1. Introduction
In recent decades, the development of big cities and industry have provoked an increase in environmental pollution and poor air quality. This situation is likely to increase in the future because climatic change is expected to decrease precipitation and increase the emission of pollutant gases. In this context, grasslands have a natural capacity to decrease the cities’ pollution and have a positive impact on citizen life. Moreover, urban green spaces have been defined as a key ingredient for city sustainability [
1]. In addition, it has been reported that the provision of urban green space and its associated benefits are important for sustainable urban development from ecological, economic, and social perspectives [
2].
However, the adequate maintenance of green space requires regular and optimum irrigation, which is difficult to apply in many regions where drought and high temperatures are frequent. Chebella has stated that one of the biggest obstacles to establishing and maintaining green space is the scarcity of water [
3]. The quality of urban green spaces could be at risk with reduced rainfall as well as greater frequency and duration of droughts [
4].
Given the high level of water deficiency in many urban areas and the high consumption of water by lawns, it is essential to select alternative mixtures of turfgrass species that allow a good maintenance of grassland with lower water consumption. The selection of turfgrass for water conservation by the use of varieties with superior drought resistance and low water use is a primary means of decreasing water needs on turfgrass sites [
5]. In this context, the water requirements of turfgrass are important in their selection, adaptation, and use, particularly in areas and times when water for turfgrass culture and maintenance is restricted [
6]. Therefore, plants with lower water needs that can remain visually acceptable under dry conditions might become preferred plants in an urban setting [
7].
Moreover, the authors of [
8] have divided turfgrass in two kinds: the C
3 species typical for cooler temperature grassland regions and the C
4 grasses adapted to persist in warmer environments [
8]. Both species differ in the photosynthetic system for the uptake of carbon dioxide. In this context, the authors of [
9] reported that the saturation of Rubisco with CO
2 eliminates photorespiration in C
4 plants under most conditions, producing higher rates of CO
2 fixation than the C
3 type, and the net CO
2 assimilation rates (A) are typically higher in C
4 than in C
3 species over a wide range of conditions. Higher uptake of CO
2 at reduced intercellular CO
2 concentration (c
i) in C
4 plants allows equivalent or greater A at lower stomatal conductance (g
s) than in C
3 species [
10,
11], indicating that C
4 is characterized by maximal rates of net leaf photosynthesis (A) at a lower stomatal conductance than in C
3 species, also by lower transpiration and conserving water, especially in hot conditions when evaporative demand is high. Moreover, the authors of [
12] conclude in their investigation that C
4 grasses in control treatments were characterized by higher CO
2 assimilation rates and water potential, but lower stomatal conductance and under drought, stomatal conductance declined more dramatically in C
3 than C
4 species. Furthermore, the authors of [
13] have indicated that lower
gs associated with C
4 photosynthesis may result in adaptation of plant hydraulics.
In the present, precision turfgrass water management using spectral reflectance was proposed as an alternative method to improve irrigation efficiency [
14]. Spectral reflectance sensing has become a crucial part of precision turfgrass management and an active area of research for many years [
15]. The normalized difference vegetation index (NDVI) is one of the most well-known vegetation indices derived from optical remote sensing and which is formulated using the following equation: (NIR − R) / (NIR + R), where R is the reflectance in the red band and NIR is the reflectance in the near-infrared light reflectance. NDVI has been used widely in the estimation of plant biomass [
16,
17,
18] and to maintained turfgrass [
19]. Alternatively, the use of information obtained by conventional digital red, green, blue (RGB) images to estimate canopy vegetation could represent a substitute way of spectroradiometrical vegetation indexing. Digital image analysis has been successfully used to assess turfgrass color and the percentage of green cover [
20]. The green area (GA) and greener area (GGA) represent two indexes derived from digital conventional images. GA describes the amount of green biomass in the picture, whereas the more yellowish-green pixels are defined by the GGA. In fact, the active photosynthetic areas and plant senescences are represented by the greener area [
21]. These indices are obtained by BreedPix open access software [
22,
23].
The chief objective of this study was to compare growth of two C3–C4 turfgrass mixtures and a standard one formed only with C3 plants, under different water conditions using economical remote sensing approaches. Furthermore, a key objective of this study was to evaluate if low-cost remote sensing techniques (Green Seeker and digital camera) used to estimate canopy green biomass were good predictors of turfgrass water stress and could be used in similar way as the more expensive technologies used to manage turfgrass irrigation. Furthermore, a key objective of this study was to determine if the turfgrass mixture (C4–C3) was more tolerant to the deficit water irrigation. It is well-known that C3 and C4 grasses actively grow during different seasons, whereas C4 grasses tend to have their period of active growth in summer and are known as summer-active plants, while the C3 turfgrasses are autumn–winter–spring growing. For this reason, in this study we evaluated two different C4-C3 mixtures (compared with only C3 mixture), with the purpose to use them for the sustainability of the urban green spaces, with a uniform turfgrass appearance throughout the year, less water irrigation consumption, and low maintenance cost.
5. Conclusions
The high demand for water resources of turfgrass represents a significant problem of this last decade due to the lack and costs of water. For this reason, the efficient management of water irrigation (an adequate amount of water irrigation contributed at the right time) is required to ensure high quality of turfgrass with lesser maintenance. This study showed the efficacy of the use of economical remote sensing devices for the turfgrass crop management. The important relations found among vegetation indexes and shoot biomass confirm the efficiency of these indexes in turfgrass crop maintenance. Therefore, the accessibility, easy use and low cost of the GreenSeeker and digital cameras makes them a perfect device for turfgrass water management and green biomass estimation, especially under limited growing conditions. However, while NDVI measures are extensively used as a method to estimate variations in turfgrass development under different growing conditions, in this study we have demonstrated that GA and GGA biomass indexes obtained by digital pictures can provide comparable estimation of biomass to the GreenSeeker and at a comparatively lower cost.
Furthermore, development and use of turfgrass species with superior drought resistance/low water use is a primary criterion to decreasing water needs on grassland [
5]. In this context, our results confirm the high performance of turfgrass mixture with C
4 species (warm weather plants) to tolerate the water deficit. Subsequently, mixtures of turfgrass with C
4 species can allow having grasslands with better appearance in summer and with less consumption in irrigation water.
Finally, we suggested that selection of C4 drought tolerant turfgrass and their combination with rustic C3 species contributes to the development of urban green space with lower water consumption and with better appearance throughout the seasons. Both a good choice of turfgrass species and the use of remote sensing techniques have a significant effect on grassland sustainability and can avoid unnecessary costs in irrigation and help in the prevention of the environment. In future work, we suggest continuing the evaluation of the same two C4 species (Cynodon and Buchloe) studied here, but mixed with two other different C3 turfgrass, in order to select the most tolerant mixture to the deficit irrigation and suitable for the sustainability of the natural grassland. In addition, we propose to decrease the level irrigation to 60% of container capacity, since the results of the present study showed that DI-50 (deficit irrigated to 50% of container capacity) presents the most severe stress conditions, while under DI-75 (deficit irrigated to 75% of container capacity) the results are almost equal to the full irrigated conditions in some cases of the C3-C4 turfgrass mixture. Additionally, we will use a drone for future research, which permits taking thermal and RGB images for continuous monitoring of the vegetation and water status of the turfgrass plots.