Assimilation of Remote Sensing Data into Crop Growth Model for Yield Estimation: A Case Study from India

• Published in: Journal of the Indian Society of Remote Sensing Vol. 50; no. 2; pp. 257 - 270
• Main Authors: Gumma, Murali Krishna, Kadiyala, M. D. M., Panjala, Pranay, Ray, Shibendu S., Akuraju, Venkata Radha, Dubey, Sunil, Smith, Andrew P., Das, Rajesh, Whitbread, Anthony M.
• Format: Journal Article
• Language: English
• Published: New Delhi Springer India 01.02.2022; Springer Nature B.V
• Subjects: Agricultural production; Appraisals; case studies; Crop growth; Crop insurance; Crop management; crop models; Crop yield; Data collection; Earth and Environmental Science; Earth Sciences; equations; Growth models; India; Information management; Landsat; Leaf area index; Leaves; Logistics management; Nutrient transport; Parameterization; Remote observing; Remote sensing; Remote Sensing/Photogrammetry; Research Article; Simulation; Time series; time series analysis; Vegetation index; villages; India; Leaf area index (LAI); Sentinel-2; Crop classification; Landsat 8 and India; Remote sensing; Crop cutting experiments (CCEs); Yield assessment
• ISSN: 0255-660X; 0974-3006
• DOI: 10.1007/s12524-021-01341-6

Crop yield estimation is important to inform logistics management such as the prescription of nutrient inputs, financing, storage and transport, marketing as well as to inform for crop insurance appraisals due to loss incurred by abiotic and biotic stresses. In this study, we used a suite of methods to assess yields at the village level (< 5 km 2 ) using remote sensing technology and crop modeling in Indian states of Telangana, Andhra Pradesh and Odisha. Remote sensing products were generated using Sentinel-2 and Landsat 8 time series data and calibrated with data collected from farmers’ fields. We derived maps showing spatial variation in crop extent, crop growth stages and leaf area index (LAI), which are crucial in yield assessment. Crop classification was performed on Sentinel-2 time series data using spectral matching techniques (SMTs) and crop management information collected from field surveys along with ground data. The locations of crop cutting experiments (CCEs) was identified based on crop extent maps. LAI was derived based on the SAVI (soil-adjusted vegetation index) equation were using Landsat 8-time series data. We used the technique of re-parametrization of crop simulation models based on the several iterations using remote sensing leaf area index (LAI). The data assimilation approach helps in fine-tuning the initial parameters of the crop growth model and improving simulation with the help of remotely sensed observations. Results clearly show a good correlation between observed and simulated crop yields ( R 2 is greater than 0.7) for all the crops studied. Our study showed that by assimilation of remotely sensed data in to crop models, crop yields at harvest could be successfully predicted.