Precision crop mapping: within plant canopy discrimination of crop and soil using multi-sensor hyperspectral imagery.

Leveraging diverse optomechanical and imaging technologies for precision agriculture applications is gaining attention in emerging economies. The precise spatial detection of plant objects in farms is crucial for optimizing plant-level nutrition and managing pests and diseases. High-resolution remote sensors mounted on drones have been increasingly deployed for large-scale crop mapping and field variability characterization.

Historical expansion of tea plantations over 150 years (1876-2023) in North Bengal, India.

The Camellia sinensis (L.) Kuntze agroforestry system has been a dominant driver of land transformation for over a century. However, most previous studies have not captured the dynamics of tea plantations since their inception.

Spatiotemporal atmospheric in-situ carbon dioxide data over the Indian sites-data perspective.

In the current study, atmospheric carbon dioxide (CO) data covering multiple locations in the Indian subcontinent are reported. This data was collected using a dedicated ground-based in-situ network established as part of the Geosphere-Biosphere Programme (CAP-IGBP) of the Climate and Atmospheric Processes of the Indian Space Research Organisation (ISRO). Data are collected over Ponmudi, Ooty, Sriharikota, Gadanki, Shadnagar, Nagpur, and Dehradun during 2014-2015, 2017-2020, 2012, 2011-2015, 2014-2017, 2017 and 2008-2011, respectively.

LiDAR-based reference aboveground biomass maps for tropical forests of South Asia and Central Africa.

Accurate mapping and monitoring of tropical forests aboveground biomass (AGB) is crucial to design effective carbon emission reduction strategies and improving our understanding of Earth's carbon cycle. However, existing large-scale maps of tropical forest AGB generated through combinations of Earth Observation (EO) and forest inventory data show markedly divergent estimates, even after accounting for reported uncertainties. To address this, a network of high-quality reference data is needed to calibrate and validate mapping algorithms.

Multi-resolution terrestrial hyperspectral dataset for spectral unmixing problems.

Recent developments in the miniaturization of hyperspectral imaging sensors have given rise to the increased use of hyperspectral imagery as the primary data for evaluating spectral unmixing algorithms in applications such as industrial quality control, agriculture, mineral mapping, military, etc. This article presents an ultra-high-resolution hyperspectral imagery dataset for undertaking benchmark studies on spectral unmixing. A terrestrial hyperspectral imager (THI) is used for imaging the target scene with the camera sensor pointing horizontally towards the target scene.