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We are sorry to report that we recently discovered a nasty bug (or rather, a stupid mistake…) in the MODIStsp package. The bug led to improper computation of custom spectral indices in the case that their formula included addition or subtraction operations on reflectance values (e.g., something like \(\frac{(\rho_{NIR}+0.1)}{\rho_{Red}}\), with \(\rho\) indicating a reflectance). What is affected Values of the following Additional Spectral Indices selectable using the MODIStsp GUI:

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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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In the last months, I started increasingly using Rmd documents for preparing scientific reports, blog posts, etcetera. While I really like the flexibility offered by the system, one thing that I thought could be improved is the support for easily inserting tables. So, “inspired” also by the recent addition of the excellent insert image addin in blogdown, I decided to give it a go and try to implement some kind of addin to facilitate table insertion in Rmd documents.

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Selected Publications

(2018). Analysing spatial–temporal changes in rice cultivation practices in the Senegal River Valley using MODIS time-series and the PhenoRice algorithm. International Journal of Applied Earth Observation and Geoinformation, (75), pp. 15-28, https://doi.org/10.1016/j.jag.2018.09.016.

(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

More Publications

. 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.

. Analysing spatial–temporal changes in rice cultivation practices in the Senegal River Valley using MODIS time-series and the PhenoRice algorithm. International Journal of Applied Earth Observation and Geoinformation, (75), pp. 15-28, https://doi.org/10.1016/j.jag.2018.09.016, 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.

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