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Deep learning is an effective method for hyperspectral image (HSI) classification, where CNN-based and Transformer-based methods have achieved excellent performance. However, there are some drawbacks to the existing CNN-based and Transformer-based HSI classification approaches: 1) CNN-based methods are deficient in showing the extraction of multiscale features and localized features owing to the fixed-size input patch. 2) the MHSA module ignores the interaction capability between multiple attention heads, which leads to insufficient feature fusion in various directions. 3) The weights of attention heads in various directions are disregarded in the MHSA and attention heads are simply concatenated horizontally. To address the above-mentioned limitations, a novel multihead interacted and adaptive integrated transformer (MHIAIFormer) with spatial-spectral attention, which integrates the respective advantages of convolutions and transformers is proposed in this study. A pyramidal spatial-spectral attention (PS2A) feature extraction module is adopted to efficiently capture the localized and multiscale feature information of HSI. The output of PS2A is then sent to the transformer encoder stage through a grouped multiscale cross-dimension embedding module, which includes additive self-attention using multihead interaction and MHSA with adaptive multihead merging to capture the long-range dependencies of the features. Extensive experiments on four datasets verify that our proposed approach achieves more satisfactory classification accuracy when compared with state-of-the-art models. The overall accuracy of the proposed model achieved 95.97%, 98.68%, 92.68%, and 99.49% on four datasets.

The accurate monitoring of crop areas is essential for food security and agriculture, but accurately extracting multiple-crop distribution over large areas remains challenging. To solve the above issue, in this study, the Pixel-based One-dimensional convolutional neural network (PB-Conv1D) and Pixel-based Bi-directional Long Short-Term Memory (PB-BiLSTM) were proposed to identify multiple-crop cultivated areas using time-series NaE (a combination of NDVI and EVI) as input for generating a baseline classification. Two approaches, Snapshot and Stochastic weighted averaging (SWA), were used in the base-model to minimize the loss function and improve model accuracy. Using an ensemble algorithm consisting of five PB-Conv1D and seven PB-BiLSTM models, the temporal vegetation index information in the base-model was comprehensively exploited for multiple-crop classification and produced the Pixel-Based Conv1D and BiLSTM Ensemble model (PB-CB), and this was compared with the PB-Transformer model to validate the effectiveness of the proposed method. The multiple-crop cultivated area was extracted from 2005, 2010, 2015, and 2020 in North China by using the PB-Conv1D combine Snapshot (PB-CDST) and PB-CB models, which are a performance-optimized single model and an integrated model, respectively. The results showed that the mapping results of the multiple-crop cultivated area derived by PB-CDST (OA: 81.36%) and PB-BiLSTM combined with Snapshot (PB-BMST) (OA: 79.40%) showed exceptional accuracy compared to PB-Transformer combined with Snapshot and SWA (PB-TRSTSA) (OA: 77.91%). Meanwhile, the PB-CB (OA: 83.43%) had the most accuracy compared to the pixel-based single algorithm. The MODIS-derived PB-CB method accurately identified multiple-crop areas for wheat, corn, and rice, showing a strong correlation with statistical data, exceeding 0.7 at the municipal level and 0.6 at the county level.

Tea is a regular export item for Bangladesh, but due to slow growth in production, the country's tea exports have declined. However, organic tea has a great future in this regard. Exploring the underlying factors for the adoption of organic tea farming has great potential for the development of tea production in a sustainable manner. Therefore, our main objective was to identify factors influencing tea growers’ attitudes, perceptions, and adoption of organic tea farming in the Panchagarh district of Bangladesh. We adopted the Theory of Planned Behavior (TPB) to investigate the underlying factors affecting the farmers' belief in the adoption of organic farming were measured based on control factors (marketing factors and cost and benefit factors), attitude towards organic farming, and social factors (extension factors) through a binary logistic regression model. Results of this study revealed that organic growers had a more favorable perception, which was significantly different from non-organic growers reading of various factors related to organic farming. Our results demonstrated that education and knowledge significantly influence the farmers to form a highly favorable attitude towards organic tea farming, which together explained 20.6 percent (R2 = 0.206) of the overall variance. Moreover, attitudes towards organic farming and cost-benefit (CBF) factors had a significant impact on the adoption of organic tea farming. Results also revealed that the predicted adoption probability was found to be higher among those farmers who had a highly favorable attitude and a high perceived CBF level towards organic farming. Finally, based on the findings, we concluded that a participatory extension program to enhance the level of knowledge related to organic tea farming could significantly impact farmers’ attitudes and behaviors towards the practice of organic tea farming.

Increased human activity and dynamic climate variables have radically altered the global land use land cover (LULC) pattern and the ecosystem services (ES) they offer. However, assessing the impact of such LULC dynamics on ecological resources has rarely been attempted, especially in Bangladesh. Here, we assessed the dynamics of LULC change and associated ecosystem service values (ESVs) of coastal Bangladesh during 1999–2019 by analyzing historical Landsat LULC images and economic valuation techniques, respectively. Findings revealed a high prevalence of rural settlement-based Tree Outside Forest (TOF) land sprawl over agricultural land. Besides, LULC transition analysis revealed an increase in built-up, mangrove forest, water bodies, and saltpan/aquaculture areas and a decrease in agricultural and bare land areas. Overall, the ESV of coastal Bangladesh decreased by 0.47 billion US dollars, from US$ 25.18 billion in 1999 to US$ 24.71 billion in 2019. However, the values of different ES functions, such as food (−10.7%) and raw materials (−7.7%), were found most affected, while slight progress was evident in biodiversity protection (+2.2%), and water regulation (+1.1%) functions. Strong spatial dynamics of ESV changes were observed, with the largest decreases in the districts of Satkhira (−7.90%), Cox'sbazar (−5.75%), and Noakhali (−4.61%), and the highest increases in the districts of Lakshmipur (+4.65%) and Patuakhali (+3.18%). The findings regarding mangrove and TOF growth have important implications for building a buffer against ESV degradation due to the substantial decline in agricultural lands in other parts of the world with related socio-geographical and environmental problems.

Abstract Understanding present and future impacts of drought on the terrestrial carbon budget is of great significance to the evaluation of terrestrial ecosystem disturbance and terrestrial carbon sink. Here, we evaluate the effect of vegetation net primary productivity (NPP) associated with drought through the difference between the mean NPP in the drought and normal years during a specific time period (30 years). Then, the NPP effects in different vegetation types and climatic zones under baseline stage (1981–2010) and future climate scenarios (RCP2.6, RCP4.5, and RCP8.5) is assessed. The results indicate that the negative NPP extremes are captured in most regions, except for the high‐latitude in the Northern Hemisphere. The NPP loss caused by extreme droughts in 2071–2100 is largest under RCPs, followed by the effects of severe and moderate droughts. Regionally, central United States, southern Africa, central Asia, India, Amazon tropical rainforest, and Australia are projected to experience a significant increase in negative NPP extremes and most of these regions are in arid and semi‐arid and tropical rain forest areas. In contrast, tropical Asia suffers little drought effects. For different vegetation, Evergreen Broadleaf Forest, Closed Shrubland, Open Shrubland, Croplands, and Grassland are the most affected by drought. The largest NPP loss occurs in most part of regions under RCP4.5 scenario, not RCP8.5. Climate change is projected to play the largest role in aggravating the risk of drought‐induced NPP reduction. And meanwhile, the adverse effects of drought on vegetation may be resisted through rational fertilizer utilization and land management in future.

Study region: East Africa (EA), lake basins (Tana, Abaya-Chamo, Turkana, Victoria, and Tanganyika), 6472,404 km2 of total area. Study focus: Total water storage change (TWSC) exposes serious challenges in sustainable water resources management and ecosystem sustenance. While TWS changes have been increasingly studied based on Gravity Recovery and Climate Experiment (GRACE) derived TWS dataset, spatio-temporal changes in TWS and the driving factors remain unexplored. Herein, Mann–Kendall test, Theil-Sen's slope estimator, empirical orthogonal functions (EOFs), component contribution ratio (CCR), and Soil and Water Assessment Tool (SWAT) models were employed. We found a slight spatial fluctuation of TWS, and groundwater could affect TWS variability in the EA region. New hydrological insights: High annual TWSC was observed in the southwestern and northern regions, reaching values of 220 and − 100 mm/yr, respectively. Among lake basins, the maximum and minimum TWSC values attained 97.5 ± 28.2 mm/yr and 24.8 ± 7.7 mm/yr, in Victoria and Tana basins, respectively. The regional level groundwater contribution was about 42%, while 48%, 53%, and 46% were in Abaya-Chamo, Turkana, and Victoria basins, respectively. However, surface runoff contribution was 52% and 35% in Tana and Tanganyika basins, respectively. The seasonal TWS changes show substantial linkage with the lake basins' seasonal precipitation distribution. Our findings highlight the driving factors of TWS variability, which could help guiding water management policies, and promote satellite remote sensing datasets.

Drought is a very complex natural hazard and has a negative impact on the global ecosystem as a whole. Recently Bangladesh has been experiencing by different degree of dryness as a consequence of high climate variability, affecting the crop production to a great extent in the last couple of decades. In this context, the present study was made an effort to assess and analyse drought characteristics based on two drought indices, i.e., Standardized Precipitation Index (SPI) and Vegetation Condition Index (VCI), and model agricultural drought risk with Fast-and-frugal decision tree (FFT) model in Bangladesh from 2001 to 2016. We identified drought occurrence and its dynamics with three-time scale, i.e., SPI3J (November-January), SPI3A (February-April) and SPI6A (November-April), and three rice-growing seasons, i.e., Aus (March-July), Aman (June-November), and Boro (November-May) from TRMM (Tropical Rainfall Measuring Mission) and MODIS (Moderate Resolution Imaging Spectroradiometer) data. The results demonstrate that TRMM had good consistency with rain gauge measurement compared to CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) and PERSIANN-CDR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record) data to derive SPI3J, SPI3A and SPI6A. Overall results confirmed that more drought frequency observed in SPI6A than SPI3J and SPI3A time scale, representing moderate to severe drought throughout the country. Regarding agricultural drought resulting from VCI demonstrated Boro rice-growing season as more vulnerable crop growing season affected by severe to extreme drought event. Validation results of VCI exhibited a high correlation with rice yield data than in-situ soil moisture data. Results of the FFT model show that out of ten predictor variables SPI3J and SPI6A caused agricultural drought with SPI value less than -1.08 and -1.21 respectively. Additionally, the model characterized SPI3J and SPI6A as the most critical driving factors with the highest balanced accuracy triggering agricultural drought risk in Bangladesh.

Assessing and modeling the changes in land use and land cover (LULC) patterns and associated ecosystem service values (ESV) has become an important global agenda for formulating sustainable land management policies. Taking the coastal region of Bangladesh (CRB) as a case study, we utilized remote sensing and GIS techniques to analyze Landsat data of 1999 and 2019 to estimate the effect of LULC changes on the ESVs. The LULC classification, ESV quantification, and spatial variations were performed by semi-automated classification, per-unit value transfer, and spatial autocorrelation techniques, respectively. First, between 1999 and 2019, LULC altered dramatically (agricultural land and coastal wetlands decreased, while all other LULC types increased). Second, the total ESV decreased by 1.87%, which was mostly attributed to a 70% growth in both rural settlement and aquaculture/saltpan, and a 30% decline in both agricultural land and wetlands. Third, significant spatial correlation and moderately high spatial clustering were observed, which consisted mostly of mangrove forests, waterbodies, and wetland zones. Both high-high and low-low values increased, but spatial outliers remained unchanged. Conserving agricultural land should be prioritized in future land use plans of CRB to meet the ever-increasing food demands, control natural land conversion, and make land use sustainable.

The quantitative attribution of changes in streamflow to climate change (CC) and land cover change (LCC) for the Yangtze River Source Region (YRSR), China, was assessed. We used a combination of the SWAT model along with the statistical technique one factor at a time (OFAT) and innovative trend analysis (ITA) to achieve the study objectives. The climate and hydrology data from 1961 to 2016 and land-cover maps of 5 years’ difference from 1985 to 2015 were used. The model was calibrated (1964–1989) using a land-cover map of 1985 and validated for 1990–2016. This validated model was further validated for all other land-cover maps used in this study. The SWAT model simulation showed that streamflow had been significantly influenced by CC compared to LCC using land-cover maps of 1985–1990, 1990–1995. However, the SWAT model simulations did not result in further changes in streamflow for land cover maps of 2000–2005, 2005–2010, and 2010–2015 because there have not been any significant changes in land cover after 2000 while the main contributing factor was climate change. The SWAT model simulations showed that the main driver of changes in streamflow in the Yangtze River Source Region is climate change. This study shows that the individual impacts are more critical than combined impacts for designing hydraulic structures, water resources planning and management, and decision-making policies at the regional/basin scale.

Urbanization is an increasing phenomenon around the world, causing many adverse effects in urban areas. Urban heat island is are of the most well-known phenomena. In the present study, surface urban heat islands (SUHI) were studied for seven megacities of the South Asian countries from 2000–2019. The urban thermal environment and relationship between land surface temperature (LST), land use landcover (LULC) and vegetation were examined. The connection was explored with remote-sensing indices such as urban thermal field variance (UTFVI), surface urban heat island intensity (SUHII) and normal difference vegetation index (NDVI). LULC maps are classified using a CART machine learning classifier, and an accuracy table was generated. The LULC change matrix shows that the vegetated areas of all the cities decreased with an increase in the urban areas during the 20 years. The average LST in the rural areas is increasing compared to the urban core, and the difference is in the range of 1–2 (°C). The SUHII linear trend is increasing in Delhi, Karachi, Kathmandu, and Thimphu, while decreasing in Colombo, Dhaka, and Kabul from 2000–2019. UTFVI has shown the poor ecological conditions in all urban buffers due to high LST and urban infrastructures. In addition, a strong negative correlation between LST and NDVI can be seen in a range of −0.1 to −0.6.

Drought has devastating impacts on agriculture and other ecosystems, and its occurrence is expected to increase in the future. However, its spatiotemporal impacts on net primary productivity (NPP) in Mongolia have remained uncertain. Hence, this paper focuses on the impact of drought on NPP in Mongolia. The drought events in Mongolia during 2003–2018 were identified using the Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI). The Boreal Ecosystem Productivity Simulator (BEPS)-derived NPP was computed to assess changes in NPP during the 16 years, and the impacts of drought on the NPP of Mongolian terrestrial ecosystems was quantitatively analyzed. The results showed a slightly increasing trend of the growing season NPP during 2003–2018. However, a decreasing trend of NPP was observed during the six major drought events. A total of 60.55–87.75% of land in the entire country experienced drought, leading to a 75% drop in NPP. More specifically, NPP decline was prominent in severe drought areas than in mild and moderate drought areas. Moreover, this study revealed that drought had mostly affected the sparse vegetation NPP. In contrast, forest and shrubland were the least affected vegetation types.

Basin geomorphology is a complete system of landforms and topographic features that play a crucial role in the basin-scale flood risk evaluation. Nepal is a country characterized by several rivers and under the influence of frequent floods. Therefore, identifying flood risk areas is of paramount importance. The East Rapti River, a tributary of the Ganga River, is one of the flood-affected basins, where two major cities are located, making it crucial to assess and mitigate flood risk in this river basin. A morphometric calculation was made based on the Shuttle Radar Topographic Mission (SRTM) 30-m Digital Elevation Model (DEM) in the Geographic Information System (GIS) environment. The watershed, covering 3037.29 km2 of the area has 14 sub-basins (named as basin A up to N), where twenty morphometric parameters were used to identify flash flood potential sub-basins. The resulting flash flood potential maps were categorized into five classes ranging from very low to very high-risk. The result shows that the drainage density, topographic relief, and rainfall intensity have mainly contributed to flash floods in the study area. Hence, flood risk was analyzed pixel-wise based on slope, drainage density, and precipitation. Existing landcover types extracted from the potential risk area indicated that flash flood is more frequent along the major Tribhuvan Rajpath highway. The landcover data shows that human activities are highly concentrated along the west (Eastern part of Bharatpur) and the east (Hetauda) sections. The study concludes that the high human concentrated sub-basin “B” has been categorized as a high flood risk sub-basin; hence, a flood-resilient city planning should be prioritized in the basin.

Drought, a climate-related disaster impacting a variety of sectors, poses challenges for millions of people in South Asia. Accurate and complete drought information with a proper monitoring system is very important in revealing the complex nature of drought and its associated factors. In this regard, deep learning is a very promising approach for delineating the non-linear characteristics of drought factors. Therefore, this study aims to monitor drought by employing a deep learning approach with remote sensing data over South Asia from 2001–2016. We considered the precipitation, vegetation, and soil factors for the deep forwarded neural network (DFNN) as model input parameters. The study evaluated agricultural drought using the soil moisture deficit index (SMDI) as a response variable during three crop phenology stages. For a better comparison of deep learning model performance, we adopted two machine learning models, distributed random forest (DRF) and gradient boosting machine (GBM). Results show that the DFNN model outperformed the other two models for SMDI prediction. Furthermore, the results indicated that DFNN captured the drought pattern with high spatial variability across three penology stages. Additionally, the DFNN model showed good stability with its cross-validated data in the training phase, and the estimated SMDI had high correlation coefficient R2 ranges from 0.57~0.90, 0.52~0.94, and 0.49~0.82 during the start of the season (SOS), length of the season (LOS), and end of the season (EOS) respectively. The comparison between inter-annual variability of estimated SMDI and in-situ SPEI (standardized precipitation evapotranspiration index) showed that the estimated SMDI was almost similar to in-situ SPEI. The DFNN model provides comprehensive drought information by producing a consistent spatial distribution of SMDI which establishes the applicability of the DFNN model for drought monitoring.

The primary concentration of this study was to assess the marketing risks faced by hybrid rice growers and explore the trajectories. With this notion, data were collected from randomly selected 300 hybrid rice growers in purposively chosen areas with the application of a structured interview schedule and conducting five focus group discussions with the farmers. The study identified seven key risks responsible for the poor marketing of hybrid rice in Bangladesh. Perceived marketing risks were assessed by farmers opinion on likelihood and severity of each risk source through using a fuzzy-Likert scale. According to the finding high fluctuation of hybrid rice price was the most serious risk in hybrid rice marketing in Bangladesh. The regression result found several socio-demographic factors of the farmers are significantly related with perceived risk. This study, therefore, suggests government taking pro policies for hybrid rice growers organized around farmers those socio-demographic characteristics for minimizing marketing risk.

The Himalayan region, a major source of fresh water, is recognized as a water tower of the world. Many perennial rivers originate from Nepal Himalaya, located in the central part of the Himalayan region. Snowmelt water is essential freshwater for living, whereas it poses flood disaster potential, which is a major challenge for sustainable development. Climate change also largely affects snowmelt hydrology. Therefore, river discharge measurement requires crucial attention in the face of climate change, particularly in the Himalayan region. The snowmelt runoff model (SRM) is a frequently used method to measure river discharge in snow-fed mountain river basins. This study attempts to investigate snowmelt contribution in the overall discharge of the Budhi Gandaki River Basin (BGRB) using satellite remote sensing data products through the application of the SRM model. The model outputs were validated based on station measured river discharge data. The results show that SRM performed well in the study basin with a coefficient of determination (R2) >0.880. Moreover, this study found that the moderate resolution imaging spectroradiometer (MODIS) snow cover data and European Centre for Medium-Range Weather Forecasts (ECMWF) meteorological datasets are highly applicable to the SRM in the Himalayan region. The study also shows that snow days have slightly decreased in the last three years, hence snowmelt contribution in overall discharge has decreased slightly in the study area. Finally, this study concludes that MOD10A2 and ECMWF precipitation and two-meter temperature products are highly applicable to measure snowmelt and associated discharge through SRM in the BGRB. Moreover, it also helps with proper freshwater planning, efficient use of winter water flow, and mitigating and preventive measures for the flood disaster.