Crop yield estimation
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Early-season crop yield mapping in Finland
UN Handbook on Remote Sensing for Agricultural Statistics
1
Welcome
2
How to use this handbook
3
Introduction
Foundations
4
Remote Sensing images: optical, SAR
5
Land cover and crop classification schemas
6
Quality control of training sets for agricultural statistics
7
Machine learning classification of remote sensing images
8
Spatial map uncertainty estimation and active learning in crop classification
9
Map validation and use of maps for area estimation
10
EO Big Data Sources
11
Remote Sensing in the Design of Sampling Frames
12
Automatic Extraction of Parcels
Use Cases in Crop Type Mapping
13
Crop monitoring with SAR images in Poland
14
Crop classification in Mexico
15
Multi-seasonal crop mapping in Senegal
16
Crop classification in Zimbabwe
17
Crop classification and land use mapping in Chile
18
Crop classification using Digital Earth Africa
Crop yield estimation
19
Early-season crop yield mapping in Finland
20
Rice Paddy Phenology in Indonesia
21
Yield Forecasting in Poland
22
Rice Phenology in Colombia
23
Crop type classification and crop yield estimation in China
Additional Topics
24
Extraction of crop statistics from crop type and crop yield maps
25
WorldCereal - A Global Effort for Crop Mapping
26
UAV use in Agricultural Statistics
27
Remote Sensing for Agricultural Disaster Response
28
Data Governance for Agricultural Statistics
Table of contents
19.1
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19
Early-season crop yield mapping in Finland
Author
Maria Yli-Heikkilä
19.1
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Crop yield estimation
20
Rice Paddy Phenology in Indonesia