| 000 | 03555nab|a22004937a|4500 | ||
|---|---|---|---|
| 001 | 69093 | ||
| 003 | MX-TxCIM | ||
| 005 | 20250815093232.0 | ||
| 008 | 20258s2025|||||ne ||p|op||||00||0|eng|dd | ||
| 022 | _a2352-9385 (Online) | ||
| 024 | 8 | _ahttps://doi.org/10.1016/j.rsase.2025.101660 | |
| 040 | _aMX-TxCIM | ||
| 041 | _aeng | ||
| 100 | 1 |
_aRohden Prudente, V.H. _939768 |
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| 245 | 1 | 0 | _aMapping grain crop sowing date in smallholder systems using optical imagery |
| 260 |
_aAmsterdam (Netherlands) : _bElsevier B.V., _c2025. |
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| 500 | _aPeer review | ||
| 500 | _aOpen Access | ||
| 520 | _aSowing date prediction using Earth observation data is challenging in smallholder systems due to small field sizes, heterogeneity in management practices, and a lack of reference data. This study aims to develop a generalizable algorithm that does not require any ground data for calibration to map sowing date using the Normalized Difference Vegetation Index (NDVI) from three optical datasets: MODIS, Harmonized Landsat and Sentinel (HLS), and Sentinel-2. We applied Savitzky-Golay (SG) and spline smoothing algorithms to each dataset and developed a derivative approach to identify the inflection point that represents the Start of Season (SoS), which was then converted to sowing date. We applied our methodology to map the sowing date of winter wheat in Bihar, India and spring-summer maize in the state of Mexico, Mexico. Overall, Sentinel-2 data led to the highest accuracies, but the performance of the smoothing algorithm differed across locations. In India, prediction models using SG achieved an R2 of 0.45 and a root mean square deviation (RMSD) of 11.44 days. In Mexico, prediction models using spline performed best, with an R2 of 0.19 and an RMSD of 4.24 weeks. The lower accuracy in Mexico was due to more complex cropping patterns as well as noise in the observed sowing date dataset. Our algorithm shows potential to identify SoS, and ultimately sowing date, at scale using Sentinel-2 imagery. However, challenges from low-quality validation datasets, small field sizes, cloud cover, and landscape complexity continue to pose challenges to predict sowing date using Earth observation data products. | ||
| 546 | _aText in English | ||
| 591 | _aMcDonald, A. : No CIMMYT Affiliation | ||
| 597 | _dNASA Land-Cover and Land-Use Change (LCLUC) | ||
| 650 | 7 |
_aLandsat _2AGROVOC _915758 |
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| 650 | 7 |
_aModerate resolution imaging spectroradiometer _2AGROVOC _913736 |
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| 650 | 7 |
_aMaize _2AGROVOC _91173 |
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| 650 | 7 |
_aWheat _2AGROVOC _91310 |
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| 650 | 7 |
_aSowing date _2AGROVOC _914747 |
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| 650 | 7 |
_aSmallholders _2AGROVOC _91763 |
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| 700 | 1 |
_aGarcia-Medina, M. _917293 |
|
| 700 | 1 |
_aKrishna, V.V. _8INT2994 _gSustainable Agrifood Systems _9558 |
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| 700 | 1 |
_8001712728 _aEuler, M.A. _gSustainable Agrifood Systems _921084 |
|
| 700 | 1 |
_aBhattarai, N. _920314 |
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| 700 | 1 |
_aLerner, A.M. _939769 |
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| 700 | 1 |
_aMcDonald, A. _gSustainable Intensification Program _8INT3034 _9883 |
|
| 700 | 1 |
_aSherpa, S.R. _8001712516 _gSustainable Agrifood Systems _928790 |
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| 700 | 1 |
_aRajan, H. _931205 |
|
| 700 | 1 |
_aUrfels, A. _8001711637 _gFormerly Sustainable Agrifood Systems _94925 |
|
| 700 | 1 |
_aCarneiro de Santana, C.T. _939770 |
|
| 700 | 0 |
_aMeha Jain _93814 |
|
| 773 | 0 |
_tRemote Sensing Applications: Society and Environment _gv. 39, art. 101660 _dAmsterdam (Netherlands) : Elsevier B.V., 2025. _x2352-9385 |
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| 856 | 4 |
_yOpen Access through DSpace _uhttps://hdl.handle.net/10883/35830 |
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| 942 |
_cJA _n0 _2ddc |
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| 999 |
_c69093 _d69085 |
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