Urban Climate & Heat$39
Urban Heat Island Mapping
Research report in urban climate & heat focused on Urban Heat Island Mapping.
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Agriculture Monitoring$39
Agricultural Crop Health Monitoring
Agricultural Crop Health Monitoring framed as a research-led agriculture monitoring study.
Category
Agriculture Monitoring
Format
PDF Report
Delivery
Digital download after purchase
Price
$39
Summary
Reliable, spatially explicit crop health monitoring is essential for food security under a changing climate, yet most remote-sensing studies evaluate vegetation indices within a single agro-climatic zone, limiting their transferability. This study presents a systematic comparison of six Sentinel-2-derived vegetation indices—Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil-Adjusted Vegetation Index (SAVI), Normalized Difference Water Index (NDWI), Normalized Difference Red-Edge Index (NDRE), and Modified Soil-Adjusted Vegetation Index 2 (MSAVI2)—across eight agricultural regions spanning seven Köppen climate classes on five continents.
What the report covers
Reliable, spatially explicit crop health monitoring is essential for food security under a changing climate, yet most remote-sensing studies evaluate vegetation indices within a single agro-climatic zone, limiting their transferability.
This study presents a systematic comparison of six Sentinel-2-derived vegetation indices—Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil-Adjusted Vegetation Index (SAVI), Normalized Difference Water Index (NDWI), Normalized Difference Red-Edge Index (NDRE), and Modified Soil-Adjusted Vegetation Index 2 (MSAVI2)—across eight agricultural regions spanning seven Köppen climate classes on five continents.
Using Level-2A surface-reflectance imagery acquired during the 2022–2023 growing seasons, we quantify index sensitivity to crop type and climate, delineate stress using threshold and z-score methods, cluster anomalous patches with DBSCAN, and relate peak-season index values to reference yields via linear regression, Random Forest, and multilayer perceptron models evaluated under leave-oneout cross-validation.
Why it matters
Crop-health monitoring supports food-system analysis, agricultural advisory work, and stress detection across growing regions.
Intended sectors and users
AgricultureFood systemsInsuranceGovernment
Methodology framing
The report is framed around Sentinel-2, using the methods described in the source abstract and paper title.
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Applications and use cases
Vegetation health screening
Crop stress monitoring
Agricultural reporting
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