Resumen
Multiple cropping systems constitute an essential agricultural practice that will ensure food security within the increasing demand of basic cereals as a consequence of global population growth and climate change effects. In this regard, there is a need to develop new methodologies to adequately monitor cropland intensification. The main objective of this research was to assess cropland intensification by means of spectral analysis of MODIS NDVI time series in a high cloudiness tropical area such as Ecuador. A surface of 89,225 ha of the main staple crops in this country, which are rice and maize crops, was monitored to assess the evolution of the number of crop cycles. The 20-year period of NDVI time series was used to calculate the periodograms across four subperiods (2001?2005, 2006?2010, 2011?2015, 2016?2020). The maximum ordinate value of each periodogram was used as an indicator of the number of growing crop cycles per year identifying single-, double-, and triple-cropping systems in each subperiod. Cropland intensification was assessed by comparing the cropping system between the subperiods. Results reveal that more than half of the studied croplands experienced changes in the cropping systems, and 40% showed positive trends in terms of the number of growing crop cycles, being principally located near the main rivers where irrigation facilitates crop development during the dry season. Therefore, the area under single cropping decreased from over 60,000 ha in the first subperiod to less than 50,000 ha in the last two subperiods. The cropland surface subjected to multi-cropping practices increased during the second decade of the study period, with a double-cropping system being more widely used than growing three crops per year, reaching surfaces of 24,400 ha and 10,450 ha in the last subperiod, respectively. The robust results obtained in this research show the great potential of the periodogram approach for the discrimination of cropping systems and for mapping intensification areas in tropical regions where dealing with noisy remote sensing time series as a consequence of high cloudiness is a great challenge.