRS) portal. The Moderate Resolution Imaging Spectroradiometer (MODIS) products were used to derive the LST and NDVI using Google Earth Engine (GEE). Four different machine learning models were used to predict the NDVI of the Mahanadi basin: linear regression (LR), random forest (RF), support vector regression (SVR), and k-nearest neighbors (KNN). The coefficient of determination (R2), root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE), and explained variance score (EVS) were calculated to evaluate the performance of the models. The results show that the RF model has the highest R2 value in both the training and testing sets among these models, indicating that it is the most optimal among these models for predicting NDVI. The SVR model has the lowest RMSE value in the training set, but the KNN model has the lowest RMSE value in the testing set. The results also show that there is a positive correlation between precipitation and NDVI, a negative correlation between precipitation and LST, and between NDVI and LST. This study provides insights into the relationship between NDVI, LST, and precipitation, and the best machine-learning model for predicting NDVI. The findings of this study can be used to improve the management of river basins and to predict the effects of climate change on vegetation. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) is a key variable used in climatic modeling, monitoring, and assessing urban heat islands (UHI) impacts. The extraction of LST and normalized difference vegetation index (NDVI) from earth observation satellite data for continuous monitoring at a local or regional scale is a challenging task. In this paper, a Python-based semi-automatic tool (LS8pyCalc) has been developed based on USGS-provided equations to calculate LST and NDVI using LANDSAT-8 (LS8) images with minimum input parameters using graphical user interface (GUI). The developed tool was successfully implemented in the Punjab Rice Wheat Agroclimatic Zone (PRW-ACZ). The tool's accuracy was validated through in situ observations and compared with Google Earth Engine (GEE) derived Moderate Resolution Imaging Spectroradiometer (MODIS) product using eight statistical parameters at 43 sites having varieties of geo-features (sparse and dense vegetated, built-up area, and water bodies). Land use land cover (LULC) extracted from Copernicus Global Land Cover Layers was used to correlate the outcomes. The derived LS8-LST showed that the study area experiences a wide range of surface temperature from 39.70 to 21.18 °C same as NDVI ranges −0.19 to 0.60. The results showed mean absolute error (MAE) and R2 ranges of 0.83–1.08 and 0.88–0.96, respectively, with field observation. The value of MAE and R2 with the MODIS dataset was observed as 4.26–5.42 and 0.46–0.87, respectively, with an overall insignificant error (max < 12%). The study would be helpful in LST, UHI, and vegetation monitoring at national and international scales due to the ease of large data processing. [ABSTRACT FROM AUTHOR]

Mapping target crops earlier than the harvest period is an essential task for improving agricultural productivity and decision-making. This paper presents a new method for early crop mapping for the entire conterminous USA (CONUS) land area using the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) data with a dynamic ecoregion clustering approach. Ecoregions, geographically distinct areas with unique ecological patterns and processes, provide a valuable framework for large-scale crop mapping. We conducted our dynamic ecoregion clustering by analyzing soil, climate, elevation, and slope data. This analysis facilitated the division of the cropland area within the CONUS into distinct ecoregions. Unlike static ecoregion clustering, which generates a single ecoregion map that remains unchanged over time, our dynamic ecoregion approach produces a unique ecoregion map for each year. This dynamic approach enables us to consider the year-to-year climate variations that significantly impact crop growth, enhancing the accuracy of our crop mapping process. Subsequently, a Random Forest classifier was employed to train individual models for each ecoregion. These models were trained using the time-series MODIS (Moderate Resolution Imaging Spectroradiometer) 250-m NDVI and EVI data retrieved from Google Earth Engine, covering the crop growth periods spanning from 2013 to 2017, and evaluated from 2018 to 2022. Ground truth data were sourced from the US Department of Agriculture's (USDA) Cropland Data Layer (CDL) products. The evaluation results showed that the dynamic clustering method achieved higher accuracy than the static clustering method in early crop mapping in the entire CONUS. This study's findings can be helpful for improving crop management and decision-making for agricultural activities by providing early and accurate crop mapping. [ABSTRACT FROM AUTHOR]

An analysis of the changes in vegetation cover on the territory of the Republic of Khakassia in 2000–2021 due to climatic trends was carried out based on the MODIS data. The changes in vegetation cover were estimated based on trends in Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). In general, in the 21st century, an increase in the biomass of vegetation cover is observed. Positive trends were observed in 16%–22% of the territory, and negative only in 1%–3%. For about 20% of the analyzed territory, a significant influence of climate on the changes in vegetation cover was revealed. The most pronounced negative impact on vegetation cover was caused by summer air and soil temperatures, spring temperature, and summer winds, and the positive impact was caused by summer precipitation and soil moisture. The response of the vegetation cover to climate was non-uniform concerning the topography. Thus, a significant correlation with the amount of precipitation was observed for ∼20%–35% of vegetation growing below 600 m above sea level and for less than 5% above this elevation. The negative effect of summer temperatures on plants prevailed mainly at an elevation below ∼1400 m above sea level. Projected climate change is likely to lead to significant degradation of vegetation in the steppe and forest-steppe in Khakassia in the coming decades. [ABSTRACT FROM AUTHOR]

Forests affect regional climates, livelihoods and global cycles of water, carbon and nitrogen. Anthropogenic activities and climatic change affect forest health and national growth. Therefore, developing effective forest management plans requires understanding of the drivers of forest growth. The primary objective of this study was to understand the long-term effect of abiotic factors on the growth of forests in the region. This study used Moderate Resolution Imaging Spectroradiometer (MODIS) data for vegetation indices like NDVI and EVI and NASA's Land Assimilation datasets (wind speed, evapotranspiration, soil moisture and temperature) to understand their role on forest growth through statistical techniques such as Pearson's correlation and Multiple Linear Regression. The study examined the relationship between standard monthly vegetation indices and abiotic variables (i.e., moisture and temperature at different soil profiles up to 2 m depth, land surface temperature, evapotranspiration, relative humidity, wind velocity, and air temperature at 2 m height) in selected forests of the Eastern Himalayas for two decades (2001–2020, n = 240). Rainfall, temperature, and other associated factors significantly affected forest growth in the region. It was observed that rainfall alone affected forest growth in the region. However, its impact was maximum after two months of the rain events, reflecting a significant lag effect. Soil moisture at different depths affected vegetation growth in all forest types. Reduced soil moisture had a more significant effect on old-growth forests than younger forests. Multiple Linear Regression models developed with the abiotic factors explained higher variability in forest growth. In conclusion, this study reveals that rainfall, temperature, and their associated variables significantly affected forest growth in the study area. The study has significant implications for forest management in the region for formulating better strategies to mitigate climate change effects on forests in the region in future.

The landscapes and Eco-Regions of West Africa have a unique biodiversity that reflects climatic regime and human interaction. Changes and trends in the Eco-Region land cover and its productivity was assessed using Aqua MODIS NDVI C6 satellite images on a geographical mapping grid at approximately 250m from between 2003 and 2021. Using cell statistics and geoprocessing tools within a GIS environment, the satellite images were processed and analysed. The processed and analyzed satellite images document changes across the region over 19 years. The results show that; Guinea Savanna Eco-Region records highest changes, with an increase in green productivity between 2007 and 2012 by 9.24 km2; and decrease in green productivity in green productivity between 2007 and 2012 by 9.24km of 4.37 km2 from 2012 to 2017 and by 9.30 km2 from 2017 to 2021. The Grassland/Sudan Savanna fluctuated over the last 19 years with an overall reduction in green productivity by 3.43 km2 while the Guinea Savanna increased by 1.0 km2 over the same period. The Tropical Rainforest Eco-Region had the highest increase in productivity from 2003 to 2007 by 11.25 km2 and less than a km2 increase from 2007 to 2012. This study concluded that there was fluctuation in area coverages among the Eco-Regions across West Africa which varies seasonally and periodically. These Eco-Regions are vital for the development and economic suitability of West Africa, thus the need for continuous monitoring and study. Also, the fluctuation in area coverages detected in the study suggest that certain interventions by policy makers and stakeholders have brought about these changes, of which reforestation and land use policies are inclusive. Policymakers and government agencies are therefore apprised to maintain policies that support green productivity in the region.

Abstract Proper and reasonable estimation of actual evapotranspiration is critical for the design, operation, and management of irrigation systems. However, the density of climatic stations is low in many parts of Ethiopia to estimate spatially reasonable reference evapotranspiration (ETo), and the lack of spatial variability of crop coefficient (Kc) is clear barrier to the proper management of irrigation water. Therefore, the main objective of this study was to estimate reasonable crop evapotranspiration (ETc) by deriving spatially and temporally varying crop coefficients using remote sensing products in 10 wheat plots at the Koga irrigation scheme. The moderate-resolution imaging spectroradiometer (MODIS) potential evapotranspiration was calibrated at two class-I climate stations (Bahir Dar and Dangila) based on Penman–Monteith estimates and Normalized Difference Vegetation Index (NDVI) was derived from Sentinel 2B, which was later used to derive Kc. The correlation between reference evapotranspiration from MODIS Penman Monteith was acceptable for both Dangila (R 2 = 0.64) and Bahir Dar (R 2 = 0.74) stations. The MODIS calibration constant for Koga irrigation schemes was 0.27 and 1.99 (regression slope and constant) on an 8-day basis. Similarly, a strong correlation (R 2 = 0.95) was found between Sentinel-based NDVI and FAO crop coefficient, which indicated an alternative pathway for estimating crop coefficient. The value of Kc varies in space (across the 10 plots) from 0.16 to 0.42 at the initial stage and from 1.20 to 1.32 at the mid-stage. Similarly, the mean value of Kc varies in time from 0.29 at the initial stage to 1.26 at the mid-stage. On the other hand, evaluation of MODIS and WaPOR ETc found a significant difference (p

This study investigates the usefulness of MODIS (Moderate Resolution Imaging Spectroradiometer) satellite imagery for determining the start, end, and length of the growing season of selected deciduous tree species. Vegetation indices derived from satellite imagery provide consistent observations in a similar temporal sequence and are useful for determining phenological phases. Using time series of NDVI (Normalised Difference Vegetation Index) vegetation index from MODIS imagery, phenological patterns were detected at several points in Slovenia and different approaches to determine seasonal phases were compared. In addition, the derived seasonal phases with field phenological and meteorological data were also compared. It has been found that the success of determining phenological phases from satellite imagery depends on many factors: the spatial resolution of the satellite data, the smoothing method for the time series data, the method for determining phenological parameters, and the field data used for comparison. The results of the study show that phenological phases determined by using MODIS data with a resolution of 250 m best match the phenological data maintained by the Slovenian Forestry Institute using the mean seasonal values method.

Abstract Observation of the growth phase of rice plants generally takes a long time and cannot cover a large area. With remote sensing, the speed and accuracy of information in agricultural management is easier. The use of MODIS satellite image data in remote sensing technology plays a role in determining the growth phase of rice plants by utilizing the vegetation index value of rice plants. The aim of the study was to determine the regression equation and the range of NDVI (Normalized Difference Vegetation Index) values to predict the growth phase of rice plants in Harau District, Lima Puluh Kota District. Research in the field was carried out based on work maps obtained from the results of the imaging process in ArcGis. The location used as a sample of field observations is Grid ≥45% which is occupied by rice fields. Regression analysis was carried out to obtain a regression equation between plant age and NDVI values, then a range of values was made based on the rice growing phase. The regression equation used to predict the growth phase of rice plants in Harau District based on the NDVI value is y = -0.0000838932x² + 0.0123160507x + 0.2966130658 where x is the age of the plant (days after planting) and y is the NDVI value. The correlation coefficient (r) of the regression equation is 0.966. The range of NDVI values based on the growth phase for Harau District is the Water Phase 0.4722, the Vegetative Phase (1) 0.4722-0.6945; Vegetative phase (2) 0.6945-0.7412; Generative phase (1) 0.7412-0.6363; Generative phase (2) 0.6363-0.5665; Bera 0.5665-0.4799. Keywords:: growth phase; vegetation index; MODIS; NDVI Abstrak Pengamatan fase pertumbuhan tanaman padi pada umumnya membutuhkan waktu yang lama dan tidak bisa menjangkau area yang luas. Dengan penginderaan jauh, maka kecepatan dan ketepatan informasi dalam pengelolaan pertanian lebih mudah. Penggunaan data citra satelit MODIS pada teknologi penginderaan jauh berperan dalam menentukan fase tumbuh tanaman padi dengan memnafaatkan nilai indeks vegetasi dari tanaman padi. Tujuan penelitian adalah menentukan persamaan regresi dan rentang nilai NDVI (Normalized Difference Vegetation Index) untuk memprediksi fase tumbuh tanaman padi di Kecamatan Harau Kabupaten Lima Puluh Kota. Penelitian di lapangan dilakukan berdasarkan peta kerja yang diperoleh dari hasil proses pencitraan pada ArcGis. Lokasi yang dijadikan sampel observasi lapangan adalah Grid ≥45% yang ditempati oleh sawah. Analisis regresi dilakukan untuk mendapatkan persamaan regresi antara umur tanaman dan nilai NDVI, kemudian dibuat rentang nilai berdasarkan fase tumbuh padi. Persamaan regresi yang digunakan untuk memprediksi fase tumbuh tanaman padi di Kecamatan Harau adalah y = -0,0000838932x² + 0,0123160507x + 0,2966130658 dengan x sebagai umur tanaman (hari setelah tanam) dan y sebagai nilai NDVI. Koefisien korelasi (r) dari persamaan regresi adalah 0,966. Rentang nilai NDVI berdasarkan fase tumbuh untuk Kecamatan Harau adalah Fase Air 0,4722, Fase Vegetatif (1) 0,4722-0,6945; Fase vegetatif (2) 0,6945-0,7412; Fase generatif (1) 0,7412-0,6363; Fase generatif (2) 0,6363-0,5665; Bera 0,5665-0,4799.

The influence of climate change on forest condition is noticeable. Forest ecosystem stress caused by climate change has already been manifested in several parts of Europe, including Poland. Thus, the main objective of this paper is to investigate for the entire area of Poland a long‐term trend and variability of forest greenness expressed as the Normalized Difference Vegetation Index (NDVI), derived from two decades (2002–2021) of remote sensing Moderate Resolution Imaging Spectroradiometer (MODIS) data. In the next step, selected meteorological elements – temperature (T), precipitation (P) and evapotranspiration (ETo), derived from ERA5‐Land reanalysis—were used to determine the influence of climatic conditions on the variability of NDVI in forests. The study documents the general greening of forests in Poland in 2002–2021. The greening is mostly visible in central‐eastern Poland, where the annual mean NDVI increased by 0.030 in 20 years, while it is weaker in the Baltic coast and in the southern edges of Poland (increase by 0.009 in 20 years). Overall, the positive, statistically significant trends in annual NDVI prevail over the negative, statistically significant trends and account for 32.5% of forest area, whereas the negative trends account for 3.9%. The study indicates an overall moderate impact of meteorological elements on variability of NDVI in forests in Poland. The most important factors affecting forest condition are P and ETo. The strongest correlations between NDVI and P and ETo reach 0.55 and are located in central Poland, in the form of a belt from western to eastern borders. [ABSTRACT FROM AUTHOR]

One of the basic factors that should be investigated and monitored in the field of urban heat islands is the exploration and detection of their spatiotemporal changes, which have been well addressed in spatial statistics. The current study aimed to detect the spatiotemporal changes of surface urban heat islands (SUHI) in Tehran metropolis during the daytime/nighttime at monthly and seasonal scales and over the warm and cold periods of the year. The consequences of many elements like as daytime/nighttime land surface temperature (LST) extracted by the MODIS/006/MOD11A1 and the NDVI extracted by MODIS/006/MOD13A2 over a 20-year period (2001–2020) were first investigated. Then, the SUHI index was computed for the study area. The correlations between the heat islands and urban land use (traffic, population density, airport, etc.), air pollutants (CO, NO2, SO2, etc.), and NDVI were investigated in the next stage. Finally, Moran's algorithm was used to measure the spatial autocorrelation, and Gi statistic was used to analyze the cold and warm spots. The results indicated that the LST trend was constant during the daytime/nighttime, and the NDVI also had a slight rising trend. The results of the SUHI maps indicated that the zones with heat islands during the daytime over the seasons' warm and cold times are located in the south, southeast, and west of the city. During the nighttime, the central zones of the city as well as some parts in the east and southeast have had higher heat islands. The results of the correlation between the heat islands and land use, vegetation, and air pollutants indicated a direct correlation between the heat islands and the airport and industrial land use over time, while it was inversely correlated with other land uses. During the nighttime, all land uses had a direct correlation with the heat islands. Regarding the air pollutants, PM2.5 and PM10 were most correlated with the heat islands during both daytime/nighttime while other pollutants have been inversely correlated. The heat islands and the NDVI were also inversely correlated during both daytime/nighttime. The OLS (ordinary least-squares) model results also indicated that the R2 values during the daytime/nighttime were 0.70 and 0.59, respectively, over the cold period of the year, compared to values of 0.69 and 0.68 over the warm period of the year. The results of global Moran's I and G*i statistics also indicated that the heat islands of the Tehran metropolis had a spatial structure distributed in a cluster in which the southern, western, southwestern, and northern parts had warm spots during the daytime and cold spots during the nighttime. Moreover, the northern and northeastern parts had cold spots during the daytime, and the central and eastern parts had warm spots during the nighttime. [ABSTRACT FROM AUTHOR]

Proper and reasonable estimation of actual evapotranspiration is critical for the design, operation, and management of irrigation systems. However, the density of climatic stations is low in many parts of Ethiopia to estimate spatially reasonable reference evapotranspiration (ETo), and the lack of spatial variability of crop coefficient (Kc) is clear barrier to the proper management of irrigation water. Therefore, the main objective of this study was to estimate reasonable crop evapotranspiration (ETc) by deriving spatially and temporally varying crop coefficients using remote sensing products in 10 wheat plots at the Koga irrigation scheme. The moderate-resolution imaging spectroradiometer (MODIS) potential evapotranspiration was calibrated at two class-I climate stations (Bahir Dar and Dangila) based on Penman–Monteith estimates and Normalized Difference Vegetation Index (NDVI) was derived from Sentinel 2B, which was later used to derive Kc. The correlation between reference evapotranspiration from MODIS Penman Monteith was acceptable for both Dangila (R2 = 0.64) and Bahir Dar (R2 = 0.74) stations. The MODIS calibration constant for Koga irrigation schemes was 0.27 and 1.99 (regression slope and constant) on an 8-day basis. Similarly, a strong correlation (R2 = 0.95) was found between Sentinel-based NDVI and FAO crop coefficient, which indicated an alternative pathway for estimating crop coefficient. The value of Kc varies in space (across the 10 plots) from 0.16 to 0.42 at the initial stage and from 1.20 to 1.32 at the mid-stage. Similarly, the mean value of Kc varies in time from 0.29 at the initial stage to 1.26 at the mid-stage. On the other hand, evaluation of MODIS and WaPOR ETc found a significant difference (p < 0.05) with the calibrated MODIS-Sentinel 2B derived ETc. This indicated the need to calibrate both MODIS and WaPOR for the proper estimation of crop water needs. Underestimation of ET was observed from MODIS, and the reverse is true for WaPOR. Our findings showed that calibrating and integrating MODIS with Sentinel 2B would be a feasible approach to estimate Kc and hence ETc that varies in time and space. This would assist water managers in estimating crop water needs for better productivity in the region. [ABSTRACT FROM AUTHOR]

The spatial patterns of species richness can be used as indicators for conservation and restoration, but data problems, including the lack of species surveys and geographical data gaps, are obstacles to mapping species richness across large areas. Lack of species data can be overcome with remote sensing because it covers extended geographic areas and generates recurring data. We developed a Deep Learning (DL) framework using Moderate Resolution Imaging Spectroradiometer (MODIS) products and modeled potential species richness by stacking species distribution models (S-SDMs) to ask, “What are the spatial patterns of potential plant species richness across the Korean Peninsula, including inaccessible North Korea, where survey data are limited?” First, we estimated plant species richness in South Korea by combining the probability-based SDM results of 1574 species and used independent plant surveys to validate our potential species richness maps. Next, DL-based species richness models were fitted to the species richness results in South Korea, and a time-series of the normalized difference vegetation index (NDVI) and leaf area index (LAI) from MODIS. The individually developed models from South Korea were statistically tested using datasets that were not used in model training and obtained high accuracy outcomes (0.98, Pearson correlation). Finally, the proposed models were combined to estimate the richness patterns across the Korean Peninsula at a higher spatial resolution than the species survey data. From the statistical feature importance tests overall, growing season NDVI-related features were more important than LAI features for quantifying biodiversity from remote sensing time-series data.

Ukraine was one of the main exporters of plant products. However, as a result of the aggression, the country's agriculture has suffered greatly, export volumes are decreasing, which may provoke a shortage of agricultural products on world markets. It is impossible to assess crop yield and forecast the harvest volume locally, as the collection of information has become difficult due to the active conduct of hostilities and the occupation of a large part of the territories. Therefore, it is necessary to use land remote sensing data to assess crop yield. In this research, we will build regression models based on a random forest for each region of Ukraine to estimate crop yield based on 16-day composites of the NDVI time series during the summer vegetation period from Sentinel-2 (10m) and MODIS (500m) satellites, involving in the calculation NDVI crop maps. The official yield of maize, sunflower, soybean, rapeseed, and wheat for the years 2016-2021 was used as training data. According to the results of the analysis, models based on NDVI from the MODIS satellite showed better accuracy (relative error within 8-18%), but models based on NDVI data from Sentinel-2 better described the variance of the predicted yield. During the research, we found a sharp drop in land productivity indicators compared to the productivity of 2021 for the territories of central, southern and eastern Ukraine. According to our estimates based on MODIS data, the average yield at the country level is expected to be 40.98 t/ha for wheat, 57.66 t/ha for maize, 23.57 t/ha for sunflower, 21.06 t/ha for soybeans, 21.15 t/ha for rapeseed. Estimates based on Sentinel-2 data: 43.22 t/ha for wheat, 71.93 t/ha for maize, 26.86 t/ha for sunflower, 22.94 t/ha for soybeans, 28.23 t/ha for rapeseed.

Vegetation degradation is correlated with drought. The more drought intensifies, the more degraded vegetation increases. Therefore, this study aimed to assess the correlation between rainfall and changes in the Normalized Difference Vegetation Index (NDVI) under arid and semi-arid conditions in Syria. This study was carried out using annual rainfall data for 2012–2022, obtained from the Agricultural cloud seeding Project, to determine the average rainfall in the study area and to link it to the NDVI of MODIS image data processed using the Google Earth Engine (GEE) for April of each year for the same time series. The results showed that the lowest NDVI value (0.098) was in 2016, representing the driest year during the studied series, while the highest NDVI value (0.24) was in 2019, which coincided with the highest rainfall rate of 206.67 mm, thus representing the least arid year during the same series. It also found a strong correlation (R = 0.7) between the overall average rainfall and the overall NDVI values of the studied time series. This study shows that changes in the NDVI are associated with changes in rainfall, indicating that they can be used to estimate and study drought as a simple method derived from satellite data in isolation from ground data.

Abstract The influence of climate change on forest condition is noticeable. Forest ecosystem stress caused by climate change has already been manifested in several parts of Europe, including Poland. Thus, the main objective of this paper is to investigate for the entire area of Poland a long‐term trend and variability of forest greenness expressed as the Normalized Difference Vegetation Index (NDVI), derived from two decades (2002–2021) of remote sensing Moderate Resolution Imaging Spectroradiometer (MODIS) data. In the next step, selected meteorological elements – temperature (T), precipitation (P) and evapotranspiration (ETo), derived from ERA5‐Land reanalysis—were used to determine the influence of climatic conditions on the variability of NDVI in forests. The study documents the general greening of forests in Poland in 2002–2021. The greening is mostly visible in central‐eastern Poland, where the annual mean NDVI increased by 0.030 in 20 years, while it is weaker in the Baltic coast and in the southern edges of Poland (increase by 0.009 in 20 years). Overall, the positive, statistically significant trends in annual NDVI prevail over the negative, statistically significant trends and account for 32.5% of forest area, whereas the negative trends account for 3.9%. The study indicates an overall moderate impact of meteorological elements on variability of NDVI in forests in Poland. The most important factors affecting forest condition are P and ETo. The strongest correlations between NDVI and P and ETo reach 0.55 and are located in central Poland, in the form of a belt from western to eastern borders.

The present study has been carried out to analyze the seasonal variation of the Urban Heat and Cool Island over the nine developing cities of India. The magnitude of urban heat/cool island and vegetation gradient (∆NDVI) were measured from the daytime satellite datasets. Results of this study show that during the pre-monsoon (March to May) season, the maximum magnitude of the Surface Urban Heat Island (SUHI) was experienced over Kolhapur city, whereas, in the winter, the highest intensity of SUHI was noticed over Pune city. Subsequently, outcomes also depict that the changes in ∆NDVI restrain the pre-monsoon means and the seasonal alterations in SUHI magnitude. However, during the winter (November to February) season, it is controlled by the temperature–vegetation conditions of the rural areas. For pre-monsoon and seasonal changes in SUHI, with the aid of ∆NDVI and the surface temperature of the urban area, regression equations were fitted for pre-monsoon and seasonal changes in SUHI, which explains nearly 90% of SUHI variation. Similarly, the variation of SUHI has been modeled for winter, which elucidates up to 84% of SUHI discrepancy. The study reveals that, on a seasonal scale, a decrement of 0.1 in seasonal ∆NDVI leads to an increase in the seasonal intensity of SUHI by 1.74 °C, which is quite a significant augmentation. [ABSTRACT FROM AUTHOR]

In this article, meteorological and agricultural droughts across the Erbil province, Iraq, were assessed using remote sensing data and satellite products. To this end, the long-term (2000–2022) Standardized Precipitation Evapotranspiration index (SPEI) at 1- and 3-month accumulation periods (SPEI-1 and SPEI-3) as well as the Normalized Difference Vegetation Index (NDVI) across Erbil were utilized. While the former was retrieved from the global SPEI data repository, the latter was derived from Moderate Resolution Imaging Spectroradiometer (MODIS) products. The spatiotemporal variations in the SPEI indices indicated that two to nine extreme drought events occurred in the province with an increasing northward pattern. An increasing trend in the long-term NDVI series was also detected, having more diversity in vegetation coverage in the northern part of the province. The relationship between the SPEI and MODIS-NDVI was found to be positive but insignificant. Thus, we concluded that short-term meteorological droughts were not the only reason for the agricultural droughts in Erbil. Furthermore, the climate characteristics related to the cumulative water balance over a previous season is not an important trigger for the spatial variation in vegetation coverage across the province. [ABSTRACT FROM AUTHOR]

This study investigates the usefulness of MODIS (Moderate Resolution Imaging Spectroradiometer) satellite imagery for determining the start, end, and length of the growing season of selected deciduous tree species. Vegetation indices derived from satellite imagery provide consistent observations in a similar temporal sequence and are useful for determining phenological phases. Using time series of NDVI (Normalised Difference Vegetation Index) vegetation index from MODIS imagery, phenological patterns were detected at several points in Slovenia and different approaches to determine seasonal phases were compared. In addition, the derived seasonal phases with field phenological and meteorological data were also compared. It has been found that the success of determining phenological phases from satellite imagery depends on many factors: the spatial resolution of the satellite data, the smoothing method for the time series data, the method for determining phenological parameters, and the field data used for comparison. The results of the study show that phenological phases determined by using MODIS data with a resolution of 250 m best match the phenological data maintained by the Slovenian Forestry Institute using the mean seasonal values method. [ABSTRACT FROM AUTHOR]

The calculation of crop productivity has now been facilitated by technological development using remote sensing technology or data generated by satellites. Determining the value of productivity using images will shorten the time and does not require much effort. A remote sensing model that connects satellite image reflectance data with rice plant parameters will be handy for monitoring biomass growth and predicting crop yields more quickly and efficiently. This study aimed to determine the regression equation to estimate the productivity of regional rice in Harau District, Lima Puluh Kota Regency. This study consisted of several stages: data collection, data processing, and calculation of rice productivity, NDVI regression analysis with rice productivity, and Nash-Sutcliffe Efficiency (NSE) test against the obtained equations. The regression equation obtained from the results of data analysis to estimate rice productivity in Harau District, Lima Puluh Kota Regency is y = - 82152x4 + 208465x3 - 197654x2 + 82986x - 13014, with an NSE value of 0.64 which is categorized as ‘sufficient’.

This study aims to develop a workflow methodology for collecting substantial amounts of Earth Observation data to investigate the effectiveness of landscape restoration actions and support the implementation of the Above Ground Carbon Capture indicator of the Ecosystem Restoration Camps (ERC) Soil Framework. To achieve this objective, the study will utilize the Google Earth Engine API within R (rGEE) to monitor the Normalized Difference Vegetation Index (NDVI). The results of this study will provide a common scalable reference for ERC camps globally, with a specific focus on Camp Altiplano, the first European ERC located in Murcia, Southern Spain. The coding workflow has effectively acquired almost 12 TB of data for analyzing MODIS/006/MOD13Q1 NDVI over a 20-year span. Additionally, the average retrieval of image collections has yielded 120 GB of data for the COPERNICUS/S2_SR 2017 vegetation growing season and 350 GB of data for the COPERNICUS/S2_SR 2022 vegetation winter season. Based on these results, it is reasonable to asseverate that cloud computing platforms like GEE will enable the monitoring and documentation of regenerative techniques to achieve unprecedented levels. The findings will be shared on a predictive platform called Restor, which will contribute to the development of a global ecosystem restoration model. [ABSTRACT FROM AUTHOR]

Remote sensing image composites are crucial for a wide range of remote sensing applications, such as multi-decadal time series analysis. The Advanced Very High Resolution Radiometer (AVHRR) instrument has provided daily data since the early 1980s at a spatial resolution of 1 km, allowing analyses of climate change-related environmental processes. For monitoring vegetation conditions, the Normalized Difference Vegetation Index (NDVI) is the most widely used metric. However, to actually enable such analyses, a consistent NDVI time series over the AVHRR time-span needs to be created. In this context, the aim of this study is to thoroughly assess the effect of different compositing procedures on AVHRR NDVI composites, as no standard procedure has been established. Thirteen different compositing methods have been implemented; daily, decadal, and monthly composites over Europe and Northern Africa have been calculated for the year 2007, and the resulting data sets have been thoroughly evaluated according to six criteria. The median approach was selected as the best-performing compositing algorithm considering all the investigated aspects. However, the combination of the NDVI value and viewing and illumination angles as the criteria for the best-pixel selection proved to be a promising approach, too. The generated NDVI time series, currently ranging from 1981–2018, shows a consistent behavior and close agreement to the standard MODIS NDVI product. The conducted analyses demonstrate the strong influence of compositing procedures on the resulting AVHRR NDVI composites. [ABSTRACT FROM AUTHOR]

The normalized differential vegetation index (NDVI) for Landsat is not continuous on the time scale due to the long revisit period and the influence of clouds and cloud shadows, such that the Landsat NDVI needs to be filled in and reconstructed. This study proposed a method based on the genetic algorithm–artificial neural network (GA-ANN) algorithm to reconstruct the Landsat NDVI when it has been affected by clouds, cloud shadows, and uncovered areas by relying on the MODIS characteristics for a wide coverage area. According to the self-validating results of the model test, the RMSE, MAE, and R were 0.0508, 0.0557, and 0.8971, respectively. Compared with the existing research, the reconstruction model based on the GA-ANN algorithm achieved a higher precision than the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) and the flexible space–time data fusion algorithm (FSDAF) for complex land use types. The reconstructed method based on the GA-ANN algorithm had a higher root mean square error (RMSE) and mean absolute error (MAE). Then, the Sentinel NDVI data were used to verify the accuracy of the results. The validation results showed that the reconstruction method was superior to other methods in the sample plots with complex land use types. Especially on the time scale, the obtained NDVI results had a strong correlation with the Sentinel NDVI data. The correlation coefficient (R) of the GA-ANN algorithm reconstruction's NDVI and the Sentinel NDVI data was more than 0.97 for the land use types of cropland, forest, and grassland. Therefore, the reconstruction model based on the GA-ANN algorithm could effectively fill in the clouds, cloud shadows, and uncovered areas, and produce NDVI long-series data with a high spatial resolution. [ABSTRACT FROM AUTHOR]

Satellite imagery is gaining popularity as a valuable tool to lower the impact on natural resources and increase profits for farmers. The purpose of this study is twofold: to mine the scientific literature to reveal the structure of this research domain, and to investigate to what extent scientific results can reach a wider public audience. To meet these two objectives, a Web of Science and a Twitter dataset were retrieved and analysed, respectively. For the academic literature, different performances of various countries were observed: the USA and China resulted as the leading actors, both in terms of published papers and employed researchers. Among the categorised keywords, "resolution", "Landsat", "yield", "wheat" and "multispectral" are the most used. Then, analysing the semantic network of the words used in the various abstracts, the different facets of the research in satellite remote sensing were detected. The importance of retrieving meteorological parameters through remote sensing and the broad use of vegetation indexes emerged from these analyses. As emerging topics, classification tasks for land use assessment and crop recognition stand out, alongside the use of hyperspectral sensors. Regarding the interaction of academia with the public, the analysis showed that it is practically absent on Twitter: most of the activity therein stems from private companies advertising their business. This shows that there is still a communication gap between academia and actors from other societal sectors. [ABSTRACT FROM AUTHOR]

Agricultural drought monitoring in Niger is relevant for the implementation of effective early warning systems and for improving climate change adaptation strategies. However, the scarcity of in situ data hampers an efficient analysis of drought in the country. The present dataset was created for agricultural drought characterization in the Sahelian climate zone of Niger. The dataset comprises the three-month scale and monthly time series of a composite drought index (CDI) and their corresponding drought classes at a spatial resolution of 1 km2 for the period 2000–2020. The CDI was generated from remote sensing data, namely CHIRPS (Climate Hazards Group InfraRed Precipitation with Stations), normalized difference vegetation index (NDVI) and land surface temperature (LST) from MODIS (Moderate Resolution Imaging Spectroradiometer). A weighing technique combining entropy and Euclidian distance was applied in the CDI derivation. From the present dataset, the extraction of the CDI time series can be performed for any location of the study area using its geographic coordinates. Therefore, seasonal drought characteristics, such as onset, end, duration, severity and frequency can be computed from the CDI time series using the theory of runs. The availability of the present dataset is relevant for the socio-economic assessment of drought impacts at small spatial scales, such as district and household level. This dataset is also important for the assessment of drought characteristics in remote areas or areas inaccessible due to civil insecurity in the country as it was entirely generated from remote sensing data. Finally, by including temperature data, the dataset enables drought modelling under global warming. [ABSTRACT FROM AUTHOR]

Understanding how natural ecosystems are and will be responding to climate change is one of the primary goals of ecological research. Plant phenology is accepted as one of the most sensitive bioindicators of climate change due to its strong interactions with climate dynamics, and a vast number of studies from all around the world present evidence considering phenological shifts as a response to climatic changes. Land surface phenology (LSP) is also a valuable tool in the absence of observational phenology data for monitoring the aforementioned shift responses. Our aim was to investigate the phenological shifts of Fagus orientalis forests in Turkey by means of daily MODIS surface reflectance data (MOD09GA) for the period between 2002 and 2020. The normalized difference vegetation index (NDVI) was calculated for the entire Turkey extent. This extent was then masked for F. orientalis. These "Fagus pixels" were then filtered by a minimum of 80% spatial and an annual 20% temporal coverage. A combination of two methods was applied to the time series for smoothing and reconstruction and the start of season (SOS), end of season, and length of season parameters were extracted. Trends in these parameters over the 19-year period were analyzed. The results were in concert with the commonly reported earlier SOS pattern, by a Sen's slope of −0.8 days year−1. Lastly, the relationships between SOS and mean, maximum and minimum temperature, growing degree days (GDD), and chilling hours (CH) were investigated. Results showed that the most significant correlations were found between the mean SOS trend and accumulated CH and accumulated GDD with a base temperature of 2 °C, both for the February–March interval. The immediate need for a phenological observation network in Turkey and its region is discussed. [ABSTRACT FROM AUTHOR]

Critical applications of satellite data products include monitoring vegetation dynamics and assessing vegetation health conditions. Some indicators like normalized difference vegetation index (NDVI) and land surface temperature (LST) are used to assess the status of vegetation growth and health. But one of the major problems with passive remote sensing satellite data products is cloud and shadow cover that leads to data gaps in the images. The present study proposes temporal aggregation of images over a short time span and developing short span harmonic analysis of time series (SS-HANTS) and pixel-wise multiple linear regression (PMLR) algorithms for retrieving cloud contaminated NDVI and LST information from Landsat-8 (L8) data products, respectively. The developed algorithms were applied in the northeastern part of Thailand to recover the missing NDVI and LST values from time series L8 images acquired in 2018. The predicted NDVI and LST values at artificially clouded locations were compared with the corresponding clear pixel values. Additionally, the model predicted LST and NDVI values were also compared with MODIS LST and NDVI datasets. The calculated root mean square (RMSE) values were ranging from 0.03 to 0.11 and 1.50 to 2.98 °C for NDVI and LST variables, respectively. The validation statistics show that these models can be satisfactorily applied to retrieve NDVI and LST values from cloud-contaminated pixels of L8 images. Furthermore, a vegetation health index (VHI) computed from cloud retrieved continuous NDVI and LST images at province level shows that most of the western provinces have healthy vegetation condition than other provinces in the northeast of Thailand. [ABSTRACT FROM AUTHOR]