Marine environments are delicate ecosystems which directly influence local climates, flora, fauna, and human activities. Their monitorization plays a key role in their preservation, which is most commonly done through the use of environmental sensing buoy networks. These devices transmit data by means of satellite communications or close-range base stations, which present several limitations and elevated infrastructure costs. Unmanned Aerial Vehicles (UAV) are another alternative for remote environmental monitoring which provide new types of data and ease of use. These aircraft are mainly used in video capture related applications, in its various light spectrums, and do not provide the same data as sensing buoys, nor can they be used for such extended periods of time. The aim of this research is to provide a flexible, easy to deploy and cost-effective Wireless Sensor Network (WSN) for monitoring marine environments. This proposal uses a UAV as a mobile data collector, low-power long-range communications and sensing buoys as part of a single WSN. A complete description of the design, development, and implementation of the various parts of this system is presented, as well as its validation in a real-world scenario.

The acquisition of reliable and accurate data to assess environmental changes over large spatial scales is one of the main limitations to determine the impact of eutrophication, and the effectiveness of management strategies in coastal systems. Here, we used a continuous in situ Chl-a fluorometry sensor and L8/OLI satellite data to develop an algorithm and map Chl-a spatial distribution to assess the impact of freshwater diversions and associated high nutrient loading rates in the Barataria Basin (BB) complex, a coastal system in the northern Gulf of Mexico. We collected water quality samples at 24 sampling stations and high-frequency continuous fluorometry in situ [Chl-a] data along a ~87 km transect from 2019–2020. Field [Chl-a] values were highly correlated (r = 0.86; p < 0.0001) with continuous in situ [Chl-a] fluorometry values. These continuous in situ [Chl-a] values were significantly related to a surface reflectance ratio ([B1 + B4]/B3) estimated using L8/OLI data (exponential model; R² = 0.46; RMSE = 4.8, p < 0.0001). The statistical model replicated [Chl-a] spatial patterns across the BB complex. This work shows the utility of high-frequency continuous Chl-a fluorometry sampling coupled with L8/OLI image analysis to increase the frequency and number of field data sets to assess water quality conditions at large spatial scales in highly dynamic deltaic regions.

To optimize irrigation, agronomists need to modulate crop water productivity (CWP) throughout phenology. We compared regulated deficit irrigation (RDI) and sustained deficit irrigation (SDI) in chickpea (Cicer arietinum L. var. Blanoro), expecting RDI during vegetative growth (VG) to enhance CWP, as opposed to flowering (F) and pod-filling (PF) stages. The effects of RDI and SDI on grain yield, plant height, weight, grain caliber, pods and grains per plant, harvest index, and CWP, were tested through a complete randomized block experiment during the years 2020 and 2021, comparing full irrigation (FI, ETc = 100%), SDI (SDI₇₅, ETc = 75% during all stages), and six RDI treatments varying in ETc% across phenology: VG₅₀, VG₇₅, F₅₀, F₇₅, PF₅₀, and PF₇₅. VG₇₅ had higher CWP while minimizing impacts on productivity. During 2020, the plants were taller (0.44 ± 4.4 m), and increased in harvest index (0.47 ± 0.06), and CWP (0.90 ± 0.2 kg m⁻³) (p < 0.05), while in 2021, plants were heavier (11.4 ± 2.8 g) and increased in caliber (46.1 ± 3.0 grains); grain yield did not differ between the years (p ˃ 0.05), reaching 861.8 (2020) and 944.7 kg ha⁻¹ (2021). Our results highlight the relevance of maintaining 100% ETc during flowering, and the maintenance of RDI at 75% ETc during vegetative growth.