ORCID Profile
0000-0003-2749-3549
Current Organisations
University of Michigan
,
University of Oklahoma
,
USDA-ARS Beltsville Agricultural Research Center
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Publisher: American Geophysical Union (AGU)
Date: 28-10-2017
DOI: 10.1002/2017GL075421
Publisher: Copernicus GmbH
Date: 03-03-2021
DOI: 10.5194/EGUSPHERE-EGU21-2186
Abstract: & & & #8216 Aerodynamic resistance& #8217 (hereafter r& sub& a& /sub& ) is a preeminent variable in the modelling of evapotranspiration (ET), and its accurate quantification plays a critical role in determining the performance and consistency of thermal remote sensing-based surface energy balance (SEB) models for estimating ET at local to regional scales. Atmospheric stability links r& sub& a& /sub& with land surface temperature (LST) and the representation of their interactions in the SEB models determines the accuracy of ET estimates.& & & & The present study investigates the influence of r& sub& a& /sub& and its relation to LST uncertainties on the performance of three structurally different SEB models by combining nine OzFlux eddy covariance datasets from 2011 to 2019 from sites of different aridity in Australia with MODIS Terra and Aqua LST and leaf area index (LAI) products. Simulations of the latent heat flux (LE, energy equivalent of ET in W/m& sup& & /sup& ) from the SPARSE (Soil Plant Atmosphere and Remote Sensing Evapotranspiration), SEBS (Surface Energy Balance System) and STIC (Surface Temperature Initiated Closure) models forced with MODIS LST, LAI, and in-situ meteorological datasets were evaluated using observed flux data across water-limited (semi-arid and arid) and radiation-limited (mesic) ecosystems.& & & & Our results revealed that the three models tend to overestimate instantaneous LE in the water-limited shrubland, woodland and grassland ecosystems by up to 60% on average, which was caused by an underestimation of the sensible heat flux (H). LE overestimation was associated with discrepancies in r& sub& a& /sub& retrievals under conditions of high atmospheric instability, during which errors in LST (expressed as the difference between MODIS LST and in-situ LST) apparently played a minor role. On the other hand, a positive bias in LST coincides with low r& sub& a& /sub& and causes slight underestimation of LE at the water-limited sites. The impact of r& sub& a& /sub& on the LE residual error was found to be of the same magnitude as the influence of errors in LST in the semi-arid ecosystems as indicated by variable importance in projection (VIP) coefficients from partial least squares regression above unity. In contrast, our results for mesic forest ecosystems indicated minor dependency on r& sub& a& /sub& for modelling LE (VIP& .4), which was due to a higher roughness length and lower LST resulting in dominance of mechanically generated turbulence, thereby diminishing the importance of atmospheric stability in the determination of r& sub& a& /sub& .& &
Publisher: MDPI AG
Date: 11-05-2021
DOI: 10.3390/RS13101870
Abstract: Remote sensing offers a way to map crop types across large spatio-temporal scales at low costs. However, mapping crop types is challenging in heterogeneous, smallholder farming systems, such as those in India, where field sizes are often smaller than the resolution of historically available imagery. In this study, we examined the potential of relatively new, high-resolution imagery (Sentinel-1, Sentinel-2, and PlanetScope) to identify four major crop types (maize, mustard, tobacco, and wheat) in eastern India using support vector machine (SVM). We found that a trained SVM model that included all three sensors led to the highest classification accuracy (85%), and the inclusion of Planet data was particularly helpful for classifying crop types for the smallest farms ( m2). This was likely because its higher spatial resolution (3 m) could better account for field-level variations in smallholder systems. We also examined the impact of image timing on the classification accuracy, and we found that early-season images did little to improve our models. Overall, we found that readily available Sentinel-1, Sentinel-2, and Planet imagery were able to map crop types at the field-scale with high accuracy in Indian smallholder systems. The findings from this study have important implications for the identification of the most effective ways to map crop types in smallholder systems.
Publisher: American Association for the Advancement of Science (AAAS)
Date: 09-2023
Publisher: American Association for the Advancement of Science (AAAS)
Date: 26-02-2021
Abstract: Groundwater depletion will reduce cropping intensity in India, and canal irrigation cannot fully substitute for this loss.
Publisher: IOP Publishing
Date: 20-07-2021
Abstract: India relies on groundwater irrigation to produce staple grain crops that provide over half of the calories consumed by its over 1.3 billion people. While groundwater has helped India achieve grain self-sufficiency, aquifers have been overexploited across much of the country and its implications for crop production are unclear. To understand how groundwater depletion affects staple grain (wheat, rice, maize, pearl millet, and sorghum) production in India, we ran district-level panel regressions using agricultural census, groundwater observation, and gridded weather datasets over a ten-year study period (2004–2013). We find that nationally, declining groundwater levels are associated with significant reductions in yield, cropped area, and production for wheat, rice, and maize in the winter season. Despite the negative impacts of groundwater depletion on crop production, we find little evidence that farmers are switching from planting more water-intensive to less water-intensive grains. Using profit-based decision modeling, we further investigated the effects of agricultural energy prices on crop choice in the monsoon season across Haryana and Punjab, which are responsible for over 60% of India’s grain production, have high electricity subsidies, and have rapidly depleting water tables. We find that eliminating energy subsidies for groundwater pumping would likely not encourage farmers to switch to planting less water-intensive crops, though sensitivity analyses suggest that it could encourage the adoption of increased water conservation efforts. In summary, our analyses reveal a discernable impact of groundwater depletion on crop production in India and suggest that reducing or removing energy subsidies may largely affect water use but not crop choice.
Location: United States of America
Location: United States of America
No related grants have been discovered for Nishan Bhattarai.