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Recent applications of Landsat 8 Operational Land Imager (L8/OLI) and Sentinel-2 MultiSpectral Instrument (S2/MSI) data for acquiring information about land use and land cover (LULC) provide a new perspective in remote sensing data analysis. Jointly, these sources permit researchers to improve operational classification and change detection, guiding better reasoning about landscape and intrinsic processes, as deforestation and agricultural expansion. However, the results of their applications have not yet been synthesized in order to provide coherent guidance on the effect of their applications in different classification processes, as well as to identify promising approaches and issues which affect classification performance. In this systematic review, we present trends, potentialities, challenges, actual gaps, and future possibilities for the use of L8/OLI and S2/MSI for LULC mapping and change detection. In particular, we highlight the possibility of using medium-resolution (Landsat-like, 10–30 m) time series and multispectral optical data provided by the harmonization between these sensors and data cube architectures for analysis-ready data that are permeated by publicizations, open data policies, and open science principles. We also reinforce the potential for exploring more spectral bands combinations, especially by using the three Red-edge and the two Near Infrared and Shortwave Infrared bands of S2/MSI, to calculate vegetation indices more sensitive to phenological variations that were less frequently applied for a long time, but have turned on since the S2/MSI mission. Summarizing peer-reviewed papers can guide the scientific community to the use of L8/OLI and S2/MSI data, which enable detailed knowledge on LULC mapping and change detection in different landscapes, especially in agricultural and natural vegetation scenarios.

High-resolution airborne thermal infrared (TIR) together with sun-induced fluorescence (SIF) and hyperspectral optical images (visible, near- and shortwave infrared; VNIR/SWIR) were jointly acquired over an experimental site. The objective of this study was to evaluate the potential of these state-of-the-art remote sensing techniques for detecting symptoms similar to those occurring during water stress (hereinafter referred to as ‘water stress symptoms’) at airborne level. Flights with two camera systems (Telops Hyper-Cam LW, Specim HyPlant) took place during 11th and 12th June 2014 in Latisana, Italy over a commercial grass (Festuca arundinacea and Poa pratense) farm with plots that were treated with an anti-transpirant agent (Vapor Gard®; VG) and a highly reflective powder (kaolin; KA). Both agents affect energy balance of the vegetation by reducing transpiration and thus reducing latent heat dissipation (VG) and by increasing albedo, i.e., decreasing energy absorption (KA). Concurrent in situ meteorological data from an on-site weather station, surface temperature and chamber flux measurements were obtained. Image data were processed to orthorectified maps of TIR indices (surface temperature (Ts), Crop Water Stress Index (CWSI)), SIF indices (F687, F780) and VNIR/SWIR indices (photochemical reflectance index (PRI), normalised difference vegetation index (NDVI), moisture stress index (MSI), etc.). A linear mixed effects model that respects the nested structure of the experimental setup was employed to analyse treatment effects on the remote sensing parameters. Airborne Ts were in good agreement (∆T < 0.35 K) compared to in situ Ts measurements. Maps and boxplots of TIR-based indices show diurnal changes: Ts was lowest in the early morning, increased by 6 K up to late morning as a consequence of increasing net radiation and air temperature (Tair) and remained stable towards noon due to the compensatory cooling effect of increased plant transpiration; this was also confirmed by the chamber measurements. In the early morning, VG treated plots revealed significantly higher Ts compared to control (CR) plots (p = 0.01), while SIF indices showed no significant difference (p = 1.00) at any of the overpasses. A comparative assessment of the spectral domains regarding their capabilities for water stress detection was limited due to: (i) synchronously overpasses of the two airborne sensors were not feasible, and (ii) instead of a real water stress occurrence only water stress symptoms were simulated by the chemical agents. Nevertheless, the results of the study show that the polymer di-1-p-menthene had an anti-transpiring effect on the plant while photosynthetic efficiency of light reactions remained unaffected. VNIR/SWIR indices as well as SIF indices were highly sensitive to KA, because of an overall increase in spectral reflectance and thus a reduced absorbed energy. On the contrary, the TIR domain was highly sensitive to subtle changes in the temperature regime as induced by VG and KA, whereas VNIR/SWIR and SIF domain were less affected by VG treatment. The benefit of a multi-sensor approach is not only to provide useful information about actual plant status but also on the causes of biophysical, physiological and photochemical changes.

Early and accurate diagnosis is a critical first step in mitigating losses caused by plant diseases. An incorrect diagnosis can lead to improper management decisions, such as selection of the wrong chemical application that could potentially result in further reduced crop health and yield. In tomato, initial disease symptoms may be similar even if caused by different pathogens, for example early lesions of target spot (TS) caused by the fungus Corynespora cassicola and bacterial spot (BS) caused by Xanthomonas perforans. In this study, hyperspectral imaging (380–1020 nm) was utilized in laboratory and field (collected by an unmanned aerial vehicle; UAV) settings to detect both diseases. Tomato leaves were classified into four categories: healthy, asymptomatic, early and late disease development stages. Thirty-five spectral vegetation indices (VIs) were calculated to select an optimum set of indices for disease detection and identification. Two classification methods were utilized: (i) multilayer perceptron neural network (MLP), and (ii) stepwise discriminant analysis (STDA). Best wavebands selection was considered in blue (408–420 nm), red (630–650 nm) and red edge (730–750 nm). The most significant VIs that could distinguish between healthy leaves and diseased leaves were the photochemical reflectance index (PRI) for both diseases, the normalized difference vegetation index (NDVI850) for BS in all stages, and the triangular vegetation index (TVI), NDVI850 and chlorophyll index green (Chl green) for TS asymptomatic, TS early and TS late disease stage respectively. The MLP classification method had an accuracy of 99%, for both BS and TS, under field (UAV-based) and laboratory conditions.

Canopy reflectance sensors are a viable technology to optimize the fertilization management of crops. In this research, canopy reflectance was measured through a passive sensor to evaluate the effects of either crop features (N fertilization, soil mulching, appearance of red fruits, and cultivars) or sampling methods (sampling size, sensor position, and hour of sampling) on the reliability of vegetation indices (VIs). Sixteen VIs were derived, including seven simple wavelength reflectance ratios (NIR/R460, NIR/R510, NIR/R560, NIR/R610, NIR/R660, NIR/R710, NIR/R760), seven normalized indices (NDVI, G-NDVI, MCARISAVI, OSAVI, TSAVI, TCARI), and two combined indices (TCARI/OSAVI; MCARI/OSAVI). NIR/560 and G-NDVI (Normalized Difference Vegetation Index on Greenness) were the most reliable in discriminating among fertilization rates, with results unaffected by the appearance of maturing fruits, and the most stable in response to different cultivars. Black mulching film did not affect NIR/560 and G-NDVI behavior at the beginning of the growing season, when the crop is more responsive to N management. Due to a moderate variability of NIR/560 and G-NDVI, a small sample size (5–10 observations) is sufficient to obtain reliable measurements. Performing the measurements at 11:00 and 14:00 and maintaining a greater distance (1.8 m) between plants and instrument enhanced measurement consistency. Accordingly, NIR/560 and G-NDVI resulted in the most reliable VIs.

One of the significant challenges in urbanization is the air pollution. This highlights the need of the green city concept with reconsideration of houses, factories, and traffic in a green viewpoint. The literature review confirms that this reconsideration for green space has a positive effect on the air quality of large cities and to reduce the air pollution. The purpose of this study is to evaluate the annual vegetation changes in the green space of Mashhad, Iran as a very populated city in the middle east to study the air pollution. To investigate the relationship between the air pollution and vegetation, the Landsat 8 satellite images for summer seasons of 2013–2019 were used to extract changes in vegetation by calculating the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and the optimized soil adjusted vegetation index (OSAVI). The main contribution in comparison with the relevant studies is to study the relationship between clean, healthy, and unhealthy days with the green space area for the first time in Mashhad, Iran. The results show that the implementation of green city concept in Mashhad, Iran, has been increased by 64, 81, and 53% by NDVI, EVI, and OSAVI, respectively, during the study period. The vegetation area of this city is positively correlated to clean and healthy days and has a negative correlation to unhealthy days, in which the greatest values for NDVI, EVI and OSAVI are 0.33, 0.52, and −0.53, respectively.

Monitoring and prediction of within-field crop variability can support farmers to make the right decisions in different situations. The current advances in remote sensing and the availability of high resolution, high frequency, and free Sentinel-2 images improve the implementation of Precision Agriculture (PA) for a wider range of farmers. This study investigated the possibility of using vegetation indices (VIs) derived from Sentinel-2 images and machine learning techniques to assess corn (Zea mays) grain yield spatial variability within the field scale. A 22-ha study field in North Italy was monitored between 2016 and 2018; corn yield was measured and recorded by a grain yield monitor mounted on the harvester machine recording more than 20,000 georeferenced yield observation points from the study field for each season. VIs from a total of 34 Sentinel-2 images at different crop ages were analyzed for correlation with the measured yield observations. Multiple regression and two different machine learning approaches were also tested to model corn grain yield. The three main results were the following: (i) the Green Normalized Difference Vegetation Index (GNDVI) provided the highest R2 value of 0.48 for monitoring within-field variability of corn grain yield; (ii) the most suitable period for corn yield monitoring was a crop age between 105 and 135 days from the planting date (R4–R6); (iii) Random Forests was the most accurate machine learning approach for predicting within-field variability of corn yield, with an R2 value of almost 0.6 over an independent validation set of half of the total observations. Based on the results, within-field variability of corn yield for previous seasons could be investigated from archived Sentinel-2 data with GNDVI at crop stage (R4–R6).

Beaver dams are gaining popularity as a low-tech, low-cost strategy to build climate resiliency at the landscape scale. They slow and store water that can be accessed by riparian vegetation during dry periods, effectively protecting riparian ecosystems from droughts. Whether or not this protection extends to wildfire has been discussed anecdotally but has not been examined in a scientific context. We used remotely sensed Normalized Difference Vegetation Index (NDVI) data to compare riparian vegetation greenness in areas with and without beaver damming during wildfire. We include data from five large wildfires of varying burn severity and dominant landcover settings in the western United States in our analysis. We found that beaver-dammed riparian corridors are relatively unaffected by wildfire when compared to similar riparian corridors without beaver damming. On average, the decrease in NDVI during fire in areas without beaver is 3.05 times as large as it is in areas with beaver. However, plant greenness rebounded in the year after wildfire regardless of beaver activity. Thus, we conclude that, while beaver activity does not necessarily play a role in riparian vegetation post-fire resilience, it does play a significant role in riparian vegetation fire resistance and refugia creation.

Improvements in irrigated areas’ classification accuracy are critical to enhance agricultural water management and inform policy and decision-making on irrigation expansion and land use planning. This is particularly relevant in water-scarce regions where there are plans to increase the land under irrigation to enhance food security, yet the actual spatial extent of current irrigation areas is unknown. This study applied a non-parametric machine learning algorithm, the random forest, to process and classify irrigated areas using images acquired by the Landsat and Sentinel satellites, for Mpumalanga Province in Africa. The classification process was automated on a big-data management platform, the Google Earth Engine (GEE), and the R-programming was used for post-processing. The normalised difference vegetation index (NDVI) was subsequently used to distinguish between irrigated and rainfed areas during 2018/19 and 2019/20 winter growing seasons. High NDVI values on cultivated land during the dry season are an indication of irrigation. The classification of cultivated areas was for 2020, but 2019 irrigated areas were also classified to assess the impact of the Covid-19 pandemic on agriculture. The comparison in irrigated areas between 2019 and 2020 facilitated an assessment of changes in irrigated areas in smallholder farming areas. The approach enhanced the classification accuracy of irrigated areas using ground-based training samples and very high-resolution images (VHRI) and fusion with existing datasets and the use of expert and local knowledge of the study area. The overall classification accuracy was 88%.

Kayseri, a densely urbanized province in Türkiye, grapples with pressing challenges of air pollution and limited green spaces, accentuating the need for strategic urban planning. This study, utilizing Landsat 8 and Landsat 9 satellite imagery, investigates the evolution of land surface temperatures (LST) and urban heat island (UHI) effects in key districts—Kocasinan, Melikgazi, Talas, and Hacılar—between 2013 and 2022. This research has been complemented with an analysis of the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Built-Up Index (NDBI), exploring correlations among the LST, UHI, NDVI, and NDBI changes. The findings indicate that a significant portion (65% and 88%) of the study area remained unchanged with respect to the NDVI and NDBI differences. This research’s findings reveal that a substantial portion (65% and 88%) of the study area exhibited consistency in the NDVI and NDBI. Noteworthy increases in the NDVI were observed in 20% of the region, while only 4% exhibited higher NDBI. Strikingly, the UHI displayed strong negative correlations with the NDVI and robust positive correlations with the NDBI. The LST changes demonstrated a reduced temperature range, from 21 to 51 °C in 2013, to 18 to 40 °C in 2022. Localized environmental factors, notably at the National Garden site, showcased the most significant temperature variations. Notably, the UHI exhibited strong negative correlations with the NDVI and strong positive correlations with the NDBI. The study’s results emphasize the interplay among the NDBI, LST, and UHI and an inverse relationship with the NDVI and NDBI, LST, and UHI. These findings hold implications for urban planning and policymaking, particularly in the context of resilient and sustainable land use planning and the UHI mitigation. This research underscores the intricate interplay among the NDBI, LST, and UHI, highlighting an inverse relationship with the NDVI. These findings hold crucial implications for resilient and sustainable urban planning, particularly in mitigating the UHI effects. Despite limited vacant spaces in Kayseri, geospatial techniques for identifying potential green spaces can facilitate swift UHI mitigation measures. Acknowledging Kayseri’s complex dynamics, future research should delve into the UHI responses to urban morphology and design, extending this methodology to analyze the UHI effects in other Turkish cities. This research contributes to a broader understanding of UHI dynamics and sustainable urban planning practices, offering valuable insights for policymakers, urban planners, and researchers alike.

Cellular Automata models are used for simulating spatial distributions and Markov Chain models are used for simulating temporal changes. The main aim of this study is to investigate the effect of urban growth on Faisalabad. This research is aimed at predicting seasonal Land-Surface-Temperature (LST) as well as Land-Use and Land-cover (LULC) with a Cellular-Automata-Markov-Chain (CA-Markov-Chain). Landsat 5, 7 and 8 data were used for mapping seasonal LULC and LST distributions during the months of May and November for the years 1990, 1998, 2004, 2008, 2013 and 2018. A CA-Markov-Chain was developed for simulating long-term landscape changes at 10-year time steps from 2018 to 2048. Furthermore, surface temperature during summers and winters were predicted well by Urban Index (UI), a non-vegetation index, demonstrating the highest correlation of R2 = 0.8962 and R2 = 0.9212 with respect to retrieved summer and winter surface temperature. Through the CA-Markov Chain analysis, we can expect that high density and low-density residential areas will grow from 22.23 to 24.52 km2 and from 108.53 to 122.61 km2 in 2018 and 2048, as inferred from the changes occurred from 1990 to 2018. Considering UI as the predictor of seasonal LST, we predicted that the summer and winter temperature 24–28 °C and 14–16 °C and regions would decrease in coverage from 10.75 to 3.14% and from 8.81 to 3.47% between 2018 and 2048, while the summer and winter temperature 35–42 °C and winter 26–32 °C regions will increase in the proportion covered from 12.69 to 24.17% and 6.75–15.15% of city.