Abstract:A continuous spatio-temporal database of accurate soil moisture (SM) measurements is an important asset for agricultural activities, hydrologic studies, and environmental monitoring. The Advanced Microwave Scanning Radiometer 2 (AMSR2), which was launched in May 2012, has been providing SM data globally with a revisit period of two days. It is imperative to assess the quality of this data before performing any application. Since resources of accurate SM measurements are very limited in Puerto Rico, this research will assess the quality of the AMSR2 data by comparing it with ground-based measurements, as well as perform a downscaling technique to provide a better description of how the sensor perceives the surface soil moisture as it passes over the island. The comparison consisted of the evaluation of the mean error, root mean squared error, and the correlation coefficient. Two downscaling techniques were used, and their performances were studied. The results revealed that AMSR2 products tend to underestimate soil moisture. This is due to the extreme heterogeneous distributions of elevations, vegetation densities, soil types, and weather events on the island. This research provides a comprehensive study on the accuracy and potential of the AMSR2 products over Puerto Rico. Further studies are recommended to improve the AMSR2 products.

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Abstract:A continuous spatio-temporal database of accurate soil moisture (SM) measurements is an important asset for agricultural activities, hydrologic studies, and environmental monitoring. The Advanced Microwave Scanning Radiometer 2 (AMSR2), launched in May 2012, has been providing SM data globally with a revisit period of two days. It is imperative to assess the quality of this data before performing any application. Since resources of accurate SM measurements are very limited in Puerto Rico, this research will assess the quality of the AMSR2 data by comparing with ground-based measurements and perform a downscaling technique to provide a better description of how the sensor perceives the surface soil moisture as it passes over the island. The comparison consisted of the evaluation of the mean error, root mean squared error, and the correlation coefficient. Two downscaling techniques were used and their performances were studied. The results revealed that AMSR2 products tend to underestimate. This is due to the extreme heterogeneous distributions of elevations, vegetation densities, soil types, and weather events on the island. This research provides a comprehensive study on the accuracy and potential of the AMSR2 products over Puerto Rico. Further studies are recommended to improve the AMSR2 products.

Irrigation scheduling (IS) and fertilization are among the most important practices in the production of horticultural crops because they affect fruit quality and quantity directly. Thus, a 15-year-old avocado orchard (cv. ‘Simmonds’) was used to determine precise IS, based on monitoring soil moisture content (SMC), remote sensing technologies [Unmanned Aerial Vehicle (UAV)] under two fertilization levels using granular formulation 15-3-19. In October 2015, all trees were pruned (topped and hedged) to 3.05 m height and 2.44 m diameter. In December 2015, soil moisture (SM) sensors were installed at five (10, 30, 50, 70 and 90 cm) soil depths in six locations. Trees received two fertilizer treatments: F1-9.06 kg and F2-12.07 kg of 15-3-19/tree/year every three months. Precipitation and SM data were recorded daily for 21 months; SM data was corrected with a quadratic equation (y = -4.1881x2 + 3.6886x - 0.3083) generated specifically for the Coto soil series (Typic Hapludox). The SM values recorded were always greater than 41%, indicating that the avocado orchard was growing under water saturation conditions; thus, micro-irrigation was not needed. The UAV data at 5, 13 and 20 months after pruning (MAP) showed quick closure of the avocado canopy; acquiring a denser and more cylindrical shape (from 17.6 ± 2.65 m2 to 52.7 ± 6.10 m2), regardless of fertilizer level. Based on correlation of UAV and manual results, F2-treated trees indicated stronger correlation at 13 and 20 MAP (R2 >0.75) than F1-trees. Yield production (110 avocados per tree = 13,200 per hectare) and leaf nutrient content did not differ significantly with fertilizer level. For commercial avocado farmers the use of SMC sensors and UAV technology could be an advantage, albeit an expensive one. Soil moisture content sensors have been shown to be very effective in irrigation water conservation. In terms of fertilization, the results suggest not using more than 9.06 kg of 15-3-19/tree/year as this amount seems enough to satisfy avocado requirements, under the experiment’s conditions. Future evaluations will determine if it is possible to use less fertilizer and still maintain an optimal avocado production.