000			02666nab|a22003737a|4500
001			65290
003			MX-TxCIM
005			20230829234754.0
008			20225s2022||||mx |||p|op||||00||0|eng|d
022			_a2072-4292
024	8		_ahttps://doi.org/10.3390/rs14091995
040			_aMX-TxCIM
041			_aeng
100	1		_aTiedeman, K. _927487
245	1	0	_aField data collection methods strongly affect satellite-based crop yield estimation
260			_bMDPI, _c2022. _aBasel (Switzerland) :
500			_aPeer review
500			_aOpen Access
520			_aCrop yield estimation from satellite data requires field observations to fit and evaluate predictive models. However, it is not clear how much field data collection methods matter for predictive performance. To evaluate this, we used maize yield estimates obtained with seven field methods (two farmer estimates, two point transects, and three crop cut methods) and the “true yield” measured from a full-field harvest for 196 fields in three districts in Ethiopia in 2019. We used a combination of nine vegetation indices and five temporal aggregation methods for the growing season from Sentinel-2 SR data as yield predictors in the linear regression and Random Forest models. Crop-cut-based models had the highest model fit and accuracy, similar to that of full-field-harvest-based models. When the farmer estimates were used as the training data, the prediction gain was negligible, indicating very little advantage to using remote sensing to predict yield when the training data quality is low. Our results suggest that remote sensing models to estimate crop yield should be fit with data from crop cuts or comparable high-quality measurements, which give better prediction results than low-quality training data sets, even when much larger numbers of such observations are available.
546			_aText in English
650		7	_aData Collection _2AGROVOC _99145
650		7	_aForecasting _2AGROVOC _92701
650		7	_aRemote sensing _2AGROVOC _91986
650		7	_aMaize _2AGROVOC _91173
650		7	_aCrop yield _2AGROVOC _91066
700	1		_aChamberlin, J. _gFormerly Socioeconomics Program _gSustainable Agrifood Systems _8I1706801 _92871
700	1		_aKosmowski, F. _919573
700	0		_aHailemariam Ayalew _99696
700	0		_aSida, T.S. _8001711262 _gSustainable Intensification Program _gSustainable Agrifood Systems _95724
700	1		_aHijmans, R.J. _99465
773	0		_tRemote Sensing _gv. 14, no. 9, art. 1995 _dBasel (Switzerland) : MDPI, 2022 _w57403 _x2072-4292
856			_yOpen Access through DSpace _uhttps://hdl.handle.net/10883/22074
942			_cJA _n0 _2ddc
999			_c65290 _d65282