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High soil moisture due to frequent excessive precipitation can lead to reductions in maize grain yields and increased nitrogen (N) loss. The traditional methods of computing N status in crops are destructive and time-consuming, especially in waterlogged fields. Therefore, in this study, we used unmanned aerial vehicle (UAV) remote sensing to evaluate the status of maize under different N rates and excessive soil moisture conditions. The experiment was performed using a split plot design with four replications, with soil moisture conditions as main plots and different N rates as sub-plots. The artificial intelligence SciPy (version 1.5.2) optimization algorithm and spherical function were used to estimate the economically optimum N rate under the different treatments. The computed EONR for CRS 2022 was 157 kg N ha−1 for both treatments, with the maximum net return to N of USD 1203 ha−1. In 2023, the analysis suggested a lower maximum attainable yield in excessive water conditions, with EONR pushed up to 197 kg N ha−1 as compared to 185 kg N ha−1 in the control treatment, resulting in a lower maximum net return to N of USD 884 ha−1 as compared to USD 1019 ha−1 in the control treatment. This study reveals a slight reduction of the fraction of NDRE at EONR to maximum NDRE under excessive water conditions, highlighting the need for addressing such abiotic stress circumstances when arriving at an N rate recommendation based on an N-rich strip concept. This study confirms the importance of sensing technology for N monitoring in maize, particularly in supporting decision making in nutrient management under adverse weather conditions.

With the advance of industrialisation and urbanisation, land use change and climate change have significant impacts on the global ecosystem. Focusing on Chongqing Municipality, a municipality that plays a central role in regional economic development and national strategies, this study explores the impacts of land use and climate change on the evolution of its NDVI spatial and temporal patterns between 2000 and 2020 and reveals the driving mechanisms behind them. By analysing remote sensing image data and climate data, it was found that Chongqing Municipality experienced significant land use changes during the study period, especially urban expansion and the reduction of agricultural land, which led to the reduction of vegetation cover. Meanwhile, precipitation in climate change positively affected vegetation growth and coverage, while the increase in surface temperature during urbanisation negatively affected vegetation cover and exacerbated the urban heat island effect. NDVI was positively correlated with precipitation and negatively correlated with air temperature, suggesting that moderate precipitation promotes vegetation growth, while high temperatures may adversely affect vegetation activities. The results of this study can provide a scientific basis for urban planning and ecological conservation, especially in formulating effective urban management and land management strategies to protect the ecological environment and rationally utilise land resources.

Remote sensing has proven to be a vital tool for monitoring and forecasting the quality and yield of crops. The utilization of innovative technologies such as Solar-Induced Fluorescence (SIF) and satellite measurements of column-averaged CO2 (xCO2) can enhance these estimations. SIF is a signal emitted by crops during photosynthesis, thus indicating photosynthetic activities. The concentration of atmospheric CO2 is a critical factor in determining the efficiency of photosynthesis. The aim of this study was to investigate the correlation between satellite-derived Solar-Induced Chlorophyll Fluorescence (SIF), column-averaged CO2 (xCO2), and Normalized Difference Vegetation Index (NDVI) and their association with sugarcane yield and sugar content in the field. This study was carried out in south-central Brazil. We used four localities to represent the region: Pradópolis, Araraquara, Iracemápolis, and Quirinópolis. Data were collected from orbital systems during the period spanning from 2015 to 2016. Concurrently, monthly data regarding tons of sugarcane per hectare (TCH) and total recoverable sugars (TRS) were gathered from 24 harvest locations within the studied plots. It was observed that TRS decreased when SIF values ranged between 0.4 W m−2 sr−1 μm−1 and 0.8 W m−2 sr−1 μm−1, particularly in conjunction with NDVI values below 0.5. TRS values peaked at 15 kg t−1 with low NDVI and xCO2 values, alongside SIF values lower than 0.4 W m−2 sr−1 μm−1 and greater than 1 W m−2 sr−1 μm−1. These findings underscore the potential of integrating SIF, xCO2, and NDVI measurements in the monitoring and forecasting of yield and sugar content in sugarcane crops.

This paper presents the implementation of statistical and remote sensing techniques to analyze the spectral response, grain yield, and infestation of fall armyworm (Spodoptera frugiperda) in corn (Zea mays) based on the application of edaphic and foliar treatments with silicon, comparing the results with those reported in the literature where it has been demonstrated that the incorporation of this nutrient in different crops improves the activity of the enzyme nitrate reductase and dry matter weight gain. The results show that the foliar application of silicon tends to increase grain production in the crop, while the soil treatment does not improve yield. Similarly, foliar silicon application improves the Normalized Difference Vegetation Index, which improves plant health and could be correlated with higher grain yield of the crop. An inverse correlation was detected between the use of foliar silicon and the Normalized Difference Water Index and a direct relationship in the case of direct field application. As for the analysis of the data to verify the influence of the use of silicon on fall armyworm infestation, no statistically significant evidence was found that would lead to the conclusion that the application of this element, whether in soil or foliar form, could lead to a decrease in crop infestation.

Remote sensing technology is widely used to obtain information on floating green tides, and thresholding methods based on indices such as the normalized difference vegetation index (NDVI) and the floating algae index (FAI) play an important role in such studies. However, as the methods are influenced by many factors, the threshold values vary greatly; in particular, the error of data extraction clearly increases in situations of high-turbidity water (HTW) (NDVI > 0). In this study, high spatial resolution, multispectral images from the Sentinel-2 MSI mission were used as the data source. It was found that the International Commission on Illumination (CIE) hue angle calculated using remotely sensed equivalent multispectral reflectance data and the RGB method is extremely effective in distinguishing floating green tides from areas of HTW. Statistical analysis of Sentinel-2 MSI images showed that the threshold value of the hue angle that can effectively eliminate the effect of HTW is 218.94°. A test demonstration of the method for identifying the floating green tide in HTW in a Sentinel-2 MSI image was carried out using the identified threshold values of NDVI > 0 and CIE hue angle < 218.94°. The demonstration showed that the method effectively eliminates misidentification caused by HTW pixels (NDVI > 0), resulting in better consistency of the identification of the floating green tide and its distribution in the true color image. The method enables rapid and accurate extraction of information on floating green tide in HTW, and offers a new solution for the monitoring and tracking of green tides in coastal areas.

Growing season freeze events pose a threat to quaking aspen (Populus tremuloides Michx.), leading to canopy defoliation, reduced vigor, and increased mortality, especially for declining montane populations western North America. Detecting the spatial distribution and progression of this damage is challenging due to limited in situ observations in this region. This study represents the first attempt to comprehensively resolve the spatial extent of freeze-induced aspen canopy damage in southern Utah using multispectral remote sensing data. We developed an approach to detect the spatial and temporal dynamics of freeze-damaged aspen stands, focusing on a freeze event from 8–9 June 2020 in southern Utah. By integrating medium- (~250 to 500 m) and high-resolution (~10 m) satellite data, we employed the Normalized Difference Vegetation Index (NDVI) to compare post-freeze conditions with historical norms and pre-freeze conditions. Our analysis revealed NDVI reductions of 0.10 to 0.40 from pre-freeze values and a second flush recovery. We introduced a pixel-based method to evaluate freeze vulnerability, establishing a strong correlation (R values 0.78 to 0.82) between the onset of the first flush (NDVI > 0.50) and the accumulation of 100 growing degree days (GDD). These methods support the potential for retrospective assessments, proactive forest monitoring, and forecasting future risks.

Urban heat poses significant challenges in rapidly developing cities, particularly in countries like Bangladesh. This study investigates the cooling effects of urban green spaces in Rajshahi city, addressing a critical research gap in developing urban contexts. We examined the relationships among urban vegetation, heat mitigation, and temperature variables using the InVEST Urban Cooling Model and spatial analysis techniques. This study focused on three key relationships: Normalized Difference Vegetation Index (NDVI) and Heat Mitigation Index (HMI), HMI and Land Sur face Temperature (LST), and HMI and Air Temperature (AT). Analysis revealed a strong positive correlation between NDVI and HMI, indicating the effectiveness of vegetation in enhancing urban cooling. A robust inverse relationship between HMI and LST was observed (R2 = 0.78, r = −0.88), with every 0.1 unit increase in HMI corresponding to a 0.53 °C decrease in LST. The HMI−AT relationship showed an even stronger correlation (R2 = 0.84, r = −0.87), with each unit increase in HMI associated with a 2.80 °C decrease in air temperature. These findings quantify the significant role of urban green spaces in mitigating heat and provide valuable insights for urban planning in developing cities, underscoring the importance of integrating green infrastructure into urban-development strategies to combat urban heat and improve livability.

Agroecosystems are facing the adverse effects of climate change. This study explored how the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) can give new insight into irrigation allocation and plant health. Leveraging the global coverage and 70-m spatial resolution of the Evaporative Stress Index (ESI) from ECOSTRESS, we processed over 200 overpasses and examined patterns over 3 growing seasons across the Maipo River Basin of Central Chile, which faces exacerbated water stress. We found that ECOSTRESS ESI varies substantially based on the overpass time, with ESI values being systematically higher in the morning and lower in the afternoon. We also compared variations in ESI against spatial patterns in the environment. To that end, we analyzed the vegetation greenness sensed from Landsat 8 and compiled the referential irrigation allocation from Chilean water regulators. Consistently, we found stronger correlations between these variables and ESI in the morning time (than in the afternoon). Based on our findings, we discussed new insights and potential applications of ECOSTRESS ESI in support of improved agricultural monitoring and sustainable water management.

Rapid urbanization worldwide has significantly altered urban climates, creating a need to balance urban growth with thermal environmental quality for sustainable development. This study examines the relationship between land surface temperature (LST) and urban characteristics, particularly focusing on how green cover can mitigate urban heat and how air pollution can increase temperatures. Recognizing the predictive value of LST for urban heat island (UHI) intensity, we analyzed three distinct U.S. cities—Chicago, San Francisco, and Phoenix—each characterized by unique climate and urban planning features. This study investigates the relationship between atmospheric pollutants (SO2, NO2, CO, O3) and the Normalized Difference Vegetation Index (NDVI) with land surface temperature (LST) using regression and correlation analyses. The analysis aims to elucidate how changes in atmospheric pollutants and NDVI affect variations in land surface temperature. Regression analysis is employed to estimate the coefficients of independent variables and quantify their impact on LST. Correlation analysis assesses the linear relationships between variables, providing insights into their pairwise associations. The study also examines multicollinearity between independent variables to identify potential confounding factors. The results reveal significant associations between atmospheric pollutants, NDVI, and land surface temperature, contributing to our understanding of the environmental factors influencing LST dynamics and informing climate change mitigation strategies. The observed inconsistencies in correlations across cities highlight the importance of the local context in environmental studies. Understanding these variations can aid in developing tailored urban planning policies that consider unique city characteristics for more effective climate resilience. Furthermore, a positive association was consistently obtained between pollutants and LST, indicating that increased pollution levels contribute to higher surface temperatures across different urban settings.

Drought may considerably decrease the growth and yield of potatoes. Small tubers may have lower performance and be more sensitive to abiotic stresses than larger tubers. Since an increase in drought areas may be expected, the development of potato varieties with drought tolerance has become necessary. Two-year greenhouse experiments were conducted to test the drought tolerance of potato breeding lines (C103, C107, C20) with great osmotic stress tolerance. Minitubers with diameters of 25–35, 20–24, 15–19 and 10–14 mm were planted. Treatments were the optimal irrigated control (100%) and moderate and severe drought (60% and 20% of optimum water supply). To study the after-effects of drought, tubers from different treatments were planted separately the following year because seed tuber priming may increase drought tolerance. Seed tubers (25–35 mm), two irrigation treatments (control and severe drought), and two control cultivars were used in the second year. We observed the rate of emergence from day-after-planting (DAP) 20 to 30 and flowering from 48 to 54. NDVI measurements were performed on the DAP35-45-75. Plant height and fresh weight of aboveground biomass (AGB) were recorded on DAP76. Harvested tubers were counted, weighed, and size-categorized, and then the number and fresh tuber yield per plant (TN and TY) were calculated. Stress indices (SI) were calculated as percentages of the results of control plots to compare the responses of genotypes to drought stress. We found that each breeding line showed adequate drought tolerance, although only the C103 and C107 breeding lines were stable in in vivo conditions. SI values for tuber number/tuber yield were 103/57; 102/63; 83/52; 80/58 and 55/41 in C103, C107, C20, ‘Boglárka’ and ‘Desiree’ (the last two were control varieties), respectively. The size of the seed tuber significantly affected each character, and usually minitubers larger than 20 mm performed better than smaller ones. No significant after-effect of drought stress on the next generation was found. Although we found a positive correlation (r = 0.83) between NDVI values and yield parameters, the correlations in our study were not consistent in all genotypes and water treatments.

In urban settings, trees and greenery play a vital role in environmental well-being and community vitality. This study explores the impact of Miami-Dade County’s tree-planting initiative on urban greenness and considers the influence of climate dynamics. Using Landsat data from 2006 to 2019, we find stable overall greenness, with 5.64% of the Census blocks exhibiting significant changes. Seasonal analysis reveals winter as prominent, with 61.47% of Census blocks showing increased greenness. Temperature and precipitation, especially post-2010, correlate with greenness changes. Despite a reported increase in tree cover from 14% to 20%, our findings show only 5–6% of Census blocks with statistically significant changes, highlighting the complexity of achieving substantial improvements in green canopy coverage. The study raises questions about the efficacy of large-scale tree-planting initiatives in densely urbanized areas when human factors are not well understood. Implications for urban planning stress the importance of preserving green spaces and informed decision-making for enhancing vegetation cover in Miami-Dade County, emphasizing the need to consider local conditions, seasonal variations, policies, and human factors in urban greening efforts.

As vegetation plays a critical role in terrestrial ecosystems, understanding its status and variation is vital for preserving the stability of an ecosystem. Central Asia serves as a representative example of an arid and semi-arid region characterized by sparse vegetation and poor soils, making its vegetation particularly fragile and sensitive. To investigate the vegetation condition in the region, this study examined the spatial and temporal characteristics of vegetation variation from 2001 to 2020, utilizing the normalized difference vegetation index (NDVI) as an indicator. Meanwhile, trend analysis, Mann–Kendall abrupt change point test, geodetector, and correlation analysis were used to quantitatively analyze the natural and anthropogenic drivers of these variations over the past two decades. The results suggest that vegetation coverage in Central Asia was relatively low, with an annual average NDVI of 0.16 over the past 20 years. Moreover, the spatial distribution of NDVI in Central Asia exhibited significant spatial heterogeneity, with vegetation coverage declining from north to south and from east to west. Furthermore, the NDVI exhibited a slightly increasing trend during the period of 2001 to 2020 with an increased rate of 0.00025/yr. However, we detected an abrupt change point in vegetation dynamics in Central Asia around 2010, which indicated a significant shift in vegetation variation in the region. Land-use type has a great influence on the spatial heterogeneity of NDVI in Central Asia, which can explain 46% of the vegetation distribution in this region. Moisture factors such as precipitation and soil water content followed with 35% and 32% contributions, respectively. Regarding the temporal variation of NDVI, it is mainly driven by the fluctuation in precipitation, with the degree of influence of precipitation on NDVI varying for different regions in various geographical conditions. This study offers a more comprehensive insight into the spatial and temporal dynamics of NDVI in Central Asia and indicates that precipitation plays a significant role in driving the spatial heterogeneity and temporal variation of NDVI. These findings are essential for predicting vegetation changes in arid regions under future environmental conditions and formulating effective strategies to prevent and alleviate vegetation degradation.

Precipitation is a critical driver of vegetation productivity and dynamics in dryland environments, especially in areas with intense livestock farming. Availability and access to accurate, reliable, and timely rainfall data are essential for natural resources management, environmental monitoring, and informing hydrological rainfall-runoff models. Gauged precipitation data in drylands are often scarce, fragmented, and with low spatial resolution; therefore, satellite-estimated precipitation becomes a valuable dataset for overcoming this constraint. Using statistical indices, we compared satellite-derived precipitation data from four products (CHIRPS, GPM, TRMM, and PERSIANN-CDR) against gauged data at different temporal scales (daily, monthly, and yearly). Spatial correlations were calculated for GPM and CHIRPS estimates against interpolated gauged precipitation. We then estimated NDVI response to Antecedent Accumulated Precipitation (AAP) for 1, 3, 6, 9, and 12 months of four major vegetation types typical of the region. Statistical metrics varied with temporal scales being highest and acceptable for periods of 1 month or 1 year. At monthly scale GPM presented the best Pearson’s Correlation Coefficient (r), Root Mean Square Error (RMSE) and RMSE-observations standard deviation ratio (RSR) and CHIRPS resulted in lower Mean Error (ME) and Bias. On an annual basis CHIRPS showed the best adjustment for all indicators except for r. NDVI responses to 3 months of AAP were significant for all vegetation types in the study area. The findings of this study show that estimated precipitation data from GPM and CHIRPS satellites are accurate and valuable as a tool for analysing the relationships between precipitation and vegetation in the drylands of Mendoza.

Drought causes significant losses in vegetation net primary productivity (NPP). However, the lack of real-time, large-scale NPP data poses challenges in analyzing the relationship between drought and NPP. Solar-induced chlorophyll fluorescence (SIF) offers a real-time approach to monitoring drought-induced NPP dynamics. Using two drought events in the Huang–Huai–Hai Plain from 2010 to 2020 as examples, we propose a new SIF/NPP ratio index to quantify and evaluate SIF’s capability in monitoring drought-induced NPP dynamics. The findings reveal distinct seasonal changes in the SIF/NPP ratio across different drought events, intensities, and time scales. SIF demonstrates high sensitivity to commonly used vegetation greenness parameters for NPP estimation (R2 > 0.8, p < 0.01 for SIF vs NDVI and SIF vs LAI), as well as moderate sensitivity to land surface temperature (LST) and a fraction of absorbed photosynthetically active radiation (FAPAR) (R2 > 0.5, p < 0.01 for SIF vs FAPAR and R2 > 0.6, p < 0.01 for SIF vs LST). However, SIF shows limited sensitivity to precipitation (PRE). Our study suggests that SIF has potential for monitoring drought-induced NPP dynamics, offering a new approach for real-time monitoring and enhancing understanding of the drought–vegetation productivity relationship.

Walnut (Juglans regia) planting is the main economic pillar industry in southern Xinjiang. Based on the Google Earth Engine (GEE) cloud platform, the NDVI maximum synthesis method was used to estimate changes in the walnut cultivation area in Ganquan Town, South Xinjiang, from 2017 to 2021. The simultaneous difference between NDVI and meteorological conditions was also used to monitor the growth and correlation analysis of walnuts from April to September 2021. To improve the classification accuracy of the extracted walnut plantation area, Sentinel-2 image data were selected, and features were trained using the random forest algorithm, and by combining topographic features, texture features, NDVI, and EVI. The results show that, compared with Statistical Yearbook data, the average error of the extracted walnut planted area is less than 10%, the overall classification accuracy is 92.828%, the average kappa coefficient is 90.344%, and the average walnut classification accuracy is 94.4%. The accuracy of the data was significantly improved by adding vegetation indices EVI and NDVI compared with the single vegetation index. An analysis of the results from monitoring comparative growth shows that the growth of walnuts in Ganquan was better during the hardcore and oil transformation stages compared with 2020, and in the fruit development stage, the growth was the same as in 2020, and overall, the growth of walnuts in 2021 was better than in previous years.

Frequent droughts may have negative influences on the ecosystem (i.e., terrestrial vegetation) under a warming climate condition. In this study, the linear regression method was first used to analyze trends in vegetation change (normalized difference vegetation index (NDVI)) and drought indices (Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI)). The Pearson Correlation analysis was then used to quantify drought impacts on terrestrial vegetation in the Weihe River Basin (WRB); in particular, the response time of vegetation to multiple time scales of drought (RTVD) in the WRB was also investigated. The trend analysis results indicated that 89.77% of the area of the basin showed a significant increasing trend in NDVI from 2000 to 2019. There were also significant variations in NDVI during the year, with the highest rate in June (0.01) and the lowest rate in January (0.002). From 2000 to 2019, SPI and SPEI at different time scales in the WRB showed an overall increasing trend, which indicated that the drought was alleviated. The results of correlation analysis showed that the response time of vegetation to drought in the WRB from 2000 to 2019 was significantly spatially heterogeneous. For NDVI to SPEI, the response time of 12 months was widely distributed in the north; however, the response time of 24 months was mainly distributed in the middle basin. The response time of NDVI to SPI was short and was mainly concentrated at 3 and 6 months; in detail, the response time of 3 months was mainly distributed in the east, while a response time of 6 months was widely distributed in the west. In autumn and winter, the response time of NDVI to SPEI was longer (12 and 24 months), while the response time of NDVI to SPI was shorter (3 months). From the maximum correlation coefficient, the response of grassland to drought (SPEI and SPI) at different time scales (i.e., 6, 12, and 24 months) was higher than that of cultivated land, forestland, and artificial surface. The results may help improve our understanding of the impacts of climatic changes on vegetation cover.

The Lluta River is the northernmost coastal wetland in Chile, representing a unique ecosystem and an important source of water in the extremely arid Atacama Desert. During peak season, the wetland is home to more than 150 species of wild birds and is the first stopover point for many migratory species that arrive in the country along the Pacific migratory route, thereby representing a priority site for avian influenza virus (AIV) surveillance in Chile. The aim of this study was to determine the prevalence of influenza A virus (IAV) in the Lluta River wetland, identify subtype diversity, and evaluate ecological and environmental factors that drive the prevalence at the study site. The wetland was studied and sampled from September 2015 to October 2020. In each visit, fresh fecal samples of wild birds were collected for IAV detection by real-time RT-PCR. Furthermore, a count of wild birds present at the site was performed and environmental variables, such as temperature, rainfall, vegetation coverage (Normalized Difference Vegetation Index—NDVI), and water body size were determined. A generalized linear mixed model (GLMM) was built to assess the association between AIV prevalence and explanatory variables. Influenza positive samples were sequenced, and the host species was determined by barcoding. Of the 4349 samples screened during the study period, overall prevalence in the wetland was 2.07% (95% CI: 1.68 to 2.55) and monthly prevalence of AIV ranged widely from 0% to 8.6%. Several hemagglutinin (HA) and neuraminidase (NA) subtypes were identified, and 10 viruses were isolated and sequenced, including low pathogenic H5, H7, and H9 strains. In addition, several reservoir species were recognized (both migratory and resident birds), including the newly identified host Chilean flamingo (Phoenicopterus chilensis). Regarding environmental variables, prevalence of AIV was positively associated with NDVI (OR = 3.65, p < 0.05) and with the abundance of migratory birds (OR = 3.57, p < 0.05). These results emphasize the importance of the Lluta wetland as a gateway to Chile for viruses that come from the Northern Hemisphere and contribute to the understanding of AIV ecological drivers.

Several earth science investigations depend heavily on knowing the surface energy budget and determining surface temperature. The primary factor affecting the energy balance in the surface physical processes of the planet is the land surface temperature (LST). Even in the case of small-scale green areas like local parks, plants have a significant impact on the climate of cities. The goal of this study was to estimate the construction-related impacts of the Karizland green town (green belt) on the LST of its surroundings over time, for the years 2013 (before construction began), 2015, 2020 and 2022 (after construction was completed). LST values and hot spot analyses were employed for thermal condition evaluation purposes on Landsat-8 satellite images, and normalized difference vegetation index (NDVI) and fractional vegetation cover (FVC) indices were used for examining the vegetation change. The results showed that after the establishment of the green town, the mean NDVI and FVC grew by 275% and 950%, respectively, compared to the initial period, which resulted in the addition of approximately 208.35 ha of green space to the study area. In this regard, the results showed that after these changes, compared to the first period, the mean LST decreased by 8%. In addition, the area of the class of hotspot analysis with less than 90% confidence increased by 9%. The results illustrated that almost 20% of the data in the LST range was below 55 °C in 2013, near 57 °C in 2015, and around 51 °C in 2020 and 2022. The results also showed a negative relationship between the distance from the established settlement and the values of NDVI and FVC in 2022 of 91% and 89% and in 2020 of 67% and 69%, respectively. Every year, LST has had a significant negative relationship with the NDVI and FVC of that year and a positive relationship with the LST of the following years, such that the correlation decreases in later years. In order to control LST and the temperature surrounding cities, this research strongly advises managers to develop these green towns.

Vegetation indices provide information for various precision-agriculture practices, by providing quantitative data about crop growth and health. To provide a concise and up-to-date review of vegetation indices in precision agriculture, this study focused on the major vegetation indices with the criterion of their frequency in scientific papers indexed in the Web of Science Core Collection (WoSCC) since 2000. Based on the scientific papers with the topic of “precision agriculture” combined with “vegetation index”, this study found that the United States and China are global leaders in total precision-agriculture research and the application of vegetation indices, while the analysis adjusted for the country area showed much more homogenous global development of vegetation indices in precision agriculture. Among these studies, vegetation indices based on the multispectral sensor are much more frequently adopted in scientific studies than their low-cost alternatives based on the RGB sensor. The normalized difference vegetation index (NDVI) was determined as the dominant vegetation index, with a total of 2200 studies since the year 2000. With the existence of vegetation indices that improved the shortcomings of NDVI, such as enhanced vegetation index (EVI) and soil-adjusted vegetation index (SAVI), this study recognized their potential for enabling superior results to those of NDVI in future studies.

Precision agriculture (PA) practices based on the use of sensors and vegetation indices have great potential for optimizing herbicide use and improving weed management in field crops. The objective of this research was to evaluate the efficacy of commercial herbicide products and their selectivity in durum wheat by measuring the Normalized Difference Vegetation Index (NDVI). Field trials were conducted in Velestino and Kozani, Greece (2020–2021 and 2021–2022) in four site-years with the following treatment list: untreated control (T1), 2,4-D at 300 and 600 g a.e. ha−1 (T2 and T3, respectively), pyroxsulam + florasulam at 18.82 + 3.71 g a.i. ha−1 + cloquintocet-mexyl at 18.82 g a.i. ha−1 (T4), and mesosulfuron-methyl + iodosulfuron-methyl-sodium at 15 + 3 g a.i. ha−1 + mefenpyr-diethyl at 45 g a.i. ha−1 (T5). Site-years and treatments affected weed NDVI, weed biomass, crop NDVI, and grain yield (p ≤ 0.05). At Kozani, weed NDVI was lowest in T4 plots in 2020–2021 (0.31) and 2021–2022 (0.33). Treatments T4 and T5 resulted in lowest weed biomass in 2020-2021 (14–16 g m−2) and 2020-2021 (19–22 g m−2). At Velestino, T3 reduced weed biomass by 92 and 87% when compared to T5 in 2020–2021 and 2021–2022, respectively. Approximately, 67% and 73% of the variability in weed biomass in 2020–2021 and 2021–2022, respectively, at Kozani could be explained by weed NDVI. These parameters were strongly correlated in Velestino (R2 ≥ 90%). Low crop NDVI at Kozani indicated herbicide injury in T3 plots, confirmed by yield losses. During 2020-2021, yield was 30, 38, and 40% higher in T4 plots than in T2, T1, and T3 plots, respectively. At Velestino, yield in T1 plots was 25, 27, 27, and 29% lower than in T2, T4, T5, and T3 plots, respectively, in 2020–2021. Similar results were obtained in 2021–2022. The current study indicates that NDVI can be used as a reliable, non-subjective indicator of herbicide efficacy and selectivity in winter cereals. The methodology used in this work should also be evaluated in other crops and under different soil and climatic conditions.

Revealing the spatial dynamics of vegetation change in Chongqing and their driving mechanisms is of major value to regional ecological management and conservation. Using several data sets, including the SPOT Normalized Difference Vegetation Index (NDVI), meteorological, soil, digital elevation model (DEM), human population density and others, combined with trend analysis, stability analysis, and geographic detectors, we studied the pattern of temporal and spatial variation in the NDVI and its stability across Chongqing from 2000 to 2019, and quantitatively analyzed the relative contribution of 18 drivers (natural or human variables) that could influence vegetation dynamics. Over the 20-year period, we found that Chongqing region’s NDVI had an annual average value of 0.78, and is greater than 0.7 for 93.52% of its total area. Overall, the NDVI increased at a rate of 0.05/10 year, with 81.67% of the areas undergoing significant expansion, primarily in the metropolitan areas of Chongqing’s Three Gorges Reservoir Area (TGR) and Wuling Mountain Area (WMA). The main factors influencing vegetation change were human activities, climate, and topography, for which the most influential variables respectively were night light brightness (NLB, 51.9%), annual average air temperature (TEM, 47%), and elevation (ELE, 44.4%). Furthermore, we found that interactions between differing types of factors were stronger than those arising between similar ones; of all pairwise interaction types tested, 92.9% of them were characterized by two-factor enhancement. The three most powerful interactions detected were those for NLB ∩ TEM (62.7%), NLB ∩ annual average atmospheric pressure (PRS, 62.7%), and NLB ∩ ELE (61.9%). Further, we identified the most appropriate kind or range of key elements shaping vegetation development and dynamics. Altogether, our findings can serve as a timely scientific foundation for developing a vegetative resource management strategy for the Yangtze River basin that duly takes into account local climate, terrain, and human activity.

Alfalfa (Medicago sativa L.) is one of the most widely planted forages due to its useful characteristics. Although alfalfa spatial distribution is an important source of basic data, manual surveys incur high survey costs, require large workloads and confront difficulties in collecting data over large areas; remote sensing compensates for these shortcomings. In this study, the time-series variation characteristics of different vegetation types were analyzed, and the extraction method of alfalfa mapping was established according to different spatial- and temporal-resolution remote sensing data. The results provided the following conclusions: (1) when using the wave peak and valley number of normalized difference vegetation index (NDVI) curves, in the study area, the number of wave peak needed to be greater than 2 and the number of wave valley needed to be greater than 1; (2) 91.6% of alfalfa sampling points were extracted by moderate resolution imaging spectroradiometer (MODIS) data using the wave peak and valley method, and 5.0% of oats sampling points were extracted as alfalfa, while no other vegetation types met these conditions; (3) 85.3% of alfalfa sampling points were identified from Sentinel-2 multispectral instrument (MSI) data using the wave peak and valley method; 6.0% of grassland vegetation and 8.7% of oats satisfied the conditions, while other vegetation types did not satisfy this rule; and (4) the temporal phase selection was very important for alfalfa extraction using single-time phase remote sensing images; alfalfa was easily separated from other vegetation at the pre−wintering stage and was more difficult to separate at the spring regreening stage due to the variability in the alfalfa overwintering rate; the overall classification accuracy was 92.9% with the supervised classification method using support vector machine (SVM) at the pre-wintering stage. These findings provide a promising approach to alfalfa mapping using different remote sensing data.

The grassland condition in Qinghai Province has changed dramatically after implementation of the Grassland Ecological Compensation Policy (GECP). The changes in grassland Normalized Difference Vegetation Index (NDVI) and those in grassland area and grassland Remote Sensing Ecological Index (RESI) reveal that the policy has indeed played a crucial role in grassland ecological recovery. Although in the first round of policy implementation from 2011–2015, the quality of grassland has not improved significantly, and degradation even occurred in some areas, the second round of the policy implementation reversed this trend. As the world’s only ecological subsidy policy for grassland ecosystems, it should continue to be implemented and monitored for better recovery of grassland ecology and production activities of herders that are concerned.

The current study aimed to evaluate the potential of the normalized difference vegetation index (NDVI), and the normalized difference yellowness index (NDYI) derived from red–green–blue (RGB) imaging to monitor the growth status of winter oilseed rape from seeding to the ripening stage. Subsequently, collected values were used to evaluate their correlations with the yield of oilseed rape. Field trials with three seed densities and three nitrogen rates were conducted for two years in Salzdahlum, Germany. The images were rapidly taken by an unmanned aerial vehicle carrying a Micasense Altum multi-spectral camera at 25 m altitudes. The NDVI and NDYI values for each plot were calculated from the reflectance at RGB and near-infrared (NIR) bands’ wavelengths pictured in a reconstructed and segmented ortho-mosaic. The findings support the potential of phenotyping data derived from NDVI and NDYI time series for precise oilseed rape phenological monitoring with all growth stages, such as the seedling stage and crop growth before winter, the formation of side shoots and stem elongation after winter, the flowering stage, maturity, ripening, and senescence stages according to the crop calendar. However, in comparing the correlation results between NDVI and NDYI with the final yield, the NDVI values turn out to be more reliable than the NDYI for the real-time remote sensing monitoring of winter oilseed rape growth in the whole season in the study area. In contrast, the correlation between NDYI and the yield revealed that the NDYI value is more suitable for monitoring oilseed rape genotypes during flowering stages.

The knowledge of soil apparent thermal diffusivity (k) is important for investigating soil surface heat transfer and temperature. Long-term k determined using the near-surface soil temperature is limited on the Tibetan Plateau (TP). The main objective of this study is to determine k with a conduction–convection method using the near-surface soil temperature measured at three sites during 2014–2016 on the TP. The hourly, daily, and monthly k values of the 0.0 m to 0.20 m layer were obtained. The hourly and daily k values ranged from 0.3 × 10−6 m2 s−1 to 1.9 × 10−6 m2 s−1 at the wet site, and from 1.0 × 10−7 m2 s−1 to 4.0 × 10−7 m2 s−1 at the two dry sites. For the monthly timescale, k ranged from 0.4 (±0.0) × 10−6 m2 s−1 to 1.1 (±0.2) × 10−6 m2 s−1 at the wet site, and varied between 1.7 (±0.0) × 10−7 m2 s−1 and 3.3 (±0.2) × 10−7 m2 s−1 at the two dry sites. The k was not constant over a day, and it varied seasonally to different degrees at different sites and years. The variation of k with soil moisture (θ) appeared to be roughly similar for unfrozen soil at these sites and years, namely, k increased sharply before reaching the peak as θ increased, and then it tended to be stable or varied slightly with further increases in θ. This variation trend was consistent with previous studies. However, the relationship between k and θ changed when soil temperature was below 0 °C, because ice had higher k than water. The correlation coefficients (r) between k and θ ranged from 0.37 to 0.80, and 0.80 to 0.92 on hourly and monthly timescales, respectively. The monthly and annual k values were significantly correlated (r: 0.73~0.93) to the Normalized Difference Vegetation Index (NDVI). The results broaden our understanding of the relationship between in situ k and θ. The presented values of k at various timescales can be used as soil parameters when modeling land–atmosphere interactions at these TP regions.

Vegetation is the main component of the terrestrial Earth, and it plays an imperative role in carbon cycle regulation and surface water/energy exchange/balance. The coupled effects of climate change and anthropogenic forcing have undoubtfully impacted the vegetation cover in linear/non-linear manners. Considering the essential benefits of vegetation to the environment, it is vital to investigate the vegetation dynamics through spatially and temporally consistent workflows. In this regard, remote sensing, especially Normalized Difference Vegetation Index (NDVI), has offered a reliable data source for vegetation monitoring and trend analysis. In this paper, two decades (2000 to 2020) of Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI datasets (MOD13Q1) were used for vegetation trend analysis throughout Iran. First, the per-pixel annual NDVI dataset was prepared using the Google Earth Engine (GEE) by averaging all available NDVI values within the growing season and was then fed into the PolyTrend algorithm for linear/non-linear trend identification. In total, nearly 14 million pixels (44% of Iran) were subjected to trend analysis, and the results indicated a higher rate of greening than browning across the country. Regarding the trend types, linear was the dominant trend type with 14%, followed by concealed (11%), cubic (8%), and quadratic (2%), while 9% of the vegetation area remained stable (no trend). Both positive and negative directions were observed in all trend types, with the slope magnitudes ranging between −0.048 and 0.047 (NDVI units) per year. Later, precipitation and land cover datasets were employed to further investigate the vegetation dynamics. The correlation coefficient between precipitation and vegetation (NDVI) was 0.54 based on all corresponding observations (n = 1785). The comparison between vegetation and precipitation trends revealed matched trend directions in 60% of cases, suggesting the potential impact of precipitation dynamics on vegetation covers. Further incorporation of land cover data showed that grassland areas experienced significant dynamics with the highest proportion compared to other vegetation land cover types. Moreover, forest and cropland had the highest positive and negative trend direction proportions. Finally, independent (from trend analysis) sources were used to examine the vegetation dynamics (greening/browning) from other perspectives, confirming Iran’s greening process and agreeing with the trend analysis results. It is believed that the results could support achieving Sustainable Development Goals (SDGs) by serving as an initial stage study for establishing conservation and restoration practices.

In recent decades, vegetation coverage and land use/land cover (LULC) have constantly changed, especially in southwest China. Therefore, it is necessary to conduct in-depth research into the temporal–spatial variation patterns of vegetation greening, LULC, and gross primary productivity (GPP). Here, we used remote sensing to analyze the spatial and temporal variation in the normalized difference vegetation index (NDVI) and GPP in the growing season under different LULCs in southwest China. Results showed: (1) From 2000–2019, the forest area in southwest China had increased by 2.1%, while the area of cropland and grassland had decreased by 3.2% and 5.5%, respectively. Furthermore, there are significant differences in spatial variation patterns. (2) NDVI and GPP in the growing season showed a general increasing trend (p < 0.01); vegetation coverage is dominated by high coverage to highest coverage and medium coverage to high coverage transfer. (3) Under different LULCs, the migration directions of NDVI and GPP were different. The center of gravity migration of highest and medium coverage shifted to the southeast by 1.69° and to the northwest by 1.81°, respectively. The results showed the ecosystem evolution and will help to guide the maintenance measure of ecosystem balance and sustainable development.

In terrestrial ecosystems, vegetation is sensitive to climate change and human activities. Its spatial-temporal changes also affect the ecological and social environment. In this paper, we considered the Beijing–Tianjin–Hebei region to study the spatio-temporal vegetation patterns. The detailed analysis of a moderate-resolution imaging spectroradiometer (MODIS) data were carried out through the Google Earth Engine (GEE) platform. Our results show a slow and tortuous upward trend in the average leaf area index (LAI) in the study region for the periods 2001–2020. Specifically, Beijing had the highest LAI value, with an average of 1.64 over twenty years, followed by Hebei (1.30) and Tianjin (1.04). Among different vegetation types, forests had the highest normalized difference vegetation index (NDVI) with the range of 0.62–0.78, followed by shrubland (0.58–0.75), grassland (0.34–0.66), and cropland (0.38–0.54) over the years. Spatially, compared to the whole study area, index value in the northwestern part of the Beijing–Tianjin–Hebei region increased greatly in many areas, such as northwest Beijing, Chengde, and Zhangjiakou, indicating a significant ecological optimization. Meanwhile, there was ecological degradation in the middle and southeast regions, from Tangshan southeastward to Handan, crossing Tianjin, Langfang, the east part of Baoding, Shijiazhuang, and the west part of Cangzhou. Air temperature and precipitation were positively and significantly correlated with net primary production (NPP) and precipitation stood out as a key driver. Additionally, an intensification of the urbanization rate will negatively impact the vegetation NPP, with the shrubland and forest being affected most relative to the cropland.

Remotely sensed land surface temperature (LST) is often used as a proxy for air temperature in urban heat island studies, particularly to illustrate relative temperature differences between locations. Two sensors are used predominantly in the literature, Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS). However, each has shortcomings that currently limit its utility for many urban applications. Landsat has high spatial resolution but low temporal resolution, and may miss hot days, while MODIS has high temporal resolution but low spatial resolution, which is inadequate to represent the fine grain heterogeneity in cities. In this paper, we overcome this inadequacy by combining high spatial frequency Environmental Services (ES), Landsat-driven Normalized Difference Vegetation Index (NDVI), and MODIS low spatial frequency background LST at different spatial frequency bands (spatial spectral composition). The method is able to provide fine scale LST four times daily on any day of the year. Using data from Paris in 2019 we show that (1) daytime cooling by vegetation reaches a maximum of 30 °C, above which there is no further increase in cooling. In addition, (2) the cooling is relatively local and does not extend further than 200 m beyond the boundary of the NBS. This model can be used to quantify the benefits of NBS in providing cooling in cities.

Vegetation in the terrestrial ecosystem, sensitive to climate change and human activities, exerts a crucial influence on the carbon cycles in land, ocean, and atmosphere. Discrimination between climate and human-induced vegetation dynamics is advocated but still limited, especially in coastal China, which is characterized by a developed economy, a large population, and high food production, but also by unprecedented climate change and warming. Taking coastal China as the research area, our study used the normalized difference vegetation index (NDVI) in growing seasons, as well as precipitation, temperature, and sunlight hours datasets, adopted residual trend analysis at pixel and regional scales in coastal China from 2000–2019 and aims to (1) delineate the patterns and processes of vegetation changes, and (2) separate the relative contributions of climate and human activities by adopting residual trend analysis. The results indicated that (1) coastal China experienced the most vegetation greening (83.04% of the whole region) and partial degradation (16.86% of the whole region) with significant spatial heterogeneity; (2) compared with climate change, human activities have a greater positive impact on NDVI, and the regions were mainly located in the north of the North China Plain and the south of southern China; (3) the relative contribution rates of climate change and human activities were detected to be 0–60% and 60–100%, respectively; (4) in the northern coastal areas, the improvement of cultivated land management greatly promoted the greening of vegetation and thus the increase of grain yield, while in southern coastal areas, afforestation and the restoration of degraded forest were responsible for vegetation restoration; and (5) similar results obtained by partial correlation between nighttime lights and NDVI indicated the reliability of the residual trend analysis. The linear relationships of precipitation, temperature, and radiation on NDVI may limit the accurate estimation of climate drivers on vegetation, and further ecosystem process-modeling approaches can be used to estimate the relative contribution of climate change and human activities. The findings in our research emphasized that the attribution for vegetation dynamics with heterogeneity can provide evidence for the designation of rational ecological conservation policies.

The normalized difference vegetation index (NDVI) contains important data for providing vegetation-cover information and supporting environmental analyses. However, understanding long-term vegetation cover dynamics remains challenging due to data outliers that are found in cloudy regions. In this article, we propose a sliding-window-based tensor stream analysis algorithm (SWTSA) for reconstructing outliers in NDVI from multitemporal optical remote-sensing images. First, we constructed a tensor stream of NDVI that was calculated from clear-sky optical remote-sensing images corresponding to seasons on the basis of the acquired date. Second, we conducted tensor decomposition and reconstruction by SWTSA. Landsat series remote-sensing images were used in experiments to demonstrate the applicability of the SWTSA. Experiments were carried out successfully on the basis of data from the estuary area of Salween River in Southeast Asia. Compared with random forest regression (RFR), SWTSA has higher accuracy and better reconstruction capabilities. Results show that SWTSA is reliable and suitable for reconstructing outliers of NDVI from multitemporal optical remote-sensing images.

Background: Green spaces reduce the risk of multiple adverse health outcomes by encouraging physical activity. This study examined correlations between urban green space and residents’ health outcomes in hilly neighborhoods: if they are mediated by social cohesion, visual aesthetics, and safety. Methods: We used multiple green space indicators, including normalized difference vegetation index (NDVI) extracted from satellite imagery, green view index (GVI) obtained from street view data using deep learning methods, park availability, and perceived level of greenery. Hilly terrain was assessed by the standard deviation of the elevation to represent variations in slope. Resident health outcomes were quantified by their psychological and physiological health as well as physical activity. Communities were grouped by quartiles of slopes. Then a mediation model was applied, controlling for socio-demographic factors. Results: Residents who perceived higher quality greenery experienced stronger social cohesion, spent more time on physical activity and had better mental health outcomes. The objective greenery indicators were not always associated with physical activity and might have a negative influence with certain terrain. Conclusions: Perceived green space offers an alternative explanation of the effects on physical activity and mental health in hilly neighborhoods. In some circumstances, geographical environment features should be accounted for to determine the association of green space and resident health outcomes.

As a unique ecosystem with multiple ecological functions but high fragility, grassland in arid areas is very vulnerable to changes in the natural environment or human activities, resulting in various ecological and environmental problems. In order to study the degree and spatial extent of the influence of climatic conditions and human activities, especially mining activities, on grasslands in arid regions, we used remote sensing data to monitor the vegetation of the Xilin Gol grassland over a long period. The significant greening and browning areas of Xilin Gol grassland vegetation from 2000 to 2020 were extracted by a time series analysis. At the same time, the correlation analysis method was used to obtain the response of the Xilin Gol grassland vegetation to climatic factors and social and economic factors. In addition, we propose a new method based on buffer analysis and correlation analysis to calculate the influence range of vegetation degradation due to mining. We used this method to determine the influence range of vegetation degradation in the main mining area of the Xilin Gol grassland. The results showed that the vegetation condition of the Xilin Gol grassland were slightly improved from 2000 to 2020. Its vegetation was significantly affected by precipitation, and more than 50% of the area’s vegetation changes were highly correlated with precipitation changes. However, the area with the most serious vegetation degradation was mainly affected by human factors, and this part accounted for about 0.13% of the total area. In the form of direct damage and indirect effects (pulling population and economic growth to expand built-up areas), coal mining has become the main driving factor in the most significant areas of vegetation damage in the study area. Vegetation coverage in areas with significant greening and significant browning was highly correlated with economic factors, indicating that the vegetation changes were significantly affected by economic development. This study can reflect the vegetation changes and main driving factors in the overall and key areas of the Xilin Gol League and is a meaningful reference for the local balance of economic development and environmental protection.

The quantitative and qualitative assessment of post-disaster vegetation damage and recovery in the core area of the Wenchuan earthquake is of great significance for the restoration and reconstruction of natural ecosystems and the construction of human settlements in China. This study used time series analysis to determine the time of MODIS data and used the data to study the vegetation damage and restoration in the core area of the Wenchuan earthquake. The determined MODIS images were used to quantitatively analyze a series of vegetation damage changes and the vegetation recovery rate in the core area of the Wenchuan earthquake before and after the earthquake. By applying the topographic factors, we analyzed the spatial and temporal characteristics of the dynamic changes of vegetation damage and the recovery rate in the disaster area. The results show that the study area’s vegetation damage was correlated to topographic factors and distance from towns. Besides, the overall vegetation restoration after the disaster was relatively optimistic. In some areas, the vegetation restoration level even exceeded the vegetation coverage level before the disaster. The recovery study of MODIS-NDVI showed a specific lag delay effect on the image of vegetation cover. The vegetation damage and the recovery rate of vegetation cover were significantly correlated with the distance from towns and the topographic factor. Overall, the results contribute to the theoretical support for the damage and recovery of vegetation in the core area affected by the earthquake.

Crop growth monitoring is an important component of agricultural information, and suitable soil temperature (ST), soil moisture content (SMC) and soil electrical conductivity (SEC) play a key role in crop growth. Real-time monitoring of the three soil parameters to predict the growth of tea plantation helps tea trees grow healthily and to accurately grasp the growth trend of tea trees. In this paper, five different models based on the polynomial model and power model were used to construct the soil temperature, soil water content and soil conductivity and tea plantation growth monitoring models. Experiments proved that tea plantation growth were positively correlated with ST and negatively correlated with SMC and SEC, and among the constructed models, the ternary cubic polynomial model was the best, and R square (R2) of the constructed models were 0.6369, 0.4510 and 0.5784, respectively, indicating that SEC was the most relevant to tea plantation growth maximum. To improve the prediction accuracy, a model based on sum of soil temperature (SST), sum of soil water content (SSMC) and sum of soil conductivity (SSEC) was proposed, and the experiments also showed that the ternary cubic polynomial model was the best, with 0.9638, 0.9733 and 0.9660, respectively. At the same time, a model incorporating three parameters such as soil temperature, soil water content and soil conductivity was also suggested, with 0.6605 and 0.9761, respectively, which effectively improved the prediction accuracy. Validation experiments were conducted. Twelve data sets were utilized to verify the performance of the model. The experiments showed that the regressions in the polynomial models achieved a better prediction effect. Finally, a long short-term memory (LSTM) network prediction model optimized by the bald eagle search algorithm (BES) was also constructed, and R2, root mean square error (RMSE), mean squared error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of prediction were 0.8666, 0.0629, 0.0040, 0.0436 and 10.5257, respectively, which significantly outperformed the LSTM network and achieved better performance. The model proposed in this paper can be used to predict the actual situation during the growing period of tea leaves, which can improve the production management of tea plantations and also provide a scientific basis for accurate tea planting and a decision basis for agricultural policy formulation, as well as provide technical support for the realization of agricultural modernization.

Wheat accounts for more than 50% of Australia’s total grain production. The capability to generate accurate in-season yield predictions is important across all components of the agricultural value chain. The literature on wheat yield prediction has motivated the need for more novel works evaluating machine learning techniques such as random forests (RF) at multiple scales. This research applied a Random Forest Regression (RFR) technique to build regional and local-scale yield prediction models at the pixel level for three southeast Australian wheat-growing paddocks, each located in Victoria (VIC), New South Wales (NSW) and South Australia (SA) using 2018 yield maps from data supplied by collaborating farmers. Time-series Normalized Difference Vegetation Index (NDVI) data derived from Planet’s high spatio-temporal resolution imagery, meteorological variables and yield data were used to train, test and validate the models at pixel level using Python libraries for (a) regional-scale three-paddock composite and (b) individual paddocks. The composite region-wide RF model prediction for the three paddocks performed well (R2 = 0.86, RMSE = 0.18 t ha−1). RF models for individual paddocks in VIC (R2 = 0.89, RMSE = 0.15 t ha−1) and NSW (R2 = 0.87, RMSE = 0.07 t ha−1) performed well, but moderate performance was seen for SA (R2 = 0.45, RMSE = 0.25 t ha−1). Generally, high values were underpredicted and low values overpredicted. This study demonstrated the feasibility of applying RF modeling on satellite imagery and yielded ‘big data’ for regional as well as local-scale yield prediction.

Future investment feasibility studies concerning post-mining repurposing utilities and economic transitions should focus on regional water resource management and the hydraulic protection of any utilities. Satellite images in different bands and Digital Elevation Models (DEM) of the Ptolemais basin were processed, leading to a more accurate estimation of the runoff ratio and percolation ratio. Furthermore, the saturated and unsaturated areas were delineated, leading to the recognition of potential artificial ground water recharge zones and zones where appropriate hydraulic protection measures are necessary.

The normalized difference vegetation index (NDVI) is an important agricultural parameter that is closely correlated with crop growth. In this study, a novel method combining the dynamic time warping (DTW) model and the long short-term memory (LSTM) deep recurrent neural network model was developed to predict the short and medium-term winter wheat NDVI. LSTM is well-suited for modelling long-term dependencies, but this method may be susceptible to overfitting. In contrast, DTW possesses good predictive ability and is less susceptible to overfitting. Therefore, by utilizing the combination of these two models, the prediction error caused by overfitting is reduced, thus improving the final prediction accuracy. The combined method proposed here utilizes the historical MODIS time series data with an 8-day time resolution from 2015 to 2020. First, fast Fourier transform (FFT) is used to decompose the time series into two parts. The first part reflects the inter-annual and seasonal variation characteristics of winter wheat NDVI, and the DTW model is applied for prediction. The second part reflects the short-term change characteristics of winter wheat NDVI, and the LSTM model is applied for prediction. Next, the results from both models are combined to produce a final prediction. A case study in Hebei Province that predicts the NDVI of winter wheat at five prediction horizons in the future indicates that the DTW–LSTM model proposed here outperforms the LSTM model according to multiple evaluation indicators. The results of this study suggest that the DTW–LSTM model is highly promising for short and medium-term NDVI prediction.

This study explored the long-term trends and breakpoints of vegetation, rainfall, and temperature in Taiwan from overall and regional perspectives in terms of vertical differences from 1982 to 2012. With time-series Advanced Very-High-Resolution Radiometer (AVHRR) normalized difference vegetation index (NDVI) data and Taiwan Climate Change Estimate and Information Platform (TCCIP) gridded monthly climatic data, their vertical dynamics were investigated by employing the Breaks for Additive Seasonal and Trend (BFAST) algorithm, Pearson’s correlation analysis, and the Durbin–Watson test. The vertical differences in NDVI values presented three breakpoints and a consistent trend from positive (1982 to 1989) to negative at varied rates, and then gradually increased after 2000. In addition, a positive rainfall trend was discovered. Average and maximum temperature had similar increasing trends, while minimum temperature showed variations, especially at higher altitudes. In terms of regional variations, the vegetation growth was stable in the north but worse in the central region. Higher elevations revealed larger variations in the NDVI and temperature datasets. NDVI, along with average and minimum temperature, showed their largest changes earlier in higher altitude areas. Specifically, the increasing minimum temperature direction was more prominent in the mid-to-high-altitude areas in the eastern and central regions. Seasonal variations were observed for each region. The difference between the dry and wet seasons is becoming larger, with the smallest difference in the northern region and the largest difference in the southern region. Taiwan’s NDVI and climatic factors have a significant negative correlation (p < 0.05), but the maximum and minimum temperatures have significant positive effects at low altitudes below 500 m. The northern and central regions reveal similar responses, while the south and east display different feedbacks. The results illuminate climate change evidence from assessment of the long-term dynamics of vegetation and climatic factors, providing valuable references for establishing correspondent climate-adaptive strategies in Taiwan.

The assessment of rice panicle initiation is crucial for the management of nitrogen fertilizer application that affects yield and quality of grain. The occurrence of panicle initiation could be determined via either green ring, internode-elongation, or a 1–2 mm panicle, and was observed through manual dissection. The quadratic polynomial regression model was used to construct the model of the trend of normalized difference vegetation index-based vegetation indexes (NDVI-based VIs) between pre-tillering and panicle differentiation stages. The slope of the quadratic polynomial regression model tended to be alleviated in the period in which the panicle initiation stage should occur. The results indicated that the trend of the NDVI-based VIs was correlated with panicle initiation. NDVI-based VIs could be a useful indicator to remotely assess panicle initiation.

In the Loess Plateau (LP) of China, the vegetation degradation and soil erosion problems have been shown to be curbed after the implementation of the Grain for Green program. In this study, the LP is divided into the northwestern semi-arid area and the southeastern semi-humid area using the 400 mm isohyet. The spatial–temporal evolution of the vegetation NDVI during 2000–2015 are analyzed, and the driving forces (including factors of climate, environment, and human activities) of the evolution are quantitatively identified using the geographical detector model (GDM). The results showed that the annual mean NDVI in the entire LP was 0.529, and it decreased from the semi-humid area (0.619) to the semi-arid area (0.346). The mean value of the coefficient of variation of the NDVI was 0.1406, and it increased from the semi-humid area (0.1165) to the semi-arid area (0.1926). The annual NDVI growth rate in the entire LP was 0.0079, with the NDVI growing faster in the semi-humid area (0.0093) than in the semi-arid area (0.0049). The largest increments of the NDVI were from grassland, farmland, and woodland. The GDM results revealed that changes in the spatial distribution of the NDVI could be primarily explained by the climatic and environmental factors in the semi-arid area, such as precipitation, soil type, and vegetation type, while the changes were mainly explained by the anthropogenic factors in the semi-humid area, such as the GDP density, land-use type, and population density. The interactive analysis showed that interactions between factors strengthened the impacts on the vegetation change compared with an individual factor. Furthermore, the ranges/types of factors suitable for vegetation growth were determined. The conclusions of this study have important implications for the formulation and implementation of ecological conservation and restoration strategies in different regions of the LP.

The study uses 30 years of the third generation of Advanced Very-High-Resolution Radiometer (AVHRR) NDVI3g monthly data from 1982 to 2012 to identify the natural clusters and important driving factors of the upstream watersheds in Taiwan through hierarchical cluster analysis (HCA) and redundancy analysis (RDA), respectively. Subsequently, as a result of HCA, six clusters were identified based on the 30 years of monthly NDVI data, delineating unique NDVI characteristics of the upstream watersheds. Additionally, based on the RDA results, environmental factors, including precipitation, temperature, slope, and aspect, can explain approximately 52% of the NDVI variance over the entire time series. Among environmental factors, nine factors were identified significantly through RDA analysis for explaining NDVI variance: average slope, temperature, flat slope, northeast-facing slope, rainfall, east-facing slope, southeast-facing slope, west-facing slope, and northwest-facing slope, which reflect an intimate connection between climatic and orthographic factors with vegetation. Furthermore, the rainfall and temperature represent different variations in all scenarios and seasons. With consideration of the characteristics of the clusters and significant environmental factors, corresponding climate change adaptation strategies are proposed for each cluster under climate change scenarios. Thus, the results provide insight to assess the natural clustering of the upstream watersheds in Taiwan, benefitting future sustainable watershed management.

Zhejiang province in China experienced an extreme climate phenomenon in August 2014 with temperature rises, sunshine duration decreases, and precipitation increases, particularly, the successive heavy rainfall events occurring from 16 to 20 August 2014 that contributed to this climate anomaly. This study investigates the spatial-temporal variation characteristics of precipitable water vapor (PWV) and the normalized difference vegetation index (NDVI) associated with this phenomenon. Multiple sources of PWV values derived from the Global Positioning System (GPS), Radiosonde (RS) and European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim data are used with different spatiotemporal resolutions. The monthly averaged PWV in August 2014 exceeded the 95% percentiles of climatological value (53 mm) while the monthly averaged temperature was less than the 5% percentiles of climatological value (26.6 °C). Before the extreme precipitation, the PWV increased from the yearly averaged value of about 35 mm to more than 60 mm and gradually returned to the August climatological average of 50 mm after the precipitation ended. A large-scale atmospheric water vapor was partially conveyed by the warm wet air current of anticyclones which originated over the South China Sea (25° N, 130° E) and the Western Pacific Ocean. The monthly NDVI variation over the past 34 years (1982–2015) was investigated in this paper and the significant impact of extreme climate on vegetation growth in August 2014 was found. The extreme negative temperature anomaly and positive PWV anomaly are the major climate-driven factors affecting vegetation growth in the north and south of Zhejiang province with correlation coefficients of 0.83 and 0.72, respectively, while the extreme precipitation does not show any apparent impact on NDVI.

Monitoring the impact of changes in land use/land cover (LULC) and land surface temperature (LST) is of great importance in environmental and urban studies. In this context, this study aimed to analyze the dynamics of LULC and its impact on the spatiotemporal variation of the LST in the two largest urban cities in Syria, Damascus, and Aleppo. To achieve this, LULC changes, normalized difference vegetation index (NDVI), and LST were calculated from multi-temporal Landsat data for the period 2010 to 2018. The study revealed significant changes in LULC, which were represented by a decrease in agricultural land and green areas and an increase in bare areas in both cities. In addition, built-up areas decreased in Aleppo and increased in Damascus during the study period. The temporal and spatial variation of the LST and its distribution pattern was closely related to the effect of changes in LULC as well as to land use conditions in each city. This effect was greater in Aleppo than in Damascus, where Aleppo recorded a higher increase in the mean LST, by about 2 °C, than in Damascus, where it was associated with greater degradation and loss of vegetation cover. In general, there was an increasing trend in the minimum and maximum LST as well as an increasing trend in the mean LST in both cities. The negative linear relationship between LST and NDVI confirms that vegetation cover can help reduce LST in both cities. This study can draw the attention of relevant departments to pay more attention to mitigating the negative impact of LULC changes in order to limit the increase in LST.

Understanding changes in urban vegetation is essential for ensuring sustainable and healthy cities, mitigating disturbances due to climate change, sustaining urban biodiversity, and supporting human health and wellbeing. This study investigates and describes the distribution and dynamic changes in urban vegetation over a 15-year period in Greater Melbourne, Australia. The study investigates how vegetation cover across Melbourne has changed at five-yearly intervals from 2001 to 2016 using the newly proposed dynamic change approach that extends the net change approach to quantify the amount of vegetation gain as well as loss. We examine this question at two spatial resolutions: (1) at the municipal landscape scale to capture broadscale change regardless of land tenure; and (2) at the scale of designated public open spaces within the municipalities to investigate the extent to which the loss of vegetation has occurred on lands that are intended to provide public access to vegetated areas in the city. Vegetation was quantified at four different times (2001, 2006, 2011, 2016), using the normalized difference vegetation index (NDVI). Dynamic changes of gain and loss in urban vegetation between the three periods were quantified for six local government areas (LGAs) and their associated public open spaces using a change matrix. The results showed an overall net loss of 64.5 square kilometres of urban vegetation from 2001 to 2016 in six LGAs. When extrapolated to the Greater Melbourne Area, this is approximately equivalent to 109 times the size of Central Park in New York City.

The aims of this study were to determine surface urban heat island (SUHI) effects and to analyze the land use/land cover (LULC) and land surface temperature (LST) changes for 11 time periods from the years 2002 to 2020 using Landsat time series images. Bursa, which is the fourth largest metropolitan city in Turkey, was selected as the study area, and Landsat multi-temporal images of the summer season were used. Firstly, the normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), modified normalized difference water index (MNDWI) and index-based built-up index (IBI) were created using the bands of Landsat images, and LULC classes were determined by applying automatic thresholding. The LST values were calculated using thermal images and SUHI effects were determined. The results show that NDVI, SAVI, MNDWI and IBI indices can be used effectively for the determination of the urban, vegetation and water LULC classes for SUHI studies, with overall classification accuracies between 89.60% and 95.90% for the used images. According to the obtained results, generally the LST values increased for almost all land cover areas between the years 2002 and 2020. The SUHI magnitudes were computed by using two methods, and it was found that there was an important increase in the 18-year time period.

Rapid urbanization in cities and urban centers has recently contributed to notable land use/land cover (LULC) changes, affecting both the climate and environment. Therefore, this study seeks to analyze changes in LULC and its spatiotemporal influence on the surface urban heat islands (UHI) in Abuja metropolis, Nigeria. To achieve this, we employed Multi-temporal Landsat data to monitor the study area’s LULC pattern and land surface temperature (LST) over the last 29 years. The study then analyzed the relationship between LULC, LST, and other vital spectral indices comprising NDVI and NDBI using correlation analysis. The results revealed a significant urban expansion with the transformation of 358.3 sq. km of natural surface into built-up areas. It further showed a considerable increase in the mean LST of Abuja metropolis from 30.65 °C in 1990 to 32.69 °C in 2019, with a notable increase of 2.53 °C between 2009 and 2019. The results also indicated an inverse relationship between LST and NDVI and a positive connection between LST and NDBI. This implies that urban expansion and vegetation decrease influences the development of surface UHI through increased LST. Therefore, the study’s findings will significantly help urban-planners and decision-makers implement sustainable land-use strategies and management for the city.

Understanding the patterns and relationships between vegetation productivity and climatic conditions is essential for predicting the future impacts of climate change. Climate change is altering precipitation patterns and increasing temperatures in the Southwest United States. The large-scale and long-term effects of these changes on vegetation productivity are not well understood. This study investigates the patterns and relationships between seasonal vegetation productivity, represented by Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI), and the Standardized Precipitation Evapotranspiration Index (SPEI) across the Mojave, Sonoran, and Chihuahuan Deserts and the Apache Highlands of the Southwest United States over 16 years from 2000 to 2015. To examine the spatiotemporal gradient and response of vegetation productivity to dry and wet conditions, we evaluated the linear trend of different SPEI timescales and correlations between NDVI and SPEI. We found that all four ecoregions are experiencing more frequent and severe drought conditions in recent years as measured by negative SPEI trends and severe negative SPEI values. We found that changes in NDVI were more strongly correlated with winter rather than summer water availability. Investigating correlations by vegetation type across all four ecoregions, we found that grassland and shrubland productivity were more dependent on summer water availability whereas sparse vegetation and forest productivity were more dependent on winter water availability. Our results can inform resource management and enhance our understanding of vegetation vulnerability to climate change.

The normalized difference vegetation index (NDVI) is a simple but powerful indicator, that can be used to observe green live vegetation efficiently. Since its introduction in the 1970s, NDVI has been used widely for land management, food security, and physical models. For these applications, acquiring NDVI in both high spatial resolution and high temporal resolution is preferable. However, there is generally a trade-off between temporal and spatial resolution when using satellite images. To relieve this problem, a convolutional neural network (CNN) based downscaling model was proposed in this research. This model is capable of estimating 10-m high resolution NDVI from MODIS (Moderate Resolution Imaging Spectroradiometer) 250-m resolution NDVI by using Sentinel-1 10-m resolution synthetic aperture radar (SAR) data. First, this downscaling model was trained to estimate Sentinel-2 10-m resolution NDVI from a combination of upscaled 250-m resolution Sentinel-2 NDVI and 10-m resolution Sentinel-1 SAR data, by using data acquired in 2019 in the target area. Then, the generality of this model was validated by applying it to test data acquired in 2020, with the result that the model predicted the NDVI with reasonable accuracy (MAE = 0.090, ρ = 0.734 on average). Next, 250-m NDVI from MODIS data was used as input to confirm this model under conditions replicating an actual application case. Although there were mismatch in the original MODIS and Sentinel-2 NDVI data, the model predicted NDVI with acceptable accuracy (MAE = 0.108, ρ = 0.650 on average). Finally, this model was applied to predict high spatial resolution NDVI using MODIS and Sentinel-1 data acquired in target area from 1 January 2020~31 December 2020. In this experiment, double cropping of cabbage, which was not observable at the original MODIS resolution, was observed by enhanced temporal resolution of high spatial resolution NDVI images (approximately ×2.5). The proposed method enables the production of 10-m resolution NDVI data with acceptable accuracy when cloudless MODIS NDVI and Sentinel-1 SAR data is available, and can enhance the temporal resolution of high resolution 10-m NDVI data.

Accurate monitoring of hybrid rice phenology (RP) is crucial for breeding rice cultivars and controlling fertilizing amount. The aim of this study is to monitor the exact date of hybrid rice initial heading stage (IHSDAS) based on low-altitude remote sensing data and analyze the influence factors of RP. In this study, six field experiments were conducted in Ezhou city and Lingshui city from 2016 to 2019, which involved different rice cultivars and nitrogen rates. Three low-altitude remote sensing platforms were used to collect rice canopy reflectance. Firstly, we compared the performance of normalized difference vegetation index (NDVI) and red edge chlorophyll index (CIred edge) for monitoring RP. Secondly, double logistic function (DLF), asymmetric gauss function (AGF), and symmetric gauss function (SGF) were used to fit time-series CIred edge for acquiring phenological curves (PC), the feature: maximum curvature (MC) of PC was extracted to monitor IHSDAS. Finally, we analyzed the influence of rice cultivars, N rates, and air temperature on RP. The results indicated that CIred edge was more appropriate than NDVI for monitoring RP without saturation problem. Compared with DLF and AGF, SGF could fit CIred edge without over fitting problem. MC of SGF_CIred edge from all three platforms showed good performance in monitoring IHSDAS with good robustness, R2 varied between 0.82 and 0.95, RMSE ranged from 2.31 to 3.81. In addition, the results demonstrated that high air temperature might cause a decrease of IHSDAS, and the growth process of rice was delayed when more nitrogen fertilizer was applied before IHSDAS. This study illustrated that low-altitude remote sensing technology could be used for monitoring field-scale hybrid rice IHSDAS accurately.