1. Introduction
The irrigation of golf courses presents important difficulties due to the heterogeneous distribution of the terrain, the variations in soil composition at different areas, and high water consumption. Furthermore, some areas of the world may present added difficulties due to water scarcity, which may lead to the use of treated wastewater to irrigate the golf course [
1]. Therefore, it is necessary to improve the efficiency of the irrigation to both reduce water consumption and increase the quality of the grass at the golf courses. Golf courses are comprised of differentiated areas such as the green, the fairway, or the rough. These areas have different grass lengths, and the activities performed in each area may lead to varied hazards such as uprooting [
2]. The differences between these areas in vegetation, soil composition, and terrain lead to different irrigation needs. Nowadays, the popular irrigation methods of golf courses in Spain are comprised of sprinkler systems where the control options allow either the management of sprinkler lines as a group or, in some cases, the individualized management of the sprinklers. However, in order to adjust the amount of irrigation for each area or sprinkler, the soil moisture at each part of the golf course must be monitored.
The soil moisture monitoring process can be performed with varied methodologies. The use of sensors is one of the most utilized forms of soil moisture monitoring [
3]. The most utilized soil moisture sensors are capacitance sensors. Multiple capacitance soil moisture sensors are available in the market and have been tested for different types of soils to determine their calibration equations [
4]. Solid-state resistance soil moisture sensors are also available, but they are less utilized [
5]. These sensors are placed at multiple depths to assess the water content in the plant’s root zone. The methodology of deploying soil multiple soil moisture sensors is widely used for precision agriculture and has been extended to other types of environments such as urban lawns and golf courses [
6]. The staff of the golf course may perform measures by physically accessing the desired area. On the other hand, the sensors may be deployed on the golf courses to obtain soil moisture values periodically. However, this solution presents increased cost as the number of deployed sensors would determine the precision acquired for each area. As it was previously noted, the variations in the terrain lead to areas where the water is accumulated and areas with slopes where the water does not reach the desired depth. The differences in soil moisture content of these different types of areas are difficult to monitor with sensors because it would require a great number of them. Therefore, to obtain a very detailed soil moisture map, countless sensors should be deployed in addition to the network devices necessary to transmit the data to the database. As a result, remote sensing solutions were studied to reduce the cost and difficulties of the sensor deployments.
One of the options is the use of drones that obtain images of the fields through devices such as multispectral cameras [
7]. These images are then processed to obtain the soil moisture of the fields [
8]. Hybrid systems that perform remote sensing through drones and use the drone as a mobile gateway to obtain the data from sensors deployed on the fields are a feasible option as well [
9]. The other option is the use of satellites that provide useful data, such as images and microwave data from satellite sensors, and process it to obtain different metrics regarding the soil moisture content and the state of the plants [
10]. The advantage of using satellite data is the obtention of a detailed map where the soil moisture is assessed for all the terrain without the difficulties due to the variations in the terrain faced by the sensor deployments. There are different types of available satellites to choose from, such as Sentinel or Landsat. The cost of utilizing these types of solutions may be reduced as the obtention of images from satellites such as Sentinel are free of charge. Furthermore, the spatial resolution of 10 m pixel size provided by Sentinel is adequate to monitor soil moisture. Other satellites may present different resolutions. Therefore, the characteristics of each satellite should be studied before deciding on which resources to use.
The contribution of this paper is the obtention of soil moisture indexes specific for the studied region to monitor soil moisture in a golf course and its surroundings by utilizing correlating sensors’ data with satellite information. The currently available indexes are developed for other regions, and their accuracies are low. Nine sensors were deployed on three types of soil for approximately three months. The sensors were calibrated for each type of soil ranging from clay soil to soil with both clay and sand. Sentinel images were obtained for the same time period to assess its adequacy for soil moisture monitoring of golf courses. Furthermore, the need for the satellite to be calibrated as well according to each soil type was evaluated as well. The main objectives of this study are twofold. Firstly, we aim to obtain a training dataset with the data from both sensors and satellites and a validation dataset. Secondly, we determine the best form of sprinkler management according to the soil moisture results.
The rest of the paper is organized as follows.
Section 2 discusses the related works regarding soil moisture monitoring through satellite data. The description of the methodology of our study is presented in
Section 3. The results of the comparison between soil moisture monitoring through sensors and satellite images are provided in
Section 4.
Section 5 discusses our results. Lastly,
Section 6 presents the conclusion and future work.
2. Related Work
In this section, we outline the existing options for monitoring soil moisture with remote sensing resources. First, we describe the existing solutions, and then we analyze the main gap of each one. Finally, we detail the contributions of our paper based on the gaps of the existing proposals.
The use of satellite data in agriculture has expanded to include numerous functionalities. Deepak Gautam et al. reviewed the current remote sensing applications for agriculture [
10]. Regarding monitoring soil moisture based on satellites, the authors comment on the use of L-band microwave radiometry such as the Soil Moisture Active Passive (SMAP) satellites from the NASA or the Soil Moisture Ocean Salinity (SMOS) sensor from ESA. There are, however, some limitations to this technique, such as the spatial resolution and the 5cm depth of retrieval. Therefore, it is impossible to use this type of solution due to its low spatial resolution, which precludes its application in heterogenic scenarios such as golf courses, urban laws, or other similar areas. Another review of agricultural applications based on remote sensing is performed by L. Karthikeyan et al. [
11]. The authors also comment on the Microwave Imaging Radiometer using Aperture Synthesis (MIRAS) and SMAP satellites. Furthermore, these satellites can also be used for other purposes, such as quantifying irrigation water use. Furthermore, the new applications and features of the Sentinel-2 satellites were discussed by Joel Segarra et al. [
12]. The authors also compared the performance of other satellites to the Sentinel-2 and determined that the latter provides more capabilities for crop management and agricultural monitoring. Furthermore, the results show that the combination of other remote sensing techniques as well as field data is still necessary. Particularly, regarding soil moisture, determination coefficients between 65% and 83% were obtained from the combination of the NDVI obtained from Sentinel-2 and soil moisture models. The use of correlations between NDVI and soil moisture is not valid for our purpose. The main reason is that in the selected scenario is that the soil moisture changes faster than the NDVI. Therefore, when the change in NDVI is detected, the stress of the plats might be too high. An earlier detection method is required.
One of the most interesting functionalities is the detection of soil moisture and crop water content to determine irrigation needs without the need of deploying sensing devices. Chengyang Xu et al. performed data fusion to MODIS and Landsat images to determine the crop water content of soybean and corn crops [
13]. A spatial resolution of 30 m and a revisit cycle of 16 days were the parameters for the Landsat image acquisition, and a spatial resolution of 500 m was used in the case of MODIS. Normalized Difference Water Index (NDWI) was obtained after performing the data fusion. Furthermore, measures were taken during the Soil Moisture Active Passive Validation Experiment to obtain the plan and canopy Vegetation Water Content (VWC). The results showed higher correlations for the soybean plants than that of the corn plants. As for [
10], this solution cannot be applied in our scenario, given the spatial resolution of the remote sensing source selected. Hamed Adab et al. used machine learning to estimate soil moisture from satellite pictures from Landsat 8 with 30 m resolution [
14]. The utilized techniques were Artificial Neural Networks (ANN), Random Forest (RF), Elastic Net Regression (EN), and Support Vector Machine (SVM). The results of the near-surface soil moisture showed that a Nash-Sutcliffe efficiency value of 0.73 was obtained for the RF technique, which was the highest value. Again, this proposal has a limitation regarding the spatial resolution of selected images (spatial resolution of 30 m) compared with the used satellite (spatial resolution of 10 m and 20 m). A study on different soil moisture indexes from SMOS and MODIS satellite data was presented by Miriam Pablos et al. [
15]. The Soil Wetness Deficit Index (SWetDI), Soil Water Deficit Index (SWDI), Soil Moisture Deficit Index (SMDI), and the Soil Moisture Agricultural Drought Index (SMADI) were compared to the crop moisture index (CMI) and atmospheric water deficit (AWD) to determine the correlation and similarity of the studied indexes. The obtained results indicated that the SMADI and SWDI were the best options for drought monitoring, with correlation coefficients ranging between 0.5 and 0.8. Anudeep Sure et al. studied soil moisture monitoring from the use of the microwave satellite sensors named Advanced Microwave Scattering Radiometer—2 (AMSR-2) and Soil Moisture Active Passive (SMAP) [
16]. The study was performed for rice and wheat crops, and the soil wetness index was estimated for depths of 10 and 40 cm. The coefficient of determination was calculated, obtaining a result of 0.9 for the SMAP sensor and 0.65 for the AMSR-2 sensor. Furthermore, a higher time delay was observed for the ascending pass. Moreover, Mireia Fontanet et al. performed a comparison of results from soil moisture sensors and results from the DISaggregation based on Physical and Theoretical scale Change (DISPATCH) algorithm, which SMOS and MODIS satellite data as well as NDVI and land surface temperature (LST) to estimate soil moisture [
17]. The authors concluded that the DISPATCH algorithm performed appropriate estimations in the case of rainfall but not for sprinkler irrigation, where the water is distributed in a heterogeneous manner. Both [
15] and [
17] used MODIS, characterized by a low spatial resolution, which impedes the application of their results in our scenario. The same problems apply to the results of [
16].
Lastly, Jesús Garrido-Rubio et al. presented a framework based on the Remote Sensing-based Soil Water Balance (RS-SWB) from satellite images and the dual crop coefficient from the FAO56 paper to calculate a Remote Sensing-based Irrigation Water Accounting (RS-IWA) metric [
18]. For the plot scale, a 12% root square mean error (RMSE) was obtained, comparing the results to those of the farmers. For the case of the water user association management, the obtained RMSE was 15%. Therefore, the authors concluded that their proposed RS-IWA is appropriate to perform soil water balance estimations. Nevertheless, their proposal is specific for certain crops (wheat, maize, and barley). Therefore, its application in other vegetation such as grasses of urban lawns or golf courses or even for natural vegetation cannot be ensured.
Most studies on soil moisture that use satellite data are focused on crops such as wheat. However, this functionality is of interest for other types of vegetation, such as the grass in golf courses. In this paper, satellite data is analyzed to estimate soil moisture for a golf course composed of different types of grasses and areas with natural vegetation (which is not irrigated). Therefore, our results will be applicable for multiple scenarios. Moreover, we will include validation of proposed index or indexes by including data of other periods. The main contributions of this paper are the following ones:
The combination of sensors and remote sensing generates a tailored index (or indexes) for estimating the soil moisture variations of the studied zone.
The use of remote sensing imagery is characterized by higher spatial resolution (10 m and 20 m) than the most used imagery, characterized by lower spatial resolution such as MODIS.
The data verification and clear presentation of indexes, R, and errors in both training and validation datasets.
The heterogeneity of employed datasets is characterized by different vegetation and different soil types.
3. Materials and Methods
This section describes the sampling area, the sensor network, the image gathering, and the data processing followed to estimate the soil moisture in the golf course during the summer of 2021.
3.1. Studied Zone
We selected the “Encín Golf Course” located in Alcalá de Henares (Madrid, Spain) (Lat. 40°31′21′′; Long. 3°18′43′′) as the sampling area to deploy a batch of soil moisture sensors provided by PLANTAE. This region is characterized by very hot and short summers; and dry, very cold, and long winters. Generally, throughout the year, the temperature varies from 1 to 33 °C. The temperature rarely cools down below −4 °C or increases above 37 °C [
7]. Therefore, the golf manager must water the grass regularly in summer to keep it healthy and in optimal conditions to play.
The golf course was built on a plot used for agriculture previously, on the upper terraces of the Henares River. Originally this area was characterized by a slope of less than 0.5% and surface horizons Ap and AB with the following characteristics: Ap between 0 cm and 18 cm, clay loam texture (22.7% gravel, 44.1% silt, 33.2% clay), weak structure, medium angular blocks, firm consistency when wet and hard when dry, thin and zonal clay films, few very fine pores, abundant roots of all sizes; and AB between 18 cm and 32 cm, clay loam texture (24.9% gravel, 41.1% silt, 34.0% clay), weak structure, medium angular blocks, firm consistency when wet and hard when dry, thin and zonal clay films, few pores fine and very fine, with an abundance of roots of all sizes [
19]. In addition, it was designed in different areas, which account for different types of soil and grass management: (1) tee, fairway, and green, where the grass is tightly trimmed, regularly watered, and the soils are sandy; (2) rough surrounding the fairways, where the grass is kept longer, regularly watered and soils are sandy-clay; and (3) wild areas, with native plants and zero maintenance or watering, except from the rainfall (see
Figure 1). Thus, only soil 1 and 2 were amended to adapt their texture to that required by the golf course designer.
We placed three sensors, see
Figure 2a in different spots of every area, at a depth of 5 cm and covered by soil, to measure the shallow soil moisture. Thus, we have nine different sets of data. Small patches of grass were cut, and soil was removed to install the sensors; see
Figure 2b. After placing the sensor, the hole was filled with soil, and the patch of grass was relocated. The soil moisture sensors, provided by PLANTAE, are a resistive sensor type, with temperature compensation [
20]. The GPS coordinates and the sensor number were registered for each point. Then, they were configured to send data every 15 min. The data was received in a hub placed close to the golf house. These records were uploaded and processed in a cloud service managed by PLANTAE. Data was provided as gravimetric water content (%): the mass of water per mass of dry soil. The soil moisture sensors were calibrated by the service provider (PLANTAE), according to the texture of soil samples taken during the installation of the sensors.
The moisture values during the sampling period ranged between 5% and 35%, depending on the irrigation, rainfall, and environmental temperature. The sandy and sandy-clay areas showed a lower humidity value (average 10% to 15%) compared to clay soil ones (average 20%) due to the better drainage of these soils.
3.2. Sentinel-2 Image Gathering
To estimate and correlate the field data with the remote sensing data, we used Copernicus Sentinel-2 mission images provided by the European Space Agency. Sentinel-2 comprises a constellation of two polar-orbiting satellites placed in the same sun-synchronous orbit, phased at 180° to each other. As they provide a ten-day revisit time with one satellite and five days with both satellites under cloud-free conditions, we gathered both Sentinel-2 A and Sentinel-2 B images. Therefore, the images available for the summer of 2021 include the following dates: 12th and 27th of June; 2nd, 12th, 17th, 22nd and 27th of July; 1st, 6th, 11th, 16th, 21st, 26th, and 31st of August; 5th and 29th of September. The initial and final dates were used for the verification of the regression models. The rest of the data were used for the generation of models.
We used both 10 m and 20 m resolution images. Sentinel-2 offers information at three scales, 10 m, 20 m, and 60 m. At each scale, information from different bands is accessible. We consider that the images with resolution at 60 m have a too low spatial resolution to be used in this study due to the heterogeneity of the land covers in the golf course. The 10 m resolution images were available for band 2 (490 nm), band 3 (560 nm), band 4 (665 nm), and band 8 (842 nm) spectral bands. Meanwhile, the 20m resolution images were available for the previous bands and band 5 (705 nm), band 6 (740 nm), band 7 (783 nm), band 8a (865 nm), band 11 (1.610 nm), and band 12 (2.190 nm). Although we consider that more accurate information will be obtained with information with a spatial resolution of 10 m, the facto of having more available information (more bands) at 20 m justifies its inclusion. The statistical analyses do not join bands with different spatial resolutions to avoid the problems linked to uncertainties regarding differing pixel sizes.
3.3. Data Processing
Through the ArcMap [
21] “Extract Values to Points” tool [
22], we collected each pixel value from the multiple layers image (bands and days) at the specified sampling points (sensor). Data of different bands of the Sentinel-2 were processed together with the moisture values sensed by in-situ sensors. Soil moisture from the sensors was estimated as the average values gathered from 9:00 a.m. to 1:00 p.m. since the Sentinel-2 image was always taken at 10:56 a.m. The calculated average soil moisture value is used for the statistical analyses.
The data matrix was processed with Statgraphics Centurion XVIII [
23] to analyze if there was any correlation between the moisture values and the values from the satellite images. First, general multivariate analyses, with all sorts of soil, and specific multivariate analyses, for individual sort of soil, were performed to find which satellite image bands correlate with the soil moisture. The multivariate analyses were performed according to the predefined options of Statgraphics Centurion XVIII [
23]. No modifications were applied to the obtained results. The data of dispersion matrix and correlation matrix were used. All cases were included in the analyses. No outlier results were deleted for this initial test.
Then, simple and multiple regression models are compared according to the results of multivariate analyses. Regarding the simple regression models, a total of 27 possible regression models are evaluated. All of them include the use of a constant number, and the adjustment was performed according to the value of least squares. Concerning the multiple regression models, only linear regression is evaluated. The regression models will be calculated for all soil classes, a group of soils, or individual soils classes (sandy, sandy-clay, and clay) according to multivariate analyses. We assume R2 as the best indicator of the accuracy of the model. Attending to previous experiences, models characterized by R2 higher than 50% should be considered as good models for this case due to the heterogeneity of the soil. This issue will be further analyzed in the discussion.
Once the mathematical models are obtained, the validation includes qualitative and quantitative analyses. The first one applies both mathematical models in ArcMap using the tool “Raster Calculator” [
24], and results are checked and compared with other soil moisture indexes. The “Raster Calculator” is a simple tool that allows applying a mathematical model to one or more bands. This tool applies the mathematical model based on the pixel values of the bands included in the mathematical model generating a new raster. Then, the predicted value of soil moisture obtained in ArcMap was extracted and compared with the sensed one.
6. Conclusions
The estimation of accurate soil moisture using satellite imagery is essential for evaluating irrigation efficiency. It can be applied to several areas such as in the urban lawn, golf courses, agriculture, or at a higher scale considering a whole river basin. Nonetheless, the existing proposed models have relatively low accuracies.
This paper shows the combined use of remote sensing and a soil moisture sensor network to generate soil sensor indexes based on satellite imagery. Our results indicate that it is feasible to evaluate the soil moisture at different points of the golf course to achieve better water efficiency by using Sentinel-2 images. Nonetheless, indexes offered accurate results only in the natural soil, precluding remote soil moisture estimation in the fairways and greens. The accuracy of obtained models upgrades the existing solutions based on the R2 and the MAEs of the calibration dataset. We added a validation dataset to verify our findings. The validation indicated that the model obtained with bands at 20 m has greater MAE than the training dataset. This aspect was not considered in previous contributions, and it is essential to ensure its application to new areas. To verify our results, a comparison with other indexes is performed. The indexes were applied to the nearby area of the golf course, and the obtained results correspond to the observed changes in the different land uses.
As future work, we will include data of other seasons to decrease the MAEs of the validation dataset. Our main objective with future work is to evaluate the option of creating seasonal soil moisture indexes. Moreover, other remote sensing sources will be combined with Sentinel-2 data to attain the generation of soil moisture models suitable for fairways and green areas.