Remote Sensing Applications in Agricultural, Earth and Environmental Sciences

In response to the urgent global threat posed by human-induced extreme climate hazards, heatwaves are still systematically under-reported and under-researched in Thailand. This region is confronting a significant rise in heat-related mortality, which has resulted in hundreds of deaths, underscoring a pressing issue that needs to be addressed. This research article is one of the first to present a solution for assessing heatwave dynamics, using machine learning (ML) algorithms and geospatial technologies in this country. It analyzes heatwave metrics like heatwave number (HWN), heatwave frequency (HWF), heatwave duration (HWD), heatwave magnitude (HWM), and heatwave amplitude (HWA), combining satellite-derived land surface temperature (LST) data with ground-based air temperature ($T_{\mathrm{air}}$) observations from 1981 to 2019. The result reveals significant marked increases in both the frequency and intensity of daytime heatwaves in peri-urban areas, with the most pronounced changes being a 0.45-day/year in HWN, a 2.00-day/year in HWF, and a 0.27-day/year in HWD. This trend is notably less pronounced in urban areas. Conversely, rural regions are experiencing a significant escalation in nighttime heatwaves, with increases of 0.39 days/year in HWN, 1.44 days/year in HWF, and 0.14 days/year in HWD. Correlation analysis ($p<0.05$) reveals spatial heterogeneity in heatwave dynamics, with robust daytime correlations between $T_{\mathrm{air}}$ and LST in rural (HWN, HWF, HWD, $r>0.90$) and peri-urban (HWM, HWA, $r>0.65$) regions. This study emphasizes the importance of considering microclimatic variations in heatwave analysis, offering insights for targeted intervention strategies. It demonstrates how enhancing remote sensing with ML can facilitate the spatial–temporal analysis of heatwaves across diverse environments. This approach identifies critical risk areas in Thailand, guiding resilience efforts and serving as a model for managing similar microclimates, extending the applicability of this study. Overall, the study provides policymakers and stakeholders with potent tools for climate action and effective heatwave management. Furthermore, this research contributes to mitigating the impacts of extreme climate events, promoting resilience, and fostering environmental sustainability. Full article

This study delves into the analysis of a vineyard in Carinthia, Austria, focusing on the automated derivation of ecosystem structures of individual vine parameters, including vine heights, leaf area index (LAI), leaf surface area (LSA), and the geographic positioning of single plants. For the derivation of these parameters, intricate segmentation processes and nuanced UAS-based data acquisition techniques are necessary. The detection of single vines was based on 3D point cloud data, generated at a phenological stage in which the plants were in the absence of foliage. The mean distance from derived vine locations to reference measurements taken with a GNSS device was 10.7 cm, with a root mean square error (RMSE) of 1.07. Vine height derivation from a normalized digital surface model (nDSM) using photogrammetric data showcased a strong correlation (R² = 0.83) with real-world measurements. Vines underwent automated classification through an object-based image analysis (OBIA) framework. This process enabled the computation of ecosystem structures at the individual plant level post-segmentation. Consequently, it delivered comprehensive canopy characteristics rapidly, surpassing the speed of manual measurements. With the use of uncrewed aerial systems (UAS) equipped with optical sensors, dense 3D point clouds were computed for the derivation of canopy-related ecosystem structures of vines. While LAI and LSA computations await validation, they underscore the technical feasibility of obtaining precise geometric and morphological datasets from UAS-collected data paired with 3D point cloud analysis and object-based image analysis. Full article

The extensive identification of mowing events on a territory holds significant potential to help monitor shifts in biodiversity and contribute to assessing the impacts of drought events. Additionally, it provides valuable insights into farming practices and their consequential economic and ecological effects. To overcome challenges in obtaining reference grazing information directly from the field, this study introduces a novel methodology leveraging the compressed sward height (CSH) derived from Sentinel-1, Sentinel-2, and meteorological data, boasting an accuracy of 20 mm. Our central hypothesis posits that the mowing status of a parcel can be automatically discerned by analyzing the distribution and variation of its CSH values. Employing a two-step strategy, we first applied unsupervised algorithms, specifically k-means and isolation forest, and subsequently amalgamated the outcomes with a partial least squares analysis on an extensive dataset encompassing 194,657 pastures spanning the years 2018 to 2021. The culmination of our modeling efforts yielded a validation accuracy of 0.66, as ascertained from a focused dataset of 68 pastures. Depending on the studied year and with a threshold fixed at 0.50, 21% to 57% of all the parcels in the Wallonia dataset were tagged as mown by our model. This study introduces an innovative approach for the automated detection of mown parcels, showcasing its potential to monitor agricultural activities at scale. Full article

The high-altitude Hindu Kush-Himalayan region (HKH, average ~5 km from msl) and the adjacent Indo-Gangetic plains (IG plains, ~0–250 m msl), due to their geographical location and complex topography, are reported to be highly sensitive to climatic changes. Recent studies show that the impacts of climate change and associated changes in water resources (glacial/snow melt water and rainfall) in this region are multifaceted, thereby affecting ecosystems, agriculture, industries, and inhabitants. In this study, 45 years of Microwave Sounding Unit/Advanced Microwave Sounding Unit (MSU/AMSU)-derived mid-tropospheric temperature (TMT, 3–7 km altitude) and lower tropospheric temperature (TLT, 0–3 km altitude) data from the Remote Sensing Systems (RSS Version 4.0) were utilized to analyze the overall changes in tropospheric temperature in terms of annual/monthly trends and anomalies. The current study shows that the mid-tropospheric temperature (0–3 km altitude over the HKH region) has already alarmingly increased (statistically significant) in Tibet, the western Himalayas, and the eastern Himalayas by 1.49 °K, 1.30 °K, and 1.35 °K, respectively, over the last 45 years (1978–2022). As compared to a previous report (TMT trend for 30 years, 1979–2008), the present study of TMT trends for 45 years (1978–2022) exhibits a rise in percent change in the trend component in the high-altitude regions of Tibet, the western Himalayas, and the eastern Himalayas by approximately 310%, 80%, and 170%, respectively. In contrast, the same for adjacent plains (the western and eastern IG plains) shows a negligible or much lower percent change (0% and 40%, respectively) over the last 14 years. Similarly, dust source regions in Africa, Arabia, the Middle East, Iran, and Pakistan show only a 130% change in warming trends over the past 14 years. In the monthly breakup, the ‘November to March’ period usually shows a higher TMT trend (with peaks in December, February, and March) compared to the rest of the months, except in the western Himalayas, where the peak is observed in May, which can be attributed to the peak dust storm activity (March to May). Snow cover over the HKH region, where the growing season is known to be from September to February, is also reported to show the highest snow cover in February (with the peak in January, February, or March), which coincides with the warmest period in terms of anomaly and trend observed in the long-term mid-tropospheric temperature data (1978–2022). Thus, the current study highlights that the statistically significant and positive TMT warming trend (95% CI) and its observed acceleration over the high-altitude region (since 2008) can be attributed to being one of the major factors causing an acceleration in the rate of melting of snow cover and glaciers, particularly in Tibet and the Eastern Himalayas. Full article

Vegetation plays an active role in ecosystem dynamics, and monitoring its patterns and changes is vital for effective environmental resource management. This study explores the possibility of machine learning techniques and remote sensing data to improve the accuracy of forest detection. The research focuses on the southeastern part of the Republic of Serbia as a case study area, using Sentinel-2 multispectral bands. The study employs publicly accessible satellite data and incorporates different vegetation indices to improve classification accuracy. The main objective is to examine the practicability of expanding the input parameters for forest detection using a machine learning approach. The classification process is performed by employing support vector machines (SVM) algorithm and utilising the SVM module in the scikit-learn package. The results demonstrate that including vegetation indices alongside the multispectral bands significantly improves the accuracy of vegetation detection. A comprehensive assessment reveals an overall classification accuracy of up to 99.01% when the selected vegetation indices (MCARI, RENDVI, NDI45, GNDVI, NDII) are combined with the Sentinel-2 bands. This research highlights the potential of machine learning and remote sensing in forest detection and monitoring. The findings underscore the importance of incorporating vegetation indices to enhance classification accuracy using the Python programming language. The study’s outcomes provide valuable insights for environmental resource management and decision-making processes, particularly in regions with diverse forest ecosystems. Full article

Since the initiation of economic reforms and opening up, China has witnessed an unprecedented rate of development across all sectors. However, the country has also experienced severe ecological damage, surpassing that of many other nations. The rapid economic growth has come at the expense of the environment, revealing a significant lack of coordination between urbanization and eco-environmental protection in China. Consequently, there is an urgent need for a comprehensive and continuous historical dataset of China’s eco-environmental quality (EEQ) based on remote sensing, allowing for the analysis of spatial and temporal changes. Such data would provide objective, scientific, and reliable support for China’s eco-environmental protection and pollution prevention policies, while addressing potential ecological risks resulting from urbanization. To achieve this, the entropy value method is employed to integrate multi-source remote sensing data and construct an evaluation system for China’s EEQ. Historical data from 2000 to 2017 is plotted to illustrate China’s EEQ over time. The findings of this study are as follows: (1) The entropy method effectively facilitates the construction of China’s eco-environmental quality assessment system. (2) From 2000 to 2017, approximately 39.7% of China’s regions witnessed a decrease in EEQ, while 60.3% exhibited improvement, indicating an overall enhancement in EEQ over the past eighteen years. (3) The Yangtze and Yellow River basins experienced improved EEQ due to China’s ecological restoration projects. (4) The future EEQ in China demonstrates a subtle positive trend across diverse contexts. This study departs from conventional approaches to EEQ evaluation by leveraging the advantages of multivariate remote sensing big data, including objectivity, timeliness, and accessibility. It provides a novel perspective for future eco-environmental quality evaluation. Full article

The role of biodiversity in improving the primary productivity within terrestrial ecosystems is well documented. Each species in an ecosystem has a role to play in the overall productivity of an ecosystem. Grass species nitrogen (N) estimation is essential in rangelands, especially in rugged terrain such as mountainous regions. It is an indicator of forage quality, which has nutritional implications for grazing animals. This research sought to improve and test the predictability of grass N by applying a combination of remotely sensed spectral bands and vegetation indices as input. Recursive feature selection was used to select the optimal spectral bands and vegetation indices for predicting grass N. Subsequently, the selected vegetation indices and bands were used as input into the non-parametric random forest (RF) regression to predict grass N. The prediction of grass N improved slightly in the vegetation indices model (81%) compared to the bands model (80%), and the highest prediction was achieved by combining the two (85%). This research ascertains that including red-edge-based vegetation indices improves the prediction of grass N. S2 MSI remains the ideal remote sensing tool for estimating grass N because of its strategically positioned red-edge bands, which are highly correlated with chlorophyll content in plants. Full article

This paper established a mathematical method for the spectral feature extraction of ratholes, based on UAV hyperspectral imaging technology. The degradation of grasslands is a major challenge to terrestrial ecosystems. Rodents not only promote soil erosion and accelerate the process of grassland degradation, but also carry diseases that can easily cause epidemics. The calculation of the number of rodent holes and grassland vegetation cover is an important indicator for monitoring and evaluating grassland degradation. Manual surveys have drawbacks in efficiently monitoring large areas and are human- and material-costly, hardly meeting the current needs of grassland degradation monitoring. Therefore, there is an urgent need to conduct real-time dynamic monitoring of grassland rathole distributions and grassland degradation processes. In this study, a low-altitude remote sensing platform was constructed by integrating a hyperspectral imager with a UAV to collect spectral data of the desert steppes in central Inner Mongolia Autonomous Region, China. Then, the spectral features of ratholes were extracted via radiation correction, noise reduction, and principal component analysis (PCA). Meanwhile, the spectral features of vegetation and bare soil were extracted based on the normalized difference vegetation index (NDVI), which was inputted to calculate the vegetation cover. The results showed that the single-band map extracted based on PCA could effectively determine the location of ratholes, where the overall accuracy and kappa coefficient were 97% and 0.896, respectively. Therefore, the method proposed in this study can accurately identify the location of desert steppe rodent holes. It provides a high-precision technical means for scientific and effective control of grassland rodent infestation and also provides a higher technical means for grassland degradation. Full article

A pot experiment was conducted to determine the impact of water availability on the discriminatory status of nitrogen (N) in plants using hyperspectral imaging. Nitrogen deficiency causes a significant decrease in chlorophyll concentration in plant leaves regardless of water availability. Five different classification algorithms were used to discriminate between nitrogen concentrations in plants at different levels of water availability. Several statistical parameters, including kappa and overall classification accuracy for calibration and prediction, were used to determine the efficiency and accuracy of the models. The Random Forest model had the highest overall accuracy of over 81% for sugar beet and over 78% for celery. Additionally, characteristic electromagnetic wavelengths were identified in which reflectance correlated with nitrogen and water content in plants could be recorded. It was also noted that the spectral resolution between the N and High Water (HW)/Low Water (LW) treatments was lower in the short-wave infrared (SWIR) region than in the visible and near-infrared (VNIR) region. Full article

The availability of geospatial data infrastructure and earth observation technology can play an essential role in facilitating the monitoring of sustainable urban development. However, in most developing countries, a spatiotemporal evaluation of urban growth is still lacking. Maseru, Lesotho’s capital and largest city, is growing rapidly due to various socioeconomic and demographic driving forces. However, urban expansion in developing countries has been characterized by entangled structures and trends exacerbating numerous negative consequences such as ecological degradation, the loss of green space, and pollution. Understanding the urban land use and land cover (LULC) dynamic is essential to mitigate such adverse impacts. This study focused on mapping and quantifying the urban extension in Maseru, using Landsat imagery from 1988 to 2019, based on the Support Vector Machines (SVM) classifier. We also simulated and predicted LULC changes for the year 2050 using the cellular automata model of an artificial neural network (ANN-CA). Our results showed a notable increase in the built-up area from 15.3% in 1988 to 48% in 2019 and bare soil from 12.3% to 35.3%, while decreased agricultural land (21.7 to 1.7%), grassland (43.3 to 10.5%) and forest vegetation (5.5 to 3.2%) were observed over the study period. The classified maps have high accuracy, between 88% and 95%. The ANN-CA projections for 2050 show that built-up areas will continue to increase with a decrease in agricultural fields, bare soil, grasslands, water bodies and woody vegetation. To our knowledge, this is the first detailed, long-term study to provide insights on urban growth to planners and other stakeholders in Maseru in order to improve the implementation of the Maseru 2050 urban plan. Full article

Traditional land-use systems can be modified under the conditions of climate change. Higher air temperatures and loss of productive soil moisture lead to reduced crop yields. Irrigation is a possible solution to these problems. However, intense irrigation may have contributed to land degradation. This research assessed the ameliorative potential of soil and produced large-scale digital maps of soil properties for arable plot planning for the construction and operation of irrigation systems. Our research was carried out in the southern forest–steppe zone (Southern Ural, Russia). The soil cover of the site is represented by agrochernozem soils (Luvic Chernozem). We examined the morphological, physicochemical and agrochemical properties of the soil, as well as its heavy metal contents. The random forest (RF) non-linear approach was used to estimate the spatial distribution of the properties and produce maps. We found that soils were characterized by high organic carbon content (SOC) and neutral acidity and were well supplied with nitrogen and potassium concentrations. The agrochernozem was characterized by favorable water–physical properties and showed good values for water infiltration and moisture categories. The contents of heavy metals (lead, cadmium, mercury, cobalt, zinc and copper) did not exceed permissible levels. The soil quality rating interpretation confirms that these soils have high potential fertility and are convenient for irrigation activities. The spatial distribution of soil properties according to the generated maps were not homogeneous. The results showed that remote sensing covariates were the most critical variables in explaining soil properties variability. Our findings may be useful for developing reclamation strategies for similar soils that can restore soil health and improve crop productivity. Full article

In the last decade, many SAR missions have been launched to reinforce the all-weather observation capacity of the Earth. The precise modeling of radar signals becomes crucial in order to translate them into essential biophysical parameters for the management of natural resources (water, biomass and energy). The objective of this study was to demonstrate the capabilities of two statistical algorithms (i.e., multiple linear regression (MLR) and random forest (RF)) to accurately simulate the backscattering coefficients observed over bare agricultural soil surfaces. This study was based on satellite and ground data collected on bare soil surfaces over an agricultural region located in southwestern France near Toulouse. Multi-configuration backscattering coefficients were acquired by TerraSAR-X and Radarsat-2 in the X- and C-bands, in co-(abbreviated σ⁰_HH and σ⁰_VV) and cross-polarization states (abbreviated σ⁰_HV and σ⁰_VH) and at incidence angles ranging from 24° to 53°. Models were independently calibrated and validated using a ground dataset covering a wide range of soil conditions, including the topsoil moisture (range: 2.4–35.3%), root-mean-square height (range: 0.5–7.9cm) and clay fraction (range: 9–58%). Higher-magnitude correlations (r) and lower errors (RMSE) were obtained when using RF (r values ranging from 0.69 to 0.86 and RMSE from 1.95 to 1.00 dB, depending on the considered signal configuration) compared to MLR (r values ranging from 0.58 to 0.77 and RMSE from 2.22 to 1.24 dB). Both surpass the performance presented in previous studies based on either empirical, semi-empirical or physical models. In the linear approach, the information is mainly provided by the surface moisture and the angle of incidence (especially in the case of co-polarized signals, regardless of the frequency), while the influence of roughness or texture becomes significant for cross-polarized signals in the C-band. On the contrary, all the surface descriptors contribute in the approach based on RF. In future work, the use of the RF algorithm developed in this paper should improve the estimation of soil parameters. Full article

Unfettered agricultural activities have severely degraded vast areas of grasslands over the last decade. To rehabilitate and restore the productivity in affected grasslands, rangeland management practices still institute vast nitrogen-based fertilization regimes. However, excessive fertilization can often have damaging environmental effects. Over-fertilization can lead to nitrogen saturation. Although early indicators of nitrogen saturation have been documented, research detailing the near-real-time nitrogen saturation status of grasslands is required to better facilitate management protocols and optimize biomass production within degraded grasslands. Hence, the aim of this study was to discriminate nitrogen-saturated tropical grasses grown under a diverse fertilization treatment trial, using Worldview-3 satellite imagery and decision tree techniques. To accomplish this, nitrogen-saturated plots were first identified through specific physiological-based criteria. Thereafter, Worldview-3 satellite imagery (400–1040 nm) and decision tree techniques were applied to discriminate between nitrogen-saturated and -unsaturated grassland plots. The results showed net nitrate (NO₃⁻-N) concentrations and net pH levels to be significantly different (α = 0.05) between saturated and non-saturated plots. Moreover, the random forest model (overall accuracy of 91%) demonstrated a greater ability to classify saturated plots as opposed to the classification and regression tree method (overall accuracy of 79%). The most important variables for classifying saturated plots were identified as: the Red-Edge (705–745 nm), Coastal (400–450 nm), Near-Infrared 3 (838–950 nm), Soil-Adjusted Vegetation Index (SAVI) and the Normalized Difference Vegetation Index 3 (NDVI3). These results provide a framework to assist rangeland managers in identifying grasslands within the initial stages of nitrogen saturation. This will enable fertilization treatments to be adjusted in near-real-time according to ecosystem demand and thereby maintain the health and longevity of Southern African grasslands. Full article

Precision agriculture and smart farming have received significant attention due to the advancements made in remote sensing technology to support agricultural efficiency. In large-scale agriculture, the role of unmanned aerial vehicles (UAVs) has increased in remote monitoring and collecting farm data at regular intervals. However, due to an open environment, UAVs can be hacked to malfunction and report false data. Due to limited battery life and flight times requiring frequent recharging, a compromised UAV wastes precious energy when performing unnecessary functions. Furthermore, it impacts other UAVs competing for charging times at the station, thus disrupting the entire data collection mechanism. In this paper, a fog computing-based smart farming framework is proposed that utilizes UAVs to gather data from IoT sensors deployed in farms and offloads it at fog sites deployed at the network edge. The framework adopts the concept of a charging token, where upon completing a trip, UAVs receive tokens from the fog node. These tokens can later be redeemed to charge the UAVs for their subsequent trips. An intrusion detection system is deployed at the fog nodes that utilize machine learning models to classify UAV behavior as malicious or benign. In the case of malicious classification, the fog node reduces the tokens, resulting in the UAV not being able to charge fully for the duration of the trip. Thus, such UAVs are automatically eliminated from the UAV pool. The results show a 99.7% accuracy in detecting intrusions. Moreover, due to token-based elimination, the system is able to conserve energy. The evaluation of CPU and memory usage benchmarks indicates that the system is capable of efficiently collecting smart-farm data, even in the presence of attacks. Full article

Land degradation (LD) is a global issue that affects sustainability and livelihoods of approximately 1.5 billion people, especially in arid/semi-arid regions. Hence, identifying and assessing LD and its driving forces (natural and anthropogenic) is important in order to design and adopt appropriate sustainable land management interventions. Therefore, using vegetation as a proxy for LD, this study aimed to distinguish anthropogenic from rainfall-driven LD in the Greater Sekhukhune District Municipality from 1990 to 2019. It is widely established that rainfall highly correlates with vegetation productivity. A linear regression was performed between the Normalized Difference Vegetation Index (NDVI) and rainfall. The human-induced LD was then distinguished from that of rainfall using the spatial residual trend (RESTREND) method and the Mann–Kendall (MK) trend. RESTREND results showed that 11.59% of the district was degraded due to human activities such as overgrazing and injudicious rangeland management. While about 41.41% was degraded due to seasonal rainfall variability and an increasing frequency of droughts. Climate variability affected vegetation cover and contributed to different forms of soil erosion and gully formation. These findings provide relevant spatial information on rainfall or human-induced LD, which is useful for policy formulation and the design of LD mitigation measures in semi-arid regions. Full article

Indicators of grass water content (GWC) have a significant impact on eco-hydrological processes such as evapotranspiration and rainfall interception. Several site-specific factors such as seasonal precipitation, temperature, and topographic variations cause soil and ground moisture content variations, which have significant impacts on GWC. Estimating GWC using multisource data may provide robust and accurate predictions, making it a useful tool for plant water quantification and management at various landscape scales. In this study, Sentinel-2 MSI bands, spectral derivatives combined with topographic and climatic variables, were used to estimate leaf area index (LAI), canopy storage capacity (CSC), canopy water content (CWC) and equivalent water thickness (EWT) as indicators of GWC within the communal grasslands in Vulindlela across wet and dry seasons based on single-year data. The results illustrate that the use of combined spectral and topo-climatic variables, coupled with random forest (RF) in the Google Earth Engine (GEE), improved the prediction accuracies of GWC variables across wet and dry seasons. LAI was optimally estimated in the wet season with an RMSE of 0.03 m⁻² and R² of 0.83, comparable to the dry season results, which exhibited an RMSE of 0.04 m⁻² and R² of 0.90. Similarly, CSC was estimated with high accuracy in the wet season (RMSE = 0.01 mm and R² = 0.86) when compared to the RMSE of 0.03 mm and R² of 0.93 obtained in the dry season. Meanwhile, for CWC, the wet season results show an RMSE of 19.42 g/m⁻² and R² of 0.76, which were lower than the accuracy of RMSE = 1.35 g/m⁻² and R² = 0.87 obtained in the dry season. Finally, EWT was best estimated in the dry season, yielding a model accuracy of RMSE = 2.01 g/m⁻² and R² = 0.91 as compared to the wet season (RMSE = 10.75 g/m⁻² and R² = 0.65). CSC was best optimally predicted amongst all GWC variables in both seasons. The optimal variables for estimating these GWC variables included the red-edge, near-infrared region (NIR) and short-wave infrared region (SWIR) bands and spectral derivatives, as well as environmental variables such as rainfall and temperature across both seasons. The use of multisource data improved the prediction accuracies for GWC indicators across both seasons. Such information is crucial for rangeland managers in understanding GWC variations across different seasons as well as different ecological gradients. Full article

Water quality deterioration is a serious problem with the increase in the urbanization rate. However, water quality monitoring uses grab sampling of physico-chemical parameters and a water quality index method to assess water quality. Both processes are lengthy and expensive. These traditional indices are biased towards the physico-chemical parameters because samples are only collected from certain sampling points. These limitations make the current water quality index method unsuitable for any water body in the world. Thus, we develop an enhanced water quality index method based on a semi-supervised machine learning technique to determine water quality. This method follows five steps: (i) parameter selection, (ii) sub-index calculation, (iii) weight assignment, (iv) aggregation of sub-indices and (v) classification. Physico-chemical, air, meteorological and hydrological, topographical parameters are acquired for the stream network of the Rawal watershed. Min-max normalization is used to obtain sub-indices, and weights are assigned with tree-based techniques, i.e., LightGBM, Random Forest, CatBoost, AdaBoost and XGBoost. As a result, the proposed technique removes the uncertainties in the traditional indexing with a 100% classification rate, removing the necessity of including all parameters for classification. Electric conductivity, secchi disk depth, dissolved oxygen, lithology and geology are amongst the high weighting parameters of using LightGBM and CatBoost with 99.1% and 99.3% accuracy, respectively. In fact, seasonal variations are observed for the classified stream network with a shift from 55:45% (January) to 10:90% (December) ratio for the medium to bad class. This verifies the validity of the proposed method that will contribute to water management planning globally. Full article

Leaf area index (LAI) is an important parameter in plant ecophysiology; it can be used to quantify foliage directly and as a measure of the photosynthetic active area and, thus, the area subject to transpiration in vegetation. The aim of this paper was to review work on remote sensing methods of estimating LAI across different forest ecosystems, crops and grasslands in terms of remote sensing platforms, sensors and models. To achieve this aim, scholarly articles with the title or keywords “Leaf Area Index estimation” or “LAI estimation” were searched on Google Scholar and Web of Science with a date range between 2010 and 2020. The study’s results revealed that during the last decade, the use of remote sensing to estimate and map LAI increased for crops and natural forests. However, there is still a need for more research concerning commercial forests and grasslands, as the number of studies remains low. Of the 84 studies related to forests, 60 were related to natural forests and 24 were related to commercial forests. In terms of model types, empirical models were most often used for estimating the LAI of forests, followed by physical models. Full article