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Multispectral data were used to estimate the above-ground forest biomass in the newly established carbon polygon in Bashkortostan. Field measurements were compared with Normalised Difference Vegetation Index (NDVI) and Normalised Green-Red Difference Index (NGRDI) obtained from the Global Inventory Monitoring and Modelling System (GIMMS) dataset. The relationship between measured biomass values and vegetation indicators was linear; NDVI has the highest correlation coefficient with above-ground biomass and greatest significance at all sites, so regression relationships with NDVI were used to map the above-ground biomass. [ABSTRACT FROM AUTHOR]

AbstractSafflower (Carthamus tinctorius L.) is a multipurpose crop and can be grown successfully under moisture-deficit environments. This study assessed the physiological attributes and yield-related traits of two safflower varieties (Dinçer and Balcı) under rainfed and irrigated environments during 2019-2020 and 2020-2021. Data relating to physiological and yield-related traits, and soil enzymes were collected. Soil attributes, nutrient acquisition, and yield-related traits were significantly altered by the interactive effect of varieties and growing environments. Higher nitrate (NO3-) accumulation (279.92 and 300 mg N g DW) was recorded in Balcı variety than Dinçer under rainfed conditions. Higher grain yield was recorded under irrigated conditions compared to rainfed environments. Balcı variety produced 2724 and 2464 kg/ha grain yield, whereas Dinçer resulted in 2664 and 2565 kg/ha under irrigated conditions during 1st and 2nd year, respectively. Nevertheless, Balcı produced a higher yield than Dinçer under rainfed conditions. The results indicate that irrigation is crucial for safflower in arid and semi-arid regions. Similarly, Dinçer variety is advantageous under rainfed conditions, whereas the Balcı variety may be more beneficial in irrigated areas. [ABSTRACT FROM AUTHOR]

Urban green spaces benefit physical, mental health, and reduses the risk of cardiovascular disease. A study in Kaunas, Lithuania collected health data from 100 patients with symptomatic heart failure (HF) during 2006–2009. Residential greenness was measured by the normalized difference vegetation index (NDVI). We assessed the impact of greenness on health indicators and on changes in health markers after 6 months. Higher greenness levels based on the NDVI 1-km radius were related to higher mean values of heart rate (HR) and ejection fraction and lower left ventricular (LV) end-diastolic diameter index (LV EDDI), LV end-systolic volume (ESV), left atrium size (LAS), and right atrium size (RAS) at baseline. After 6 months, a decrease in DBP and HR and an improvement in spiroergometric parameters were associated with exposure to high levels of greenness. The long-term rehabilitation group experienced significant changes in spiroergometric indicators. The results confirm that the greenness of the residential environment can improve health indicators in patients with HF. [ABSTRACT FROM AUTHOR]

A smart agricultural system is necessary for monitoring crop growth and stress conditions using near-ground-based remote sensing techniques. Crop growth can be estimated using several standard crop growth parameters. However, obtaining timed sequential data for observing leaf area index (LAI) is challenging, and normalized difference vegetation index (NDVI) estimation in crop fields requires the installation of sensors on frames structure that are taller than the crop. Canopy light transmittance (CLT) indicates the degree of decrease in the amount of light passing through some material. It was conventionally used to understand canopy radiative transfer. This study examined the viability of CLT as a novel crop parameter for monitoring crop growth conditions. The CLT, LAI, and NDVI of a garlic field were recorded for five years. The correlation between daily CLT and LAI was higher than that between NDVI and LAI. Thus, CLT has the potential to sequentially estimate crop LAI values, particularly for capturing the temporal patterns of LAI. In addition, like NDVI, CLT showed sensitivity in representing the canopy structure and the amount of biomass because CLT is conceptually related to the sky gap fraction. Thus, CLT has the potential to serve as a novel growth parameter for continuous crop growth monitoring. [ABSTRACT FROM AUTHOR]

The amount of water taken from non-renewable resources such as aquifers to fulfill irrigation requirements is rarely monitored, putting sustainable agriculture under threat in the face of changing climate. In the present research, an attempt was made to apply multi-sensor (Landsat suite, GRACE, GRACE-FO) satellite data to monitor spatiotemporal evolution of agriculture for the Al-Qassim region, Kingdom of Saudi Arabia (KSA). For this purpose, time series of NDVI (Normalized Difference Vegetation Index), SAVI (Soil-Adjusted Vegetation Index), and MSAVI2 (Modified Soil-Adjusted Vegetation 2) was utilized to assess vegetation pattern change in the study area. The present investigation used High-resolution Planetscope (PS) nanosatellite data to validate the vegetation results. Mann Kendall trend analysis and linear regression were performed to study the temporal pattern, and the relationship between vegetation, GRACE, and climate variables was performed from 1984 to 2020. Water extraction based on the averaged value of JPL GWS and CSR GWS showed a decreasing trend of −10.24 ± 1.4 mm/year from 2003-2020. The annual rainfall showed a decreasing trend, while the annual temperature showed an increasing trend from 1982-2020. The correlation of vegetation indices with rainfall of one-month lag showed a significantly better relationship of 0.74, 0.74, and 0.75, respectively, for NDVI, SAVI, and MSAVI2. The correlation between temperature and all three vegetation indices is a strong negative correlation: −0.85 for NDVI and −0.9 for SAVI and MSAVI. [ABSTRACT FROM AUTHOR]

There are spatial differences in the correlation between urbanization (NL) and vegetation cover (NDVI), and the driving mechanism is not clear. This study aims to explore the spatial correlation between NL and NDVI in China and identify the influencing factors. The results showed that there was a north-south difference in the correlation, and this correlation was strongest in winter. Meteorological factors were the main influencing factors, Bivariate enhancement is observed for TEM-DEM at the national scale in winter, TEM-relative humidity (RH) in cold spot areas, and TEM-RH in hot spot areas in summer. [ABSTRACT FROM AUTHOR]

This study develops an early warning system for crop yield (CY) failure based on meteorological drought and vegetation health conditions. The framework combines three drought indices – the Standardized Precipitation Evapotranspiration Index (SPEI), standardized Normalized Difference Vegetation Index (stdNDVI), and standardized CY (stdCY) values – using copulas. Datasets for five major wheat-producing cities in Turkey between 2000 and 2022 are used for analysis. Results indicate that the time periods used to calculate SPEI and NDVI are critical in determining agricultural drought and CY conditions. The critical threshold values for SPEI and NDVI, with a 10% probability of causing agricultural drought, are found to be ~0.28 and ~0.42, respectively. Using a three-dimensional copula model resulted in more precise CY simulations than a two-dimensional model. The validation efforts showed that all of the observed CYs fell within the simulated range, indicating the robustness of the methodology in capturing drought impacts on CY conditions. [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 study conducted in Jammu District, India, investigates land use and land cover (LULC) transformations over the past three decades using satellite data and remote sensing techniques. Analyzing data from 1990 to 2020, significant changes were observed. Agricultural land expanded by 157.76 km2 (6.71%), barren land by 151.69 km2 (6.45%), and settlements by 96.97 km2 (4.12%). However, vegetation decreased by 389.77 km2 (16.57%), while water bodies experienced minimal changes. Land Surface Temperature (LST) analysis, utilizing MODIS data (2000-2020), revealed warming trends, with temperatures ranging from 15.92°C to 42.77°C in 2010 and 14.04°C to 37.01°C in 2020. Notably, NDVI values peaked in 2020 (0.759) and were lowest in 1990 (−0.243), indicating healthier vegetation and lower surface temperatures. This inverse correlation highlights NDVI's potential as an indicator for assessing vegetation health and its impact on local temperature conditions. Man-Kendall Z statistics indicated negative trends for Tmax and Tmin, while rainfall data showed significant positive trend. Population growth, urbanization, climate change and agricultural intensification emerged as principal drivers of the LULC changes in the region. This study underscores the importance of geospatial tools in monitoring LULC changes, providing valuable insights for policymakers and planners to formulate sustainable land use planning and management strategies. [ABSTRACT FROM AUTHOR]

Since the initiation of the Grain for Green Project (GFGP) in 1999, dramatic change in vegetation status on the Loess Plateau. Spatially, geographical detector was employed to detect dominant variables influencing the spatial arrangement of normalized difference vegetation index (NDVI). Temporally, lagged or accumulated monthly precipitation, temperature and standardized precipitation evapotranspiration indices (SPEIs) sensitive to the monthly NDVI were first detected for every individual pixel, and the correlation between the NDVI and meteorological elements with time-lag effects was established a random forest model of unchanged land cover, followed by attributing impacts of climatic alterations and human interventions through residual examination across changed land cover. The findings indicate that (1) precipitation, slope, and soil dominantly influence the spatial arrangement of the NDVI. (2) Precipitation in current the month and cumulative temperatures of the previous 1-2 months steadily affect vegetation growth significantly, the optimal accumulation time interval for SPEI around 2000 are 8 months and 4 months, respectively. (3) Increases in the average NDVI within woodland and meadow vegetation on the Loess Plateau were primarily driven by climate change before 2000, accounting for 76.2%, whereas after 2000 it was dominantly driven by human activities, accounting for 64.16% [ABSTRACT FROM AUTHOR]

Within the Tibetan Plateau (TP), grassland degradation is of great concern, and there is a need to better understand the spatial variability and landscape patterns in grassland degradation across the TP. This study explores potential patterns in grassland degradation, estimated using Landsat-derived normalized difference vegetation index (NDVI) growing season change products generated by a prior study. Shuttle Radar Topography Mission (SRTM) digital terrain model (DTM)-derived geomorphons, topographic slope and the topographic position index (TPI) were used to explore grassland degradation and assess whether changes in growing season NDVI are associated with specific grasslands or landforms. An increase in NDVI within the TP was observed especially during the 1990 to 2018 and 2000 to 2018 time periods. Higher growing season median NDVI change values were found for the Southeast Tibet Shrublands and Meadows (SETSM) and TP Alpine Shrublands and Meadows (TPASM) grasslands in comparison to the other grassland types analysed, suggesting a more pronounced greening trend in the eastern portion of the plateau in comparison to the western portion. Small differences in NDVI change were observed for different geomorphon-based landforms, while topographic slope and TPI showed only modest correlations with NDVI change for all time periods and grassland types. More localized patterns of grassland degradation were masked by the widespread greening trends. Further analysis of only pixels that experienced a decreasing NDVI magnitude of less than or equal to −0.1 also generally suggested more change in the SETSM and TPASM. While this study generally supports the use of geomorphons as an analysis and aggregating unit for studying change patterns at the hillslope scale, more research is required to fully grasp the dynamics of landscape change at the hillslope scale in the TP. [ABSTRACT FROM AUTHOR]

To produce cotton, growers must regulate nitrogen (N) during the growing season, which is one of the most essential considerations. Any new methods, techniques, or technologies that might help farmers regulate N, a crucial choice in crop productivity, will be of great use to them. In the present study, by utilizing tractor-mounted nitrogen sensor (TMNS) technology, field experiments were conducted to design an algorithm for variable nitrogen rate application for cotton crop varieties V1 (NCS 855 BG-II) and V2 (RCH 773 BG-II). Under node formation, fruit squaring, squaring, early, mid, peak flowering, and maturity crop growth stages, relationships between the attributes of TMNS and variable nitrogen application rate (VNAR) were also created for V1 and V2. The coefficient of determination between NDVI and N content (%) in leaves was 0.933 and 0.711 at the mid-flowering stage for varieties V1 and V2, respectively. Both varieties differ significantly in their N response, with mid-flowering (76 DAS) being a crucial growth period. So, algorithms were developed for converting sensor sufficiency index values to N application rates for V1 and V2 at the mid-flowering stage. For cultivars V1 and V2, the crucial sensor-based sufficiency index thresholds for initiating VNAR were 0.960 and 0.860, respectively. The developed mid-blooming phase algorithms were useful to predict mid-season N-application rates for cotton crops. [ABSTRACT FROM AUTHOR]

Sustainable Drainage Systems (SuDS) monitoring is very often intrusive and need onsite personnel to be carried out. The application of remote sensing in SuDS still is an area for further development, especially in vegetation-based techniques, representing a gap in the field. This research proposes an exploratory method combining Synthetic Aperture Radar (SAR) images data and onsite measurements to develop models of performance. Linear regression models were obtained for the computing of the soil moisture using the following variables: backscatter coefficient (σ°), temperature, normalized difference vegetation index (NDVI) and topographic wetness index (TWI), reaching medium to high values for its predictive capacity, ranging from 0.53 and 0.66 using σ° and temperature. The most influential variable was found to be the temperature. This investigation opens the path for future research in the use of remote sensing tools in vegetation-based SuDS monitoring with homogeneous plant species, highlighting the need for further research. [ABSTRACT FROM AUTHOR]

Remote sensing and GIS reveal the overall vegetation cover and interannual vegetation change in Liangcheng National Nature Reserve, Minqin, Gansu Province from 2002 to 2020. Overall, vegetation cover is very low although the area of extremely low coverage has decreased. Woodland and sandland account for more than 70% of its area; cultivated and built over land account for less than 1%. Afforestation and aerial seeding, undertaken in 2013, have significantly increased the vegetation cover. [ABSTRACT FROM AUTHOR]

Ferrous sulfate application is important in improvement of agricultural production, elimination of micronutrient deficiencies in human and animal populations, and promotion of sustainable food and feed production. This study was carried out to investigate the effects of different doses of foliar iron sulfate (FeSO4) applications on cotton plant yield and yield components, soil enzyme activity and chlorophyll content (SPAD value) and Normalized Difference Vegetation Index (NDVI) values of cotton plants. The results of both growing periods indicated that 0.4% FeSO4 application dose was more effective than the other doses compared to the control (Co), therefore, the evaluations were carried out based on 0.4% FeSO4 application dose. The application dose increased the cotton yield by 20.53 and 19.08% in the 2020 and 2021 season, respectively. The SPAD value increased by 54.88 and 98.68% in the 2020 and 2021 season. The NDVI values were 0.83 and 0.85 in the 2020 and 2021 seasons, respectively. Soil enzymes responded positively to FeSO4 application. The urease enzyme activity at this application dose varied between 17.81 and 18.76 µg N g−1 dry soil h−1 (13.73 to 26.33% increase compared to Co), and the highest value was recorded in 2021. The Dehydrogenase (DHG) enzyme activity was between 18.38 and 16.05 µg TPF g−1 dry soil 24 h−1 (39.8 and 46.30% increase compared to Co). The Catalase enzyme activity (CAT) was 36.40 and 38.73 ml O2 g−1 dm 5 min−1 in (51.23 and 39.32% compared to control) and the highest value was recorded in 2021. Nitrate Reductase Activity (NRA) was 18.66 and 17.14 µg N g−1 dm 2 h−1 in 2020 and 2021 seasons. The values of NRA were 54.11 and 59.54% lower in 2020 and 2021 seasons compared to the NRA in Co. [ABSTRACT FROM AUTHOR]

A chaotic attractor was obtained from remote sensing data, for the first time, in the early 2010s, its Poincaré section revealing a weakly dissipative dynamics. This attractor was captured from a time series of vegetation index, for the cycles of cereal crops in semi-arid region. This attractor was fully unexpected, since it was also the first attractor of a weakly dissipative dynamics directly extracted from observational time series. The generality of this result is questioned here by applying the same modelling approach – the global modelling technique – to four provinces in coastal and inland Morocco: Safi, El Jadida, Khourigba and Khenifra. Several experimentations are considered, applying the analyses at the provinces scale, or to several provinces in association and in aggregation. The obtained models confirm the results obtained in the early 2010s: the dynamics of cereal crops is chaotic and can be approximated by a weakly dissipative three-dimensional dynamics. [ABSTRACT FROM AUTHOR]

To improve the accuracy of detecting changes in remote sensing time series, an improved algorithm based on the combination of the antileakage least-squares spectral analysis (ALLSSA) algorithm and detecting breakpoints and estimating segments in trends (DBEST) algorithm is proposed and applied. The method uses the ALLSSA algorithm to decompose the time series and identify the trend components in the time series. Then, the trend segmentation mechanism of the DBEST algorithm is used to detect the changes in the trend component. In this paper, the improved algorithm is evaluated using a simulated time series data set, a time series data set with multiple change points, and data set based on the moderate resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) remote sensing time series. The results demonstrate that the average detection accuracies of the improved algorithm and DBEST algorithm are 98.4% and 85.2%, respectively, for the simulated time series data set. For the time series data set with multiple change points, the average root mean square errors (RMSEs) of the trend data for the improved and DBEST algorithms are 0.0386 and 0.0331, respectively. The mean normalized residual norms (MNRNs) of the improved and DBEST algorithms are 0.0252 and 0.0351, respectively. Finally, the improved algorithm, DBEST algorithm, and breaks for additive season and trend (BFAST) algorithm are applied to MODIS NDVI data, and their performance with remote sensing data is compared. The improved algorithm has higher detection accuracy and a smaller MNRN, indicating that more information is included in the trend and seasonal components. Therefore, the proposed method is useful for analysing trends in remote sensing time series data. [ABSTRACT FROM AUTHOR]

Precision application of nutrient based on soil test value is important for improving nutrient use efficiency and yield of hybrid castor. Field trial was conducted during 2021 at Tapioca and Castor Research Station, Yethapur, Salem District, Tamil Nadu, India (110 ∘ 35' N, 780 ∘ 29' E). Fertilizer prescription equations (FPEs) were developed for hybrid castor by adopting inductive cum targeted yield model approach under irrigated condition on Alfisol. Initially, soil fertility gradients were established with respect to soil available N, P and K nutrients, and 24 treatments were imposed in three fertility strips under factorial randomized design. Principle component analysis revealed that all the variables accounted for developing the FPEs of hybrid castor are present at the positive quadrant which mean that all the variables are highly important toward the castor seed yield production. From the field experimental data, the basic parameters [nutrient requirement (NR) and nutrients contributions from farmyard manure (Co), fertilizer (Cf), and soil (Cs)] were computed. The nutrient required for producing one quintal of hybrid castor seed yield was evaluated as 3.20 kg of nitrogen, 1.23 kg of phosphorus pentoxide (P2O5), and 3.28 kg of potassium oxide (K2O). The study revealed that soil nutrient contribution was high in case of available phosphorus (41.87%), available nitrogen (21.56%), and available potassium (19.12%), respectively, toward P, N, and K nutrient uptake by hybrid castor. The nutrient contribution from farmyard manure (Co) toward the total uptake was 21.40% of N, 10.35% of P2O5, and 26.06% of K2O, respectively. FPEs for hybrid castor and a ready reckoner of fertilizer dosages were developed using basic data for operational range of soil test values for intended yield target under inorganic fertilization alone and Integrated plant Nutrient System (IPNS) (NPK and FYM). [ABSTRACT FROM AUTHOR]

The efficiency of cellular communication systems is hampered by cyclones. Network operator authenticated network blind spots, generated after cellular BTS destruction, were successfully recovered using AerialBTS systems by selecting Nisarga cyclone as a representative natural calamity in this case using the stated algorithm in this paper. The geographical location and parameter reconfiguration of AerialBTS have been precisely described to provide maximum coverage of the cellular network. Using the corresponding algorithm, network deployment successfully expanded network coverage by 22.31% with the Digital Elevation Model. [ABSTRACT FROM AUTHOR]

As the cloud covers the surface thermal radiation during the transmission process and the remote sensor in the air is difficult to detect, Thermal Infrared (TIR) remote sensing inversion of Land Surface Temperature (LST) must be performed under clear-sky and cloudless conditions. This study has established the functional relationship between LST and the vegetation index of cloudless vegetation pixels around the cloud coverage areas in the North China Plain based on the Normalized Difference Vegetation Index (NDVI) to address the issues of TIR remote sensing inversion for LST affected by cloud coverage. By acquiring NDVI and using short-term, relatively stable characteristics of vegetation, the LST of the cloud cover area is estimated. The findings show a linear negative correlation between LST and NDVI in vegetation pixels, with the vegetation type remaining essentially unchanged over time. When there are 20 pixels in each of the two thermal infrared channels of FY-3 D, the MAE value and RMSE value of the 24th thermal infrared channel are 0.77 and 0.88, respectively, and the MAE value and RMSE value of the 25th thermal infrared channel are 0.64 and 0.80, respectively. When the number of pixels is 200, the 24th thermal infrared channel’s MAE value and RMSE values are 0.96 and 0.99, respectively, while the 25th thermal infrared channel’s MAE value and RMSE values are 0.90 and 0.95, respectively. In other words, the estimation is more accurate and closer to the true value, and the land surface temperature retrieved by the 25th channel deviates from the true value to a lesser extent. The average absolute error and root mean square error are both less than 1, which may satisfy the accuracy demands of practical applications such as agricultural drought monitoring, ecological evaluation, and crop yield estimation. [ABSTRACT FROM AUTHOR]

Efforts of community forest users show positive changes in forest cover and quality but there was limited study regarding this. Thus, this study was objectively conducted to assess dynamics of forest cover and quality between 1990 and 2021 in Jalthal Jhapa, Nepal. Supervised classification with a maximum likelihood algorithm was applied to classify forest cover to produce maps of 1990, 2005 and 2021. Forest quality change was assessed using Normalized Difference Vegetation Index (NDVI) and Soil Moisture Index (SVI). A total of 114.0 ha non-forest was converted into forest between 2005 and 2011. Overall accuracy of classified maps of 1990, 2005 and 2021 was 85.29%, 84.08% and 88.13%, respectively. Sparse forest was converted into dense forest by 0.06% between 1990 and 2021. Principal Component Analysis showed successful implementation of community forest program and control grazing were main factors positively affecting to increase forest cover and quality. Value of NDVI showed that high vegetation forest covers around 42.27% in 2021 and SMI was the highest. Kaiser-Meyer-Olk in (KMO) (0.474) and Bartlett Sphericity tests (p = 0.001) showed there was linear relationship between factors affecting forest cover and quality. This paper will be useful for academicians and policymakers to monitor forest. [ABSTRACT FROM AUTHOR]

Evaporative fraction (EF) is the ratio of latent heat (LE in W m−2) to available energy at the land surface. It can aid in partitioning the Sun's energy available at the Earth's surface into surface energy fluxes to understand land–atmosphere interactions as well as monitoring Evapotranspiration and terrestrial drought conditions. In this study, the feasibility of estimating instantaneous, diurnal, and daily EF with minimal satellite and meteorological inputs based on the temperature difference-vegetation index framework is explored in South Florida, United States (US). The model performance is assessed against the ground observations collected at three flux stations between 2008 and 2011. It was found that the energy balance closure was improved by 9% at the long hydroperiod marsh site (i.e. US-Elm) and 6% at the short hydroperiod marsh site (i.e. US-Esm) after water heat storage (W) correction was applied on energy fluxes since W is more important than soil heat flux (G) for wetland ecosystems. The results further indicated that the model produced accurate EF estimates and performed the best on diurnal, then instantaneous, and lastly daily periods. Overall, the model can be used to produce spatially continuous EF and ET maps since the flux towers are no longer active. [ABSTRACT FROM AUTHOR]

The overpopulation of urban centres accelerates land use patterns, slum creation and generation of contaminants. As such, this study seeks to investigate the impact of rapid urbanisation on climate variability and surface water quality around Mangaung metropolis. Remote sensing was employed to analyse land use land cover (LULC), normalized differential vegetation index (NDVI) and normalized differential water index (NDWI) changes for a period of 30 years in the Mangaung metropolis. Furthermore, the historical water quality of important rivers and dams were studied in respect of urban contamination. LULC revealed that the urban areas increased exponentially which led to increased loss of vegetation cover and shortage of water availability. The rainfall and water type evolution suggests the influence of urbanisation. This suggests urbanisation accelerates the loss of vegetation cover, thus causing an increase in temperature and reduction in the amount of rainfall, thereby reducing the surface water quantity in the study area. [ABSTRACT FROM AUTHOR]

The applications of scatterometer data (σ°) are limited due to their coarser resolution (25–50 km). Some image reconstruction techniques are available to generate high-resolution products, but they require various sensor parameters and multiset observation, making them complex to use. Therefore, this paper proposes an information fusion approach to disaggregate the coarse resolution σ° product. The coarse resolution backscattering signal includes the contribution from more than one land cover class, such as short vegetation, soil, urban and tall vegetation, the information of which can be obtained from normalised difference vegetation index (NDVI), vegetation temperature condition index (VTCI), and fraction cover of urban and forests, respectively. Disaggregating this coarse resolution pixel, an optimum weight information is required that provides the distribution of each class. Since the distribution of land cover classes is not homogeneous for every pixel, a variance-based fusion approach has been used to obtain the optimum weight factors to fuse NDVI, VTCI, and fraction cover. These weight factors are used to disaggregate every coarse-resolution pixel into high-resolution pixels. The developed model is applied to Sentinel-1 and Scatsat-1 level-3 products, and the obtained results are quite satisfactory. [ABSTRACT FROM AUTHOR]

The terrestrial vegetation GPP of Qinghai Province is an important variable that characterizes the carbon cycling pattern. However, there is still a lack of a high-resolution GPP dataset for Qinghai Province. To address this issue, we processed all Landsat images of Qinghai from 1987 to 2021 using the GEE, and we combined multi-source auxiliary data to estimate GPP using the revised EC-LUE model. We compared our GPP dataset with flux observations to verify its accuracy. The results showed that our GPP dataset had a high correlation with the flux tower observations, with correlation coefficients of 0.984 at CF-AM site and 0.976 at CN-Ha2 site, respectively, and each site had an RMSE of $11.960\, \rm g C\cdot m^{-2}\cdot 16d^{-1}$ 11.960 g C ⋅ m − 2 ⋅ 16 d − 1 and 12.986 $\rm g C\cdot m^{-2}\cdot 16d^{-1}$ g C ⋅ m − 2 ⋅ 16 d − 1 , respectively. There are different deviations between our GPP dataset and the mainstream GPP datasets in various vegetation types, with the average correlation coefficient ranging from 0.431 to 0.943. By comparing with the flux observations and the related analysis, we demonstrated that our GPP dataset features better accuracy, higher spatial resolution, and more temporal coverage than mainstream GPP datasets. This study offers the first long-term high-resolution GPP dataset for Qinghai Province, and we believe that this dataset has important implications for ecological management and climate research. [ABSTRACT FROM AUTHOR]

Remote sensing-based vegetation index (VI) data are significantly impacted by cloud contamination. Spatiotemporal reconstruction methods demonstrate higher accuracy than temporal reconstruction methods. However, the computing time and random access memory (RAM) consumption of these spatiotemporal reconstruction methods for large-scale reconstruction remains unclear. In this study, a method called spatial-interannual reconstruction (SIR) was proposed to reconstruct cloud-contaminated pixels in MODIS normalized difference VI (NDVI) and enhanced VI (EVI) data. SIR has four major advantages: (1) High accuracy. The average mean absolute error of SIR was 0.0338, which was 20.2% and 23.4% lower than that of two state-of-the-art spatiotemporal reconstruction methods (i.e. interpolation of the mean anomalies (IMA) and Gapfill). (2) High computing speed. The average computing time of SIR was 99.7% and 98.8% lower than IMA and Gapfill, respectively. (3) Low RAM consumption. (4) Simultaneous reconstruction of all invalid values. Reconstructed 250 m spatial resolution and 16-day composite NDVI and EVI datasets in China from 2000 to 2022 (written as ReVIChina) were developed based on the SIR method and MODIS MOD13Q1 data. Spatiotemporal analyses revealed that the reconstructed datasets were more reliable than the original product and a similar dataset. [ABSTRACT FROM AUTHOR]

In detecting the internal health of an individual tree, "two impact factors analysis" refers to evaluation of NIR and RED separately, instead of merging them together in one formula as in NDVI assessment. We use high resolution WorldView-2/-3 satellite data for our study. From our global case studies of stressed trees, we have found the following results: (1) NDVI works when the spectral reflectance in both NIR and RED bands deteriorates together/concurrently. (2) NDVI does not work when the spectral reflectance in NIR and RED bands varies. This will happen, as shown by our case studies of Tree 1 and Tree 2 of the removed stressed trees, and Tree 4 and Tree 5 of the collapsed stressed trees. [ABSTRACT FROM AUTHOR]

The increase in deciduous shrub growth in response to climate change throughout the Arctic tundra has uncertain implications, in part due to a lack of field observations. Here we investigate how increasing alder shrub growth in alpine tundra in Interior Alaska corresponds to active layer thickness and soil physical properties. We documented increased alder growth by combining biomass harvests and dendrochronology with the analysis of remotely sensed Normalized Difference Vegetation Index and fire history. Active layer thickness was measured with a tile probe and carbon and nitrogen pools were assessed via elemental analysis. Shallower organic layers under increasing alder growth indicate that nitrogen-rich, deciduous litter inputs may play a role in accelerating decomposition. Despite the observed reduction in organic carbon stocks, active layer thickness was the same under alder and adjacent graminoid tundra, implying deeper thaw of the underlying mineral soil. This study provides further evidence that the widely observed expansion of deciduous shrubs into graminoid tundra will reduce ecosystem carbon stocks and intensify soil–atmosphere thermal coupling. Two consequences of rapid climate warming in the Arctic, where grass-like plants dominate under very cold conditions, are an increased growth and occurrence of shrubs and associated thaw of frozen ground. This exposes organic matter in soils to microbes that can decompose it into carbonaceous greenhouse gases, but some of this carbon loss may be offset by the increased plant growth. Here, we investigate the impacts of greater shrub presence on soil properties at five sites in Alaska. We documented shrub growth by analyzing satellite images, which can help us understand the productivity and/or leaf coverage at each site back in time, and annual growth rings in shrub stems, which show how old the shrubs are and how much they grow each year. We also measured the depth of soil thaw in the field and its organic matter content in a laboratory. Where shrubs were more common, we found a thinner layer of organic matter at the soil surface. Thaw depth remained the same, which may indicate that the presence of shrubs results in deeper thaw of the mineral soil. Our findings support the hypothesis that shrub expansion will further enhance warming-driven increases of greenhouse gas emissions from Arctic landscapes. Trends in dendrochronology and Normalized Difference Vegetation Index reveal increasing growth of alder shrubs in Interior Alaska. More alder cover results in the loss of the soil organic layer and thus soil C and N that is not offset by more shrub biomass. Increasing alder growth may promote permafrost thaw not captured by tile probe active layer thickness monitoring. [ABSTRACT FROM AUTHOR]

This study aimed to analyze and compare the accuracy of models to predict the grain yield (GY) of common bean cultivars with contrasting growth habits using spectral indices. The common bean cultivars used were IAC Imperador and IPR Campos Gerais, which have determinate and indeterminate growth habits, respectively. The plants were grown under five irrigation levels (54, 70, 77, 100, and 132% of the crop evapotranspiration) to generate variability. The normalized difference vegetation (NDVI) and leaf chlorophyll (LCI) indexes were measured at the following phenological stages: V4 (third trifoliate leaf), R5 (pre-flowering), R6 (full flowering), and R8 (grain filling). The spectral indices were used individually for each phenological stage and associated with simple and multiple regressions (SLR and MLR) and artificial neural networks (ANN) to predict GY. Then, stratified models by cultivar and general models were established using data from both cultivars. The accuracy of NDVI-based GY predictions for both models at R6 phenological stage (ANN and SLR average) was acceptable (R² = 0.64; RMSE = 0.37 Mg ha-1 ; MBE= 0.14 Mg ha-1 ) but poor for LCI predictions. The highest accuracies were observed at reproductive phenological stages, mainly R6. The ANNs algorithm did not show superior GY prediction accuracy compared to SLR. NDVI-based remote sensing is feasible to predict and monitor common bean yield potential using cultivarspecific and general models. [ABSTRACT FROM AUTHOR]

The use of remote sensing for examining phenological variation in tropical forests is scarce. The major objectives of the study were to characterize the intra-annual variability of phenological cycle of the Biligiri Ranganathaswamy Temple Tiger Reserve (BRT) and the potentiality of these phenological metrices in defining species assemblages by classifying the forest. Sentinel-2 derived temporal Normalized Difference Vegetation Index (NDVI) data of 2019 was used to extract the vegetation trends and to derive phenological metrics using CropPhenology R package. Seasonal trends revealed that the highest greenness was associated with high NDVI values in September and October. We identified seven vegetation classes in the region and used Random Forest classifier to prepare a community level classification map with an overall classification accuracy of 68.9%. Our results revealed that incorporating the field sampling data and NDVI data can be effectively used for identifying, mapping and monitoring phenology of the BRT landscape.. [ABSTRACT FROM AUTHOR]

Exploring the long-term spatiotemporal pattern of vegetation changes and its response to natural factors and human activities is vital for making informed decisions regarding ecosystem protection. The spatial–temporal distribution characteristics of vegetation dynamics and its driving factors in Shaanxi Province from 2000 to 2019 were analysed using the normalized difference vegetation index (NDVI). The explanatory power of solar radiation was the highest (63.45%), followed by relative humidity (59.07%), land use (56.73%), precipitation (52.17%), temperature (51.28%), and vegetation type (50.95%). In terms of climatic factors, the influences of solar radiation and relative humidity on vegetation changes were stronger than those of precipitation and temperature. The comprehensive impact of the pairwise factors was higher than those of the independent factors. Our results contribute to a better understanding of the complex mechanisms of vegetation changes and provide scientific recommendations for the prevention remediation of vegetation degradation in fragile ecosystems. [ABSTRACT FROM AUTHOR]

The Severe Acute Respiratory Syndrome-CoronaVirus Diseases 2019 (SARS-COVID-19) has sternly affected the entire world in terms of human health, loss of lives, and huge economic losses. However, pandemic-triggered lockdown (LD) events (as a preventive measure) have compelled to stop or reduce major economic activities, exerting positive impacts on the terrestrial environment. We deployed a variety of satellite products (i.e., normalized difference vegetation index (NDVI), solar-induced chlorophyll fluorescence (SIF), and aerosol optical depth (AOD)) along with gridded climatic dataset (temperature (TEMP), precipitation (PREC), and net radiation (NR)) to quantify the changes in vegetation activity (greenness and productivity) during the LD period over the Indian biogeographic provinces (BGPs) as compared to the average conditions over the previous three years (2017-2019). The analysis of the NDVI and SIF data revealed that vegetation greenness and productivity significantly enhanced during LD periods (by up to 37 to 55%, respectively). The influence of climatic drivers (PREC, TEMP, and NR) on vegetation activity was also investigated. We found that the enhancement in the vegetation activity (over BGPs) during the LD period was not entirely driven by the climatic parameters, and was therefore inferred to be also influenced by the LD events. Moreover, vegetation activity around the mining clusters were largely improved during the LD period (by up to 78%) over the coal mining, followed by iron ore mining (up to 63%), and stone mining (up to 41%) clusters) regions. In a nutshell, it can be deliberated that COVID-triggered preventive measures (i.e., country-level LD, travel bans, industry ban, curtail in mining capacity, among others) likely enhanced vegetation health and productivity. Thereby, regulatory measures can be seen as a viable option for improving the terrestrial environmental conditions in the context of climate change in the near future. [ABSTRACT FROM AUTHOR]

Mountainous terrains severely affect the sun-target-sensor geometry due to surface dispersion of solar radiation, resulting to variation in the observed radiance. The effect of topographic shadow on raw, atmospherically and topographically processed Normalized Difference Snow Index (NDSI) and Normalized Difference Vegetation Index (NDVI) values was investigated for Jhelum basin, Kashmir Himalaya. The results indicate that NDSI and NDVI derived from raw images show less area of snow and vegetation, due to underestimation of corresponding pixels in the shadow regions, in comparison to atmospherically and topographically processed datasets. Among the three datasets, raw images exhibited low values of minimum and maximum NDSI and NDVI for both shadow and sunlit slopes. All the three datasets show significantly higher values of minimum and maximum NDSI, NDVI and surface temperature in the sunlit slopes, relative to shadow slopes. This research demonstrates that topographic shadow has significant impact on the raw, DOS atmospheric corrected and C topographic processed Landsat-8 Operational Land Imager (OLI) datasets as well as on Thermal Infrared Sensor (TIRS) sensor. [ABSTRACT FROM AUTHOR]

In past studies, several researchers took potential use of multi-temporal optical data and dual-polarized SAR data to assess drought by estimating soil moisture. In this study, Modified Dubois Model (MDM) semi-empirical model with Topp's model is used for retrieval of soil moisture. It involves retrieving the backscattering coefficient from RISAT-1 and SENTINEL-1 datasets to derive the surface roughness and soil moisture conditions. The estimated soil moisture retrieved from microwave SAR parameters is validated with field measurements provides soil moisture spatial variability over different land use classes and bare soil condition. The RISAT-1 derived soil moisture has R² = 0.53, whereas SENTINEL-1 shows R² = 0.84. It also confirms the possibility of two different polarization σ°HH and σ°VV backscatter involving MDM. It observes that SENTINEL-1 was found well correlated with ground-measured soil moisture. Also, the averaged NDVI sounds reliable with soil moisture ratio, which helps to understand the impact of agricultural drought monitoring. [ABSTRACT FROM AUTHOR]

Accurate oil palm yield prediction is necessary to sustain oil palm production for food security and economic return. However, there are limited studies on comprehensive mapping and accurate oil palm yield prediction using advanced machine learning algorithms. Using multi-temporal remote sensing data, this paper proposed a new approach to predict oil palm yield based on the normalized difference vegetation index (NDVI) and ensemble machine learning algorithm. ReliefF algorithm with linear projection was employed to select the best combination of spectral indices in oil palm discrimination. Oil palm land cover was classified using random forest (RF) and modified AdaBoost algorithms. A time-series approach known as walk-forward validation was firstly introduced to train the model using the 2016-2019 data and the one-step prediction was performed for 2020 using RF and AdaBoost. Result of the study revealed that the RF model (RMSE=0.384; MSE=0.148; MAE=0.147) outperformed the AdaBoost model (RMSE=0.410; MSE=0.168; MAE=0.176). Our research has demonstrated the value of detailed mapping and subsequent yield prediction by developing a novel approach utilising time-series satellite imagery, ensemble machine learning, and NDVI, which will assist decision-makers in managing their practices related to oil palm. [ABSTRACT FROM AUTHOR]

Argania spinosa also known as the argan tree is an endemic plant of Morocco. Despite having the ability to subsist in extreme drought conditions, it is threatened by soil land clearing, overexploitation, and absence of natural regeneration, causing a worrying decline in both spatial extent and density. The spatial extent of dryland forests is debated, as estimates of forest areas in drylands are uncertain. The present study aims to map and locate the spatial distribution of the argan trees at Smimou community located in Essaouira province, south-eastern Morocco, using satellite images and a double-classification process to overcome separability problems. The work focuses on the characterization and comparison of the unique phenological patterns of argan with the other present land-cover classes. NDVI products were derived from a Sentinel-2 time-series covering one year (2018 to 2019), then ground samples were used to extract phenological profiles at parcel level then at tree level, to feed representative calibration samples to Support Vector Machine classifier. The outcome was integrated with an elevation model in a Decision Tree to reclassify mixed areas. The results indicated an F1-score and an overall accuracy of 91.27% and 92.60% respectively, a promising technique for updating argan extent at national scale. [ABSTRACT FROM AUTHOR]

Grazing behaviour influences animal productivity and the conservation of grassland ecosystem services. We used GPS tracking and remote sensing (NDVI index) to monitor the grazing patterns of lactating cows on the 'Malga Ombretta' summer farm (1,957 m asl) in the Dolomites, eastern Italian Alps, from 5th July to 5th August 2018. The pasture area (35 ha) was grazed by a mixed herd of Simmental and Alpine grey cows (stocking density = 0.6 LU/ha) under traditional management: each morning the farmer led the cows to graze in a selected sub-area of pasture, and during the afternoon he left them free to graze unrestricted until they returned to the barn for the night. GPS positions were collected every minute from 9 Simmental and 4 Alpine Grey cows with low milk production during the time they were outdoors. The farmer's choice of where to drive the herd to graze in the morning determined the distances the cows walked/day, which varied from 2.0 to 8.9 km, and favoured the use of higher and steeper areas that the cows tended otherwise to avoid. When free in the afternoon, the cows selected areas with higher NDVI values than those selected by the farmer in the morning, and Alpine Grey cows used slightly higher slopes and altitudes than Simmental cows, suggesting better adaptation to mountain pastures. The study revealed highly heterogenous grazing patterns dependent on multiple factors that can be assessed at fine temporal and spatial scales using GPS and remote sensing technologies to improve grazing management. Daily distances walked and grazing patterns were influenced differently by the farmer's decisions and the animals' choices in response to environmental features. The NDVI index of vegetation productivity suggested that cows grazed more productive areas when free than when driven by the farmer. GPS tracking and remote sensing shed light on how human and animal choices regarding grazing are influenced by environmental features. [ABSTRACT FROM AUTHOR]

High biomass and nutrient acquisition are desirable for oat (Avena sativa L.) as a cover crop. However, our understanding of oat genotypes suitable for cover crops and associated traits is limited. The objectives of this experiment on growing oat as a cover crop, after winter wheat (Triticum aestivum L.) harvest, were to determine biomass production, nutrient uptake of a set of oat genotypes, and to identify phenotypic traits that can be used as indicators to select cultivars suitable for cover crops. The results showed that the top biomass-producing genotypes took up larger amounts of soil nutrients, up to 142 kg N ha−1 and 17 kg P ha−1 in 2016, and 43.5 kg N ha−1 and 8.3 kg P ha−1 in 2017. The biomass production was significantly related to plant height and leaf area index (LAI) in both years, and to the normalized difference vegetation index (NDVI) in 2017. Both NDVI and LAI were closely related to the total amounts of N and P uptake. The poor association between biomass and NDVI in 2016 was due to vigorous growth of volunteer wheat and weeds as well as severe rust (Puccinia coronata f. sp. avenae Eriks.) infestation. Our results suggest that it is important to choose oat varieties as cover crops. Leaf area index can be used as a nondestructive indicator for final biomass and nutrient acquisition, while both NDVI and LAI are important traits for choosing oats as soil conservation cover crops. [ABSTRACT FROM AUTHOR]

Almost no systematic monitoring of bird population trends occurs in West Africa, despite rapid human population increase, habitat change, and climate change, making conservation planning problematic. We monitored bird population trends using constant-effort mist netting, in a newly protected area (Amurum Forest Reserve) on the outskirts of Jos, central Nigeria, from 2002 to 2019. We modelled the 18-year changes in trends of 10 Palearctic migrant and 41 common resident bird species and related this to any changes in annual environmental site quality using NDVI and rainfall data. The populations of most bird species were stable; 30% of migrants and 7% of residents increased, while 10% of migrants and 29% of residents declined moderately. Primary productivity, measured by NDVI, increased, and rainfall pattern was stable, suggesting that environmental conditions at the site improved slightly during the period. However, only a few species showed significant correlations of population trends with NDVI and rainfall. Overall, our results suggest that population changes were locally similar for both the Afro-Palearctic and resident bird species, being reasonably stable or increasing — although perhaps this reflected the fact that the monitoring was done within a newly protected area, which at present represents the best habitat in the wider locality. Those species that declined were mostly associated with open, grassland areas, which will have decreased as anthropogenic influences were reduced at the study site. Though we only monitored one site, the results are encouraging in that simple protection of a small habitat fragment (∼300 ha) in Nigeria yielded generally positive population benefits for both resident and Palearctic migrant species. [ABSTRACT FROM AUTHOR]

Although the impacts of dams on the environment and human populations have been widely documented, many developing countries continue constructing large dams. In Mexico, impact studies have neglected to analyze downstream effects of dams. Thus, we analyzed land use and land cover (LULC) changes downstream of El Molinito dam, constructed on the Sonora River in northwest Mexico. We used Landsat Thematic Mapper (TM) to generate classifications (for 1993, 2002 and 2011), from which an assessment is performed to identify the principal changes. In addition, we analyzed changes in photosynthetic activity using the simple but informative Normalized Difference Vegetation Index (NDVI). We found a drastic decline between 1993 and 2011 in agricultural LULC because of water diversions from El Molinito. This study demonstrates the need for agencies responsible for water management to better assess the negative effects of dam construction and operation, especially on the livelihoods of downstream rural communities. [ABSTRACT FROM AUTHOR]

A fast, precise and efficient method of savannah grassland mapping and monitoring is essential to support sustainable livestock feed management. This study aims to utilise Sentinel-2A Level-1C imagery to map and monitor tropical savannah grasslands on Sabu Island, Indonesia. Normalized Difference Vegetation Index (NDVI) images were generated to identify vegetation objects from 50 image scenes covering each month from 2016 to 2020 through the Google Earth Engine (GEE). Principal Component Analysis (PCA) was applied to the 50 NDVI data to produce monthly images (12 months). The grassland objects were classified from Sentinel-2A images using the parallelepiped algorithm and resulted in an overall accuracy of 82.86%. Results showed a range of the average monthly NDVI between 0.127 and 0.449, which falls within the grassland class. NDVI combined with GEE can quickly and accurately identify grasslands, creating highly recommended tools for monitoring tropical savannah grasslands. [ABSTRACT FROM AUTHOR]

Multispectral indices are useful to improve the knowledge of plant organic functionality. Geographically weighted regression (GWR), multispectral data from unmanned aerial vehicles (UAVs) and individual tree attributes were used in combination to generate forest parameters in an even-aged orchard of Pinus arizonica Engelm. The NDVI index was the best indicator of vegetation vigour, correlated to diameter at breast height (DBH) and total estimated tree height (UAVe) as explanatory variables. Geospatial models explained the variance of orchard vigour (R2 = 39 for DBH and R2 = 52 for UAVe), suggesting the crucial requirement for individual or zonal management of the trees. Our results thus provide timely indicators of plant health conditions in the clonal orchard that may be useful for adaptive management strategies in the face of predicted climatic change. [ABSTRACT FROM AUTHOR]

The study focused on deriving the LST of the Raipur City of India and generating the relationships of LST with six selected remote sensing indices, like MNDWI, NDBaI, NDBI, NDVI, NDWI, and NMDI. The entire study was performed by using 210 cloud-free Landsat data of different months from 1988 to 2019. The LST retrieval mono-window algorithm was applied in the study. Based on Pearson's linear correlation coefficient (r), the study finds that LST builds a strong positive correlation (r = 0.65) with NDBI, a moderate positive correlation (r = 0.30) with NDBaI, a weak positive correlation with NDWI (r = 0.19), a strong negative relation with NMDI (r = −0.54), and a moderate negative correlation (r = −0.38) with MNDWI and NDVI. These relationships were consistent and stronger in earlier years. The LST-NDBI correlation is the most consistent (CV = 9.09), while the LST-NDBaI correlation is the most variable (CV = 60.21). [ABSTRACT FROM AUTHOR]

The spectral characteristics of sunlit and shaded leaves are critical to improving the utilization of remote sensing methodology to quantify forest physiology. However, spectral characteristics within the tree canopies, especially normalized difference vegetation index (NDVI) and photochemical reflectance index (PRI), are poorly understood. Our study used an imaging observation platform to obtain hyperspectral imagery of a Manchurian Ash canopy on Changbai Mountain. A non-imaging spectrometer was employed for an assisted analysis. The study results of the corresponding spectrum obtained at two observation spatial scales were significantly different between sunlit and shaded leaves. For imaging spectral observations, there were significant differences in NDVI and PRI between sunlit and shaded leaves (P < 0.001). PRI near the petiole was significantly lower than in other parts of leaves (P = 0.049). Non-imaging spectral observations of the reflectance of sunlit and shaded leaves were different only in the visible light region. The PRI of the shaded leaves were higher than that of sunlit leaves, which was consistent with the imaging spectral observations. The complexity of light environment within the canopy, especially the differences in incident irradiance, contributed to the range of leaf attribute measurements, which resulted in the variability of spectral characteristics. [ABSTRACT FROM AUTHOR]

The Global Change Observation Mission-Climate (GCOM-C), launched in 2017, has suitable bands matching the photochemical reflectance index (PRI) definition. It also has the bands for the normalized difference vegetation index (NDVI). The PRI has a unique capability to detect plant stress caused by excessive light and drought. However, no moderate-resolution satellites had suitable bands for the PRI, requiring two narrow bands in green light in the definition. In this study, we conducted the early validation study of PRI and NDVI derived from the GCOM-C satellite and demonstrated those accuracies and characteristics in Mongolian grassland. The Mongolian Steppes (dry grasslands) are widely distributed on the plateau and therefore suitable for satellite validation. It is particularly suitable for the PRI validation because Mongolian grasslands have water stress due to the small amount of precipitation in summer. Therefore, we carried out field campaigns at three study sites in Mongolia. In this study, we found the seasonal pattern of PRI suggesting the potential to detect the water stress of vegetation, which is essential information for informed management of the grasslands. However, the correlation between the satellite-derived PRI and the in-situ PRI was negative because of the dependence of GCOM-C PRI on the soil moisture at sparse vegetation. For the accuracy assessment of PRI, which depends on rapidly changing light and soil moisture in a day, more exact synchronization of in-situ and satellite observation is required. On the other hand, we found that the NDVI derived from GCOM-C was highly accurate: The correlation coefficient (R) between the satellite-derived NDVI and the in-situ NDVI was 0.988 (RMSE=0.052). GCOM-C NDVI has enough similarities with MODIS NDVI in terms of accuracy, spatial resolution, and frequency. For example, we demonstrated that GCOM-C NDVI could detect the phenology with the same or better accuracy than MODIS NDVI. We also demonstrated their difference: the soil moisture dependence in sparse vegetation. The less dependency of GCOM-C NDVI on the soil moisture leads to a better classification of vegetation and non-vegetation in the sparse grassland than MODIS NDVI. [ABSTRACT FROM AUTHOR]

Wetland vegetation plays an important role in the environmental functioning of wetlands through the provision of ecosystem services, such as food and critical habitat for organisms that live in or near water resources. The ecosystem services provided by wetland vegetation are facing several pressures due to the impacts of drought. Drought can induce significant declines in overall plant productivity and even lead to high rates of plant mortality. Therefore, assessing vegetation response to a drought is important for wetland assessment. In this study, the subtle changes in vegetation distribution were used as a proxy to examine and quantify the extent of drought impacts on the Soetendalsvlei wetland within the Heuningnes Catchment, South Africa. First, the vegetation health information was extracted by calculating the Normalized Difference Vegetation Index (NDVI) during the wet and dry seasons for the period between 2014 and 2018. The derived NDVI results were further statistically linked to the corresponding rainfall and evapotranspiration observed during the study period. An analysis of NDVI results revealed that gradual vegetation health change occurred across the study area. The highest derived NDVI (0.5) for wetland vegetation was observed during 2014, but progressively declined over the years. Change in vegetation health indicated a significant (r = 0.8-0.92) and positive correlation to the amount of rainfall received over the same period, whereas with evapotranspiration the relationships showed an opposite trend (r = −0.7 to −0.5). The results of this study highlight the importance of integrating remotely sensed data and climate variability information in assessing wetland vegetation seasonal and long-term variations. Such information can help in decision-making on the conservation of wetlands and effective monitoring of wetland ecosystems. [ABSTRACT FROM AUTHOR]

This study explores the impact on land surface temperature due to the spatial clustering of urban landforms with normalized difference vegetation index, normalized difference water index and dry bare-soil index. In order to determine the contribution of different land use/land cover classes in affecting the land surface temperature, the contribution index was used for summer and winter seasons. For analyzing the intensity of land surface temperature at the local scale, landscape index was used. Results depicted that the contribution of the source and sink landscapes weakens the intensity of land surface temperature in the winter season. However, the contribution of the source and sink landscape promoted the intensity of land surface temperature in the summer season. Furthermore, this study evaluated the predictive performance of four machine learning models, including K-Nearest Neighbor (K-NN) regression, Neural Networks (NN), Random Trees (RT) regression and Support Vector Machine (SVM) regression for land surface temperature. [ABSTRACT FROM AUTHOR]

Rainfall and temperature are the key factors responsible for vegetation condition, health and growth. This research work analysis spatiotemporal phenomena in between SPI, LST and VIs in summer session from 2000 to 2018 in European Russia. This study used MODIS, NDVI, LST and TRMM data. The negative SPI values represent increasing drought events with reducing rainfall and vice-versa. Numerical consequences specify that mean annual rainfall, VIs variate according to SPI values and they completely change in the year 2004, 2009 and 2015. VIs also indirectly related to LST as high LST values (high temperature) associated with low VIs values (low vegetation) and vice-versa, with correlation coefficients 0.90. Correlation analysis of VIs, SPI and LST indicate close relationship in between vegetation, rainfall and temperature and this relationship can be used for near real time vegetation drought dynamics monitoring through satellite data for short term to long term changes in vegetation growth. [ABSTRACT FROM AUTHOR]

The objective of the study is to understand the pattern of land surface temperature (LST) and normalized difference vegetation index (NDVI) developed in Ranchi city during Covid-19-induced lockdown (2020) and its comparison with previous years. The study incorporated Landsat 8 (Operational land imager) data from United States Geological Survey and air temperature and relative humidity data from power.larc.nasa.gov for the years 2017, 2019 and 2020. The results exposed a drastic change in the LST and NDVI pattern of the city. The mean LST of the city during April has declined from 39.80°C in 2017 to 32.38°C in 2020. Similarly, the mean LST of May also declined from 38.41°C in 2017 to 34.84°C in 2020. On the contrary, the city experienced an ascending growth of NDVI from 0.24 to 0.26 in April and May 2017 to 0.349 and 0.37 in 2020, respectively. Additionally, the city portrays declining air temperature with enhanced relative humidity. Ranchi city also exhibited relatively maximum area under ecologically excellent category in the year 2020 and reduced area under ecologically the worst category based on urban thermal field variance index. Thus, reduced temperature with augmented humidity and NDVI developed a healthy urban environment. [ABSTRACT FROM AUTHOR]

Soil moisture is essential for water resources management, yet accurate information of soil moisture has been a challenge. The major goal was to parametrize the Modified Water Cloud Model (MWCM). The Sentinel-1A data of winter wheat crop was collected for two weeks. Concurrently, in-situ soil moisture data was collected using Time Domain Reflectometer (TDR). A parametric scheme was used for the retrieval of the VV polarization of Sentinel-1A. The effect of NDVI as a vegetation descriptors (V1 and V2) on total VV backscatter (σ0) was analyzed. The calibration showed NDVI has the potential to influence Water Cloud Model (WCM) and vegetation descriptors; hence it is recommended to calibrate the MWCM. The coefficient of determination (R2 = 0.83) showed a good agreement between observed and estimated soil moisture. Therefore, this approach help improve soil moisture prediction, and can be applied to determine soil moisture more accurately for winter crops, grasses, and pasture lands. [ABSTRACT FROM AUTHOR]