Soil moisture content (SMC) is an important factor in managing irrigated farms. SMC includes two main components: surface soil moisture (SSM) (held in the upper 10 cm of soil) and root zone soil moisture (held in the upper 200 cm of soil). Surface soil moisture is a key component for addressing energy and water exchanges at the land surface/atmosphere interface and can be estimated using different techniques, such as in situ
measurements, physically based models, remote sensing, etc.
Grayson and Western addressed the estimation of soil moisture by applying: (1) field (or in situ
) measurements; (2) remote sensing techniques; and (3) soil water balance simulation models [1
]. Soil moisture constitutes a very small volume in terms of the total global water balance, but it plays a significant role in water resources planning and management [2
]. Many current crop production management decisions that are made by growers, production managers, and crop advisors in precision agriculture are already based on observation from remotely sensed data such as satellite imagery. The objective of this research is to generate surface soil moisture (SSM) estimates using high-resolution, remotely sensed data, collected at 15 cm pixel resolution, as inputs to a learning machine algorithm (Artificial neural networks (ANNs))developed under supervised learning procedures. ANNs are used to build the SSM estimation model. To our knowledge, this is the first study to document estimation of surface soil moisture using remotely sensed data at such a fine spatial resolution and readily available in the sense of temporal resolution. The results will contribute not only to efficient and reliable high-resolution multi-spectral remote sensing validation, but also to better utilization of remotely sensed soil moisture products for enhanced irrigation modeling and scheduling.
Various techniques for retrieving soil moisture content have been the subject of research for almost four decades. Gravimetric measurements of soil moisture are very reliable but are laborious. Measuring SMC with imbedded sensors, such as time and frequency domain reflectometers (TDRs and FDRs), does not require a huge investment of time or facilities; however, most of these methods suffer from some of these same disadvantages. In situ
measurements can be exhaustive and expensive if large areas are involved, as these measurements are mainly “local,” with a particular footprint representing moisture conditions in only a fraction of a cubic meter of soil [3
]. Because of the spatial heterogeneity of soil moisture due to different soil conditions, vegetation, topography, or impact of human activities, local measurements when are carried out on a larger scale such as fields or watersheds, might result in inaccuracies [4
]. Remote-sensing techniques might provide a useful tool to address these data acquisition difficulties.
Some of the early works in estimating SMC using remote sensing [5
] established that thermal remote sensing, in concert with in situ
measurements, can be used to measure, or at least quantitatively infer, soil moisture content. The possibility of estimating SSM (0–7.6 cm) from visible and near-infrared (NIR) reflectance data has also been demonstrated [11
]. Optical and thermal remote-sensing techniques or passive and active microwave sensors offer large-scale monitoring of SSM [11
]. Some meteorological satellites, such as the Advanced Microwave Scanning Radiometer (AMSR-E), the European Remote Sensing (ERS) satellite scatterometer or the Meteorological Satellite (METEOSAT), offer the possibility of monitoring operational SSM [3
]. However, the coarse spatial resolutions (ERS-Scat: 50 km, AMSR-E: 56 km and METEOSAT: visible and infrared (IR) 5 km) of the instruments are often not consistent with the scale of hydrologic processes of interest [14
]. A number of studies on soil moisture estimations introduced the error sources that have degraded the accuracy of satellite remotely sensed soil moisture content such that it is critical to calibrate soil moisture estimation algorithms and to validate derived products using ground-truth data. The error sources comprise radio-frequency interference (RFI) [16
], vegetation water content [13
], surface roughness [16
], and land surface heterogeneity [18
]. It has been stated in the literature that a space-borne sensor designed to interpret SMC on the basis of soil microwave emission, and therefore the relationship between soil dielectric constant and water content, will show considerable systematic uncertainty of around 4% with maximum figures at relatively low water content in SMC retrieval [19
Remote-sensing measurements in the thermal IR band has given rise to the thermal inertia (TI) approach for SMC retrieval. The TI approach relates SMC to the magnitudes of the differences between daily maximum and minimum soil and crop canopy temperatures [6
]. This approach retrieves SMC from models that describe TI as a function of water content [20
]. The implementation of the TI approach is simple because knowledge of soil physical properties and climate can produce representative SMC profiles up to a depth of 1 m. The limitation of the approach, however, is its sensitivity to the uncertainty of soil physical properties, which are complex to determine spatially and are typically obtained with point measurements [22
]. The TI method provides large-scale spatial coverage, but the functions are empirical and have the drawback of being site- and time-specific, such that none of them are general enough to be applied extensively [21
]. Monitoring soil moisture by remote sensing includes another set of approaches that permits SSM retrieval from the information contained in satellite-derived surface temperature (Ts) and vegetation index (VI). However, one of the major drawbacks of the Ts-VI method is that, in order to have enough points in a remote-sensing image to use in the determination of the boundaries of extreme conditions, a sufficiently large number of pixels must be sampled. This limitation is a handicap when dealing with smaller scale imagery on the order of the size of a typical farm field [11
The difficulties associated with the above introduced approaches have led researchers to look for data-driven modeling tools, such as artificial neural networks (ANNs), support vector machines (SVMs), and relevance vector machines (RVMs), to estimate soil moisture [2
]. For example, Landsat data has been used for soil moisture estimation using relevance vector and support vector machines [3
]. One of the major advantages of the machine learning approach to SMC estimation is that it can provide estimates having resolutions commensurate with remotely sensed data [3
This paper demonstrates the application of a high resolution remote sensing technology (AggieAir) for estimating surface soil moisture as a key piece of information in irrigation water management. High-resolution multi-spectral imagery, in combination with ground sampling, provided enough information for the modelling approaches to accurately estimate spatially distributed surface soil moisture.
This paper presents the results of a modelling approach utilizing ANN in concert with time and site specific information. Parallel to other modeling approaches, such as data mining algorithms or linear regression, the ANN model is calibrated for this study within the conditions of the information collected including soil moisture measurements, soil texture, crop type information, and high resolution multi-spectral imagery. While the site-specific calibrated ANN in this study cannot be used immediately in another location, the modeling procedure (identifying spatial information with the most significant contribution to soil moisture estimation (Table 2
)) along with similar field measurements and high resolution multi-spectral imagery and the data mining algorithm, are transferable from this study.
Surface soil moisture estimation was accomplished with an ANN model (RMSE: 2.0, MAE: 1.3, r: 0.87, e: 0.75, R2:0.77) for four dates in 2013 (16 May, 1 June, 9 June, and 17 June). These results show the capability of the model to accurately estimate surface soil moisture. Compared to the traditional soil moisture estimations that are based on a farmer’s visual perceptions or a few soil moisture samples averaged across the farm, the modelling approach presented enables greater precision in the application of water and identifies dry/wet spots and water stressed crops.
AggieAir imagery, combined with appropriate analytic tools, allows spatial estimation of surface soil moisture. These estimates were made at much finer resolutions in space and time than those available from conventional remote sensing technologies (e.g., satellite or commercial aerial photography services). Also, the application of data mining algorithms to AggieAir aerial imagery allows for quantification of actionable information for precision agriculture (soil moisture values across the field). The soil moisture maps that are produced can then be related to irrigation water management for scheduling and application rates.
The results from the wrapper selection (Table 2
) prove the significance of thermal imagery as the most relevant information in surface soil moisture estimations. In the case of one input, a model with thermal imagery can estimate the soil moisture values with RMSE of approximately 3% (thermal images are provided in supplementary material section).
Soil water holding capacity as a function of soil texture plays an important role in soil moisture values. This parameter was observed by utilizing field capacity as an input to the models. Table 2
shows that field capacity is a component of most of the models. The effect of this parameter is confounded by other important inputs in the spatial distribution of soil moisture in Figure 8
. Based on the information presented in Table 2
, among the available vegetation indices, NDVI and VCI have a greater explanatory contribution in surface soil moisture estimates. The soil moisture maps have a good association with false color composite maps that allows for distinction of agricultural features in the field.