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

In the current study, atmospheric carbon dioxide (CO2) 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. The atmospheric CO2 generated as part of the CAP-IGBP network would enhance the understanding of CO2 variability in different time scales ranging from diurnal, seasonal, and annual over the Indian region. Data available under this network may be interesting to other research communities for modeling studies and spatiotemporal variability of atmospheric CO2 across the study locations. The work also evaluated the CO2 observations against the Model for Interdisciplinary Research on Climate version 4 atmospheric chemistry-transport model (MIROC4-ACTM) concentrations.

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. This study aims to generate reference AGB datasets using field inventory plots and airborne LiDAR data for eight sites in Central Africa and five sites in South Asia, two regions largely underrepresented in global reference AGB datasets. The study provides access to these reference AGB maps, including uncertainty maps, at 100 m and 40 m spatial resolutions covering a total LiDAR footprint of 1,11,650 ha [ranging from 150 to 40,000 ha at site level]. These maps serve as calibration/validation datasets to improve the accuracy and reliability of AGB mapping for current and upcoming EO missions (viz., GEDI, BIOMASS, and NISAR).

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. The datasets are acquired at various spatial resolutions ranging from 1 mm to 2 cm. The targeted scene contains several paper-based panels, each size of 2 cm x 2 cm and filled with different colours and proportions, glued to a black background board that maintains a distinguishable distance between one another. In addition to the hyperspectral imagery data acquisitions, reference spectral signatures of the candidate mixture materials are obtained by in-situ hyperspectral reflectance measurements using a spectroradiometer. The hyperspectral image acquisition and the in-situ spectral signatures of the target scene are collected under natural illumination conditions. The proposed datasets are designed for undertaking proof-of-the-concept (PoC) studies in spectral unmixing. The datasets are also valuable for evaluating the performance of different statistical and machine learning algorithms for target detection, classification, and sub-pixel classification algorithms.