-
Research Article
Field-Scale Monitoring of Rice Crop Using Open-Source Satellite Data and Digital Platforms - A Case Study of Samastipur District, Bihar, India
Issue:
Volume 13, Issue 2, December 2025
Pages:
48-72
Received:
8 September 2025
Accepted:
23 September 2025
Published:
30 October 2025
Abstract: Crop monitoring over large areas with high accuracy is of great significance in precision agriculture. The study is an attempt to assess the potential of open-source high-resolution satellite datasets and open-source digital platforms in combination with AI/ML algorithms for near-real-time crop monitoring and yield estimation at the farm level. In this research, we used Sentinel-1 and Sentinel-2 datasets, by processing them on the Google Earth Engine platform and developed several crop-based indicators to assess crop phenology as well as the distinction between a well-managed field (demo plots) vs a normal farmers' practice-managed crop (control plots) using Sentinel-1 satellite data. Further, crop yields were estimated before the harvesting of the crop by using Sentinel-1 and Sentinel-2 data with machine learning algorithms. The findings demonstrate that the effect of an improved package of practices on rice was significantly different from the farmer's practice. Among the statistical yield models developed for yield estimation, the gradient tree boosting model performed better than other models. This study proposes a novel method of near-real-time remote crop monitoring right from sowing to harvest time to estimate crop yields with an accuracy of 77 percent. There is potential in using open-source satellite data for monitoring farm fields in the future.
Abstract: Crop monitoring over large areas with high accuracy is of great significance in precision agriculture. The study is an attempt to assess the potential of open-source high-resolution satellite datasets and open-source digital platforms in combination with AI/ML algorithms for near-real-time crop monitoring and yield estimation at the farm level. In ...
Show More
-
Research Article
Evaluation of the Changes of Vegetation Cover Impact on Rainfall Using Remote Sensing in Wag Hemra Zone, Amhara Region, Ethiopia
Wendimnew Getachew Alemu*
Issue:
Volume 13, Issue 2, December 2025
Pages:
73-86
Received:
14 September 2025
Accepted:
11 October 2025
Published:
30 October 2025
Abstract: Landsat imagery has the ability to assess the effect of vegetation cover change on rainfall. CHRIPS data was also used to analyze rainfall time series and trend line from 1990-2024. The goal of this study was to examine temporary and spatial changes in vegetation cover impact on rainfall, examine the trends of rainfall and the correlation between rainfalls with vegetation cover change during the study period. The study methods were undertaken using NDVI, change detection, Mann-Kendall's and Sen's slope test and correlation analysis. This study explores using NDVI analysis, the vegetation cover was shown 13.5% in 1990, 29.8% in 2000, 20.2% in 2010, 31.3% in 2020 and 24.1 in 2024 over the study area. Therefore, the amount of vegetation cover has been regenerated by about 89,272 hectares (10.7%) in the past 35 years in the study area. this study results, the minimum, maximum and mean rainfalls have declined trend lines of 0.497, 0.81 and 0.26mm per year over the past 35 years period (1990-2024) respectively. Statistically non-significant trends were shown in maximum and mean rainfall but not in minimum rainfall. However, the analysis of the trend line explained that the minimum, maximum, and mean rainfalls were changed by the factors of -0.497 mm, -0.81mm, and -0.26 mm per year respectively. The mean rainfall of the vegetated area was greater than the mean rainfall of non-vegetated area for all reference years. This indicates areas with low vegetation cover or low NDVI values have shown low mean rainfall. Based on the coefficient of determination, 3% of vegetation cover change was caused by rainfall in the study area. All the residents of Wag Hemra zone are to strengthen the protection of the vegetation cover in the study area and encourage afforestation work.
Abstract: Landsat imagery has the ability to assess the effect of vegetation cover change on rainfall. CHRIPS data was also used to analyze rainfall time series and trend line from 1990-2024. The goal of this study was to examine temporary and spatial changes in vegetation cover impact on rainfall, examine the trends of rainfall and the correlation between r...
Show More
-
Research Article
Study and Modeling of a Photon-counting DIAL (Differential Absorption Lidar) System Applied to the Detection of Tropospheric Gas Molecules
Issue:
Volume 13, Issue 2, December 2025
Pages:
87-99
Received:
7 October 2025
Accepted:
20 October 2025
Published:
26 November 2025
Abstract: The Differential Absorption Lidar (DIAL) technique is one of the most effective methods for detecting atmospheric gases. It is based on the interaction between laser-emitted light and atmospheric molecules. The backscattered optical signal is converted into an electrical signal using photomultiplier tubes or other types of detectors. Two main detection approaches are commonly used: analog detection and photon-counting detection. While the analog mode is widely employed, it suffers from limited sensitivity. The photon-counting mode, although more suitable for detecting extremely weak signals, faces challenges in daytime measurements due to strong solar background noise. The main objective of this study is to evaluate the performance of the photon-counting technique to enable the DIAL system to detect extremely weak optical signals. To this end, modeling and simulation of the parameters influencing system performance have been carried out. Furthermore, the impact of using ultra-narrow band filter (UNBF) has been investigated and compared to that of conventional interference filter, with the aim of reducing background noise in daytime measurements and improving the transmission of the useful signal. The results show that the photon counting acquisition technique for a DIAL system or P-DIAL (Photon-counting DIAL) provides superior performance in terms of signal quality and measurement accuracy compared to analogue detection using an UNBF for noise limitation.
Abstract: The Differential Absorption Lidar (DIAL) technique is one of the most effective methods for detecting atmospheric gases. It is based on the interaction between laser-emitted light and atmospheric molecules. The backscattered optical signal is converted into an electrical signal using photomultiplier tubes or other types of detectors. Two main detec...
Show More
-
Research Article
Meteorological Drought Analysis by Using CHRIPSv2 Satellite Remote Sensing and Station Data over North Shewa Zone, Oromia, Ethiopia
Tsige Berhanu Fana*,
Mesay Tolossa Wakete
Issue:
Volume 13, Issue 2, December 2025
Pages:
100-109
Received:
22 October 2025
Accepted:
4 November 2025
Published:
11 December 2025
DOI:
10.11648/j.ajrs.20251302.14
Downloads:
Views:
Abstract: The North Shewa zone of Ethiopia is highly vulnerable to drought. The majority of the population of the zone is involved in agricultural activities for their livelihood. Agriculture is predominantly dependent on the timing, amount, duration, and distribution of natural rainfall. This makes the zone susceptible to the impacts of climatic extreme events such as drought. Yet, no drought assessment studies have been conducted on the spatial and temporal analysis of recent droughts over the zone. In view of that, this study examined the spatial and temporal characteristics of drought in the period 1990 to 2020 over the North Shewa zone of Oromia regional state, Ethiopia, using the Standardized Precipitation Index (SPI) drought index. The drought events at each of the stations had varying magnitudes and occurrences. During Kiremt (JJAS), seasonal drought was more frequent than Belg (FMAM). The years 2006, 1998, 2013, and 2016 are the top driest seasons in Belg (ordered from high to low spatial coverage); and 1992, 2009, 1990, 2012, and 2015 are the years when there was the highest spatially spread drought in the Kiremt (June-September) season. The comparison between the performance of Raw, Adjusted, and Merged datasets in terms of with relation to CORR, BR2, BIAS, and RMSE. The results show that merged data is stronger than row and adjusted data in reproducing the observed station data.
Abstract: The North Shewa zone of Ethiopia is highly vulnerable to drought. The majority of the population of the zone is involved in agricultural activities for their livelihood. Agriculture is predominantly dependent on the timing, amount, duration, and distribution of natural rainfall. This makes the zone susceptible to the impacts of climatic extreme eve...
Show More
