Annual and seasonal variations in gross primary productivity across the agro-climatic regions in India

Tropical ocean teleconnections with gross primary productivity of monsoon-Asia

The intricate oceanic climate interactions with terrestrial primary production of Asian ecosystems exert crucial social-economical-environmental repercussions. Yet, a holistic understanding of tropical sea surface temperature (SST) anomalies associated with the gross primary productivity (GPP) variations of monsoon-Asia remains constrained. This study provides a statistical framework demonstrating how SST perturbations in the tropics influence GPP fluctuations in monsoon-Asia by modulating hydrothermal conditions of different climate system components. Observation evidence explicitly illustrated the characteristic anomalous SST signatures of positive and negative GPP anomalies in South and Southeast Asia during June-August. The SST anomalies of the central-eastern tropical Pacific showed a robust negative impact on the GPP variability of South-Asia. The GPP alterations in maritime-Southeast-Asia exhibited strong connections with SST anomalies of the western Pacific (positive) and eastern equatorial Pacific (negative). The oceanic signals in the GPP variability of South-Asia and maritime-Southeast-Asia mirrored canonical El Niño and La Niña patterns. The detected SST-GPP link is feasible through large-scale atmospheric circulation variability and the consequent regional modulation of heat and moisture fluxes. The anomalous strengthening (weakening) of Walker cell enhances (reduces) water availability to plants for photosynthesis during the La Niña (El Niño) phase of the ENSO cycle and thus elevates (lowers) GPP in South-Asia and Maritime-southeast-Asia. In contrast, the enhanced GPP anomaly in mainland-Southeast-Asia depicts signs of canonical La Niña and Indian Ocean subtropical dipole (IOSD) teleconnections. The positive impact of IOSD was through the modulation of the Mascarene High and the consequent impact on the monsoon. Meanwhile, decreased GPP bears the imprint of El Niño Modoki and warm tropical Indian Ocean SSTs. The atmospheric teleconnections demonstrated the delayed impact of El Niño Modoki on GPP variability through the Indian Ocean capacitor effect. Our findings could be instrumental in forecasting the probable effects on vegetation growth in monsoon-Asia associated with high-frequency tropical oceanic changes.

Crop productivity estimation by integrating multisensor satellite, in situ, and eddy covariance data into efficiency-based model

Accurate and quantitative regional estimates of the carbon budget require an integration of eddy covariance (EC) flux-tower observations and remote sensing in ecosystem models. In this study, a simple remote sensing driven light use efficiency (LUE) model was used to estimate the primary productivity for major cropping systems using multi-temporal satellite data over the Saharanpur district in India.The model is based on radiation absorption and its conversion into biomass. The LUE model was implemented for major crop rotations derived from the time-series of Sentinel-2 and Landsat 8 with monthly satellite-based spatially explicit fields of photosynthetically active radiation (PAR), fraction of absorbed PAR (fAPAR) and down-regulated light use efficiency. Incident PAR and fAPAR were estimated on monthly basis from the ground-calibrated empirical equation using INSAT-3D insolation product and remote sensing-based vegetation indices, respectively. Spatial LUE maps created by down-regulating maximum LUE (EC tower-based) with water and temperature stressors derived from land surface water index (LSWI) and EC-based cardinal temperature, respectively. LUE-based modeled GPP over the sugarcane-wheat system was found higher than the rice-wheat system in Saharanpur district. This is because C4 crop (sugarcane) has very high photosynthetic efficiency compared to C3 crops (rice and wheat). Modeled GPP over the sugarcane-wheat system was found in good agreement with observed EC tower-based GPP (Index of Agreement = 0.93). Further regionally calibrated remote sensing-based LUE model well captures gross photosynthesis rates (GPP) over cropland ecosystem compared to globally modeled MODIS GPP product.

Species-Level Classification and Mapping of a Mangrove Forest Using Random Forest—Utilisation of AVIRIS-NG and Sentinel Data

Although studies on species-level classification and mapping using multisource data and machine learning approaches are plenty, the use of data with ideal placement of central wavelength and bandwidth at appropriate spatial resolution, for the classification of mangrove species is underreported. The species composition of a mangrove forest has been estimated utilising the red-edge spectral bands and chlorophyll absorption information from AVIRIS-NG and Sentinel-2 data. In this study, three dominant species, Heritiera fomes, Excoecaria agallocha and Avicennia officinalis, have been classified using the random forest (RF) model for a mangrove forest in Bhitarkanika Wildlife Sanctuary, India. Various combinations of reflectance/backscatter bands and vegetation indices derived from Sentinel-2, AVIRIS-NG, and Sentinel-1 were used for species-level discrimination and mapping. The RF model showed maximum accuracy using Sentinel-2, followed by the AVIRIS-NG, in discriminating three dominant species and two mixed compositions. This study indicates the potential of Sentinel-2 data for discriminating various mangrove species owing to the appropriate placement of central wavelength and bandwidth in Sentinel-2 at ≥10 m spatial resolution. The variable importance plots proved that species-level classification could be attempted using red edge and chlorophyll absorption information. This study has wider applicability in other mangrove forests around the world.