Random forest model that incorporates solar-induced chlorophyll fluorescence data can accurately track crop yield variations under drought conditions
Timely and reliable crop yield estimation is vital for ensuring both global and regional food security. Previous studies have primarily used process-based crop models or statistical regression-based models for crop yield estimates. However, these model types possess limitations, particularly in acco...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-03-01
|
| Series: | Ecological Informatics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1574954124005144 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832595397082611712 |
|---|---|
| author | Guangpo Geng Qian Gu Hongkui Zhou Bao Zhang Zuxin He Ruolin Zheng |
| author_facet | Guangpo Geng Qian Gu Hongkui Zhou Bao Zhang Zuxin He Ruolin Zheng |
| author_sort | Guangpo Geng |
| collection | DOAJ |
| description | Timely and reliable crop yield estimation is vital for ensuring both global and regional food security. Previous studies have primarily used process-based crop models or statistical regression-based models for crop yield estimates. However, these model types possess limitations, particularly in accounting for specific extreme climate events that occur during the growth stage. In this study, remote sensing data, climate data, and soil moisture data from the winter wheat growth period in northern China from 2003 to 2017 were used to construct a crop yield simulation model based on the Random Forest (RF) algorithm. The effect of drought on winter wheat yield was quantitatively evaluated by calculating the fitting accuracy of the RF model, analyzing the importance of the factors influencing yield simulations, identifying a typical drought event, and determining the yield estimation accuracy as well as the percent yield loss (PYL) under drought conditions. The results indicated that solar-induced chlorophyll fluorescence (SIF) could characterize drought stress on winter wheat yield. The fitting accuracy of the RF yield simulation model was relatively high (R2 = 0.72). Among all climate factors, SIF, enhanced vegetation index, and soil moisture were significant factors affecting wheat yield, exerting greater effect than those of all other climate factors. Furthermore, 2011 was identified as a typical drought year in the winter wheat area of northern China. The RF model simulated the accuracy of winter wheat yield for 2011 with an R2 of 0.80. The RF model simulation revealed that the yield simulation accuracy of winter wheat under drought conditions was 90.64 %. The mean simulated PYL due to drought was 5.6 %, aligning closely with the mean actual PYL of 6.1 %. This suggested that the RF model was feasible for simulating crop yields and tracking yield variations by incorporating environmental variables, especially SIF data, under drought conditions. |
| format | Article |
| id | doaj-art-56e1bc8e27864853b8eedbba7bd3d005 |
| institution | Kabale University |
| issn | 1574-9541 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Informatics |
| spelling | doaj-art-56e1bc8e27864853b8eedbba7bd3d0052025-01-19T06:24:42ZengElsevierEcological Informatics1574-95412025-03-0185102972Random forest model that incorporates solar-induced chlorophyll fluorescence data can accurately track crop yield variations under drought conditionsGuangpo Geng0Qian Gu1Hongkui Zhou2Bao Zhang3Zuxin He4Ruolin Zheng5College of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, China; Corresponding author at: College of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, China.College of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, ChinaInstitute of Digital Agriculture, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, ChinaCollege of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, ChinaCollege of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, ChinaCollege of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, ChinaTimely and reliable crop yield estimation is vital for ensuring both global and regional food security. Previous studies have primarily used process-based crop models or statistical regression-based models for crop yield estimates. However, these model types possess limitations, particularly in accounting for specific extreme climate events that occur during the growth stage. In this study, remote sensing data, climate data, and soil moisture data from the winter wheat growth period in northern China from 2003 to 2017 were used to construct a crop yield simulation model based on the Random Forest (RF) algorithm. The effect of drought on winter wheat yield was quantitatively evaluated by calculating the fitting accuracy of the RF model, analyzing the importance of the factors influencing yield simulations, identifying a typical drought event, and determining the yield estimation accuracy as well as the percent yield loss (PYL) under drought conditions. The results indicated that solar-induced chlorophyll fluorescence (SIF) could characterize drought stress on winter wheat yield. The fitting accuracy of the RF yield simulation model was relatively high (R2 = 0.72). Among all climate factors, SIF, enhanced vegetation index, and soil moisture were significant factors affecting wheat yield, exerting greater effect than those of all other climate factors. Furthermore, 2011 was identified as a typical drought year in the winter wheat area of northern China. The RF model simulated the accuracy of winter wheat yield for 2011 with an R2 of 0.80. The RF model simulation revealed that the yield simulation accuracy of winter wheat under drought conditions was 90.64 %. The mean simulated PYL due to drought was 5.6 %, aligning closely with the mean actual PYL of 6.1 %. This suggested that the RF model was feasible for simulating crop yields and tracking yield variations by incorporating environmental variables, especially SIF data, under drought conditions.http://www.sciencedirect.com/science/article/pii/S1574954124005144Yield simulationRandom forestDroughtSolar-induced chlorophyll fluorescenceWinter wheat |
| spellingShingle | Guangpo Geng Qian Gu Hongkui Zhou Bao Zhang Zuxin He Ruolin Zheng Random forest model that incorporates solar-induced chlorophyll fluorescence data can accurately track crop yield variations under drought conditions Ecological Informatics Yield simulation Random forest Drought Solar-induced chlorophyll fluorescence Winter wheat |
| title | Random forest model that incorporates solar-induced chlorophyll fluorescence data can accurately track crop yield variations under drought conditions |
| title_full | Random forest model that incorporates solar-induced chlorophyll fluorescence data can accurately track crop yield variations under drought conditions |
| title_fullStr | Random forest model that incorporates solar-induced chlorophyll fluorescence data can accurately track crop yield variations under drought conditions |
| title_full_unstemmed | Random forest model that incorporates solar-induced chlorophyll fluorescence data can accurately track crop yield variations under drought conditions |
| title_short | Random forest model that incorporates solar-induced chlorophyll fluorescence data can accurately track crop yield variations under drought conditions |
| title_sort | random forest model that incorporates solar induced chlorophyll fluorescence data can accurately track crop yield variations under drought conditions |
| topic | Yield simulation Random forest Drought Solar-induced chlorophyll fluorescence Winter wheat |
| url | http://www.sciencedirect.com/science/article/pii/S1574954124005144 |
| work_keys_str_mv | AT guangpogeng randomforestmodelthatincorporatessolarinducedchlorophyllfluorescencedatacanaccuratelytrackcropyieldvariationsunderdroughtconditions AT qiangu randomforestmodelthatincorporatessolarinducedchlorophyllfluorescencedatacanaccuratelytrackcropyieldvariationsunderdroughtconditions AT hongkuizhou randomforestmodelthatincorporatessolarinducedchlorophyllfluorescencedatacanaccuratelytrackcropyieldvariationsunderdroughtconditions AT baozhang randomforestmodelthatincorporatessolarinducedchlorophyllfluorescencedatacanaccuratelytrackcropyieldvariationsunderdroughtconditions AT zuxinhe randomforestmodelthatincorporatessolarinducedchlorophyllfluorescencedatacanaccuratelytrackcropyieldvariationsunderdroughtconditions AT ruolinzheng randomforestmodelthatincorporatessolarinducedchlorophyllfluorescencedatacanaccuratelytrackcropyieldvariationsunderdroughtconditions |