Hybridizing deep learning algorithms and geostatistical approaches for improved crop yield disaggregation
Saravanakumar R., Rajni Jain, Vaibhav Kumar Singh, Anshu Bharadwaj, Vinay Kumar Sehgal, Ankur Biswas, Alka Arora, Hari Krishna
TL;DR
This paper introduces a new method that combines deep learning and geostatistics to improve the accuracy of crop yield maps at a fine spatial scale.
Contribution
The novel contribution is a hybrid framework combining deep learning with geostatistical residual kriging for village-to-pixel crop yield disaggregation.
Findings
Combining spectral and weather data yields the best results for crop yield disaggregation.
Deep learning models produce accurate and spatially realistic yield maps but have spatially structured residuals.
Residual kriging reduces RMSE by 35–45% and improves spatial realism of yield maps.
Abstract
Reliable crop yield estimates at fine spatial resolution are essential for precision agriculture, food security planning, and insurance schemes. However, yield statistics are reported at coarser administrative levels, limiting their applicability for field-scale analysis. This study proposes a multi-stage hybridized framework that integrates deep learning (DL) models with geostatistical residual kriging to disaggregate village-level crop yield statistics to the pixel level. The proposed methodology is demonstrated using wheat and mustard crops as case study in the semi-arid districts, Haryana, India. The study identifies suitable data combination by evaluating multiple combinations of soil, weather, Sentinel-1, and Sentinel-2 bands data for yield disaggregation. Results show that datasets combining spectral and weather information consistently outperform other data combinations.…
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Taxonomy
TopicsSoil Geostatistics and Mapping · Remote Sensing in Agriculture · Soil Moisture and Remote Sensing