Shangharsha Thapa

Process-based crop models are extensively used to assess the impacts of climate change, environmental variations, and management practices on crop yields. However, parameters sourced from the literature are often not universally applicable, necessitating calibration to enhance the model performance. The in-situ observed crop yield (hereafter referred to as “observed crop yield”) is commonly used for model calibration. Satellite-based data, such as evapotranspiration (ET), offers additional insights into plant growth and holds significant potential for enhancing calibration efforts. The LPJ-GUESS (Lund-Potsdam-Jena General Ecosystem Simulator) model has been applied extensively to simulate crop yields across various scales, but it has not been calibrated for the regional-scale yield simulation. This study aims to enhance the performance of LPJ-GUESS in simulating crop yields in southern Sweden through calibration using observed crop yield and satellite-based ET data. Results demonstrated that calibrating with observed crop yield substantially improved simulation accuracy for both spring barley and winter wheat, reducing the normalized root mean square error (nRMSE) from 50.3% to 12.8% and from 15.5% to 12.2%, respectively. Sensitivity analysis identified four key parameters influencing yield simulations: minimum C:N ratio (CNmin), N demand reduction by leaves (Ndred), and the retranslocation of nitrogen and carbon (Nret and Cret). Calibration using the Penman-Monteith-Leuning Version 2 (PML-V2) ET product moderately enhanced yield simulation accuracy, particularly for winter wheat, achieving an nRMSE of 14.2%, demonstrating its potential as an alternative when especially when the long-term and continuous observed crop yield is not available. The calibrated LPJ-GUESS model effectively simulated crop yield for both crop types under drought and normal conditions, highlighting its robustness across varying environmental scenarios.

Archaeological sites hold immense economic value for Nepal, contributing to income and overall economic growth. These sites are susceptible to damage from both human activities and natural factors, needing swift and cost-effective reconstruction efforts. In addressing this challenge, 3D scene reconstruction emerges as a crucial reverse engineering method. Various techniques, such as LiDAR and Photogrammetry, are used for this purpose. While LiDAR offers precise models, its high cost has limited its accessibility. A notable development in this context has been achieved due to the introduction of consumer-grade LiDAR sensors, particularly with the integration of such sensors into iPhones. The primary focus of this research revolves around assessing the efficacy of consumer-grade LiDAR sensors in archaeological scene reconstruction. Utilizing the SiteScape and 3D Scanner apps, several tests with various settings were conducted to generate point clouds of the studied archaeological structures. The obtained point clouds were analyzed to assess point density variations across various application settings. Meshes were generated from these point clouds and the measurements were then taken from digital meshes which are compared with in-situ measurements, allowing for the calculation of relative error. Measurements obtained through SiteScape displayed a maximum difference of 4.17% and a minimum difference of 0.05%. In contrast, the 3D Scanner exhibited a maximum difference of 6.22% and a minimum difference of 0.09%. The findings suggest that the SiteScape app provides a higher point density and a higher accuracy than the 3D Scanner.

Monitoring crop growth and estimating crop yield are essential for managing agricultural production, ensuring food security, and maintaining sustainable agricultural development. Combining the mechanistic framework of a crop growth model with remote sensing observations can provide a means of generating realistic and spatially detailed crop growth information that can facilitate accurate crop yield estimates at different scales. The main objective of this study was to develop a robust estimation methodology of within-field winter wheat yield at a high spatial resolution (20 m × 20 m) by combining a light use efficiency-based model and Sentinel-2 data. For this purpose, Sentinel-2 derived leaf area index (LAI) time series were assimilated into the Simple Algorithm for Yield Estimation (SAFY) model using an ensemble Kalman filter (EnKF). The study was conducted on rainfed winter wheat fields in southern Sweden. LAI was estimated using vegetation indices (VIs) derived from Sentinel-2 data with semi-empirical models. The enhanced two-band vegetation index (EVI2) was found to be a useful VI for LAI estimation, with a coefficient of determination (R²) and a root mean square error (RMSE) of 0.80 and 0.65 m²/m², respectively. Our findings demonstrate that the assimilation of LAI derived from Sentinel-2 into the SAFY model using EnKF enhances the estimation of within-field spatial variability of winter wheat yield by 70% compared to the baseline simulation without the assimilation of remotely sensed data. Additionally, the assimilation of LAI improves the accuracy of winter wheat yield estimation by decreasing the RMSE by 53%. This study demonstrates an approach towards practical applications of freely accessible Sentinel-2 data and a crop growth model through data assimilation for fine-scale mapping of crop yield. Such information is critical for quantifying the yield gap at the field scale, and to aid the optimization of management practices to increase crop production.

The monitoring of forest phenology based on observations from near-surface sensors such as Unmanned Aerial Vehicles (UAVs), PhenoCams, and Spectral Reflectance Sensors (SRS) over satellite sensors has recently gained significant attention in the field of remote sensing and vegetation phenology. However, exploring different aspects of forest phenology based on observations from these sensors and drawing comparatives from the time series of vegetation indices (VIs) still remains a challenge. Accordingly, this research explores the potential of near-surface sensors to track the temporal dynamics of phenology, cross-compare their results against satellite observations (MODIS, Sentinel-2), and validate satellite-derived phenology. A time series of Normalized Difference Vegetation Index (NDVI), Green Chromatic Coordinate (GCC), and Normalized Difference of Green & Red (VIgreen) indices were extracted from both near-surface and satellite sensor platforms. The regression analysis between time series of NDVI data from different sensors shows the high Pearson’s correlation coefficients (r > 0.75). Despite the good correlations, there was a remarkable offset and significant differences in slope during green-up and senescence periods. SRS showed the most distinctive NDVI profile and was different to other sensors. PhenoCam_GCC tracked green-up of the canopy better than the other indices, with a well-defined start, end, and peak of the season, and was most closely correlated (r > 0.93) with the satellites, while SRS-based VIgreen accounted for the least correlation (r = 0.58) against Sentinel-2. Phenophase transition dates were estimated and validated against visual inspection of the PhenoCam data. The Start of Spring (SOS) and End of Spring (EOS) could be predicted with an accuracy of NDVI vs. satellite_NDVI and PhenoCam_GCC vs. satellite_GCC suggests that it is feasible to use PhenoCams and UAVs for satellite data validation and upscaling. Thus, a combination of these near-surface vegetation metrics is promising for a holistic understanding of vegetation phenology from canopy perspective and could serve as a good foundation for analysing the interoperability of different sensors for vegetation dynamics and change analysis.

Water is a major component in the living ecosystem. As water quality is degrading due to human intervention, continuous monitoring is necessary. One of the indicators is Chlorophyll-a (Chl-a) which indicates algal blooms which are often driven by eutrophication phenomena in freshwater. Lakes should be monitored for Chl-a because Chla-a is related to eutrophication phenomena which are an enrichment of water by nutrients salt. When the environment becomes enriched with nutrients the excessive growth can lead to the death of fish. In this study, the Remote Sensing (RS) and Geographic Information System (GIS) techniques were utilized to determine Chl-a concentration of Phewa Lake of Kaski district. We used Landsat 8 satellite imagery for estimation and mapping of the Chl-a concentration. In-situ measurements from different sample points were taken and used to form a regression model for Chl-a and its concentration over the water body was calculated. The preceding year’s (2016) in situ measurement data of Chl-a concentration at a specific location were assessed with the one evaluated from the regression model thus produced for the succeeding year (2017) using Root Mean Square Error (RMSE) technique. As a result, we concluded that the estimation and mapping of Chl-a of a lake in Nepal can be done with the help of RS and GIS techniques.

Nepal being a part of Hindu Kush Himalayan Region is one of the largest concentration of glaciers. The loss of ice on that region reflects the climate change resulting devastating consequences on billions of people living downstream. This is why regular monitoring of the glaciers on that region is important not only to have insight into the glacier change but also to mitigate the effect of it. The satellite image as an alternate to ground based approach provides much more flexibility for monitoring in a regular basis. Therefore, this research aimed to map the decadal changes in glacier extent followed by estimating volume of ice loss of Rikha Samba Glacier located in Mustang district of Nepal. Multitemporal Landsat satellite images dating back from 1995 to 2015 were utilized in the study. The current research involved the calculation of snow index like NDSI on the radiometrically corrected satellite imageries. An open source GIS software package was used to automatically delineate the boundary of glacier based on the NDSI. It has been revealed that the glacier area shrunk by 2.608 km² on an average over the period. The ice velocity of the glacier was estimated. Laminar flow approach (Cuffey and Paterson, 2010) was utilized to find the ice thickness of glacier based on the evaluated velocity information. The utilization of thus computed ice thickness gave the volume of ice loss during the study period (i.e. 310000 m³). In addition, 0.69 km glacier retreat was observed over the period.