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![Crop calendar. Source: [24]. Note: It is a modified crop calendar based on Ministry of Agriculture Development (MoAD) of Nepal and local experience of the author. We have used the NDVI values of different seasons for the evaluation of crop cycle. It may differ according to cropping pattern, topography, and environment](profile/Bhagawat-Rimal/publication/327897489/figure/fig5/AS:963468396085248@1606720174053/Crop-calendar-Source-24-Note-It-is-a-modified-crop-calendar-based-on-Ministry-of.png)
Crop calendar. Source: [24]. Note: It is a modified crop calendar based on Ministry of Agriculture Development (MoAD) of Nepal and local experience of the author. We have used the NDVI values of different seasons for the evaluation of crop cycle. It may differ according to cropping pattern, topography, and environment
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![Crop calendar. Source: [24]. Note: It is a modified crop calendar based...](profile/Bhagawat-Rimal/publication/327897489/figure/fig5/AS:963468396085248@1606720174053/Crop-calendar-Source-24-Note-It-is-a-modified-crop-calendar-based-on-Ministry-of_Q320.jpg)
Nepal is an agrarian country located in the Southeast Asian region with unique climate and geographical features. There are three geographical regions in the country: (a) mountain, (b) hill, and (c) plain (termed as himal, pahad, and tarai in the Nepali language) with distinct cropping patterns. Limited land in the hill and mountain regions is farm...
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Citations
... In the context of Nepal, national-scale agricultural land cover maps were developed using MODIS 250 m NDVI time series data for the year 2016 [25], temporal rice-growing area and land-use change maps were provided using MODIS 500 m time series data [26], and 30 m resolution maps were generated through land suitability assessments and a land allocation model [27]. However, there is a gap in the availability of local-level cropland maps utilizing high-resolution satellite imagery for evaluating long-term changes in cropland areas. ...
... As a follow-up to this study, our intention is to employ similar methodologies to generate an updated, regionally trained, high-resolution seasonal cropland classification map of Nepal, leveraging the capabilities of Sentinel-2 imagery. This effort is motivated by the current lack of detailed, high-resolution cropland maps for the region, with only a coarser resolution map utilizing MODIS NDVI time series [25] currently available. ...
With growing global concern for food and water insecurity, an efficient method to monitor irrigation projects is essential, especially in the developing world where irrigation performance is often suboptimal. In Nepal, the irrigated area has not been objectively recorded, although their assessment has substantial implications for national policy, project’s annual budgets, and donor funding. Here, we present the application of Landsat images to measure irrigated areas in Nepal for the past 17 years to contribute to the assessment of the irrigation performance. Landsat 5 TM (2006–2011) and Landsat 8 OLI (2013–2022) images were used to develop a machine learning model, which classifies irrigated and non-irrigated areas in the study areas. The random forest classification achieved an overall accuracy of 82.2% and kappa statistics of 0.72. For the class of irrigation areas, the producer’s accuracy and consumer’s accuracy were 79% and 96%, respectively. Our regionally trained machine learning model outperforms the existing global cropland map, highlighting the need for such models for local irrigation project evaluations. We assess irrigation project performance and its drivers by combining long-term changes in satellite-derived irrigated areas with local data related to irrigation performance, such as annual budget, irrigation service fee, crop yield, precipitation, and main canal discharge.
... In comparison to traditional artificial cropland mapping and change analysis, remote sensing imagery analysis is more dependable, quicker, and more economical for measuring large territories, and it can also create consistent temporal records (Onojeghuo et al. 2018). Numerous researches have either identified cropland map or farming statistics (Rimal et al. 2018;Sharma et al. 2011;Mtibaa and Irie 2016) or the detection of cropping patterns (Chen et al. 2018;Panigrahy et al. 2011;Qian et al. 2017) using a variety of techniques. Hudait and Patel (2022) mapped the diverse crop patch on the Tamluk Subdivision of the Purba Medinipur district of West Bengal using Sentinel-2 multi-spectral pictures and two machine learning algorithms: K-nearest neighbor (KNN) and random forest (RF). ...
In the Indian Sundarban region, the current study investigates the long-term dynamics of seasonal (Kharif and Rabi) land-use/land-cover change (LULC) and spatial change of seasonal croplands. Based on the random forest (RF) classifier, LULC classes are divided into eight categories. Cropland’s spatial and temporal dynamicity has been generated using land change modeler in TerrSet throughout the past 20 years (2000/01 and 2020/2021). With the aid of a confusion matrix, classification accuracy has been tested and shown to be quite acceptable and effective in identifying the long-term land-use change in the study area. It has been discovered that during the Kharif season (November 2000 to November 2020), waterlogged, built-up, and cropland expanded by approximately 57% (99.14 km2), 23% (126.06 km2), and 2% (29.90 km2), respectively, at the expense of current fallow, vegetation, and mudflats, which decreased by roughly 28% (81.9 km2), 11% (78.68 km2), and 10% (60.14 km2), respectively. Croplands and current fallows were the most dynamic land changes throughout the Rabi season (February 2001 to February 2021), increasing by about 102% (522.28 km), while the area now in fallow steadily decreased by about 56% (991.23 km2) between 2001 and 2021. The analysis of cropland changes reveals that the most changes in cropland have been recorded in the populated area’s central, northern, and northeastern regions. The results of this study will help develop agricultural and environmental management methods to ensure the sustainability of the ecosystem and agricultural resources.Graphical Abstract
... However, this dataset has not been fully explored to keep track of the phenological stages (i.e., crop calendar in the mountainous topography) of a country like Nepal. On the other hand, a high-resolution crop cycle inventory and precise crop areas are not yet available [24] although they are essential for better crop planning and management and for improving agricultural production. Therefore, knowledge about the seasonal extension of cropping patterns in Nepal remains limited and accurate cropping intensity maps are in urgent need. ...
... This study established that the Sentinel-2 based dataset is useful for automatically tracking crop dynamics in small-scale farms with heterogeneous topography. Before this study, phenology estimation had been realized based on a field survey [33,34] or coarse resolution remote sensing data such as MODIS time-series data [24,35,36]. However, field observation is time and resource consuming; hence, it cannot be extended to a larger area. ...
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.
... Thus, rice farming in SA and SEA is important to regional and global food security, which is a pivotal theme in Zhiqi Decision makers and planners depend on timely reported information on paddy rice area and vegetation growth to estimate rice yields and plan resource allocations and contingency plans accordingly. Thus, timely producing accurate rice extent maps is crucial for helping the formulation of strategic agricultural plans that ensures food security, especially for densely populated countries like Bangladesh, India, and Vietnam [3]. ...
Mapping rice area is a critical resource planning task in many South Asia countries where rice is the primary crop. Remote sensing-based methods typically rely on domain knowledge, such as crop calendar and crop phenology, and supervised classification with ground truth samples. Applying such methods on Google Earth Engine (GEE) has been proven effective especially at large scale owing to the comprehensive and up-to-date data catalog and massive server-side processing power. However, writing scripts through the code editor requires users to program in JavaScript and understand GEE Application Programming Interface (API), which can be challenging for many researchers. Thus, this paper presents a GEE-based web application that aims to eliminate the programming requirements for data selection, preprocessing, and visualizations so that users can easily produce rice maps and refine ground truth collections through intuitive Graphical User Interfaces (GUI). This software includes 3 sub-module apps, namely the ground truth collection app, threshold-based rice mapping app, and classification-based rice mapping app. Users can customize data processing flow using GUI designed with Bootstrap, and the backend server uses GEE Python API, and a Google service account for authentication to execute the workflow on Google cloud servers. The experiment shows both the overall accuracy (OA) and Kappa scores of the mapping result are higher than 0.9 which suggests RiceMapEngine can significantly reduce the complexity and time costs it takes to produce accurate rice area maps and meet the demands of real-world stakeholders.
... The lower tropical climate zone is a geographically plain area, and most of the soil in this zone is highly fertile. Thus, the lower part of this climate zone is more suitable for agricultural activities and the climate is more ideal for paddy, wheat, and maize crops (Rimal et al. 2018;Sharma 2001). Moreover, due to the fertile soil and tropical climate, the production of cash crops (sugarcane and jute), tropical fruits (mango, papaya, litchi, and banana), and vegetables is higher in this climate zone compared to the other climate zones in Nepal (Amatya 1976;Ghimire and Thakur 2013;Neupane et al. 2017). ...
... The overall climate of this zone is tropical. Paddy, wheat, potato, legume/oilseed, maize, vegetable crops are cultivated in the irrigated lowland area; similarly, maize and mustard are the main crops cultivated in the upland area within the Tarai agro-ecozone (Rimal et al. 2018). ...
Nepal is a relatively small, mountainous country in the Central Himalayas with a diverse climate within a short aerial distance due to its unique topography and altitudinal variation. The country is characterized mainly by six climatic zones, ranging from tropical in the southern plains to tundra/nival in the northern part with perpetual snow cover. In this chapter, a brief explanation of the climate zones; a description of the climate seasons; and overall climatic trends including temperature, rainfall, floods, drought, and frosts and cold waves of the country is provided. Further, the chapter covers the status of agro-ecozones, soil climate, and climate change and its impact. In recent decades, Nepal’s climate shows trends of increasing temperature and decreasing rainfall. The country’s agro-ecozones are divided into five major zones, including Tarai, River Basins, Lower Hills, High Hills, and High Mountains, ranging between 60–4800 meters (m) above mean sea level (asl). Climate is known to be one of the five soil-forming factors and has a significant influence on soil properties. For example, the properties of soils found in the High Hills and High Mountains are different than in soils found in other agro-ecozones of Nepal. Finally, we discuss the climate change impacts in Nepal and the significant risks they pose, especially on agriculture, food security, and people’s livelihoods. A broad-brush description of various climatic scenarios of Nepal Himalayas is presented.
... Based on RS technology, the winter wheat planting areas can be estimated more conveniently. The collected pre-processing MOD09Q1 data were used to calculate the NDVI (Normalized Difference Vegetation Index), which was used to identify the regionalor global-scale vegetation coverage [32,40,41]. To express the characteristics of ground features on NDVI curves, the Savitzky-Golay filter was chosen to smooth the original NDVI time series curves to better reflect the trends of the three types of NDVI time series curves of ground features [42,43]. ...
Crop production potential is an index used to evaluate crop productivity capacity in one region. The spatial production potential can help give the maximum value of crop yield and visually clarify the prospects of agricultural development. The DSSAT (Decision Support System for Agrotechnology Transfer) model has been used in crop growth analysis, but spatial simulation and analysis at high resolution have not been widely performed for exact crop planting locations. In this study, the light-temperature production potential of winter wheat was simulated with the DSSAT model in the winter wheat planting area, extracted according to Remote Sensing (RS) image data in the Jing-Jin-Ji (JJJ) region. To obtain the precise study area, a Decision Tree (DT) classification was used to extract the winter wheat planting area. Geographic Information System (GIS) technology was used to process spatial data and provide a map of the spatial distribution of the production potential. The production potential of winter wheat was estimated in batches with the DSSAT model. The results showed that the light-temperature production potential is between 4238 and 10,774 kg/ha in JJJ. The production potential in the central part of the planting area is higher than that in the south and north in JJJ due to the influences of light and temperature. These results can be useful for crop model simulation users and decision makers in JJJ.
... To overcome this problem, many studies have been using several vegetation indices that are derived from satellite data to identify agricultural land cover classes [1,31,58,64,71,81,82,84]. Furthermore, various studies proved that the Normalized Difference Vegetation Index (NDVI), developed in the early 1970s [68,75], correlates with plant productivity [75]. ...
The use of remote sensing data provides valuable information to ensure sustainable land cover management. In this paper, the potential of phenological metrics data, derived from Sentinel-2A (S2) and Landsat 8 (L8) NDVI time series, was evaluated using Random Forest (RF) classification to identify and map various crop classes over two irrigated perimeters in Morocco. The smoothed NDVI time series obtained by the TIMESAT software was used to extract profiles and phenological metrics, which constitute potential explanatory variables for cropland classification. The method of classification applied involves the use of a supervised Random Forest (RF) classifier. The results demonstrated the capability of moderate-to-high spatial resolution (10–30 m) satellite imagery to capture the phenological stages of different cropping systems over the study area. Furthermore, the classification based on S2 data presents a higher overall accuracy of 93% and a kappa coefficient of 0.91 than those produced by L8 data, which are 90% and 0.88, respectively. In other words, phenological metrics obtained from S2 time series data showed high potential for agricultural crop-types classification in semi-arid regions and thus can constitute a valuable tool for decision makers to use in managing and monitoring a complex landscape such as an irrigated perimeter.
... A classification map with a finer spatial resolution in a substudy area (according to references [4], [5], and [40]) or the use of enough samples (usually more than 500, according to references [1], [14], and [37]) is usually constructed. The purpose is to compute a confusion matrix, where strategies for the spatial distribution accuracy of crop classification or identification results are usually employed. ...
Remote sensing (RS) is a convenient technology to estimate the regional cultivation areas of crops. However, the accurate estimation of maize areas using RS over a broad region is a significant challenge due to the large phenology differences and insufficient prior knowledge in space. To address this issue, a new method was developed in this work. In this method, the correlation (r) and root mean standard error (RMSE) between the time-series moderate resolution imaging spectroradiometer enhanced vegetation index (MODIS EVI) and the standard EVI curve of maize from a reference area are computed. Pixels with a high value of r and a low value of RMSE were identified as maize areas. The phenology information observed at agro-meteorological stations was also used to recognize maize pixels from the pixel-level phenology derived from time-series MODIS EVI. The proposed method provides an accurate characterization of the phenology differences over the study area by making use of the planting and maturity dates only. In addition, the few location-dependent parameters make the recognition of maize planting areas over large regions easier than previous studies. The proposed method was implemented over the Northeast China Plain (NECP) and North China Plain (NCP). The derived results were compared with official statistical results, and a close agreement was observed. At the city level, the satellite-derived estimates agreed well with the statistics with the R2(RMSE) of 0.86(110.97 k hm2) in the NECP and 0.76(68.74 k hm2) in the NCP. At the county level, the R2(RMSE) is 0.82(25.47 k hm2) in the NECP and 0.75(5.93 k hm2) in the NCP. At both temporal levels, the R2(RMSE) results obtained in this work are higher(lower) than those published in other studies. The obtained results indicate that the proposed method is effective in maize area estimation over broad regions.
... Location of food production in Nepal is mainly determined by topography, agro-ecological zone and availability of irrigation (Rimal et al., 2018b). Paddy and wheat are mainly grown in Tarai districts of Jhapa, Morang, Sunsari and Udaypur, while maize is produced throughout the study area. ...
... Location of food production in Nepal is mainly determined by topography, agro-ecological zone and availability of irrigation (Rimal et al., 2018b). Paddy and wheat are mainly grown in Tarai districts of Jhapa, Morang, Sunsari and Udaypur, while maize is produced throughout the study area. ...
The provision of ecosystem services is directly related to the type of land use and land cover and management practices in a given area. Changes in land use and land cover can alter the supply of ecosystem services and affect the well-being of both humanity and nature. This study analyses the spatiotemporal variations of land use and land cover and quantifies the change in three important ecosystem services (food production, carbon storage, and habitat quality) in the Koshi River Basin, Nepal during 1996–2016 by using freely available data and tools such as, Landsat satellite images and the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model. During the observed time period, there was an overall gain in urban areas (190 sq.km), forests (773 sq.km) and grassland (431 sq.km); loss of cultivated land (220 sq.km) and shrub lands (847 sq.km), mostly occurring in the lowlands (≤1000 m). As a result of the land cover changes, while food production and carbon storage showed a declining trend, overall habitat quality in the basin increased. There is a need to design novel and effective landscape approaches that address local realities and that will aid the maintenance of ecosystem services. We recommend landscape level planning to improve urban and agricultural sectors and focus on halting the loss of ecosystem services.
