English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

. Large-scale mapping and monitoring of agriculture land use are very important. It helps in forecast crop yields, assesses the factors influencing the crop stress and estimate the damage due to natural hazards. Also, more essentially, aids in calculating the irrigation water demand at the farm level and better water resource management. Recent developments in remote sensing satellite sensors spatial and temporal resolutions, global coverage and open access such as Sentinel-2, created new possibilities in mapping and monitoring land use/land cover features. The present study investigated the performance and applicability of Sen2-Agri system in the heterogeneous cropping system for operational crop type mapping at parcel resolution using time series Sentinel-2 multispectral satellite imagery. The parcel level crop type information was collected in the field by systematic sampling and used to train and validate the random forest (RF) classification in the system. The classification accuracy varied from 57% to 86% for different major crops. The overall classification accuracy was 70% with KAPPA index of 61%. The very small agriculture field size and persistent cloud cover are the major constraint to the improvement of classification accuracy. Combination of the time series imagery from multiple earth observation satellites for the monsoon cropping season and development of a robust system for in-situ data collection will further increase the mapping accuracy. Sen2-Agri system has the potential to handle a large amount of earth observation data and can be scaled up to the entire country, which will help in the efficient monitoring of crops.

the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences

SEN2-AGRI – CROP TYPE MAPPING PILOT STUDY USING SENTINEL-2 SATELLITE IMAGERY IN INDIA