Search Results (65)

This study discusses the issue of determining the direction and strength of changes taking place in the structure of agricultural land in the mountain and foothill areas of the Carpathians in Poland in comparison with Slovakia, Romania and Ukraine. The most important financial institutional measures dedicated to the protection of the natural environment in Polish agriculture in the Areas facing Natural and other specific Constraints (ANCs) mountain and foothill in the first year of the CAP 2023–2027 were also established. Satellite data from 2001 to 2022 were used. The analyses used the land use classification MCD12Q1 provided by NASA and were made on the basis of satellite imagery collections from the MODIS sensor placed on two satellites: TERRA and AQUA. In EU countries, a decreasing trend in agricultural areas has been observed in areas below 350 m above sea level. In areas above 350 m, this trend weakened or even turned into an upward trend. Only in Ukraine was a different trend observed. It was found that in Poland, the degree of involvement of farmers from mountain and foothill areas in implementing financial institutional measures dedicated to protecting the natural environment during the study period was not satisfactory. Full article

NASH (non-alcoholic steatohepatitis) is a severe liver disease characterized by hepatic chronic inflammation that can be associated with the gut microbiota. In this study, we explored the therapeutic effect of Gynostemma pentaphyllum extract (GPE), a Chinese herbal extract, on methionine- and choline-deficient (MCD) diet-induced NASH mice. Based on the peak area, the top ten compounds in GPE were hydroxylinolenic acid, rutin, hydroxylinoleic acid, vanillic acid, methyl vanillate, quercetin, pheophorbide A, protocatechuic acid, aurantiamide acetate, and iso-rhamnetin. We found that four weeks of GPE treatment alleviated hepatic confluent zone inflammation, hepatocyte lipid accumulation, and lipid peroxidation in the mouse model. According to the 16S rRNA gene V3–V4 region sequencing of the colonic contents, the gut microbiota structure of the mice was significantly changed after GPE supplementation. Especially, GPE enriched the abundance of potentially beneficial bacteria such as Akkerrmansia and decreased the abundance of opportunistic pathogens such as Klebsiella. Moreover, RNA sequencing revealed that the GPE group showed an anti-inflammatory liver characterized by the repression of the NF-kappa B signaling pathway compared with the MCD group. Ingenuity Pathway Analysis (IPA) also showed that GPE downregulated the pathogen-induced cytokine storm pathway, which was associated with inflammation. A high dose of GPE (HGPE) significantly downregulated the expression levels of the tumor necrosis factor-α (TNF-α), myeloid differentiation factor 88 (Myd88), cluster of differentiation 14 (CD14), and Toll-like receptor 4 (TLR4) genes, as verified by real-time quantitative PCR (RT-qPCR). Our results suggested that the therapeutic potential of GPE for NASH mice may be related to improvements in the intestinal microenvironment and a reduction in liver inflammation. Full article

Non-alcoholic fatty liver disease (NAFLD) is a clinicopathological syndrome characterized by diffuse hepatocellular steatosis due to fatty deposits in hepatocytes, excluding alcohol and other known liver injury factors. However, there are no specific drugs for the clinical treatment of NAFLD. Therefore, research on the pathogenesis of NAFLD at the cellular and molecular levels is a promising approach to finding therapeutic targets and developing targeted drugs for NAFLD. Pin1 is highly expressed during adipogenesis and contributes to adipose differentiation, but its specific mechanism of action in NAFLD is unclear. In this study, we investigated the role of Pin1 in promoting the development of NAFLD and its potential mechanisms in vitro and in vivo. First, Pin1 was verified in the NAFLD model in vitro using MCD diet-fed mice by Western Blot, RT-qPCR and immunohistochemistry (IHC) assays. In the in vitro study, we used the oleic acid (OA) stimulation-induced lipid accumulation model and examined the lipid accumulation in each group of cells by oil red O staining as well as BODIPY staining. The results showed that knockdown of Pin1 inhibited lipid accumulation in hepatocytes in an in vitro lipid accumulation model and improved lipid indices and liver injury levels. Moreover, in vivo, WT and Pin1-KO mice were fed a methionine-choline deficient (MCD) diet for 4 weeks to induce the NAFLD model. The effects of Pin1 on lipid accumulation, hepatic fibrosis, and oxidative stress were evaluated by biochemical analysis, glucose and insulin tolerance tests, histological analysis, IHC, RT-qPCR and Western blot assays. The results indicate that Pin1 knockdown significantly alleviated hepatic steatosis, fibrosis and inflammation in MCD-induced NAFLD mice, improved glucose tolerance and alleviated insulin resistance in mice. Further studies showed that the AMPK/ACC1 signalling pathway might take part in the process by which Pin1 regulates NAFLD, as evidenced by the inhibition of the AMPK/ACC1 pathway. In addition, immunofluorescence (IF), coimmunoprecipitation (Co-IP) and GST pull-down experiments also showed that Pin1 interacts directly with ACC1 and inhibits ACC1 phosphorylation levels. Our study suggests that Pin1 promotes NAFLD progression by inhibiting the activation of the AMPK/ACC1 signalling pathway, and it is possible that this effect is achieved by Pin1 interacting with ACC1 and inhibiting the phosphorylation of ACC1. Full article

Non-alcoholic fatty liver disease (NAFLD) is the most prevalent chronic liver condition worldwide. Numerous studies conducted recently have demonstrated a connection between the dysbiosis of the development of NAFLD and gut microbiota. Rebuilding a healthy gut ecology has been proposed as a strategy involving the use of probiotics. The purpose of this work is to investigate and compare the function of probiotics Akkermansia muciniphila (A. muciniphila) and VSL#3 in NAFLD mice. Rodent NAFLD was modeled using a methionine choline-deficient diet (MCD) with/without oral probiotic delivery. Subsequently, qPCR, histological staining, and liver function tests were conducted. Mass spectrometry-based analysis and 16S rDNA gene sequencing were used to investigate the liver metabolome and gut microbiota. We found that while both A. muciniphila and VSL#3 reduced hepatic fat content, A. muciniphila outperformed VSL#3. Furthermore, probiotic treatment restored the β diversity of the gut flora and A. muciniphila decreased the abundance of pathogenic bacteria such as Ileibacterium valens. These probiotics altered the metabolism in MCD mice, especially the glycerophospholipid metabolism. In conclusion, our findings distinguished the role of A. muciniphila and VSL#3 in NAFLD and indicated that oral-gavage probiotics remodel gut microbiota and improve metabolism, raising the possibility of using probiotics in the cure of NAFLD. Full article

The Tibetan Plateau (TP) region contains maximal alpine grassland ecology at the mid-latitudes. This region is also recognized as an ecologically fragile and sensitive area under the effects of global warming. Regional climate modeling and ecosystem research depend on accurate land cover (LC) information. In order to obtain accurate LC information over the TP, the reliability and precision of five moderate/high-resolution LC products (MCD12Q1, C3S-LC, GlobeLand30, GLC_FCS30, and ESA2020 in 2020) were analyzed and evaluated in this study. The different LC products were compared with each other in terms of areal/spatial consistency and assessed with four reference sample datasets (Geo-Wiki, GLCVSS, GOFC-GOLD, and USGS) using the confusion matrix method for accuracy evaluation over the TP. Based on the paired comparison of these five LC datasets, all five LC products show that grass is the major land cover type on the TP, but the range of grass coverage identified by the different products varies noticeably, from 43.35% to 65.49%. The fully consistent spatial regions account for 43.72% of the entire region of the TP, while, in the transition area between grass and bare soil, there is still a large area of medium-to-low consistency. In addition, a comparison of LC datasets using integrated reference datasets shows that the overall accuracies of MCD12Q1, C3S-LC, GlobeLand30, GLC_FCS30, and ESA2020 are 54.29%, 49.32%, 53.03%, 53.73%, and 60.11%, respectively. The producer accuracy of the five products is highest for grass, while glaciers have the most reliable and accurate characteristics among all LC products for users. These findings provide valuable insights for the selection of rational and appropriate LC datasets for studying land-atmosphere interactions and promoting ecological preservation in the TP. Full article

Subtropical forest phenology differentiation is affected by temperature, precipitation, and topography. Understanding the primary contributing elements and their interactions with forest phenology can help people better comprehend the subtropical forest growth process and its response to climate. Meanwhile, the temporal and spatial variations of phenological rhythms are important indicators of climatic impacts on forests. Therefore, this study aimed to analyze both a total area and different forest growth environments within the whole (i.e., coastal site areas (II, IV) and inland site areas (I, III)) as to spatiotemporal patterns associated with subtropical forests in Fujian Province, which is located at the boundary between the middle and south subtropical zones. Considering the asymmetric effects of climate and forest growth, this study chose pre-seasonal and cumulative temperature and precipitation factors and utilized the GeoDetector model to analyze the dominant drivers and interactions within phenology differentiation in Fujian Province. The results show the following: (1) All of the phenological parameters were advanced or shortened over the 19-year observation period; those of shrubland and deciduous broadleaf forests fluctuated greatly, and their stability was poor. (2) The phenological parameters were more distinct at the borders of the site areas. Additionally, the dates associated with the end of the growth season (EOS) and the date-position of peak value (POP) in coastal areas (i.e., II and IV) were later than those in inland areas (i.e., I and III). Among the parameters, the length of the growth season (LOS) was most sensitive to altitude. (3) Precipitation was the main driving factor affecting the spatial heterogeneity of the start of the growth season (SOS) and the EOS. The relatively strong effects of preseason and current-month temperatures on the SOS may be influenced by the temperature threshold required to break bud dormancy, and the relationship between the SOS and temperature was related to the lag time and the length of accumulation. The EOS was susceptible to the hydrothermal conditions of the preseason accumulation, and the variation trend was negatively correlated with temperature and precipitation. Spatial attribution was used to analyze the attribution of phenology differentiation from the perspectives of different regions, thus revealing the relationships between forest phenology and meteorological time-lag effects, the result which can contribute to targeted guidance and support for scientific forest management. Full article

To investigate the susceptibility of drifting snow along the Ya’an–Qamdo section of the railway, which is located in a high-altitude and cold plateau in Southwest China with scarce meteorological information, the Weather Research and Forecasting Model (WRF) is used in this paper to simulate the spatio-temporal distribution of meteorological data. According to the varying terrain, the railway section from Ya’an to Qamdo is divided into two regions along 100.8° E for double-layer nested simulation. The original land use data of the WRF model are used in region 1. Due to the increased number of mountains in region 2, the original data are replaced by the MCD12Q1v006 land use data, and the vertical direction layers are densified near the ground to increase simulation accuracy. The simulated results are compared with the observation data. It is found that after densification, the results have been significantly improved. The results obtained by the WRF model can accurately simulate the change trends of temperature, rainfall, and wind speed, and the correlation coefficients are relatively high, which verifies the accuracy of WRF for simulating complex terrain regions. The simulation results further indicate that approximately 300 km of the Ya’an–Qamdo railway may experience drifting snow. Among them, no drifting snow events occur in Ya’an County, and the areas with higher probability are located at the border between Luding County and Tianquan County, followed by Kangding area. The remaining areas have a probability of less than 10%. The WRF model demonstrates its capability in the drifting snow protection of railways with limited meteorological data. Full article

Recently, light use efficiency (LUE) models driven by remote sensing data have been widely employed to estimate the gross primary productivity (GPP) of different terrestrial ecosystems at global or regional scales. Furthermore, the two-leaf light use efficiency (TL-LUE) model has been reported to improve the accuracy of GPP estimation, relative to the big-leaf MOD17 model, by separating the entire canopy into sunlit and shaded leaves through the use of constant clumping index estimation (Ω). However, ignoring obvious seasonal changes in the vegetation clumping index (CI) most likely results in GPP estimation errors since the CI tends to present seasonal changes, especially with respect to the obvious presence or absence of leaves within the canopy of deciduous vegetation. Here, we propose a TL-CLUE model that considers the seasonal difference in the CI based on the TL-LUE model to characterize general changes in canopy seasonality. This method composites monthly CI values into two or three Ω values to capture the general seasonal changes in CI while attempting to reduce the potential uncertainty caused during CI inversion. In theory, CI seasonality plays an essential role in the distribution of photosynthetically active radiation absorbed by the canopy (APAR). Specifically, the seasonal difference in CI values mainly considers the state of leaf growth, which is determined by the MODIS land surface phenology (LSP) product (MCD12Q2). Therefore, the one-year cycle (OYC) of leaf life is divided into two (leaf-off and leaf-on) or three seasons (leaf-off, leaf-scattering, and leaf-gathering) according to this MODIS LSP product, and the mean CI of each corresponding season for each vegetation class is computed to smoothen the uncertainties within each seasonal section. With these two or three seasonal Ω values as inputs, the TL-CLUE model by which the seasonal differences in CI are incorporated into the TL-LUE model is run and evaluated based on observations from 84 eddy covariance (EC) tower sites across North America. The results of the analysis reveal that the TL-LUE model widely overestimates GPP for most vegetation types during the leaf-on season, particularly during the growth peak. Although the TL-LUE model shows that the temporal characteristics of GPP agree with the EC observations in terms of general trends, the TL-CLUE model further improves the accuracy of GPP estimation by considering the seasonal changes in the CI. The result of GPP estimation from the TL-CLUE model shows a lower error (RMSE = 2.46 g C m⁻² d⁻¹) than the TL-LUE model (RMSE = 2.75 g C m⁻² d⁻¹) and somewhat decreases the eight-day GPP overestimation in the TL-LUE model with a constant Ω by approximately 9.76 and 8.970% when adapting three and two Ωs from different seasons, respectively. The study demonstrates that the uncertainty of seasonal disturbance in the CI, quantified by a standard deviation of approximately 0.071 relative to the mean CI of 0.746, is diminished through simple averaging. The seasonal difference in CI should be considered in GPP estimation of terrestrial ecosystems, particularly for vegetation with obvious canopy changes, where leaves go through the complete physiological processes of germination, stretching, maturity, and falling within a year. This study demonstrates the potential of the MODIS CI application in developing ecosystem and hydrological models. Full article

The accurate monitoring of vegetation phenology is critical for carbon sequestration and sink enhancement. Vegetation phenology in arid zones is more sensitive to climate responses; therefore, it is important to conduct research on phenology in arid zones in response to global climate change. This study compared the applicability of the enhanced vegetation index (EVI), which is superior in arid zones, and global solar-induced chlorophyll fluorescence (GOSIF), which has a high spatial resolution, in extracting vegetation phenology in arid zones, and explored the mechanism of the differences in the effects of environmental factors on the phenology of different vegetation types. Therefore, this study employed a global solar-induced chlorophyll fluorescence (GOSIF) dataset to determine the start and end of the vegetation growth season (SOS_SIF and EOS_SIF, respectively) in the arid zone of Northwest China from 2001 to 2019. The results were compared with those from the EVI-based MODIS climate product MCD12Q2 (SOS_EVI and EOS_EVI). Variations in the sensitivity of these climatic datasets concerning temperature, precipitation, and standardised precipitation evapotranspiration index (SPEI) were assessed through partial correlation analysis. Results: Compared to the MCD12Q2 climatic products, SOS_SIF and EOS_SIF closely matched the observed climate data in the study area. Spring onset was delayed at higher altitudes and latitudes, and the end of the growing season occurred earlier in these areas. Both SOS_SIF and EOS_SIF significantly advanced from 2001 to 2019 (trend degrees −0.22 and −0.48, respectively). Spring vegetation phenology was chiefly influenced by precipitation while autumn vegetation phenology was driven by both precipitation and SPEI. GOSIF-based climate data provides a more accurate representation of vegetation phenology compared to traditional vegetation indices. The findings of this study contribute to a deeper understanding of the potential ability of EVI and SIF to reveal the influence of vegetation phenology on the carbon cycle. Full article

The mountain systems of the Himalayan regions are changing rapidly due to climatic change at a local and global scale. The Indian Western Himalaya ecosystem (between the tree line and the snow line) is an underappreciated component. Yet, knowledge of vegetation distribution, rates of change, and vegetation interactions with snow-hydroclimatic elements is lacking. The purpose of this study is to investigate the linkage between the spatiotemporal variability of vegetation (i.e., greenness and forest) and related snow-hydroclimatic parameters (i.e., snow cover, land surface temperature, Tropical Rainfall Measuring Mission (TRMM), and Evapotranspiration (ET)) in Himachal Pradesh (HP) Basins (i.e., Beas, Chandra, and Bhaga). Spatiotemporal variability in forest and grassland has been estimated from MODIS land cover product (MCD12Q1) using Google Earth Engine (GEE) for the last 19 years (2001–2019). A significant inter- and intra-annual variation in the forest, grassland, and snow-hydroclimatic factors have been observed during the data period in HP basins (i.e., Beas, Chandra, and Bhaga basin). The analysis demonstrates a significant decrease in the forest cover (214 ha/yr.) at the Beas basin; however, a significant increase in grassland cover is noted at the Beas basin (459 ha/yr.), Chandra (176.9 ha/yr.), and Bhaga basin (9.1 ha/yr.) during the data period. Spatiotemporal forest cover loss and gain in the Beas basin have been observed at ~7504 ha (6.6%) and 1819 ha (1.6%), respectively, from 2001 to 2019. However, loss and gain in grassland cover were observed in 3297 ha (2.9%) and 10,688 ha (9.4%) in the Beas basin, 1453 ha (0.59%) and 3941 ha (1.6%) in the Chandra basin, and 1185 ha (0.92%) and 773 ha (0.60%) in the Bhaga basin, respectively. Further, a strong negative correlation (r = −0.65) has been observed between forest cover and evapotranspiration (ET). However, a strong positive correlation (r = 0.99) has been recorded between grassland cover and ET as compared to other factors. The main outcome of this study in terms of spatiotemporal loss and gain in forest and grassland shows that in the Bhaga basin, very little gain and loss have been observed as compared to the Chandra and Beas basins. The present study findings may provide important aid in the protection and advancement of the knowledge gap of the natural environment and the management of water resources in the HP Basin and other high-mountain regions of the Himalayas. For the first time, this study provides a thorough examination of the spatiotemporal variability of forest and grassland and their interactions with snow-hydroclimatic factors using GEE for Western Himalaya. Full article

Leaf Area Index (LAI) is one of the most important biophysical parameters of vegetation, and its dynamic changes can be used as a reflective indicator and differentiation basis of vegetation function. In this study, a VCA–MLC (Vertex Component Analysis–Maximum Likelihood Classification) algorithm is proposed from the perspective of multi-temporal satellite LAI image classification to monitor and quantify the spatial and temporal variability of vegetation dynamics in China since 2000. The algorithm extracts the vegetation endmembers from 46 multi-temporal images of MODIS LAI in 2011 without the aid of other a priori knowledge and uses the maximum likelihood classification method to select the categories that satisfy the requirements of the number of missing periods, absolute distance, and relative distance for the rest pixels to be classified, ultimately dividing the vegetation area of China into 10 vegetation zones called China Vegetation Functional Zones (CVFZ). CVFZ outperforms MCD12Q1 and CLCD land cover datasets in the overall differentiation of vegetation functions and can be used synergistically with other land cover datasets. In this study, CVFZ is used to cut the constant vegetation-type pixels of MCD12Q1 during 2001–2022. The results of the LAI mean time series decomposition of each subregion using the STL (Seasonal-Trend Decomposition based on Loess) method show that the rate of vegetation greening ranges from 9.02 × 10⁻⁴ m²m⁻²yr⁻¹ in shrubland subregions to 2.34 × 10⁻² m²m⁻²yr⁻¹ in savanna subregions. In relative terms, the average greening speed of forests is moderate, and savannas tend to have the fastest average greening speed. The greening speed of grasslands and croplands in different zones varies widely. In contrast, the average greening speed of shrublands is the slowest. In addition, CVFZ detected grasslands with one or two phenological cycles, broadleaf croplands with one or two phenological cycles, and shrublands with no apparent or one phenological cycle. Full article

Accurate land cover (LC) datasets are the basis for global environmental and climate change studies. Recently, numerous open-source annual LC datasets have been created due to advances in remote sensing technology. However, the agreements and sources of error that affect the accuracy of current annual LC datasets are not well understood, which limits the widespread use of these datasets. We compared four annual LC datasets, namely the CLCD, MCD12Q1, CCI-LC, and GLASS-LC, in the Yellow River Basin (YRB) to identify their spatial and temporal agreement for nine LC classes and to analyze their sources of error. The Mann–Kendall test, Sen’s slope analysis, Taylor diagram, and error decomposition analysis were used in this study. Our results showed that the main LC classes in the four datasets were grassland and cropland (total area percentage > 80%), but their trends in area of change were different. For the main LC classes, the temporal agreement was the highest between the CCI-LC and CLCD (0.85), followed by the MCD12Q1 (0.21), while the lowest was between the GLASS-LC and CLCD (−0.11). The spatial distribution of area for the main LC classes was largely similar between the four datasets, but the spatial agreement in their trends in area of change varied considerably. The spatial variation in the trends in area of change for the cropland, forest, grassland, barren, and impervious LC classes were mainly located in the upstream area region (UA) and the midstream area region (MA) of the YRB, where the percentage of systematic error was high (>68.55%). This indicated that the spatial variation between the four datasets was mainly caused by systematic errors. Between the four datasets, the total error increased along with landscape heterogeneity. These results not only improve our understanding of the spatial and temporal agreement and sources of error between the various current annual LC datasets, but also provide support for land policy making in the YRB. Full article

The accurate mapping of soil organic carbon (SOC) distribution is important for carbon sequestration and land management strategies, contributing to mitigating climate change and ensuring agricultural productivity. The Heihe River Basin in China is an important region that has immense potential for SOC storage. Phenological variables are effective indicators of vegetation growth, and hence are closely related to SOC. However, few studies have incorporated phenological variables in SOC prediction, especially in alpine areas such as the Heihe River Basin. This study used random forest (RF) and extreme gradient boosting (XGBoost) to study the effects of phenological variables (e.g., Greenup, Dormancy, etc.) obtained from MODIS (i.e., Moderate Resolution Imaging Spectroradiometer) product (MCD12Q2) on SOC content prediction in the middle and upper reaches of Heihe River Basin. The current study also identified the dominating variables in SOC prediction and compared model performance using a cross validation procedure. The results indicate that: (1) when phenological variables were considered, the ${\mathrm{R}}^2$ (coefficient of determination) of RF and XGBoost were 0.68 and 0.56, respectively, and RF consistently outperforms XGBoost in various cross validation experiments; (2) the environmental variables MAT, MAP, DEM and NDVI play the most important roles in SOC prediction; (3) the phenological variables can account for 32–39% of the spatial variability of SOC in both the RF and XGBoost models, and hence were the most important factor among the five categories of predictive variables. This study proved that the introduction of phenological variables can significantly improve the performance of SOC prediction. They should be used as indispensable variables for accurately modeling SOC in related studies. Full article

Land degradation (LD) processes are widespread in drylands worldwide and are accelerated by climate change. As a result, food security and livelihoods are at risk. Thus, there is a need to monitor LD trends, especially in agricultural areas. Mediterranean countries, including Tunisia and Greece, are concerned due to the presence of drivers and pressures causing land degradation. Through the Trends.Earth plugin, the SDG 15.3.1 indicator can be implemented to map LD status. In this study, we mapped LD in Greece and Tunisia for the recommended baseline period of 2001–2015 and the selected reporting period of 2016–2020. The land productivity was assessed within Trends.Earth using the MODIS MOD13Q1 product, while the default datasets were used for the other sub-indicators. The main findings are: (i) the percentage of degraded land decreased from the baseline to the reporting period from 4.83% to 2.62% of total area in Greece and 9.97% to 6.26% in Tunisia—degradation rates that differ from those reported to the UNCCD (United Nations Convention to Combat Desertification) by the respective national authorities; (ii) the dominant land condition in Greece was improved, while in Tunisia, it was stable; (iii) land productivity presented a similar trend through the SDG 15.3.1 indicator over both countries, including the net land productivity dynamics over croplands; (iv) based on analysis using plant functional types performed with MODIS MCD12Q1, the highest portion of degraded land in Greece was located in grasslands and in Tunisia in cereal croplands (after desert areas); and (v) with a focus on LD over cereal croplands, the portion of degraded areas appeared to decrease in both Greece and Tunisia. The percentage was higher in Tunisia, representing 16.52% of the total degraded land during the reporting period compared to 10.83% in Greece. All the above stress the need to foster the adoption of sustainable land management practices, especially in Tunisia, and speed up the implementation of measures to achieve LD neutrality. Full article

Microwave sensors have attracted interest as non-destructive metal crack detection (MCD) devices due to their low cost, simple fabrication, potential miniaturization, noncontact nature, and capability for remote detection. However, the development of multi-crack sensors of a suitable size and quality factor (Q-factor) remains a challenge. In the present study, we propose a multi-MCD sensor that combines a higher-mode substrate-integrated waveguide (SIW) and complementary split-ring resonators (CSRRs). In order to increase the Q-factor, the device is miniaturized; the MCD is facilitated; and two independent CSRRs are loaded onto the SIW, where the electromagnetic field is concentrated. The concentrated electromagnetic field of the SIW improves the Q-factor of the CSRRs, and each CSRR creates its own resonance and produces a miniaturizing effect by activating the sensor below the cut-off frequency of the SIW. The proposed multi-MCD sensor is numerically and experimentally demonstrated for cracks with different widths and depths. The fabricated sensor with a TE₂₀-mode SIW and CSRRs is able to efficiently detect two sub-millimeter metal cracks simultaneously with a high Q-factor of 281. Full article