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
DOI:
10.11648/j.ajrs.20251302.11
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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 ...
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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
DOI:
10.11648/j.ajrs.20251302.12
Downloads:
Views:
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...
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