Towards food security and mitigation of agricultural GHG emissions in India: Transforming satellite remote sensing applications for improving agronomic practices in smallholder landscapes and accounting GHG emissions

Description of the topic

From the Green Revolution era, the Indo-Gangetic Plains (IGP) plays a critical role in national food security of India, thereby shaping the country’s socioeconomic and environmental landscape. The region is under tremendous pressure with the increased demand for food and the adverse effects of climate change such as water stress and extreme heat fluxes. On the other hand, the agriculture sector itself contributes to the Global Warming, by releasing the greenhouse gases (GHG) directly or indirectly to the atmosphere. In this context, we need sustainable development solutions that are capable of enhancing the co-benefits between agricultural practices and emission reduction strategies. The options for these co-benefits include improved productivity through land management without adverse environmental side effects, access to low-cost technology, improved residue management and restoration of water availability.

A significant proportion of food grains in the IGP are produced by small-scale agricultural production systems on a diverse geographic platform. Smallholder farmers often rely on their underperforming croplands and have minimal access to the technologies necessary for optimal agrarian production. This precludes the ability to catch up with increased food demand as well as to adapt to climate change. Hence it is crucial to identify factors associated with existing yield gaps in smallholder croplands including environmental factors. Closing these gaps in this context has an additional potential of improving the livelihood of smallholder farmers. Further, deployment of new and traditional agricultural practices needs to be evaluated for its potential for the mitigation of GHG emissions in agriculture.

Over the past few decades, satellite remote sensing has played a crucial role in agricultural applications. The proposed study will attempt to explore the potential of optical and microwave remote sensing to examine water stress through parameters like soil moisture content, agriculture distribution patterns, its impact on yield, the role of technology in water stress management. Additionally, a detailed study will be conducted to quantify the role of agriculture in GHG production. GHG contribution from residual burning and paddy fields will be quantified using satellite dedicated for detection of GHG.

Objectives

  1. Assess the differences in soil moisture measures using satellite and ground data obtained from fields of different size
  2. Assess the impact of crop lifecycle on the variation in soil moisture content and quantify the impact of small field size on the effectiveness of remote sensing driven approach for estimating soil moisture.
  3. Assess the impact of levelling technology on soil moisture distribution using an ensemble crop modelling approach
  4. Quantify the role of agricultural innovations in changing GHG emissions using Sentinel-1 SAR and Sentinel-5P time series

Work expectations

The proposed study is a novel attempt to utilize remote sensing applications for improving agronomic practices with particular emphasis on smallholders. The candidate will be expected to retrieve remote sensing data, explore the use of such data in the data assimilation framework and link the technology impact on a soil parameter to the crop yield through the application of remote sensing. Additionally, the candidate is expected to deduce the contribution of different farming practices on changing GHG emissions by applying an atmospheric transport model in addition to remote sensing products.

Activities

  1. Field Soil moisture and methane measurements
  2. Satellite data acquisition and pre-processing
  3. Design the Artificial Neural Network to estimate the SMC
  4. Determining the relationship between crop life cycle as well as Laser Land Leveling (LLL) technology on SMC distribution
  5. Quantify the impact of SMC on overall crop yield using the crop model
  6. Effectively contribute toward the economic impact assessment of LLL technology in the rice-wheat systems of the Indo-Gangetic Plains of India.
  7. Inundation area mapping using SAR data from Sentinel-1
  8. Designing Inverse problem using Sentinel 5p observations and atmospheric transport model simulations to estimate agricultural emissions

 

Required skills

Background in atmospheric GHG modelling, remote sensing tools as well as programming languages (e.g., R, Fortran, Matlab, Python) is essential. Proven records in the synergistic use of Sentinel-1 C-band and Sentinel-2 optical indices, as well as Sentinel 5p retrievals, will be desirable. Knowledge of inverse modelling technique will be a plus.