Spatial Rice Yield Estimation Using Multiple Linear Regression Analysis, Semi-Physical Approach and Assimilating SAR Satellite Derived Products with DSSAT Crop Simulation Model

Abstract

Accurate and consistent information on the area and production of field crops is vital for national and state planning and ensuring food security in India. Satellite-based remote sensing offers a suitable and cost-effective technique for regional- and national-scale crop monitoring. The use of remote sensing data for crop yield estimation has been demonstrated using a semi-physical approach with reasonable success. Assimilating remote sensing data with the DSSAT model and spectral indices-based regression analysis are promising methods for spatially estimating rice crop yields. Rice area and yield in the Cauvery delta zone of Tamil Nadu, India was estimated during samba (August–January) season in the years 2020–2021 using Sentinel 1A Synthetic Aperture Radar satellite data with three different spatial yield estimation methods, namely a spectral indices-based regression analysis, semi-physical approach, and integrating remote products with DSSAT crop growth model. A rice area map was generated for the study area using a rule-based classifier approach utilizing parameterization with a classification accuracy of 94.5% and a kappa score of 0.89. The total classified rice area in Cauvery Delta Region was 379,767 ha, and the Start of Season (SoS) maps for samba season revealed that the major planting period for rice was between 22 September and 9 November in 2020. The study also aimed to identify promising spatial yield estimation techniques for optimal rice yield prediction over large areas. Regression models resulted in rice yields of 3234 to 3905 kg ha−1 with a mean of 3654 kg ha−1. The net primary product was computed using the periodical PAR, fAPAR, Wstress, Tstress, and maximum radiation use efficiency in a semi-physical approach. The resultant rice yields ranged between 2652 and 3438 kg ha−1 with the mean of 3076 kg ha−1. During the integration of remote sensing products with crop growth models, LAI values were extracted from dB images and utilized to simulate rice yields in the range of 3684 to 4012 kg ha−1 with the mean of 3855 kg ha−1. When compared to the semi-physical approach, both integrating remote sensing products with the DSSAT crop growth model and spectral indices-based regression analysis had R2 greater than 0.80, NRMSE of less than 10%, and agreement of more than 90%, indicating that these two approaches could be used for spatial rice yield estimation.

Integrating Synthetic Aperture Radar (SAR) Sentinel 1A and CROPGRO Peanut Simulation Model for Spatial Yield Gap Analysis

“…The Overall accuracy (> 80 per cent) and kappa score (>0.50) indicated that the assessment was of high quality. The results were in accordance with [28][29][30][31] has delineated the crop area estimation with high classification accuracy using multi temporal sentinel 1A data had indicated the suitability of remote sensing-based products for crop area estimation and monitoring for policy decisions, including crop insurances.…”

Growth Monitoring and Yield Estimation of Maize Plant Using Unmanned Aerial Vehicle (UAV) in a Hilly Region

“…Yield estimation and validation were part of the workflow diagram. Estimation of yield was performed as part of a model generation, where the model was generated using regression analysis, which is shown below [ 40 ]. Further, the methodology for the validation of the obtained yield is also illustrated in Figure 13 below.…”

The role of crop simulation modeling in assessing potential climate change impacts

“…A detailed review of various data assimilation methods, widely used remote sensing data, diverse crop models used, and the current status of data assimilation application with crop models can be found in Jin et al (2018) and Huang et al (2019). Such data assimilation applications have included the use of remote sensing data along with crop simulation models to estimate regional evapotranspiration, water-use efficiency, and primary production of wheat and maize in China and rice in India (Pazhanivelan et al, 2022). Data assimilation methods have been used to update LAI and the Normalized Differential Vegetation Index (NDVI) from satellite data to improve simulation of wheat yields under irrigation (Jin et al, 2022).…”

“…(2019). Such data assimilation applications have included the use of remote sensing data along with crop simulation models to estimate regional evapotranspiration, water‐use efficiency, and primary production of wheat and maize in China (Wang, Lei, Li, Huo, et al., 2023; Wang, Lei, Li, Qu, et al., 2023) and rice in India (Pazhanivelan et al., 2022). Data assimilation methods have been used to update LAI and the Normalized Differential Vegetation Index (NDVI) from satellite data to improve simulation of wheat yields under irrigation (Jin et al., 2022).…”