TY - JOUR
T1 - Rapid early-season maize mapping without crop labels
AU - You, Nanshan
AU - Dong, Jinwei
AU - Li, Jing
AU - Huang, Jianxi
AU - Jin, Zhenong
N1 - Publisher Copyright:
© 2023 Elsevier Inc.
PY - 2023/5/15
Y1 - 2023/5/15
N2 - Maize (Zea mays), the second most-produced crop worldwide, serves as the cornerstone for global food security and human livelihood. Early-season maize mapping benefits maize production forecasting and other pre-harvest decision-making applications. However, most existing early-season mapping efforts rely heavily on either the current-year or historical crop labels to train classifiers, limiting the potential applications to new regions lacking crop labels. To explore the possibility of maize mapping only using satellite data in the early season, we proposed a Multi-temporal Gaussian Mixture Model (MGMM) to map maize planting areas without any crop labels. A chlorophyll content relevant proxy, named the Red-edge position (REP), was selected as model input, based on the truth that summer maize tends to show a higher chlorophyll content than other summer crops (e.g., soybean, cotton, peanut, sunflowers, etc.) in the peak season. The novel early-season mapping framework using the REP-based MGMM (MGMM-REP) was applied in four diverse areas (Iowa and Georgia in the US, Heilongjiang province (HLJ) in China, and Grand-Est in France). The MGMM-REP could generate maize maps more than two months before harvest with reasonable accuracy (F1 ≥ 77%) using all the available Sentinel-2 (S2) images and the Google Earth Engine platform (GEE). Our early-season maps agreed well with the existing crop maps and official statistics. The correlation coefficient (R) of the maize acreage between our early-season maps and statistics was higher than 0.94. The high inter-class difference of REP between maize and other summer crops could increase the F1 score by 2–47% compared to the other commonly used Vegetation indices (VIs). Since MGMM-REP does not rely on crop labels, it had the potential to be transferred to label-scarce maize-cropped regions and contribute to the international commodity trade and food security forecast.
AB - Maize (Zea mays), the second most-produced crop worldwide, serves as the cornerstone for global food security and human livelihood. Early-season maize mapping benefits maize production forecasting and other pre-harvest decision-making applications. However, most existing early-season mapping efforts rely heavily on either the current-year or historical crop labels to train classifiers, limiting the potential applications to new regions lacking crop labels. To explore the possibility of maize mapping only using satellite data in the early season, we proposed a Multi-temporal Gaussian Mixture Model (MGMM) to map maize planting areas without any crop labels. A chlorophyll content relevant proxy, named the Red-edge position (REP), was selected as model input, based on the truth that summer maize tends to show a higher chlorophyll content than other summer crops (e.g., soybean, cotton, peanut, sunflowers, etc.) in the peak season. The novel early-season mapping framework using the REP-based MGMM (MGMM-REP) was applied in four diverse areas (Iowa and Georgia in the US, Heilongjiang province (HLJ) in China, and Grand-Est in France). The MGMM-REP could generate maize maps more than two months before harvest with reasonable accuracy (F1 ≥ 77%) using all the available Sentinel-2 (S2) images and the Google Earth Engine platform (GEE). Our early-season maps agreed well with the existing crop maps and official statistics. The correlation coefficient (R) of the maize acreage between our early-season maps and statistics was higher than 0.94. The high inter-class difference of REP between maize and other summer crops could increase the F1 score by 2–47% compared to the other commonly used Vegetation indices (VIs). Since MGMM-REP does not rely on crop labels, it had the potential to be transferred to label-scarce maize-cropped regions and contribute to the international commodity trade and food security forecast.
KW - Corn belts
KW - Early-season maize mapping
KW - Gaussian mixture model
KW - Google Earth Engine
KW - Red-edge position
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U2 - 10.1016/j.rse.2023.113496
DO - 10.1016/j.rse.2023.113496
M3 - Article
AN - SCOPUS:85150359709
SN - 0034-4257
VL - 290
JO - Remote Sensing of Environment
JF - Remote Sensing of Environment
M1 - 113496
ER -