Difference Vegetation Index Research Articles

Improvement of data imbalance for digital soil class mapping in Eastern China

A fine-resolution digital soil class map is needed. However, the problem of imbalanced data leads to an inaccurate spatial distribution of the digital soil class map, and the spatial resolution of digital soil class maps at a large scale is low in existing studies. Based on these points, an algorithm of over-sampling and under-sampling was introduced to solve the problem of imbalanced data, and to improve the performance of soil classification model. 316 topsoil samples with eight main soil classes at the great group level were collected in Eastern China. Eight out of twelve prediction variables were determined after the importance evaluation by “Mean Decrease Accuracy” in the random forest (RF) model, including digital elevation model (DEM), enhanced vegetation index (EVI), land surface wetness index (LSWI), land surface temperature (LST), normalized differenced vegetation index (NDVI), and soil texture components. RF model was also applied to complete digital soil class mapping, and the results of treated (over-sampling and under-sampling by randomly increasing or decreasing the number of samples) and untreated data were compared and discussed. Research results indicated that modeling by imbalanced data resulted in uncertain soil classes mapping, with minority classes were lost and with lower accuracies than those of balanced data (overall accuracy = 83.83 %, kappa coefficient = 0.79). After over-sampling and under-sampling treatments, these problems were well solved with an overall accuracy of 96.72 % and a kappa coefficient of 0.93. The accuracy of soil class prediction for minority classes were improved by 12.5 %–54.5 %. Compared to the existing conventional soil map, the new map with a fine resolution of 30 × 30 m is time-effective and more detailed. Validation (point-validation and map-to-map comparison) of the predicted map showed that the output is reliable and can provide a reference for other soil and environmental studies without major difficulties.

Spatial variability of hydrochemistry and environmental controls in karst aquifers of the southern Iberian Peninsula: Implications for climate change impact assessment

Carbonate aquifers are crucial water and carbon reservoirs globally, particularly in semi-arid climates. However, these systems are susceptible to the impacts of climate change, given their sensitivity to specific environmental factors. This study presents the hydrochemical (water temperature, pH, electrical conductivity, and major ions) and isotopic (δ13C) composition of 39 karst springs in the southern Iberian Peninsula, along with the parameterization of environmental factors (temperature, precipitation, recharge altitude, and vegetation cover quantified by the Normalized Differential Vegetation Index, NDVI) in their recharge areas. The spatial analysis revealed that the climatic and environmental factors follow a longitudinal pattern producing a notable west-east environmental gradient in the study area. Through a statistical analysis based on Principal Component Analysis (PCA), it was found that environmental factors control the spatial variability of groundwater hydrochemistry in these karstic aquifers. The δ13CDIC values in groundwater, ranging from −1.84 to −12.46 ‰, show a prevalence of C4 plants mainly in the more arid study sectors and indicate an origin of dissolved inorganic carbon (DIC) mainly in biological processes in the recharge area. In addition, a relationship between NDVI values, equilibrium partial pressure of CO2 (pCO2), and groundwater bicarbonate content was observed. Springs further west of the study area exhibited higher bicarbonate content (about 400 ppm), which was associated with higher pCO2 levels (about 10,000 and 15,000 ppm) and higher NDVI values (between 0.5 and 0.7). In contrast, aquifers located further to the east had lower bicarbonate levels (<200 ppm), with an average pCO2 of about 2000 ppm and the lowest NDVI values (<0.3). Furthermore, the spatial variability and the relationship between environmental factors and groundwater hydrochemistry allow for the assessment of potential climate change impacts in carbonate systems with a comparison between aquifers in wetter regions and those in semi-arid conditions.

Evapotranspiration and groundwater inputs control the timing of diel cycling of stream drying during low-flow periods

Geologic, geomorphic, and climatic factors have been hypothesized to influence where streams dry, but hydrologists struggle to explain the temporal drivers of drying. Few hydrologists have isolated the role that vegetation plays in controlling the timing and location of stream drying in headwater streams. We present a distributed, fine-scale water balance through the seasonal recession and onset of stream drying by combining spatiotemporal observations and modeling of flow presence/absence, evapotranspiration, and groundwater inputs. Surface flow presence/absence was collected at fine spatial (~80 m) and temporal (15-min) scales at 25 locations in a headwater stream in southwestern Idaho, USA. Evapotranspiration losses were modeled at the same locations using the Simultaneous Heat and Water (SHAW) model. Groundwater inputs were estimated at four of the locations using a mixing model approach. In addition, we compared high-frequency, fine-resolution riparian normalized vegetation difference index (NDVI) with stream flow status. We found that the stream wetted and dried on a daily basis before seasonally drying, and daily drying occurred when evapotranspiration outputs exceeded groundwater inputs, typically during the hours of peak evapotranspiration. Riparian NDVI decreased when the stream dried, with a ~2-week lag between stream drying and response. Stream diel drying cycles reflect the groundwater and evapotranspiration balance, and riparian NDVI may improve stream drying predictions for groundwater-supported headwater streams.

Towards a Fairer Green city: measuring unfairness in daily accessible greenery in Chengdu’s central city

ABSTRACT Urban green spaces exert beneficial effects on both individuals and communities. However, as urban sprawl intensifies, socioeconomic disparities widen, and populations burgeon, the concepts of green fairness and environmental justice confront substantial hurdles. The daily exposure to greenery emerges as a crucial determinant of these factors, yet no comprehensive methodologies currently exist to gauge the levels of daily accessible urban greenery or to probe the distribution of green inequities. In this research, we harness the capabilities of Spatial Design Network Analysis (sDNA) to scrutinize spatial choice and integration, using the metropolis of Chengdu as a case study. Three indicators representing daily accessed urban greenery are utilized, including the Green View Index (GVI) at the street level, the assessment of green spaces based on the Normalized Differential Vegetation Index (NDVI), and the level of greenery within the visible range of buildings. Green Accessibility Index (GAI) was further proposed and calculated for three states of commuting, recreation, and work to synthesize the accessibility and greenness levels. The distribution of green unfairness in the study area are evaluated using bivariate local spatial autocorrelation. Our findings reveal that (1) frequent expressway commuting and existing greenery does not satisfy urban fairness needs. (2) Significant differences in unfair areas of building visible greenery (3) Unfair areas are concentrated in high-income neighborhoods (4) Severe unfairness between greenery and population in large cities, where most people do not enjoy the benefits of adequate greenery. We provide recommendations based on these findings, thereby offering actionable insights to optimize the spatial distribution of green unfairness through enhanced accessibility of urban greenery.

Optimized Deep Learning Model for Flood Detection Using Satellite Images

The increasing amount of rain produces a number of issues in Kerala, particularly in urban regions where the drainage system is frequently unable to handle a significant amount of water in such a short duration. Meanwhile, standard flood detection results are inaccurate for complex phenomena and cannot handle enormous quantities of data. In order to overcome those drawbacks and enhance the outcomes of conventional flood detection models, deep learning techniques are extensively used in flood control. Therefore, a novel deep hybrid model for flood prediction (DHMFP) with a combined Harris hawks shuffled shepherd optimization (CHHSSO)-based training algorithm is introduced for flood prediction. Initially, the input satellite image is preprocessed by the median filtering method. Then the preprocessed image is segmented using the cubic chaotic map weighted based k-means clustering algorithm. After that, based on the segmented image, features like difference vegetation index (DVI), normalized difference vegetation index (NDVI), modified transformed vegetation index (MTVI), green vegetation index (GVI), and soil adjusted vegetation index (SAVI) are extracted. The features are subjected to a hybrid model for predicting floods based on the extracted feature set. The hybrid model includes models like CNN (convolutional neural network) and deep ResNet classifiers. Also, to enhance the prediction performance, the CNN and deep ResNet models are fine-tuned by selecting the optimal weights by the combined Harris hawks shuffled shepherd optimization (CHHSSO) algorithm during the training process. This hybrid approach decreases the number of errors while improving the efficacy of deep neural networks with additional neural layers. From the result study, it clearly shows that the proposed work has obtained sensitivity (93.48%), specificity (98.29%), accuracy (94.98%), false negative rate (0.02%), and false positive rate (0.02%) on analysis. Furthermore, the proposed DHMFP–CHHSSO displays better performances in terms of sensitivity (0.932), specificity (0.977), accuracy (0.952), false negative rate (0.0858), and false positive rate (0.036), respectively.

Hyperspectral imaging for estimating leaf, flower, and fruit macronutrient concentrations and predicting strawberry yields

Managing the nutritional status of strawberry plants is critical for optimizing yield. This study evaluated the potential of hyperspectral imaging (400–1,000 nm) to estimate nitrogen (N), phosphorus (P), potassium (K), and calcium (Ca) concentrations in strawberry leaves, flowers, unripe fruit, and ripe fruit and to predict plant yield. Partial least squares regression (PLSR) models were developed to estimate nutrient concentrations. The determination coefficient of prediction (R2P) and ratio of performance to deviation (RPD) were used to evaluate prediction accuracy, which often proved to be greater for leaves, flowers, and unripe fruit than for ripe fruit. The prediction accuracies for N concentration were R2P = 0.64, 0.60, 0.81, and 0.30, and RPD = 1.64, 1.59, 2.64, and 1.31, for leaves, flowers, unripe fruit, and ripe fruit, respectively. Prediction accuracies for Ca concentrations were R2P = 0.70, 0.62, 0.61, and 0.03, and RPD = 1.77, 1.63, 1.60, and 1.15, for the same respective plant parts. Yield and fruit mass only had significant linear relationships with the Difference Vegetation Index (R2 = 0.256 and 0.266, respectively) among the eleven vegetation indices tested. Hyperspectral imaging showed potential for estimating nutrient status in strawberry crops. This technology will assist growers to make rapid nutrient-management decisions, allowing for optimal yield and quality.

Effects of Climate Variability and Human Activities on Vegetation Dynamics across the Qinghai–Tibet Plateau from 1982 to 2020

In recent years, vegetation on the Qinghai–Tibet Plateau (QTP) has undergone significant greening. However, the causal factors underpinning this phenomenon, whether attributable to temperature fluctuations, precipitation patterns, or anthropogenic interventions, remain a subject of extensive scholarly debate. This study conducted a comprehensive analysis of the evolving vegetation across the QTP. The National Oceanic and Atmospheric Administration Climate Data Record Advanced Very High Resolution Radiometer Normalized Vegetation Difference Index (NOAA CDR AVHRR NDVI) dataset was employed to elucidate the intricate relationship between climatic variables and human activities driving vegetative transformations. The findings were as follows: The NDVI on the QTP has exhibited a significant greening trend at a rate of 0.0013/a (per year). A minor decline, accounting for only 17.6% of grasslands, was observed, which was primarily concentrated in the northwestern and northern regions. Through residual analysis, climate change was found to be the predominant driver, explaining 70.6% of the vegetation variability across the plateau. Concurrently, noticeable trends in temperature and precipitation increases were observed on the QTP, with the southern region demonstrating improved sensitivity to precipitation alterations. In summary, these results substantiate that a confluence of climatic warming, enhanced moisture availability, and a reduction in livestock population collectively creates an environment conducive to enhanced vegetation vigor on the QTP. This study highlights the significance of acknowledging the dual influence of climate and human agency in shaping vegetative dynamics, which is a critical consideration for informed land management strategies and sustainable development initiatives on this ecologically pivotal plateau.

Adaptability Evaluation of the Spatiotemporal Fusion Model in the Summer Maize Planting Area of the Southeast Loess Plateau

Precise regional crop yield estimates based on the high-spatiotemporal-resolution remote sensing data are essential for directing agronomic practices and policies to increase food security. This study used the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), the flexible spatiotemporal data fusion (FSADF), and the spatial and temporal non-local filter based fusion model (STNLFFM) to calculate the normalized differential vegetation index (NDVI) of the summer maize planting area in the Southeast Loess Plateau based on the Sentinel-2 and MODIS data. The spatiotemporal resolution was 10 m and 1 d, respectively. Then, we evaluated the adaptability of the ESTARFM, FSADF, and STNLFFM fusion models in the field from the perspectives of spatial and textural characteristics of the data, summer maize NDVI growing curves, and yield estimation accuracy through qualitative visual discrimination and quantitative statistical analysis. The results showed that the fusion of ESTARFM–NDVI, FSDAF–NDVI, and STNLFFM–NDVI could precisely represent the variation tendency and local mutation information of NDVI during the growth period of summer maize, compared with MODIS–NDVI. The correlation between STNLFFM–NDVI and Sentinel-2–NDVI was favorable, with large correlation coefficients and a small root mean square error (RMSE). In the NDVI growing curve simulation of summer maize, STNLFFM introduced overall weights based on non-local mean filtering, which could significantly improve the poor fusion results at seedling and maturity stages caused by the long gap period of the high-resolution data in ESTARFM. Moreover, the accuracy of yield estimation was as follows (from high to low): STNLFFM (R = 0.742, mean absolute percentage error (MAPE) = 6.22%), ESTARFM (R = 0.703, MAPE = 6.80%), and FSDAF (R = 0.644, MAPE = 10.52%). The FADSF fusion model was affected by the spatial heterogeneity in the semi-humid areas, and the yield simulation accuracy was low. In the semi-arid areas, the FADSF fusion model had the advantages of less input data and a faster response.

THE NDVI INDEX CHANGE OF THE CARPATHIAN REGION OF UKRAINE DURING 2000-2022

Systematic monitoring of the Carpathian region is necessary to preserve its unique natural resource potential. The NDVI index, or - Normalized Differential Vegetation Index, - is a convenient and informative indicator of the state of the vegetation cover, and is calculated on the basis of satellite data on the absorption and reflection of red and near-infrared radiation by the studied surface.&#x0D; To study the dynamics of vegetation index values and the state of the vegetation cover of the Carpathian region, images from the space projects Landsat 7 and 8 for the years 2000, 2013, 2020, 2021 and 2022 were downloaded and processed. The best indicator for analyzing changes in the state of vegetation cover in space and time is the integral index NDVImean. For 12 out of 15 processed images, the highest value of the integral index NDVImean=0.40-0.42 was found for polygon 1, which covers part of the Transcarpathian region. The lowest values of 0.13-0.16 were found for polygon 3, which covers parts of the Lviv, Ivano-Frankivsk, and Ternopil regions. The values of the areas covered with healthy or dense vegetation, for which NDVI˃0.33, were calculated. The maximum value of this indicator was 81.54% for polygon 1 in 2021, and the minimum was - 52.89% - for the polygon 3 in 2013.&#x0D; Keywords: Carpathian region; vegetation cover; vegetation index; multispectral analysis; raster; extent.

Response of vegetation to terrestrial water storage in Southwest China

To explore the responses of vegetation growth to change in terrestrial water storage in Southwest China, we analyzed the change trend and relationship between vegetation and terrestrial water storage anomaly (TWSA) in Southwest China from January 2003 to December 2021 by using TWSA data of Gravity Recovery and Climate Experi-ment (GRACE) satellite and normalized differential vegetation index (NDVI) data. The results showed that NDVI in Southwest China during the study period showed an overall upward trend. Meanwhile, TWSA showed a significant downward trend in central and southern Tibet, and a significant upward trend in northwest Tibet and southeast region of Southwest China. Results of Pearson correlation analysis showed that there were significant spatial differences in responses of NDVI to TWSA changes in Southwest China. NDVI had a significant negative response to TWSA changes in most regions of Tibet, but a significant positive response to TWSA changes in most regions of southeast region of Southwest China. Such differences were driven by climate change and topography.

Response of vegetation normalized different vegetation index to different meteorological disaster indexes in karst region of Guangxi, China

Fragile karst areas of Guangxi are the key areas of vegetation protection and restoration work of the government. It is of great significance to study the effects of meteorological disasters on vegetation change for disaster prevention and reduction and ecological protection in the karst areas. The Normalized Differential Vegetation Index (NDVI), as a tool for monitoring vegetation growth, has a strong responsiveness to vegetation and can qualitatively and quantitatively evaluate the vegetation cover and its growth vitality. Therefore, in this study, we analyzed the trend of vegetation change and the impacts of multiple meteorological hazards, such as drought, torrential rainfall, high temperature, and low temperature, on the vegetation of the karst region in Guangxi by using the linear trend method, GIS spatial analysis, and correlation analysis, using the MODIS NDVI and temperature and precipitation information from 2000 to 2020 in the karst region of Guangxi. The results show that: (1) NDVI increased significantly in the karst areas, and 32.2% of the areas had significant improvement in vegetation. The improvement was the most obvious mainly in the central part of the study area, while the vegetation degradation trend was obvious in partial scattered areas in northeast and southwest. (2) On the interannual scale, NDVI was negatively correlated with some meteorological disaster indexes, such as relative humidity index, the number of drought days, the amount of extremely heavy rainstorm, the amount of heavy rainstorm, the number of days with high temperature of ≥35 °C, the number of days with high temperature of 35–37 °C, the number of days with high temperature of ≥37 °C, the minimum temperature, and the effective accumulated temperature of ≤0 °C. The obvious negative effect area of drought on vegetation was mainly concentrated in the middle of the study area, and that of rainfall was mainly distributed in the southwest, northeast and northwest; that of high temperature was mainly distributed in the northwest and northeast, and that of low temperature was mainly concentrated in the southwest and north. (3) On the multi-year monthly scale, the responses of NDVI to drought, high temperature and low temperature disaster indexes had a lag effect, but had no lag effect on rainfall disaster indexes. The lag time of vegetation to drought was 1 month, and the lag time to high temperature and low temperature was 3 months.

Spatiotemporally consistent global dataset of the GIMMS Normalized Difference Vegetation Index (PKU GIMMS NDVI) from 1982 to 2022

Abstract. Global products of remote sensing Normalized Difference Vegetation Index (NDVI) are critical to assessing the vegetation dynamic and its impacts and feedbacks on climate change from local to global scales. The previous versions of the Global Inventory Modeling and Mapping Studies (GIMMS) NDVI product derived from the Advanced Very High Resolution Radiometer (AVHRR) provide global biweekly NDVI data starting from the 1980s, being a reliable long-term NDVI time series that has been widely applied in Earth and environmental sciences. However, the GIMMS NDVI products have several limitations (e.g., orbital drift and sensor degradation) and cannot provide continuous data for the future. In this study, we presented a machine learning model that employed massive high-quality global Landsat NDVI samples and a data consolidation method to generate a new version of the GIMMS NDVI product, i.e., PKU GIMMS NDVI (1982–2022), based on AVHRR and Moderate-Resolution Imaging Spectroradiometer (MODIS) data. A total of 3.6 million Landsat NDVI samples that were well spread across the globe were extracted for vegetation biomes in all seasons. The PKU GIMMS NDVI exhibits higher accuracy than its predecessor (GIMMS NDVI3g) in terms of R2 (0.97 over 0.94), root mean squared error (RMSE: 0.05 over 0.09), mean absolute error (MAE: 0.03 over 0.07), and mean absolute percentage error (MAPE: 9 % over 20 %). Notably, PKU GIMMS NDVI effectively eliminates the evident orbital drift and sensor degradation effects in tropical areas. The consolidated PKU GIMMS NDVI has a high consistency with MODIS NDVI in terms of pixel value (R2 = 0.956, RMSE = 0.048, MAE = 0.034, and MAPE = 6.0 %) and global vegetation trend (0.9×10-3 yr−1). The PKU GIMMS NDVI product can potentially provide a more solid data basis for global change studies. The theoretical framework that employs Landsat data samples can facilitate the generation of remote sensing products for other land surface parameters. The PKU GIMMS NDVI product is open access and available under a Creative Commons Attribution 4.0 License at https://doi.org/10.5281/zenodo.8253971 (Li et al., 2023).

Spatial prediction and uncertainty estimation of crucial GlobalSoilMap properties - A contextual study in the semi-arid area of western Iran

Information on the spatial distribution of organic carbon (OC), salinity (EC), and soil pH in the semi-arid region of the Chahardowli Plain in western Iran is limited. Though soil carbon is the most common property mapped in GlobalSoilMap, EC, and pH affect OC content and other soil properties and functions. To study variations in these properties and map their distribution, soil samples were collected at depths of 0–5, 5–15, 15–30, 30–60, and 60–100 cm. A total of 145 soil samples were collected from 30 profiles. The relationships between soil characteristics and environmental covariates were modeled using random forest (RF), decision tree (DT), and multiple linear regression (MLR) models. We used a k-fold cross-validation to assess the quality of the predictions. The RF model demonstrated the highest prediction accuracy for all three soil properties. The OC validation results for the RF model show that the R2 value was between 0.80 and 0.98, the R2 value for EC was between 0.74 and 0.98, and the R2 value for pH was between 0.80 and 0.93. The channel network base level (CNBL) was found to be the most crucial covariate in predicting EC, while CNBL and vegetation indices were the most significant covariates in predicting OC. The covariates found to be crucial in predicting pH were the difference in vegetation index (DVI) and slope (S). The bootstrap method was used to compute the prediction uncertainty. The bootstrap method provided reliable estimates of uncertainties associated with these predictions. For all layers and all points, the coverage percentage for OC, EC, and pH was between 80 and 95% at the 95% confidence level. This shows the reliability of the estimated confidence limits. This study indicated that high pH and EC levels are associated with a reduction in soil OC percentage. To provide an accurate representation of OC distribution in any region, it is necessary to report not only an OC map but also maps of EC and pH, as the spatial interrelationship between soil properties highlights the need for estimating pH and EC for better understanding OC variability and soil functioning.

Urban heat islands characterized by six thermal indicators

The urban heat island (UHI) effect alters local thermal environment and affects thermal comfort of urban dwellers. However, the similarities and differences of the UHI when characterized by different thermal indicators (i.e., different types of temperatures and thermal comfort indices) remain poorly understood. Taking 30 major Chinese cities as an example, here we compared the spatiotemporal patterns and drivers of the UHI intensity (UHII) represented by six different thermal indicators, i.e., Ia (UHII represented by surface air temperature), Is (land surface temperature), Id (dew-point temperature), IHD (Humidex), Imrt (mean radiant temperature), and IUTCI (Universal Thermal Climate Index). These thermal indicators encompass temperature factors that capture changes in the natural environment and thermal comfort indices that reflect the human perception of changes in ambient heat and humidity. The results show that the ranking of the annual mean UHII is similar between the day and night (i.e., Is > IUTCI > Imrt > Ia > IHD > Id). From a seasonal perspective, the daytime Is and Imrt exhibit the highest variability among all indicators, while the IUTCI demonstrates the largest difference among climate zones. Regarding the UHI drivers, most of the UHI indicators (except Id) are negatively correlated with the urban-rural difference in vegetation index during the day. At night, the UHI indicators are negatively related to the climatic variables and the urban-rural difference in albedo. Our results by using the IUTCI to assess outdoor thermal comfort across local climate zones (LCZs) show that the inter-LCZ standard deviation of the IUTCI is 1.94 °C during the day and 2.16 °C at night. We believe that these results deepen our understanding of the UHII when characterized by different thermal indicators, especially from the human perception perspective.

Modeling Landslide Susceptibility in Forest-Covered Areas in Lin’an, China, Using Logistical Regression, a Decision Tree, and Random Forests

Landslides are a common geodynamic phenomenon that cause substantial life and property damage worldwide. In the present study, we developed models to evaluate landslide susceptibility in forest-covered areas in Lin’an, southeastern China using logistic regression (LR), decision tree (DT), and random forest (RF) techniques. In addition to conventional landslide-related natural and human disturbance factors, factors describing forest cover, including forest type (two plantations (hickory and bamboo) and four natural forests (conifer, hardwood, shrub, and moso bamboo) and understory vegetation conditions, were included as predictors. Model performance was evaluated based on true-positive rate, Kappa value, and area under the ROC curve using a 10-fold cross-validation method. All models exhibited good performance with measures of ≥0.70, although the LR model was relatively inferior. The key predictors were forest type, understory vegetation height (UVH), normalized differential vegetation index (NDVI) in summer, distance to road (DTRD), and maximum daily rainfall (MDR). Hickory plantations yielded the highest landslide probability, while conifer and hardwood forests had the lowest values. Bamboo plantations had probability results comparable to those of natural forests. Using the RF model, areas with a shorter UVH (&lt;1.2 m), a lower NDVI (&lt;0.70), a heavier MDR (&gt;115 mm), or a shorter DTRD (&lt;500 m) were predicted to be landslide-prone. Information on forest cover is essential for predicting landslides in areas with rich forest cover, and conversion from natural forests to plantations could increase landslide risk. Across the study areas, the northwestern part was the most landslide-prone. In terms of landslide prevention, the RF model-based map produced the most accurate predictions for the “very high” category of landslide. These results will help us better understand landslide occurrences in forest-covered areas and provide valuable information for governments in designing disaster mitigation.

Soil Salinity, Its Inversion and Spatial Distribution Characteristics in Agricultural Fields Using Remote Sensing Data

Soil salinization is an urgent problem in the arid and semi-arid regions that damages soil ecology and affects agricultural growths. Timely supervision and monitoring of soil salinity are essential to reach the most sustainable improvement goals in arid and semi-arid regions. In the present study, the soil of Aktau region in Xinjiang, China was collected to build a remote sensing based inversion model for identifying soil salinity hazards. Results showed that Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Difference Vegetation Index (DVI) were correlated (P &lt; 0.01) with the model inversion, having correlation coefficients of -0.735, -0.858, and -0.774, respectively. All these were suitable for the construction of the soil salinity inversion model where the optimal parameters of model accuracy were above 85% and prediction results were accurate credible and consistent with the measured data. The NDWI extracted from multispectral images was used as the key parameter of the soil salinity inversion model, which could obtain a better spatial distribution of soil salinity. The remote sensing inversion model of soil salinity provides a theoretical basis for the management of soil salinization and sustainable utilization of agricultural resources in the Aktau region of Xinjiang. Bangladesh J. Bot. 52(2): 437-443, 2023 (June) Special

Soil Salinity, Its Inversion and Spatial Distribution Characteristics in Agricultural Fields Using Remote Sensing Data

Soil salinization is an urgent problem in the arid and semi-arid regions that damages soil ecology and affects agricultural growths. Timely supervision and monitoring of soil salinity are essential to reach the most sustainable improvement goals in arid and semi-arid regions. In the present study, the soil of Aktau region in Xinjiang, China was collected to build a remote sensing based inversion model for identifying soil salinity hazards. Results showed that Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Difference Vegetation Index (DVI) were correlated (P &lt; 0.01) with the model inversion, having correlation coefficients of -0.735, -0.858, and -0.774, respectively. All these were suitable for the construction of the soil salinity inversion model where the optimal parameters of model accuracy were above 85% and prediction results were accurate credible and consistent with the measured data. The NDWI extracted from multispectral images was used as the key parameter of the soil salinity inversion model, which could obtain a better spatial distribution of soil salinity. The remote sensing inversion model of soil salinity provides a theoretical basis for the management of soil salinization and sustainable utilization of agricultural resources in the Aktau region of Xinjiang. Bangladesh J. Bot. 52(2): 437-443, 2023 (June) Special

Dose dependent effect of nitrogen on the phyto extractability of Cd in metal contaminated soil using Wedelia trilobata

Cadmium (Cd) is one of the toxic heavy metal that negatively affect plant growth and compromise food safety for human consumption. Nitrogen (N) is an essential macronutrient for plant growth and development. It may enhance Cd tolerance of invasive plant species by maintaining biochemical and physiological characteristics during phytoextraction of Cd. A comparative study was conducted to evaluate the phenotypical and physiological responses of invasive W. trilobata and native W. chinensis under low Cd (10 µM) and high Cd (80 µM) stress, along with different N levels (i.e., normal 91.05 mg kg-1 and low 0.9105 mg kg-1). Under low-N and Cd stress, the growth of leaves, stem and roots in W. trilobata was significantly increased by 35–23%, 25–28%, and 35–35%, respectively, compared to W. chinensis. Wedelia trilobata exhibited heightened antioxidant activities of catalase and peroxidase were significantly increased under Cd stress to alleviate oxidative stress. Similarly, flavonoid content was significantly increased by 40–50% in W. trilobata to promote Cd tolerance via activation of the secondary metabolites. An adverse effect of Cd in the leaves of W. chinensis was further verified by a novel hyperspectral imaging technology in the form of normalized differential vegetation index (NDVI) and photochemical reflectance index (PRI) compared to W. trilobata. Additionally, W. trilobata increased the Cd tolerance by regulating Cd accumulation in the shoots and roots, bolstering its potential for phytoextraction potential. This study demonstrated that W. trilobata positively responds to Cd with enhanced growth and antioxidant capabilities, providing a new platform for phytoremediation in agricultural lands to protect the environment from heavy metals pollution.

Evaluation of phosphate rock as the only source of phosphorus for the growth of tall and semi-dwarf durum wheat and rye plants using digital phenotyping.

Phosphorus nutrition is important for obtaining high yields of crop plants. However, wheat plants are known to be almost incapable of taking up phosphorus from insoluble phosphate sources, and reduced height genes are supposed to decrease this ability further. We performed a pot experiment using Triticum durum Desf. tall spring variety LD222, its near-isogenic semidwarf line carrying Rht17 (Reduced height 17) gene, and winter rye (Secale cereale L.) variety Chulpan. The individual plants were grown in quartz sand. The phosphorus was provided either as phosphate rock powder mixed with sand, or as monopotassium phosphate solution (normal nutrition control) or was not supplemented at all (no-phosphorus control). Other nutrients were provided in soluble form. During experiment the plants were assessed using the TraitFinder (Phenospex Ltd., Heerlen, Netherlands) digital phenotyping system for a standard set of parameters. Double scan with 90 degrees turns of pots around vertical axis vs. single scan were compared for accuracy of phenotyping. The phenotyping showed that at least 20 days of growth after seedling emergence were necessary to get stable differences between genotypes. After this initial period, phenotyping confirmed poor ability of wheat to grow on substrate with phosphate rock as the only source of phosphorus compared to rye; however, Rht17 did not cause an additional reduction in growth parameters other than plant height under this variant of substrate. The agreement between digital phenotyping and conventionally measured traits was at previously reported level for grasses (R2 = 0.85 and 0.88 for digital biomass and 3D leaf area vs. conventionally measured biomass and leaf area, single scan). Among vegetation indices, only the normalized differential vegetation index (NDVI) and the green leaf index (GLI) showed significant correlations with manually measured traits, including the percentage of dead leaves area. The double scan improved phenotyping accuracy, but not substantially.

Spatial Mapping of Soil Salinity Using Machine Learning and Remote Sensing in Kot Addu, Pakistan

The accumulation of salt through natural causes and human artifice, such as saline inundation or mineral weathering, is marked as salinization, but the hindrance toward spatial mapping of soil salinity has somewhat remained a consistent riddle despite decades of efforts. The purpose of the current study is the spatial mapping of soil salinity in Kot Addu (situated in the south of the Punjab province, Pakistan) using Landsat 8 data in five advanced machine learning regression models, i.e., Random Forest Regressor, AdaBoost Regressor, Decision Tree Regressor, Partial Least Squares Regression and Ridge Regressor. For this purpose, spectral data were obtained between 20 and 27 of January 2017 and a field survey was carried out to gather a total of fifty-five soil samples. To evaluate and compare the model’s performances, the coefficient of determination (R2), Mean Squared Error (MSE), Mean Absolute Error (MAE) and the Root-Mean-Squared Error (RMSE) were used. Spectral data of band values, salinity indices and vegetation indices were employed to study the salinity of soil. The results revealed that the Random Forest Regressor outperformed the other models in terms of prediction, achieving an R2 of 0.94, MAE of 1.42 dS/m, MSE of 3.58 dS/m and RMSE of 1.89 dS/m when using the Differential Vegetation Index (DVI). Alternatively, when using the Soil Adjusted Vegetation Index (SAVI), the Random Forest Regressor achieved an R2 of 0.93, MAE of 1.46 dS/m, MSE of 3.90 dS/m and RMSE of 1.97 dS/m. Hence, remote sensing technology with machine learning models is an efficient method for the assessment of soil salinity at local scales. This study will contribute to mitigating osmotic stress and minimizing the risk of soil erosion by providing early warnings regarding soil salinity. Additionally, it will assist agriculture officers in estimating soil salinity levels within a shorter time frame and at a reduced cost, enabling effective resource allocation.