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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

(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.

. 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