South Asia is the world's most densely populated region and is highly reliant on agriculture. Its agricultural production has been impacted by extreme climate events viz. erratic rainfall, heat stress, hailstorms, droughts, flash floods, and landslides which are more frequent in recent years. In South Asia, the common agricultural adaptation strategies to combat the impacts of climate change on agroecosystems include the use of drought-resistant varieties of crops, crop diversification, changes in cropping patterns, appropriate tillage methods, and water management, etc. Soil and fertilizer management, as well as Climate Smart Agriculture (CSA) technologies, can considerably reduce GHGs emissions from crop fields. Hence, scaling up existing adaptation and mitigation options and discovering innovative strategies are essential to reduce vulnerability and enhance the agricultural system's resilience. Therefore, the respective government body must develop policies for sustainable agriculture production and food system amid climate change.

Sustainable agricultural management requires knowledge of where and when crops are grown, what they are, and for how long. However, such information is not yet available in Nepal. Remote sensing coupled with farmers’ knowledge offers a solution to fill this gap. In this study, we created a high-resolution (10 m) seasonal crop map and cropping pattern in a mountainous area of Nepal through a semi-automatic workflow using Sentinel-2 A/B time-series images coupled with farmer knowledge. We identified agricultural areas through iterative self-organizing data clustering of Sentinel imagery and topographic information using a digital elevation model automatically. This agricultural area was analyzed to develop crop calendars and to track seasonal crop dynamics using rule-based methods. Finally, we computed a pixel-level crop-intensity map. In the end our results were compared to ground-truth data collected in the field and published crop calendars, with an overall accuracy of 88% and kappa coefficient of 0.83. We found variations in crop intensity and seasonal crop extension across the study area, with higher intensity in plain areas with irrigation facilities and longer fallow cycles in dry and hilly regions. The semi-automatic workflow was successfully implemented in the heterogeneous topography and is applicable to the diverse topography of the entire country, providing crucial information for mapping and monitoring crops that is very useful for the formulation of strategic agricultural plans and food security in Nepal.

A deep neural network (DNN), evolved from a traditional artificial neural network, has been seamlessly adapted for the spatial data domain over the years. Deep learning (DL) has been widely applied for a number of applications and a variety of thematic domains. This article reports on a systematic review of methods adapted in major DNN applications with remote sensing data published between 2010 and 2020 aiming to understand the major application area, a framework for model development and the prospect of DL application in spatial data analysis. It has been found that image fusion, change detection, scene classification, image segmentation, and feature detection are the most commonly used application areas. Based on the publication in these thematic areas, a generic framework has been devised to guide a model development using DL based on the methods followed in the past. Finally, recent trends and prospects in terms of data, method, and application of deep learning with remote sensing data are discussed. The review finds that while DL-based approaches have the potential to unfold hidden information, they face challenges in selecting the most appropriate data, methods, and model parameterizations which may hinder the performance. The increasing trend of application of DL in the spatial domain is expected to leverage its strength at its optimum to the research frontiers.

Precipitation plays vital roles in the global water cycle, knowledge of the spatial and temporal variation of the precipitation is essential to understanding extreme environmental phenomena such as floods, landslides, and drought. In this paper, the integrated characteristics of precipitation during 1980–2016 over Nepal along with the seasonal elevation dependency of precipitation were examined for three different regions over the country using Multi-Source Weighted-Ensemble Precipitation (MSWEP) product. The spatial distribution of mean annual precipitation varies significantly with the highest (lowest) precipitation of ~5500 (~100) mm/year in the Arun valley (Manang and Mustang). The precipitation regime of the country is determined by the contribution of the monthly precipitation amount with distinct spatial gradients between the eastern and the western sides during pre-monsoon, post-monsoon, and winter seasons. On the contrary, the spatial distribution of monsoon precipitation tends to more heterogeneous with visible differences between the lowland, midland, and highlands as similar to the annual one. Further, elevation dependency of seasonal precipitation revealed that the winter and post-monsoon precipitation distribution in western and central are very similar, whereas post-monsoon precipitation was found slightly higher than winter season in the eastern region. The highest precipitation areas in eastern and central region are located between 2000-2500 m, which is between 500 and 1000 m in the western region of the country. Overall, the pre-monsoon, summer monsoon and annual precipitation increases gradually with elevation upto 2500 m and then decreases with increasing elevation, whereas winter and post-monsoon precipitation are almost identical to each elevation interval of 500 m.

Monitoring paddy rice cultivation is essential for ensuring food security and for land resource management in agrarian countries of South Asia. In this study, we investigated the spatial and temporal variation of rice cultivated area and phenological metrics in Nepal between 2003 and 2018 using the time series MODIS data and PhenoRice algorithm. Comparisons of PhenoRice outputs with ancillary data show that implementation of PhenoRice with the MODIS data can be used for long-term change analysis of rice cultivation. Results on spatial distribution illustrate: rice cultivation is concentrated in the low elevation belt in the south; the cultivation begins earlier in the western region compared to the eastern region and begins earlier in the hilly region compared to the plains. The inter-annual trend analysis found a statistically significant decrease of rice cultivated area at the rate of 19.13 thousand hectares per year during the recent decade; the loss of rice fields was more prominent in the eastern plains while rice farming expanded in the mid-hills in the western region. Our study provides insights regarding timely and cost-efficient monitoring of rice farming at a large scale in the mountainous region.

Understanding the spatial and temporal variation of precipitation is important to identify its driving potential of extreme events that impact on the socio-economic conditions at national and provincial scales. This study presents the spatial and temporal variation of precipitation and the related extreme events in provincial scale using 143 rain-gauge stations across Nepal during 2001–2016. The results show the provincial differences in the precipitation distribution, with the highest precipitation in Province 4 (Bagmati) and lowest in Province 6 (Karnali). The precipitation is in decreasing trend at the national and provincial scale, expect for Province 6. The spatial distribution of precipitation shows the wettest (Lumle) and driest (Manang and Mustang) areas of the country located within the same Province 4. The seasonal cycle reveals the longer period of monsoon in Provinces 1 and 4; meanwhile, a shorter monsoon period was observed in Provinces 6 and 7 (Farwestern). Heavy (R10mm) and extreme precipitation (R25mm) events were higher in those provinces with a more extended monsoon period and vice-versa. The result further shows that the number of Consecutive Dry Day (CCD) spells in all the provinces was higher than the number of Consecutive Wet Day (CWD) spells. The time-series of extreme events (R10mm, R25mm, CDD and CWD) show an inter-annual variation in seven provinces during 2001-2016. The spatial variation of the number of wet and dry spells was reversed for the provinces lying in the eastern and western parts of the country. This study helps to update and upgrade our understanding of precipitation variability and related extremities over different provinces of Nepal, which can further assist the provincial government in disaster management.

Land surface temperature (LST) is an important variable for assessing climate change and related environmental impacts observed in recent decades. Regular monitoring of LST using satellite sensors such as MODIS has the advantage of global coverage, including topographically complex regions such as Nepal. In order to assess the climatic and environmental changes, daytime and nighttime LST trend analysis from 2000 to 2017 using Terra-MODIS monthly daytime and nighttime LST datasets at seasonal and annual scales over the territory of Nepal was performed. The magnitude of the trend was quantified using ordinary linear regression, while the statistical significance of the trend was identified by the Modified Mann–Kendall test. Our findings suggest that the nighttime LST in Nepal increased more prominently compared to the daytime LST, with more pronounced warming in the pre-monsoon and monsoon seasons. The annual nighttime LST increased at a rate of 0.05 K yr−1 (p < 0.01), while the daytime LST change was statistically insignificant. Spatial heterogeneity of the LST and LST change was observed both during the day and the night. The daytime LST remained fairly unchanged in large parts of Nepal, while a nighttime LST rise was dominant all across Nepal in the pre-monsoon and monsoon seasons. Our results on LST trends and their spatial distribution can facilitate a better understanding of regional climate changes.