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Fusarium wilt of banana is a major production constraint in India, prompting banana growers to replace bananas with less remunerative crops. Effective disease management practices thus need to be developed and implemented to prevent further spread and damage caused by Fusarium oxysporum f. sp. cubense (Foc), the cause of Fusarium wilt. Currently, knowledge of disease incidence, affected varieties, and the geographical spread of Foc races in India are only scantily available. An extensive field survey was conducted in 53 districts of 16 major banana-growing states of and one union territory of India that covered both tropical and subtropical regions. Disease incidence ranged from 0 to 95% on farms, with Cavendish bananas (AAA) most affected. No Fusarium wilt symptoms due to Foc R1 were observed in Nendran (AAB) or Red Banana (AAA) in South India. During the survey, 293 Foc isolates were collected from Cavendish, Pisang Awak (ABB), Silk (AAB), Monthan (ABB), Neypoovan (AB), and Mysore (AAB) bananas. Isolate diversity was assessed through Vegetative Compatibility Group (VCG) analyses, sequencing of EF1α gene sequences, phylogenetic analyses, and characterisation by SIX gene composition. Thirteen VCGs were identified, of which VCGs 0124, 0125, 01220, and 01213/16 were dominant and infected Cavendish bananas. Phylogenetic analysis divided the Indian Foc isolates into race 1 (R1), subtropical race 4 (STR4), and tropical race 4 (TR4). Secreted in Xylem (SIX) gene analyses indicated that the effector genes SIX4 and SIX6 were present in the VCGs 0124, 0124/5, 0125, and 01220 of race 1, SIX7 was present only in Foc STR4, and SIX8 was found only in Foc R4 (TR4 and STR4) isolates. Insights into the geographical distribution of Foc races, and their interactions with banana varieties, can guide integrated disease management intervention strategies across India. Full article

Satellite remote sensing enables monitoring of regenerative agriculture practices, such as crop rotation, cover cropping, and conservation tillage to allow tracking and quantification at unprecedented scales. The Monitor system presented here capitalizes on the scope and scale of these data by integrating crop identification, cover cropping, and tillage intensity estimations annually at field scales across the contiguous United States (CONUS) from 2014 to 2023. The results provide the first ever mapping of these practices at this temporal fidelity and spatial scale, unlocking valuable insights for sustainable agricultural management. Monitor incorporates three datasets: CropID, a deep learning transformer model using Sentinel-2 and USDA Cropland Data Layer (CDL) data from 2018 to 2023 to predict annual crop types; the living root data, which use Normalized Difference Vegetation Index (NDVI) data to determine cover crop presence through regional parameterization; and residue cover (RC) data, which uses the Normalized Difference Tillage Index (NDTI) and crop residue cover (CRC) index to assess tillage intensity. The system calculates field-scale statistics and integrates these components to compile a comprehensive field management history. Results are validated with 35,184 ground-truth data points from 19 U.S. states, showing an overall accuracy of 80% for crop identification, 78% for cover crop detection, and 63% for tillage intensity. Also, comparisons with USDA NASS Ag Census data indicate that cover crop adoption rates were within 20% of estimates for 90% of states in 2017 and 81% in 2022, while for conventional tillage, 52% and 25% of states were within 20% of estimates, increasing to 75% and 67% for conservation tillage. Monitor provides a comprehensive view of regenerative practices by crop season for all of CONUS across a decade, supporting decision-making for sustainable agricultural management including associated outcomes such as reductions in emissions, long term yield resiliency, and supply chain stability. Full article

As a vital conservation area for water sources in the Yellow River Basin, understanding the spatial-temporal dynamics of vegetation coverage is crucial, along with the factors that affect it, to ensure ecological preservation and sustainable development of the Yellow River Source Region (YRSR). In this paper, we utilized Landsat surface reflectance data from 2000 to 2020 using de-clouding and masking methods implementing the Google Earth Engine (GEE) cloud platform. We investigated spatial-temporal changes in vegetation coverage by combining the maximum value composite (MVC), the dimidiate pixel model (DPM), the Theil–Sen median slope, and the Mann–Kendall test. The influencing factors on vegetation coverage were quantitatively analyzed using a geographic detector, and future tendencies in vegetation coverage were predicted utilizing the Future Land Use Simulation (FLUS) model. The outcomes suggested the following: (1) On the temporal scale, vegetation coverage exhibited a general upward trend between 2000 and 2020, with the YRSR showing a yearly growth rate of 0.23% (p < 0.001). In comparison to 2000, the area designated as having extremely high vegetation coverage increased by 19.3% in 2020. (2) Spatially, the central and southeast regions have higher values of vegetation coverage, whereas the northwest has lower values. In the study area, 75.5% of the region demonstrated a significant improvement trend, primarily in Xinghai County, Zeku County, and Dari County in the south and the northern portion of the YRSR; conversely, a notable tendency of degradation was identified in 11.8% of the area, mostly in the southeastern areas of Qumalai County, Chenduo County, Shiqu County, and scattered areas in the southeastern region. (3) With an explanatory power of exceeding 45%, the three influencing factors that had the biggest effects on vegetation coverage were mean annual temperature, elevation, and mean annual precipitation. Mean annual precipitation has been shown to have a major impact on vegetation covering; the interconnections involving these factors have increased the explanatory power of vegetation coverage’s regional distribution. (4) Predictions for 2030 show that the vegetation coverage is trending upward in the YRSR, with a notable recovery trend in the northwestern region. This study supplies a theoretical foundation to formulate strategies to promote sustainable development and ecological environmental preservation in the YRSR. Full article

Wetlands are dubbed the “kidneys of the earth” and are involved in climate regulation, carbon sequestration, ecological balance preservation, and reducing the surface water pollution. Ongoing economic development has introduced pressing challenges to wetland environments. In this context, extracting coastal wetland information and monitoring the dynamic changes are essential. Using long-term sequence Sentinel-2 satellite remote sensing images and field observations, this research proposed a Dynamic Bayesian Network classification model framework based on conjugate gradient updates. We compared the wetland feature extraction effects of the Fletcher–Reeves and the Polak–Ribière–Polyak algorithms of the conjugate gradient. Then, remote sensing combined with the FRDBN classification model was used to extract the information pertinent to wetland feature types and changes in wetland areas and analyze alterations in the distribution characteristics of land cover types. The results showed that the FRDBN model achieved high accuracy (above 96%), and kappa coefficients exceeded 0.96. Long-term monitoring revealed that the area of wetlands increased by 0.85 × 104 hm² from 2016 to 2021. Non-aquatic land cover types exhibited pronounced dynamic changes, with the area of change representing 58–69% of the monitored total. Specifically, the transition between salt marsh vegetation and artificial wetlands was relatively obvious. The FRDBN model provides a new method for extracting wetland feature information. Wetland protection, dynamic monitoring, and carbon sink research can provide robust technology support, facilitating investigations into coastal salt marsh carbon sinks and technological advances in carbon sink assessment. Full article

Soil salinity is considered one of the biggest constraints to crop production, particularly in arid and semi-arid regions affected by recurrent and long periods of drought, where high salinity levels severely impact plant stress and consequently agricultural production. Climate change accelerates soil salinization, driven by factors such as soil conditions, land use/land cover changes, and water deficits, over extensive spatial and temporal scales. Continuous monitoring of areas at risk of salinization plays a critical role in supporting effective land management and enhancing agricultural production. For these purposes, this work aims to propose a spatiotemporal method for monitoring soil salinization using spectral indices derived from Earth observation data. The proposed approach was tested in the Zaghouan Region in northeastern Tunisia, a region where soils are characterized by alarming levels of salinization. To address this concern, remote sensing techniques were applied for the analysis of satellite imagery generated from Landsat 5, Landsat 8, and Landsat 9 missions. A comprehensive field survey complemented this approach, involving the collection of 229 geo-referenced soil samples. These samples were representative of distinct soil salinity classes, including non-saline, slightly saline, moderately saline, strongly saline, and very strongly saline soils. Soil salinity modeling using Landsat-8 OLI data revealed that the SI-5 index provided the most accurate predictions, with an R² of 0.67 and an RMSE of 0.12 dS/m. By 2023, 42.3% of the study area was classified as strongly or very strongly saline, indicating a significant increase in salinity over time. This rise in salinity corresponds to notable land use and land cover (LULC) changes, as 55.9% of the study area experienced LULC shifts between 2000 and 2023. A decline in vegetation cover coincided with increasing salinity, showing an inverse relationship between these factors. Additionally, the results highlight the complex interplay among these variables demonstrating that soil salinity levels are significantly impacted by climate change indicators, with a negative correlation between precipitation and salinity (r = −0.85, p < 0.001). Recognizing the interconnections between soil salinity, LULC changes, and climate variables is essential for developing comprehensive strategies, such as targeted irrigation practices and land suitability assessments. Earth observation and remote sensing play a critical role in enabling more sustainable and effective soil management in response to both human activities and climate-induced changes. Full article

In recent decades, global ecosystems have increasingly faced impacts from heightened precipitation variability. Specifically, water availability is an essential factor in wetland dynamics and has ecological importance in the high-Andean wetlands in both mountains and downstream ecosystems, particularly in semi-arid regions. This study focused on a chain of twelve high-Andean wetlands within the “Estero Derecho” nature sanctuary at the headwaters of the Elqui River in north-central Chile. The analysis of the spatiotemporal dynamics of precipitation and vegetation cover used the Landsat 5 and 8 Satellite imagery-derived normalized difference vegetation index (NDVI) and normalized difference moisture index (NDMI) time series during the austral summer (December–March). We employed time series, boxplots, and least-squares regression analyses to explore vegetation cover behavior in relation to precipitation, water quality, and vegetation indices. Precipitation had a marked influence on vegetation behavior, particularly during the Chilean “megadrought” phenomenon. For both the NDVI and NDMI indices and precipitation, negative trends in the time series were observed, along with a highly significant correlation with a one-year lag between both indices and precipitation. The analysis of the individual wetlands showed different vegetation cover behaviors, which were attributable to the altitude, terrain slope, and additional water inputs from streams that have also given rise to alluvial fans that exert a shaping influence on the wetlands. In addition, significant correlations between both indices and water quality parameters (CE, Cl, Mg, Na, and Fe) were identified. The findings of this study can be incorporated into the Sanctuary’s management plan and concretely assist communities involved with wetland conservation. Full article

The rainfed cereal growing regions of Northern Kazakhstan experience significant yield fluctuations due to dependence on weather conditions. Early detection and monitoring of droughts is crucial for effective mitigation strategies in this region. This study emphasises the following objectives: (1) description of the current vegetation condition with a possible separation of short-term weather effects and (2) analysing trends of changes with their directionality and quantification. Terra MODIS satellite images from 2000 to 2023 are used. Differential indices—Normalised Difference Vegetation Index (NDVI) and Vegetation Condition Index (VCI)—are used to determine the characteristics of each current season. A key component is the comparison of the current NDVI values with historical maximum, minimum, and average values to identify early indicators of drought. NDVI deviations from multiyear norms and VCI values below 0.3 visually reflect changing vegetation conditions influenced by seasonal weather patterns. The results show that the algorithm effectively detects early signs of drought through observed deviations in NDVI values, showing a trend towards increasing drought frequency and intensity in Northern Kazakhstan. The algorithm was particularly effective in detecting severe drought seasons in advance, as was the case in June 2010 and May 2012, thus supporting early recognition of drought onset. The Integrated Vegetation Index (IVI) and Integrated Vegetation Condition Index (IVCI) time series are used for integrated multiyear assessments, in analysing temporal changes in vegetation cover, determining trends in these changes, and ranking the weather conditions of each growing season in the multiyear series. Areas with high probability of drought based on low IVCI values are mapped. The present study emphasises the value of remote sensing as a tool for drought monitoring, offering timely and spatially detailed information on vulnerable areas. This approach provides critical information for agricultural planning, environmental management and policy making, especially in arid and semi-arid regions. The study emphasises the importance of multiyear data series for accurate drought forecasting and suggests that this methodology can be adapted to other drought-sensitive regions. Emphasising the socio-economic benefits, this study suggests that the early detection of drought using satellite data can reduce material losses and facilitate targeted responses. Full article

This study accurately inverts key growth parameters of rice, including Leaf Area Index (LAI), chlorophyll content (SPAD) value, and height, by integrating multisource remote sensing data (including MODIS and ERA5 imagery) and deep learning models. Dehui City in Jilin Province, China, was selected as the case study area, where multidimensional data including vegetation indices, ecological function parameters, and environmental variables were collected, covering seven key growth stages of rice. Data analysis and parameter prediction were conducted using a variety of machine learning and deep learning models including Partial Least Squares (PLSs), Support Vector Machine (SVM), Random Forest (RF), and Long Short-Term Memory Networks (LSTM), among which the LSTM model demonstrated superior performance, particularly at multiple critical time points. The results show that the LSTM performed best in inverting the three parameters, with the LAI inversion accuracy on 21 August reaching a coefficient of determination (R²) of 0.72, root mean square error (RMSE) of 0.34, and mean absolute error (MAE) of 0.27. The SPAD inversion accuracy on the same date achieved an R² of 0.69, RMSE of 1.45, and MAE of 1.16. The height inversion accuracy on 25 July reached an R² of 0.74, RMSE of 2.30, and MAE of 2.08. This study not only verifies the effectiveness of combining multisource data and advanced algorithms but also provides a scientific basis for the precision management and decision-making of rice cultivation. Full article

This study examines climate change impacts on evapotranspiration in Inner Mongolia, analyzing potential (PET) and actual (AET) evapotranspiration shifts across diverse land-use classes using the SEBAL model and SSP2-4.5 and SSP5-8.5 projections (2030–2050) relative to a 1985–2015 baseline. Our findings reveal substantial PET increases across all LULC types, with Non-Vegetated Lands consistently showing the highest absolute PET values across scenarios (931.19 mm under baseline, increasing to 975.65 mm under SSP5-8.5) due to limited vegetation cover and shading effects, while forests, croplands, and savannas exhibit the most pronounced relative increases under SSP5-8.5, driven by heightened atmospheric demand and vegetation-induced transpiration. Monthly analyses show pronounced PET increases, particularly in the warmer months (June–August), with projected SSP5-8.5 PET levels reaching peaks of over 500 mm, indicating significant future water demand. AET increases are largest in densely vegetated classes, such as forests (+242.41 mm for Evergreen Needleleaf Forests under SSP5-8.5), while croplands and grasslands exhibit more moderate gains (+249.59 mm and +167.75 mm, respectively). The widening PET-AET gap highlights a growing vulnerability to moisture deficits, particularly in croplands and grasslands. Forested areas, while resilient, face rising water demands, necessitating conservation measures, whereas croplands and grasslands in low-precipitation areas risk soil moisture deficits and productivity declines due to limited adaptive capacity. Non-Vegetated Lands and built-up areas exhibit minimal AET responses (+16.37 mm for Non-Vegetated Lands under SSP5-8.5), emphasizing their limited water cycling contributions despite high PET. This research enhances the understanding of climate-induced changes in water demands across semi-arid regions, providing critical insights into effective and region-specific water resource management strategies. Full article

Accurate and reliable knowledge about grassland distribution is essential for farmers, stakeholders, and government to effectively manage grassland resources from agro-economical and ecological perspectives. This study developed a novel pixel-based grassland classification approach using three supervised machine learning (ML) algorithms, which were assessed in the province of Manitoba, Canada. The grassland classification process involved three stages: (1) to distinguish between vegetation and non-vegetation covers, (2) to differentiate grassland from non-grassland landscapes, and (3) to identify three specific grassland classes (tame, native, and mixed grasses). Initially, this study investigated different satellite data, such as Sentinel-1 (S1), Sentinel-2 (S2), and Landsat 8 and 9, individually and combined, using the random forest (RF) method, with the best performance at the first two steps achieved using a combination of S1 and S2. The combination was then utilized to conduct the first two steps of classification using support vector machine (SVM) and gradient tree boosting (GTB). In step 3, after filtering out non-grassland pixels, the performance of RF, SVM, and GTB classifiers was evaluated with combined S1 and S2 data to distinguish different grassland types. Eighty-nine multitemporal raster-based variables, including spectral bands, SAR backscatters, and digital elevation models (DEM), were input for ML models. RF had the highest classification accuracy at 69.96% overall accuracy (OA) and a Kappa value of 0.55. After feature selection, the variables were reduced to 61, increasing OA to 72.62% with a Kappa value of 0.58. GTB ranked second, with its OA and Kappa values improving from 67.69% and 0.50 to 72.18% and 0.58 after feature selection. The impact of raster data quality on grassland classification accuracy was assessed through multisensor image fusion. Grassland classification using the Hue, Saturation, and Value (HSV) fused images showed higher OA (59.18%) and Kappa values (0.36) than the Brovey Transform (BT) and non-fused images. Finally, a web map was created to show grassland results within the Soil Landscapes of Canada (SLC) polygons, relating soil landscapes to grassland distribution and providing valuable information for decision-makers and researchers. Future work may include extending the current methodology by considering other influential variables, like meteorological parameters or soil properties, to create a comprehensive grassland inventory across the whole Prairie ecozone of Canada. Full article

Senegalese cities have experienced rapid urbanisation, leading to profound landscape changes. Dakar, one of Senegalese’s fastest-growing cities, is experiencing rapid urban expansion, significantly reducing green spaces. These green spaces, essential for urban sustainability and resilience, have become increasingly scarce, affecting the city’s environment and the quality of life for its residents. This study aims to assess the spatiotemporal changes in Dakar’s green spaces from 1990 to 2022. Using satellite imagery, this study produces land use maps to quantify green space coverage over the years. The results show a gradual decline in green spaces in Dakar between 1990 and 2022. In 1990, green spaces covered an estimated 13.36% of Dakar’s area, which decreased significantly to 9.54% by 2022. In contrast, other land uses, such as built-up areas, increased significantly over this period, rising from 19.23% in 1990 to 39.34% in 2022. Moreover, built-up areas are not the sole contributor to the reduction of green spaces in Dakar. The study revealed that, between 1990 and 2022, 5.49% of green spaces were converted into bare soil due to excessive tree cutting. This pattern highlights the growing challenge of green space availability as built-up areas expand rapidly, particularly when growth is unplanned. This study underscores the importance of sustainable urban planning that integrates the protection and conservation of Dakar’s vegetation to preserve vital ecosystem services. Full article

Stormwater runoff and nutrient pollution are significant sources of water contamination that continue to grow in rural and suburban watersheds. The goal of this research is to analyze and evaluate the impact of urbanization and industrialization on suburban watersheds in southeast Texas. The objectives are to: (1) determine nutrient and heavy metal concentrations in soil and water samples along Spring Creek Bayou (SC), (2) analyze land cover changes over the last 30 years and (3) assess and evaluate socio-economic data within the watershed. The soil and water samples were collected from upstream, midstream and downstream locations in triplicate during the spring and fall seasons along the bayou. The samples were analyzed to determine chemical concentrations and Landsat 5, and eight imageries were used to derive thematic land cover maps. The soil and water chemical concentrations were interpolated to spatial maps for distribution analysis. The chemical analysis of water samples collected from SC Bayou revealed that N and P concentrations were at elevated levels that can pose a threat to water quality and aquatic organisms. Heavy metal concentrations of Zn were at elevated levels in water samples from the SC Bayou watershed. Land cover change patterns showed that high-vegetation surfaces decreased while low-vegetation surfaces increased slightly over the past three decades. The watershed experienced an increase in total population from 129,629 residents in 1990 to 389,977 residents in 2020. This research is important in improving our understanding on the impact of natural and human activities on suburban watersheds in the Greater Houston metropolitan region. Full article

For tropical rainforest regions with dense vegetation cover, the development of effective large-scale soil mapping methods is crucial to improve soil management practices to replace the time-consuming and laborious conventional approaches. While machine learning (ML) algorithms demonstrate superior predictability of soil properties over linear models, their practical and automated application for predicting soil properties using remote sensing data requires further assessment. Therefore, this study aims to integrate Unmanned Aerial Vehicles (UAVs)-based hyperspectral images and Light Detection and Ranging (LiDAR) points to predict the soil properties indirectly in two tropical rainforest mountains (Diaoluo and Limu) in Hainan Province, China. A total of 175 features, including texture features, vegetation indices, and forest parameters, were extracted from two study sites. Six ML models, Partial Least Squares Regression (PLSR), Random Forest (RF), Adaptive Boosting (AdaBoost), Gradient Boosting Decision Trees (GBDT), Extreme Gradient Boosting (XGBoost), and Multilayer Perceptron (MLP), were constructed to predict soil properties, including soil acidity (pH), total nitrogen (TN), soil organic carbon (SOC), and total phosphorus (TP). To enhance model performance, a Bayesian optimization algorithm (BOA) was introduced to obtain optimal model hyperparameters. The results showed that compared with the default parameter tuning method, BOA always improved models’ performances in predicting soil properties, achieving average R² improvements of 202.93%, 121.48%, 8.90%, and 38.41% for soil pH, SOC, TN, and TP, respectively. In general, BOA effectively determined the complex interactions between hyperparameters and prediction features, leading to an improved model performance of ML methods compared to default parameter tuning models. The GBDT model generally outperformed other ML methods in predicting the soil pH and TN, while the XGBoost model achieved the highest prediction accuracy for SOC and TP. The fusion of hyperspectral images and LiDAR data resulted in better prediction of soil properties compared to using each single data source. The models utilizing the integration of features derived from hyperspectral images and LiDAR data outperformed those relying on one single data source. In summary, this study highlights the promising combination of UAV-based hyperspectral images with LiDAR data points to advance digital soil property mapping in forested areas, achieving large-scale soil management and monitoring. Full article

This study examines the evolution of eco-environmental quality and its driving forces in the Qinghai-Tibet Plateau, with a particular focus on the Qinghai Lake region (QLR). By employing principal component analysis (PCA) on nearly 20 years of remote sensing data, we reveal the dynamic characteristics of ecological quality in this sensitive area. The results indicate that the ecological quality of the QLR has exhibited significant fluctuations over the past two decades, influenced by multiple factors such as climate change, human activities, and policy adjustments. Specifically, the fluctuations in ecological quality are closely associated with key ecological indicators, including the Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), Wetness Index (WET), and Normalized Differential Bare Soil Index (NDBSI). Vegetation cover and moderate humidity have substantial positive effects on ecological quality, while high temperatures and dry soil conditions exert negative impacts. Full article

Exploring NPP changes and their corresponding drivers is significant for the achievement of sustainable ecosystem management and in addressing climate change. This study aimed to explore the spatiotemporal variation in NPP and analyze the effects of vegetation and climate change on the global NPP from 2003 to 2020. Methodologically, the Theil–Sen and Mann–Kendall methods were used to study the spatiotemporal characteristics of global NPP change. Moreover, a ridge regression model was built by selecting the vegetation indicators of the leaf area index (LAI) and fraction vegetation coverage (FVC) and the climate factors of CO₂, shortwave downward solar radiation (R_sd), precipitation (P), and temperature (T). Then, the relative contributions of each factor were evaluated. The results showed that, over the previous two decades, the global mean NPP reached 503.43 g C m⁻² yr⁻¹, with a fluctuating upward trend of 1.52 g C m⁻² yr⁻¹. The regions with a significant increase in NPP (9.22 g C m⁻² yr⁻¹) were mainly located in Central Africa, while the regions with decreasing NPP (−3.21 g C m⁻² yr⁻¹) were primarily in the Amazon Rainforest in northern South America. Additionally, CO₂, the LAI, and the FVC exhibited positive contributions to the NPP trend, with the predominant factors being CO₂ (relative contribution of 32.22%) and the LAI (relative contribution of 21.96%). In contrast, the contributions of R_sd and precipitation were relatively low (<10%). In addition, the contributions varied at different land cover and climate zone scales. The CO₂, LAI, FVC, and temperature were the predominant factors affecting NPP across the vegetation types. At the scale of climate zones, CO₂ was the predominant factor influencing changes in vegetation NPP. As the climate gradually transitioned towards temperate and cold regions, the contribution of the LAI to NPP increased. The findings of this study help to clarify the effects of vegetation and climate change on the ecosystem, providing theoretical support for ecological environmental protection and other related initiatives. Full article