Search Results (1,656)

Accurately identifying medium- and low-yield farmlands (MLYF) and diagnosing their constraints are essential for targeted improvement of productivity and national food security. However, traditional evaluation is usually limited by coarse spatial resolution and high labor costs, and a methodological gap remains between large-scale MLYF classification and system constraints diagnosis. To address the current methodological gaps, this study developed a comprehensive framework to determine the spatial distribution of MLYF in northern China and clarify their key constraints. The framework combined the Spatio-Temporal Random Forest (STRF) algorithm with vegetation indices (VIs), climate, and soil data to delineate MLYF and uses interpretable machine learning to diagnose major constraints. The model showed high explanatory power and ensured the reliability of attribution results. The results showed that MLYF exhibited obvious spatial heterogeneity, accounting for 48.66% of the total cultivated land in the study area. These MLYF are primarily concentrated in the northwestern Loess Plateau (LP), the central Along the Great Wall (ATGW) region, and the peripheries of the Huang-Huai-Hai (HHH) Plain. In addition to spatial classification, our analysis revealed significant differences in constraint mechanisms: soil structural, nutrient, and salinization constraints predominantly restrict productivity in the HHH Plain, whereas water stress and soil erosion are the primary drivers of yield gaps in the LP and ATGW regions. These findings provide new data and insights for understanding the spatial heterogeneity of farmland quality in typical dryland agricultural regions in northern China, and offer a scientific basis for targeted land improvement and regional agricultural sustainability. Full article

Accurately characterizing the spatiotemporal variations in precipitation in China’s drylands is important for solving water scarcity in the region, guaranteeing security in the ecological environment, and conducting precise drought disaster management. To reduce the uncertainty in the existing precipitation products, we developed a two-stage machine-learning framework combining extreme gradient boosting (XGBoost) and random forest (RF) residual corrections. Based on the ground-based observation data from 1030 meteorological stations and numerous high-precision precipitation products (GPM IMERG Final V6, MSWEP V2, CMFD 2.0, TerraClimate), a monthly fused precipitation dataset (XGB-RF) for China’s drylands was produced during the 2001–2020 period at the 0.1° resolution. The validation results showed that the XGB-RF had a monthly Kling–Gupta Efficiency (KGE) of 0.941, and it improved 20.6–62.2% relatively with that of input individual products. For the dataset as a whole, we found very consistent, reliable performance in all seasons and topography, in particular in winter time and data-scarce western areas where individual products have large biases. More importantly, the XGB-RF was employed for drought monitoring based on the 1-month Standardized Precipitation Index that calculated the median KGE of 0.888, which made good drought trend tracking and drought features possible. Notably, the KGE for the mean drought intensity was 0.757, which was higher than that of independent original products. This study provides a high-resolution precipitation forcing dataset and demonstrates the effectiveness of two-stage machine learning strategies in enhancing hydroclimatic monitoring and drought risk assessment in arid and semi-arid regions. Full article

Dryland farming systems in South Dakota face rainfall variability and rising water demand, which can reduce crop productivity and threaten long-term soil health. We combined field experiments across three dryland sites in South Dakota (Roscoe, Selby, Fort Pierre) with continuous soil moisture monitoring (0–15, 15–30, 30–45 cm) and HYDRUS-1D modeling to evaluate cover crops and soil amendments (biochar, manure) on water retention. During the active cover crop growth period, plots with cover crops consistently exhibited lower soil water content than plots without cover crops, likely due to increased transpiration. Plots with no cover crop (NCC) retained more water than cover crop (CC) plots (Roscoe: 26.27% vs. 24.16% at 0–15 cm). During the primary crop growing season, biochar consistently increased soil moisture (θ) compared with manure and unamended plots. Following a 43-day dry spell (1 July–13 August 2024), soil moisture declined by approximately 0.096 m³ m⁻³ in the biochar plots, compared with 0.125 m³ m⁻³ under manure and 0.216 m³ m⁻³ in the unamended control, exhibiting differences in water retention capacity among treatments. HYDRUS inverse modeling reproduced observed soil moisture dynamics (R² ~ 0.91) and demonstrated higher water content under biochar. Scenario analysis using representative wet (2008) and dry (2012) years showed the cover crop + biochar combination maintained the highest average water content. Results support integrating biochar with cover cropping to buffer drought and improve soil water availability in dryland farming. Full article

Safe and sustainable water sources are a serious global concern because of growing population, urbanization, industrialization, and climate change. The conventional water surveillance systems that rely on periodic sampling and laboratory analysis fail to provide time-sensitive and high-resolution data utilized for proactive water management. Artificial Intelligence of Things (AIoT) offers a viable solution, as they can provide tools of constant active monitoring and predictive analytics. The integration of IoT sensor networks with machine learning (ML) methods enables real-time data-driven water resource monitoring and intelligent decision-making, enhances water quality assessment, supports early detection of anomalies, improves predictive capabilities for floods and droughts, and facilitates efficient irrigation and reservoir management, ultimately leading to sustainable and resilient water management systems. The paper presents an extensive overview of AIoT solutions for water quality monitoring and water resource management, including IoT sensor networks for real-time data acquisition, machine learning methods for prediction, classification, anomaly detection, and edge computing platforms for data processing and decision support. This study also highlights existing possibilities, obstacles, and research gaps identified through a review of the recent literature. Key challenges reported across multiple studies include limited data availability, sensor calibration bias, integration of heterogeneous data, and insufficient model interpretability. Advanced paradigms such as digital twin systems, TinyML, federated learning, and explainable AI (XAI) are examined as enabling technologies to enhance system efficiency, flexibility, and transparency. Future research directions are outlined to develop scalable, interpretable, and real-time water management solutions. Full article

Climate variability and rising water scarcity are major challenges to agricultural sustainability, particularly in Mediterranean climates with high spatial heterogeneity. Agroclimatic zoning is a fundamental analytical tool for digital agriculture and climate-resilient agriculture. The current effort proposes an integrated agroclimatic and micro-agroclimatic zoning approach for Greece, based on the Aridity Index (AI), CORINE Land Cover 2018 land-use data, and topographic factors. Daily precipitation and reference evapotranspiration data from 139 meteorological stations and 382 rain gauges were spatially interpolated using Empirical Bayesian Kriging, identifying eight agroclimatic classes adapted to the country’s specific conditions. The results indicate a high degree of variability in space, with most agricultural areas being classified as dry to sub-humid, suggesting higher irrigation requirements and sensitivity to drought. Micro-agroclimatic zones have been identified by combining agroclimatic classes, land use, and elevation. Consequently, the derived zones can be used as groundwork for designing methodologies towards more efficient agrometeorological monitoring through the improved localization of IoT agrometeorological stations. Validation with the Köppen–Geiger climate classification reveals high spatial and statistical agreement (χ² = 248,454.09, df = 49, p < 0.001), proving the climatic validity of the proposed approach and its higher sensitivity to local water balance conditions. Full article

Climate-induced drought and intensifying land-use pressures threaten ecosystem services and agricultural productivity, particularly in regions with distinctive soil and ecological characteristics. Alabama’s Black Belt, defined by its clay-rich soils and shaped by a legacy of plantation agriculture, uneven land tenure, and persistent socioeconomic disadvantage, is increasingly vulnerable to these interacting stressors. This study analyzes long-term (2000–2023) spatiotemporal patterns of Land Use Land Cover (LULC) change and vegetation response to drought to inform sustainable resource management. Multi-temporal Landsat imagery and National Land Cover Database (NLCD) products were used to quantify LULC dynamics. At the same time, vegetation condition and moisture stress were assessed using the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Moisture Index (NDMI). Drought conditions were evaluated using the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI), which incorporates temperature-driven evaporative demand. Results indicate substantial landscape change, including declines in deciduous forest (−17.78%) and pasture/hay (−13.17%), alongside increases in medium-intensity developed land (+20.25%) and evergreen forest (+10.62%). Declining NDVI and NDMI values indicate increasing vegetation stress, particularly during prolonged droughts. Vegetation response exhibited a weak relationship with SPI (R = 0.37) but a stronger association with SPEI (R = 0.59), underscoring the importance of accounting for atmospheric water demand. These findings highlight the growing vulnerability of Black Belt ecosystems to coupled climate and land-use pressures and provide insights to strengthen climate-resilient agricultural management. Full article

Persistent droughts and shifting precipitation regimes continue to threaten water security across the United States, with arid and semi-arid regions remaining the most vulnerable. This study examines the spatial and temporal patterns of aridity and water scarcity across drought-prone stations (111) and regions of the U.S. using 30 years (1991–2020) of precipitation records from xmACIS II. Weather stations were categorized into arid (<10 inches/year), semi-arid (10–20 inches/year), and non-arid (>20 inches/year) zones, revealing a distinct west–east gradient: arid and semi-arid conditions prevail across the western and central U.S., while the eastern regions remain largely non-arid. Drought frequency analysis spanning 2000–2019 indicates that certain regions experienced exceptional drought conditions (D3 or higher) for more than 50% of the study period, with localized areas enduring over 300 weeks of extreme drought. Long-term precipitation trends (1920–2020) in Texas, Washington, and South Dakota reflect a modest increase in precipitation; however, CMIP6 multi-model ensemble projections under a 2 °C and 4 °C warming scenario point to divergent future trajectories, with some regions experiencing increased wetness while others face progressive drying. These findings offer actionable insights for drought monitoring and climate adaptation strategies, underscoring the heightened vulnerability of arid and semi-arid zones to intensify water scarcity. Full article

Remote sensing has become a cornerstone of modern agricultural monitoring, addressing the dual challenges of increasing production while ensuring environmental sustainability. Based on a conceptual framework developed over the past decade, key application areas include yield estimation, phenology, stress assessment (e.g., drought), crop mapping, and land-use change detection. In Central Europe, regionally specific conditions such as fragmented land ownership, small and irregular plots, and high climate variability shape these applications. Annual field crops, such as cereals, oilseeds, maize, and forage crops dominate production and represent the primary focus of monitoring efforts. Optical data from Sentinel-2 are effective for mapping crop types and analyzing phenology, especially when dense time series are available. However, persistent cloud cover during critical growth phases limits the effectiveness of optical approaches, prompting the integration of radar data from Sentinel-1. Multi-sensor strategies increase the robustness of classification and temporal continuity, supporting monitoring under adverse conditions. Reliable reference data from systems such as the Land Parcel Identification System enables parcel-level validation and facilitates object-oriented analyses in line with management needs. Future developments will increasingly rely on advanced time-series analysis, machine learning, and the integration of agrometeorological and crop model data. As climate change intensifies drought frequency and yield variability, remote sensing will play a pivotal role in enabling near-real-time monitoring and decision support within the evolving landscape of digital agriculture ecosystems. The aim of this review article is to provide an overview of crop monitoring in the Central European region over approximately the past fifteen years, emphasizing trends in subsequent technological and procedural developments. Full article

The Moroccan locust (Dociostaurus maroccanus) is one of the most economically significant locust species in the Caucasus and Central Asia. In the past, the Mediterranean region also experienced severe damage to crops and pastures, until widespread grassland conversion to cropland began in the second half of the 20th century. However, climate change, environmental shifts, land-use changes, cropland abandonment, and overgrazing are likely to alter the spatial distribution and outbreak patterns of this pest. Understanding potential changes and geographic shifts is essential for proactive pest management, including effective monitoring and control strategies. In this study, we apply Ecological Niche Modelling (ENM) using 12 machine learning algorithms, historical survey data covering the species’ full distribution range, and relevant abiotic variables to identify the most suitable areas for potential mass breeding during 1991–2020 and the near future (2021–2040), based on the “middle-of-the-road” Shared Socioeconomic Pathway (SSP2-4.5) scenario. Our results indicate significant regional shifts. Notably, breeding suitability is projected to increase in parts of Greece, Turkey, Armenia, Georgia, Kyrgyzstan, and Tajikistan. In contrast, countries such as Turkmenistan, Afghanistan, Pakistan, and Spain are likely to experience a decline in optimal breeding areas. The forecast results support field observations of a geographical shift northward and toward higher altitudes. Additionally, higher temperatures in suitable areas suggest more drought-like conditions, which typically promote locust population explosions and outbreaks. If left unaddressed, such outbreaks can cause severe economic damage to affected regions. Full article

The Numidian cypress (Cupressus sempervirens var. numidica, C. numidica hereafter) is a rare, almost unknown, endemic taxon of Tunisia whose conservation has long been hampered by human activities, taxonomic uncertainty and limited ecological knowledge, with only 64.33 ha of its populations remaining. Although recent genetic studies have confirmed its native status and long-term isolation, detailed information on its distribution, population structure and threats remain lacking. This study provides the first comprehensive assessment of C. numidica across its remaining range. Field surveys revealed that the species persists in only three small, fragmented forests, Bou Abdallah, Sidi Amer, and Dir Satour, covering a total of 64.33 ha. Soil analysis revealed some differences among sites, with Bou Abdallah showing higher clay content and Dir Satou exhibiting the highest levels of nitrogen, organic carbon, Olsen P, and available Mn and Mo. Climatic analyses indicate a semi-arid Mediterranean environment with pronounced summer droughts and a clear warming trend. Trees showed widespread damages, due to intensive grazing, tree cutting, crown dieback (drought), and pest and pathogen attacks. Natural regeneration was limited, and the condition of affected trees ranged from moderate to severe, with Bou Abdallah showing the highest levels of degradation. Notably, the severe fungal pathogen Seiridium cardinale, causal agent of cypress canker, was detected on C. numidica for the first time, highlighting an urgent conservation concern. Our results point to a staged conservation approach over time. In the immediate term (within 1 year), urgent monitoring and management of S. cardinale is needed. In the short term, efforts should focus on protecting carefully selected areas, about 5–10 regeneration microsites per forest, from grazing to support natural regeneration, reduce ongoing soil degradation, and establish clonal and seed-production plantations along with long-term seed storage. In the long term, the survival of C. numidica will only be possible with the active involvement of local communities, through awareness campaigns, adapting traditional practices such as gdel, and developing small-scale ecotourism that provides sustainable livelihoods while reinforcing support for conservation. Full article

Masson Pine (Pinus massoniana), an important afforestation species in southern China, is severely threatened by pine wilt disease caused by pine wood nematode (Bursaphelenchus xylophilus, PWN). To differentiate mortality induced by B. xylophilus from that caused by abiotic environmental factors, hyperspectral imaging and needle chlorophyll content were measured and analyzed for the early detection physiological changes in Masson pine seedlings under various environmental stressors. Four-year-old Masson pine seedlings were subjected to PWN inoculation, mechanical injury, drought, and waterlogging treatments. Hyperspectral reflectance and needle chlorophyll content of Masson pine were measured concurrently at 7-day intervals. The results showed that hyperspectral imaging responses varied among the stressors. Both PWN and waterlogging stress induced rapid mortality, with spectral changes observed as early as the 3rd week and reaching statistical significance by the 5th week. Under PWN infection, hyperspectral reflectance increased markedly in the 405–580 nm range, accompanied by a pronounced blue-shift of the red edge position (680–750 nm), while needle chlorophyll content declined sharply from approximately 0.8 mg g⁻¹ to 0.48 mg g⁻¹. Waterlogging stress produced a uniform increase in reflectance within the 500–580 nm range, with the hyperspectral curve gradually flattening, and needle chlorophyll content decreasing from 0.75 mg g⁻¹ to 0.6 mg g⁻¹. Conversely, drought-stressed seedlings exhibited only minor hyperspectral changes and maintained relatively stable chlorophyll levels, demonstrating the inherent drought tolerance of Masson pine. The RF and XGBoost models performed best in fitting the entire process of pine wood nematode infection and waterlogging stress, with all R² values greater than 0.69. The distinct hyperspectral imaging patterns under nematode infection and water-related stresses provide a reliable basis for early diagnosis and monitoring pine wilt disease in Masson pine stands. Full article

Under ongoing climate change and rapid urbanization, urban hydrothermal regimes are being reshaped, intensifying drought hazards and increasing stress on urban forests. Yet, systematic assessments of drought-induced stability dynamics of urban vegetation remain limited. We identified drought events across 330 Chinese cities during 2000–2022 and quantified vegetation resistance and resilience using multi-source remote sensing data. Pronounced latitudinal divergence emerged: high-latitude cities showed lower resistance but higher resilience, whereas low-latitude cities exhibited stronger resistance but weaker recovery. Across climatic zones, resistance was greater in humid and arid cities, whereas resilience was stronger in sub-humid and semi-arid cities, indicating a climate-dependent trade-off between disturbance buffering and recovery capacity. From 2000–2011 to 2012–2022, resistance increased significantly, whereas resilience declined. Seasonally, resistance was lowest and resilience highest in summer. Drought severity and climatic background—especially drought intensity and duration—primarily governed stability patterns: stronger droughts reduced resistance but enhanced recovery. Anthropogenic factors, including population density, economic development, and CO₂ emissions, also played a significant role in shaping vegetation stability. These findings highlight the need for long-term drought monitoring and climate-specific urban forest management to strengthen ecosystem stability in rapidly urbanizing regions. Full article

The study presents a comprehensive spatio-temporal assessment of meteorological drought in the Upper Vistula basin, a region located in southern Poland. The analysis was based on monthly precipitation data from 30 meteorological stations covering the period 1961–2022. These data were used to calculate the Standardized Precipitation Index (SPI) for accumulation periods of 3, 6, 9, 12, 24, and 48 months. Drought events were identified using run theory, adopting a threshold of SPI < −1 for all accumulation periods. On this basis, drought characteristics were determined, including the number of identified drought episodes (N), average drought duration (ADD), average drought severity (ADS), and average drought intensity (ADI). The multi-scale analysis revealed a clear dependence of drought characteristics on the time scale. Short-term droughts (SPI-3 and SPI-6) occurred frequently and were characterized by high monthly intensity but short duration. In contrast, long-term droughts (SPI-24 and SPI-48) occurred less frequently, but were marked by much longer duration and greater cumulative severity, despite lower average intensity. Spatial analyses showed substantial heterogeneity of drought characteristics within the Upper Vistula basin. The western and south-western parts of the region were particularly exposed to frequent short-term droughts, whereas long-term droughts were less frequent, but more regional in nature and resulted from accumulated, multi-year precipitation deficits affecting groundwater resources and catchment retention. The presented findings provide valuable information for improving drought monitoring systems and adaptation strategies in the Upper Vistula basin and in other climatically diverse regions of Central Europe. Full article

Hurricanes routinely damage forests across the North Atlantic Basin, yet efforts to characterize their impacts have had mixed subregional success. To elucidate these challenges, this study analyzed pre- and post-hurricane land surface phenology (LSP) for 44 moderate and strong hurricanes over 22 years using the Enhanced Vegetation Index (EVI). We statistically grouped storms based on their long-term climate attributes, then compared subregional impacts with wind speed and land cover. After accounting for wind speed, responses differed among the six subregions. The Southeast U.S. showed declines in EVI for the first winter and first year post storm, but this response was weak or absent elsewhere. The Central America region declined in the first winter but not in the subsequent growing season, while four other regions showed no increased impact with wind speed in either season. We then examined six category 4 hurricanes using a forest mask. In dry areas, drought-sensitive vegetation explained weak responses, whereas in the humid tropics, rapid refoliation or sprouting was common. These factors complicate optical remote sensing assessments. Rapid evaluations can mistake defoliation for more substantial damage, and delayed assessments can confuse EVI recovery with structural recovery. Results underscore the need for ecologically tailored monitoring approaches. Full article

Accurate soil moisture assessment is essential for effective agricultural management in the southern US, where water availability has a significant impact on crop productivity. This study evaluates the Soil Moisture Active Passive (SMAP) Level-4 daily soil moisture product using in situ measurements from Natural Resources Conservation Service (NRCS) Soil Climate Analysis Network (SCAN) stations and the US. Climate Reference Network (USCRN) across diverse agroecosystems in Texas from 2016 to 2024. SMAP’s performance was examined across ten climate zones and six major land cover types, including urban regions, pastureland, grassland, rangeland, shrubland, and deciduous forests. Statistical metrics, including the coefficient of determination (R²), Root Mean Square Error (RMSE), Bias, and unbiased RMSE (ubRMSE) were used to evaluate the agreement between SMAP-derived and in situ soil moisture measurements. Results show that SMAP effectively captures seasonal soil moisture dynamics but exhibits spatially variable accuracy. The highest agreement was observed at Panther Junction (R² = 0.57, RMSE = 2.29%), followed by Austin (R² = 0.57, RMSE = 9.95%). While a weaker coefficient of determination was observed at PVAMU (R² = 0.28, RMSE = 11.28%) and Kingsville (R² = 0.11, RMSE = 7.33%), likely due to heterogeneity in land cover, and urbanized landscapes in these stations. Applying the quantile mapping bias correction methods significantly reduced RMSE and improved the accuracy of SMAP soil moisture data at some in situ measurement stations. The results highlight the importance of station-specific calibration and the integration of satellite and ground-based measurements to improve soil moisture monitoring for agriculture and drought management in Texas and similar regions. Full article