Scholarlink

<p>Accurate and timely production forecasts of soybean (<i>Glycine max L. Merril</i>), an economically significant crop worldwide, has immense importance for market intelligence as well as for the decision makers. Space-based observation provides synoptic coverage and information over a large area regarding crop condition at different spatial resolutions over the whole crop season and hence is important for estimating yield estimation at required spatial scale. In some parts of India, it is being cultivated as mono crop while in some other parts in combination with red gram as intercrop. Crop mapping, acreage estimation and yield estimation of soybean in the intercropped region poses challenge. This study was carried out to estimate production of soybean (Glycine max), at three different scales (block, district and state) in two major soybean-growing states (Madhya Pradesh and Maharashtra) of India those contain areas with monocrop soybean as well as inter crop soybean (along with red gram) using input data from multiple satellite sensors (both optical and microwave). For production estimation at state and district level, crop mask and acreage estimates were generated using optical data from TERRA-MODIS (Moderate Resolution Imaging Spectrometer) while backscattering coefficient from C-band-SAR of Sentinel 1 was used for generating crop mask and acreage estimates at block level. Yield was estimated using the semi-physical model with inputs derived from multiple sensors at both GEO (INSAT-3D) and LEO (TERRA-MODIS) platforms. Production was estimated using the acreage and yield of the respective administrative unit. The estimated acreage, yield and production were validated at block, district and state level using the reported statistics. Validation of soybean production in the study area indicated a correlation coefficient of 0.81 to 0.89 at block level and 0.91 at district level. The study suggested use of optical data for estimating soybean production at state and district scale and use of C-band SAR data for block level production estimation. This study also suggested to use the correction factor for estimating area and yield in the areas where soybean is cultivated in combination with red gram as intercrop for improving the production estimation.</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

Estimating Soybean Production at Multiple Scale Using Remote Sensing Based Crop Model and Data from Both Optical and Microwave Sensor

  • 期刊论文

  • 发表日期：

    2026年5月27日

查看全文
  • Rojalin Tripathy,
  • Nita Bhagia,
  • Mukesh Kumar,
  • Bimal Kumar Bhattacharya,
  • Govind Bairagi,
  • Indal Ramteke

摘要

Accurate and timely production forecasts of soybean (Glycine max L. Merril), an economically significant crop worldwide, has immense importance for market intelligence as well as for the decision makers. Space-based observation provides synoptic coverage and information over a large area regarding crop condition at different spatial resolutions over the whole crop season and hence is important for estimating yield estimation at required spatial scale. In some parts of India, it is being cultivated as mono crop while in some other parts in combination with red gram as intercrop. Crop mapping, acreage estimation and yield estimation of soybean in the intercropped region poses challenge. This study was carried out to estimate production of soybean (Glycine max), at three different scales (block, district and state) in two major soybean-growing states (Madhya Pradesh and Maharashtra) of India those contain areas with monocrop soybean as well as inter crop soybean (along with red gram) using input data from multiple satellite sensors (both optical and microwave). For production estimation at state and district level, crop mask and acreage estimates were generated using optical data from TERRA-MODIS (Moderate Resolution Imaging Spectrometer) while backscattering coefficient from C-band-SAR of Sentinel 1 was used for generating crop mask and acreage estimates at block level. Yield was estimated using the semi-physical model with inputs derived from multiple sensors at both GEO (INSAT-3D) and LEO (TERRA-MODIS) platforms. Production was estimated using the acreage and yield of the respective administrative unit. The estimated acreage, yield and production were validated at block, district and state level using the reported statistics. Validation of soybean production in the study area indicated a correlation coefficient of 0.81 to 0.89 at block level and 0.91 at district level. The study suggested use of optical data for estimating soybean production at state and district scale and use of C-band SAR data for block level production estimation. This study also suggested to use the correction factor for estimating area and yield in the areas where soybean is cultivated in combination with red gram as intercrop for improving the production estimation.