Scholarlink

<p>The accurate identification of crop types and estimation of crop acreage are crucial for land management, food security, and policy planning. This study explores methodologies for crop area estimation using machine learning algorithms applied to remote sensing imagery, with Hisar, Haryana, as a case study. It compares the proposed remote sensing-based methods for the years 2018-19 to 2022-23 with traditional statistical approaches using historical datasets for the years 1998-2021 for estimating mustard and wheat acreage. The enhanced spatial and temporal variations in the fused dataset improve crop identification and help overcome the limitations of traditional methods. Our proposed methodology integrates unsupervised clustering, remapping with a supervised classifier using ground truth data, and validation. The results indicate that the proposed approach outperforms traditional methods in accuracy and effectiveness. Based on experimental evidence, the study recommends using a fusion dataset, spatially diverse ground truth data for a given year, and a hybrid machine learning algorithm to improve crop acreage estimation for years beyond the current period. The proposed hybrid KRF method applied to the fusion dataset achieved superior accuracy compared to the RF classifier and statistical models, with the lowest MAPE of 2.55% for wheat and 4.25% for mustard, and RMSE% of 2.73% for wheat and 4.29% for mustard. This approach is applicable to other crops and regions where optical imagery alone is insufficient for crop area estimation due to cloud cover during the cropping season.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Fusion of Optical and SAR Imagery for Crop Area Estimation: A Case Study of Wheat and Mustard in Semi-arid India

  • 期刊论文

  • 发表日期：

    2026年5月26日

查看全文
  • Ravisankar Saravanakumar,
  • Rajni Jain,
  • Anshu Bharadwaj,
  • Vinay Kumar Sehgal,
  • Dilip Kumar,
  • Sapna Nigam,
  • Alka Arora,
  • Sudeep Marwaha,
  • Ankur Biswas

摘要

The accurate identification of crop types and estimation of crop acreage are crucial for land management, food security, and policy planning. This study explores methodologies for crop area estimation using machine learning algorithms applied to remote sensing imagery, with Hisar, Haryana, as a case study. It compares the proposed remote sensing-based methods for the years 2018-19 to 2022-23 with traditional statistical approaches using historical datasets for the years 1998-2021 for estimating mustard and wheat acreage. The enhanced spatial and temporal variations in the fused dataset improve crop identification and help overcome the limitations of traditional methods. Our proposed methodology integrates unsupervised clustering, remapping with a supervised classifier using ground truth data, and validation. The results indicate that the proposed approach outperforms traditional methods in accuracy and effectiveness. Based on experimental evidence, the study recommends using a fusion dataset, spatially diverse ground truth data for a given year, and a hybrid machine learning algorithm to improve crop acreage estimation for years beyond the current period. The proposed hybrid KRF method applied to the fusion dataset achieved superior accuracy compared to the RF classifier and statistical models, with the lowest MAPE of 2.55% for wheat and 4.25% for mustard, and RMSE% of 2.73% for wheat and 4.29% for mustard. This approach is applicable to other crops and regions where optical imagery alone is insufficient for crop area estimation due to cloud cover during the cropping season.