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    <responseDate>2026-10-13T22:16:23Z</responseDate>
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    <identifier>10.57760/sciencedb.IGA.001eq</identifier>
    <datestamp>2026-09-15T13:23:49Z</datestamp>
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  <dc:date>2026-09-15</dc:date>
  <dc:title>Qiangu Irrigation Area Rice Leaf Coverage Distribution Map</dc:title>
  <dc:identifier>doi:10.57760/sciencedb.iga.001eq</dc:identifier>
  <dc:language>en</dc:language>
  <dc:description>This dataset represents the results of systematic field-measured leaf canopy coverage data and high-precision spatial inversion for rice plants during the jointing stage, collected in the Qiangu Irrigation Area in 2023. The dataset comprises 135 field-level canopy measurement sampling points gathered from rice fields within the Qiangu Irrigation Area in August 2023. The sampling distribution covers various irrigation zones, soil fertility levels, cropping systems, and rice varieties across the irrigation area, encompassing both contiguous large-scale fields and scattered individual plots, thereby ensuring broad regional representativeness.
Rice leaf canopy cover (unit:%) was determined at each sampling point using a canopy analyzer combined with UAV aerial photography verification methodology; this value is defined as the percentage of the vertical projected leaf area relative to the total plot area, with a numerical range between 0% and 100%. Based on the field-measured data from each sampling point and multi-temporal remote sensing variables, a Random Forest (RF) model was employed for the spatial inversion and prediction of rice leaf canopy cover, resulting in a leaf canopy cover raster map covering the entire irrigation area (resolution: 10 m), with a total file size of approximately 12.68 GB.
The model accuracy validation results show that the R&amp;sup2; value for the inversion of leaf canopy cover during the jointing stage was 0.84, with an RMSE of 3.41%. This dataset provides a detailed characterization of the spatial heterogeneity of leaf canopy cover in rice plants during the jointing stage across the irrigated area, offering high-precision baseline data for monitoring rice growth dynamics, implementing precise water and fertilizer management, and assessing crop growth patterns throughout the growing season in the Qianguo Irrigated Area.</dc:description>
  <dc:subject>Remote sensing inversion; Rice; Leaf coverage</dc:subject>
  <dc:creator>刘露诗</dc:creator>
  <dc:rights>PUBLIC</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by/4.0/</dc:rights>
  <dc:type>dataset</dc:type>
  <dc:publisher>Science Data Bank</dc:publisher>
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