Articles | Volume 19, issue 11
https://doi.org/10.5194/hess-19-4441-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hess-19-4441-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
05 Nov 2015
Research article |
| 05 Nov 2015
SACRA – a method for the estimation of global high-resolution crop calendars from a satellite-sensed NDVI
RIKEN Advanced Institute for Computational Science, Kobe, Japan
K. Tanaka
Disaster Prevention Research Institute, Kyoto University, Kyoto, Japan
Viewed
Total article views: 5,789 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 29 Jan 2015)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 3,355 | 2,249 | 185 | 5,789 | 216 | 244 |
- HTML: 3,355
- PDF: 2,249
- XML: 185
- Total: 5,789
- BibTeX: 216
- EndNote: 244
Total article views: 4,258 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 05 Nov 2015)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 2,416 | 1,674 | 168 | 4,258 | 186 | 215 |
- HTML: 2,416
- PDF: 1,674
- XML: 168
- Total: 4,258
- BibTeX: 186
- EndNote: 215
Total article views: 1,531 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 29 Jan 2015)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 939 | 575 | 17 | 1,531 | 30 | 29 |
- HTML: 939
- PDF: 575
- XML: 17
- Total: 1,531
- BibTeX: 30
- EndNote: 29
Cited
25 citations as recorded by crossref.
- RICA: A rice crop calendar for Asia based on MODIS multi year data B. Mishra et al. https://doi.org/10.1016/j.jag.2021.102471
- Optimising Phenological Metrics Extraction for Different Crop Types in Germany Using the Moderate Resolution Imaging Spectrometer (MODIS) X. Xu et al. https://doi.org/10.3390/rs9030254
- Reproduction of historical water balance in the Aral Sea Basin: The physically-based framework to quantify water consumption components in endorheic lake Y. Touge et al. https://doi.org/10.1016/j.jhydrol.2024.131711
- Optimality-based modelling of wheat sowing dates globally S. Qiao et al. https://doi.org/10.1016/j.agsy.2023.103608
- Assessing and addressing the global state of food production data scarcity E. Kebede et al. https://doi.org/10.1038/s43017-024-00516-2
- Beyond Fixed Dates and Coarse Resolution: Developing a Dynamic Dry Season Crop Calendar for Paddy in Indonesia from 2001 to 2021 A. Irawan & D. Komori https://doi.org/10.3390/agronomy14030564
- A Remote Sensing Approach for Biomass Assessment in Winter Wheat Using the NDVI Second Derivative in Terms of NIR A. Atanasov et al. https://doi.org/10.3390/su17167299
- GCPE: The global dataset of crop phenological events for agricultural and earth system modeling A. MORI et al. https://doi.org/10.2480/agrmet.D-23-00004
- A Data-Intensive Approach to Address Food Sustainability: Integrating Optic and Microwave Satellite Imagery for Developing Long-Term Global Cropping Intensity and Sowing Month from 2001 to 2015 A. Sakti & W. Takeuchi https://doi.org/10.3390/su12083227
- Comparative analysis of flash and traditional droughts in the Kashkadarya region, Uzbekistan V. Rakhmatova et al. https://doi.org/10.3178/hrl.25-00021
- Copernicus global crop productivity indicators: An evaluation based on regionally reported yields S. Chevuru et al. https://doi.org/10.1016/j.cliser.2023.100374
- RiceStageSeg: A Multimodal Benchmark Dataset for Semantic Segmentation of Rice Growth Stages J. Zhang et al. https://doi.org/10.3390/rs17162858
- Monsoon Asia Rice Calendar (MARC): a gridded rice calendar in monsoon Asia based on Sentinel-1 and Sentinel-2 images X. Zhao et al. https://doi.org/10.5194/essd-16-3893-2024
- ChinaRiceCalendar – seasonal crop calendars for early-, middle-, and late-season rice in China H. Li et al. https://doi.org/10.5194/essd-16-1689-2024
- A comparison of global agricultural monitoring systems and current gaps S. Fritz et al. https://doi.org/10.1016/j.agsy.2018.05.010
- Incorporating climate data with machine learning can improve rice phenology estimation Y. Liu et al. https://doi.org/10.1088/1748-9326/ada56e
- Threats of tropical cyclone on cropping systems and crop calendar of rice in India: Issues, policy practice gap and adaptation strategies I. Chowdhuri & S. Pal https://doi.org/10.1016/j.ijdrr.2024.104722
- Field-Scale Crop Seeding Date Estimation from MODIS Data and Growing Degree Days in Manitoba, Canada T. Dong et al. https://doi.org/10.3390/rs11151760
- GCI30: a global dataset of 30 m cropping intensity using multisource remote sensing imagery M. Zhang et al. https://doi.org/10.5194/essd-13-4799-2021
- Detecting Recent Crop Phenology Dynamics in Corn and Soybean Cropping Systems of Kentucky Y. Yang et al. https://doi.org/10.3390/rs13091615
- Characterizing spatiotemporal patterns of crop phenology across North America during 2000–2016 using satellite imagery and agricultural survey data Y. Yang et al. https://doi.org/10.1016/j.isprsjprs.2020.10.005
- A review of global gridded cropping system data products K. Kim et al. https://doi.org/10.1088/1748-9326/ac20f4
- Toward hyper-resolution global hydrological models including human activities: application to Kyushu island, Japan N. Hanasaki et al. https://doi.org/10.5194/hess-26-1953-2022
- Integration of prognostic sowing and harvesting schemes to enhance crop dynamic growth simulation in Noah-MP-Crop model F. Wang et al. https://doi.org/10.1016/j.ecoinf.2024.102785
- Modeling the Global Sowing and Harvesting Windows of Major Crops Around the Year 2000 T. Iizumi et al. https://doi.org/10.1029/2018MS001477
25 citations as recorded by crossref.
- RICA: A rice crop calendar for Asia based on MODIS multi year data B. Mishra et al. https://doi.org/10.1016/j.jag.2021.102471
- Optimising Phenological Metrics Extraction for Different Crop Types in Germany Using the Moderate Resolution Imaging Spectrometer (MODIS) X. Xu et al. https://doi.org/10.3390/rs9030254
- Reproduction of historical water balance in the Aral Sea Basin: The physically-based framework to quantify water consumption components in endorheic lake Y. Touge et al. https://doi.org/10.1016/j.jhydrol.2024.131711
- Optimality-based modelling of wheat sowing dates globally S. Qiao et al. https://doi.org/10.1016/j.agsy.2023.103608
- Assessing and addressing the global state of food production data scarcity E. Kebede et al. https://doi.org/10.1038/s43017-024-00516-2
- Beyond Fixed Dates and Coarse Resolution: Developing a Dynamic Dry Season Crop Calendar for Paddy in Indonesia from 2001 to 2021 A. Irawan & D. Komori https://doi.org/10.3390/agronomy14030564
- A Remote Sensing Approach for Biomass Assessment in Winter Wheat Using the NDVI Second Derivative in Terms of NIR A. Atanasov et al. https://doi.org/10.3390/su17167299
- GCPE: The global dataset of crop phenological events for agricultural and earth system modeling A. MORI et al. https://doi.org/10.2480/agrmet.D-23-00004
- A Data-Intensive Approach to Address Food Sustainability: Integrating Optic and Microwave Satellite Imagery for Developing Long-Term Global Cropping Intensity and Sowing Month from 2001 to 2015 A. Sakti & W. Takeuchi https://doi.org/10.3390/su12083227
- Comparative analysis of flash and traditional droughts in the Kashkadarya region, Uzbekistan V. Rakhmatova et al. https://doi.org/10.3178/hrl.25-00021
- Copernicus global crop productivity indicators: An evaluation based on regionally reported yields S. Chevuru et al. https://doi.org/10.1016/j.cliser.2023.100374
- RiceStageSeg: A Multimodal Benchmark Dataset for Semantic Segmentation of Rice Growth Stages J. Zhang et al. https://doi.org/10.3390/rs17162858
- Monsoon Asia Rice Calendar (MARC): a gridded rice calendar in monsoon Asia based on Sentinel-1 and Sentinel-2 images X. Zhao et al. https://doi.org/10.5194/essd-16-3893-2024
- ChinaRiceCalendar – seasonal crop calendars for early-, middle-, and late-season rice in China H. Li et al. https://doi.org/10.5194/essd-16-1689-2024
- A comparison of global agricultural monitoring systems and current gaps S. Fritz et al. https://doi.org/10.1016/j.agsy.2018.05.010
- Incorporating climate data with machine learning can improve rice phenology estimation Y. Liu et al. https://doi.org/10.1088/1748-9326/ada56e
- Threats of tropical cyclone on cropping systems and crop calendar of rice in India: Issues, policy practice gap and adaptation strategies I. Chowdhuri & S. Pal https://doi.org/10.1016/j.ijdrr.2024.104722
- Field-Scale Crop Seeding Date Estimation from MODIS Data and Growing Degree Days in Manitoba, Canada T. Dong et al. https://doi.org/10.3390/rs11151760
- GCI30: a global dataset of 30 m cropping intensity using multisource remote sensing imagery M. Zhang et al. https://doi.org/10.5194/essd-13-4799-2021
- Detecting Recent Crop Phenology Dynamics in Corn and Soybean Cropping Systems of Kentucky Y. Yang et al. https://doi.org/10.3390/rs13091615
- Characterizing spatiotemporal patterns of crop phenology across North America during 2000–2016 using satellite imagery and agricultural survey data Y. Yang et al. https://doi.org/10.1016/j.isprsjprs.2020.10.005
- A review of global gridded cropping system data products K. Kim et al. https://doi.org/10.1088/1748-9326/ac20f4
- Toward hyper-resolution global hydrological models including human activities: application to Kyushu island, Japan N. Hanasaki et al. https://doi.org/10.5194/hess-26-1953-2022
- Integration of prognostic sowing and harvesting schemes to enhance crop dynamic growth simulation in Noah-MP-Crop model F. Wang et al. https://doi.org/10.1016/j.ecoinf.2024.102785
- Modeling the Global Sowing and Harvesting Windows of Major Crops Around the Year 2000 T. Iizumi et al. https://doi.org/10.1029/2018MS001477
Saved (final revised paper)
Latest update: 24 Jun 2026
Short summary
This study aims to develop a new global data set of a satellite-derived crop calendar (SACRA) and to reveal its advantages and disadvantages compared to other global products. The cultivation period of SACRA is identified from the time series of NDVI; therefore, SACRA considers current effects of human decisions and natural disasters. The difference between the estimated sowing dates and other existing products is less than 2 months (< 62 days) in most areas.
This study aims to develop a new global data set of a satellite-derived crop calendar (SACRA)...
