Estimation and analysis of terrestrial net primary productivity over India by remote-sensing-driven terrestrial biosphere model
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In the present study, the Carnegie–Ames–Stanford Approach (CASA), a terrestrial biosphere model, has been used to investigate spatiotemporal pattern of net primary productivity (NPP) during 2003 over the Indian subcontinent. The model drivers at 2-min spatial resolution were derived from National Oceanic and Atmospheric Administration advanced very high resolution radiometer normalized difference vegetation index, weather inputs, and soil and land cover maps. The annual NPP was estimated to be 1.57 Pg C (at the rate of 544 g C m − 2), of which 56% contributed by croplands (with 53% of geographic area of the country (GAC)), 18.5% by broadleaf deciduous forest (15% of GAC), 10% by broadleaf evergreen forest (5% of GAC), and 8% by mixed shrub and grassland (19% of GAC). There is very good agreement between the modeled NPP and ground-based cropland NPP estimates over the western India (R2 = 0.54; p = 0.05). The comparison of CASA-based annual NPP estimates with the similar products from other operational algorithms such as C-fix and Moderate Resolution Imaging Spectroradiometer (MODIS) indicate that high agreement exists between the CASA and MODIS products over all land covers of the country, while agreement between CASA and C-Fix products is relatively low over the region dominated by agriculture and grassland, and the agreement is very low over the forest land. Sensitivity analysis suggest that the difference could be due to inclusion of variable light use efficiency (LUE) across different land cover types and environment stress scalars as downregulator of NPP in the present CASA model study. Sensitivity analysis further shows that the CASA model can overestimate the NPP by 50% of the national budget in absence of downregulators and underestimate the NPP by 27% of the national budget by the use of constant LUE (0.39 gC MJ − 1) across different vegetation cover types.
KeywordsCASA Net primary productivity Remote sensing Carbon cycle India
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