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We are happy to report that our MODIStsp package for automatic preprocessing of MODIS time series has been recently approved for being included in the rOpenSci ecosystem of R packages for reproducible science! We wish to thank reviewers Leah Wasser and Jeffrey Hanson for providing really valuable insights during the onboarding review process. We think their contribution really helped in improving the package! Please also note that MODIStsp website was also migrated, and is now available at http://ropensci.

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We are happy to report that a new version of MODIStsp (1.3.4) is on CRAN as of today ! The new version introduces a strongly improved GUI (thanks mainly to @lwasser comments in her review for MODIStsp onboarding on ropensci). The new GUI facilitates the selection of layers to be processed, and allows interactive selection of the processing spatial extent over a map (thanks to @timsalabim and @timelyportfolio for implementing some changes on mapview to allow this!

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Selected Publications

(2017). Downstream Services for Rice Crop Monitoring in Europe: From Regional to Local Scale. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, (10), 12, pp. 5423-5441, https://doi.org/10.1109/JSTARS.2017.2679159.

(2017). PhenoRice: A method for automatic extraction of spatio-temporal information on rice crops using satellite data time series. Remote Sensing of Environment, (194), pp. 347-365, https://doi.org/10.1016/j.rse.2017.03.029.

Recent Publications

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. A Critical Comparison of Remote Sensing Leaf Area Index Estimates over Rice-Cultivated Areas: From Sentinel-2 and Landsat-7/8 to MODIS, GEOV1 and EUMETSAT Polar System. Remote Sensing, (10), 5, https://doi.org/10.3390/rs10020293, 2018.

. A high-resolution, integrated system for rice yield forecasting at district level. Remote Sensing, (10), 5, https://doi.org/10.1016/j.agsy.2018.05.007, 2018.

. Early season weed mapping in rice crops using multi-spectral UAV data. International Journal of Remote Sensing, pp. 1-21, https://doi.org/10.1080/01431161.2018.1441569, 2018.

. Spatial rice yield estimation based on MODIS and Sentinel-1 SAR data and ORYZA crop growth model. Remote Sensing, (10), 2, https://doi.org/10.3390/rs10020293, 2018.

Projects

MODIStsp

An R package for automatic download and preprocessing of MODIS Land Products Time Series

SPRAWL

sprawl - Spatial Processing (in) R: Amorphous Wrappers Library