Open access peer-reviewed chapter

Satellite Data and Supervised Learning to Prevent Impact of Drought on Crop Production: Meteorological Drought

By Leonardo Ornella, Gideon Kruseman and Jose Crossa

Submitted: November 22nd 2018Reviewed: February 26th 2019Published: June 6th 2019

DOI: 10.5772/intechopen.85471

Downloaded: 791


Reiterated and extreme weather events pose challenges for the agricultural sector. The convergence of remote sensing and supervised learning (SL) can generate solutions for the problems arising from climate change. SL methods build from a training set a function that maps a set of variables to an output. This function can be used to predict new examples. Because they are nonparametric, these methods can mine large quantities of satellite data to capture the relationship between climate variables and crops, or successfully replace autoregressive integrated moving average (ARIMA) models to forecast the weather. Agricultural indices (AIs) reflecting the soil water conditions that influence crop conditions are costly to monitor in terms of time and resources. So, under certain circumstances, meteorological indices can be used as substitutes for AIs. We discuss meteorological indexes and review SL approaches that are suitable for predicting drought based on historical satellite data. We also include some illustrative case studies. Finally, we will survey rainfall products existing at the web and some alternatives to process the data: from high-performance computing systems able to process terabyte-scale datasets to open source software enabling the use of personal computers.


  • remote sensing
  • supervised learning
  • meteorological index
  • wavelet

1. Introduction

Climate change is shifting the rainfall patterns and increasing the severity of droughts and floods around the Earth. Australia [1], Europe, and the rest of the continents have been affected by a number of major drought events [2]. In 2018, drought and heat waves reduced harvests up to 40–50% in some countries of northern and central Europe [3].

Drought is by far the Earth’s most costly natural disaster and can have widespread impacts [4]. Globally, it is responsible for 22% of the economic damage caused by natural disasters and 33% of the damage in terms of the number of people affected [5]. Though average yields rose steadily between 1947 and 2008, there is no evidence that relative stress tolerance has improved [6, 7]. Therefore, until breeding programs develop adapted germplasm, drought forecasting will be important to determine when to take contingency actions to prevent drought and mitigate its risk and impacts.

The practice of drought forecasting remains challenging and is subject to great uncertainty partly due to the instability of the components of the hydrologic cycle (e.g., rainfall, soil moisture, groundwater level, etc.); temporal variability involving trends, oscillating behavior, and sudden shifts that appear in hydroclimatic records, thus posing challenges to drought prediction [8, 9].

Although agricultural indices (AIs) better reflect the soil water situation that influences crop conditions, monitoring of soil moisture is costly in terms of time and resources [10]. On the other hand, some meteorological indices (e.g., the Standard Precipitation Index) can be calculated just knowing the precipitation (pp) data, and then the expert can give a very close condition of the vegetation [11].

A variety of methods has been developed to predict drought occurrence: statistical run theory [12], Markov chain [13], loglinear [14], renewal process [15], and Poisson process [16], among others.

A valuable alternative to the aforementioned methods is machine learning (ML), a branch of artificial intelligence that studies how to extract information from big data sets with minimal human intervention. ML has been successfully tested in very different areas such as bioinformatics [17], crop protection [18], and economics [19], among others. Therefore, its potential for predicting the climate seems far from being fully exploited.

The remainder of this chapter is organized as follows: in Section 2, we introduce some representative ML methods that have been proposed for drought forecasting; in Section 3, we present the concept of meteorological drought, in particular the standardized precipitation index that is considered a primary drought indicator. Section 4 describes some forecast examples using the abovementioned methods. In Sections 5 and 6, we review satellite precipitation products and how to access and process them; and finally, in Section 7 we present the conclusions of this work.


2. Machine learning

ML is the science of algorithms and statistical models that computer systems use to progressively improve their performance of a specific task. They can be broadly categorized into supervised and unsupervised learning. In SL (classification or regression), the algorithm builds a function from a set of data relating the inputs to the outputs. In regression, the outputs are continuous, meaning they may have any value within a range (e.g., temperature and moisture), while in classification, the outputs are restricted to a limited set of values.

In unsupervised learning, the algorithm builds a mathematical model of a data set that contains inputs and no outputs. These unsupervised learning procedures are used to find structure in the data (e.g., cluster data) or reduce its dimensionality.

Examples of ML are land classification using remote sensing [20, 21, 22], amending satellite data assimilation [23], or decomposing the causes of climate change [24].

2.1 Support vector regression (SVR) and least squares support vector regression (LS-SVR)

SVR is based on the Vapnik-Chervonenkis (VC) theory [25], which characterizes the properties of learning machines that enable them to generalize the unobserved data well.

Starting with the simplest example, that is, linear regression, the objective of both SVR [26] and LS-SVR [27] is to fit a linear relation y=wTx+bbetween the x regressors and the dependent variable y in the so-called feature space. In SVR, the problem is solved by minimizing


under the constraints


while for LS-SVR, the objective is to minimize


under the constraints


Both methods are very similar, but in LS-SVR, the objective is to minimize the more usual sum of the squares of the errors, by replacing the ε-tube or ε-insensitive loss of SVR, that is, by ignoring all regression errors smaller than ε (Figure 1). Solving a nonlinear regression demands a “kernel trick” [26]. This trick uses kernel functions to transform the data of the input space into a higher dimensional feature space to make it possible to perform a linear regression.Common kernels are

Gaussian radial basis functionkxixj=expxixj22σ2E6

Figure 1.

(A) Kernel trick: mapping the data from the input space into a feature space. (B) Loss function used in support vector regression (ε-insensitive loss) and least squares support regression (quadratic).

LS-SVR is an economic alternative to the original SVR model. It only relies on the cost function on a sum-of-squared-error (SSE) and equality constraints, instead of the computationally complex and time-consuming quadratic programming problem in SVR [28].

For optimal performance, parameter tuning is necessary [29]: for SVR, C and εand the kernel-related parameters (e.g., σ2for the RBF kernel) and for LS-SVR, g (the regularization parameter determining the trade-off between the fitting error and the smoothness of the estimated function) and the kernel-related parameters. For further information about SVR in general, the reader should refer to [30].

2.2 Artificial neural network (ANN)

An ANN is a supervised learning model based on the operation of biological neurons. There are many architectures and training algorithms for ANN. The multilayer perceptron network (MLPN), the most common ANN architecture used for forecasting, consists of a feedforward neural network with at least three layers of neurons: an input layer, one or more hidden layers, and an output layer with a directed acyclic graph representation network (Figure 2). The input layer receives the data vector x, while the output layer gives the output vector y. An activation function is applied to activate the neurons in the hidden layer. For a three-layer network system, the nonlinear mapping between input x and output y is given by the equation:


Figure 2.

Architectures of forecasting artificial neural networks. Recursive multistep neural network versus direct multistep neural network.

An ANN is usually learned by adjusting the weights and biases in order to minimize a cost function, usually MSE using the error back-propagation algorithm.

Of the activation functions, we should mention the hyperbolic tangentfx=exexex+exand the sigmoidal function: x=ex1+ex.

The number of hidden neurons is no less important, since a wrong number may cause either overfitting or underfitting problems. Normally it is selected via trial and error, but this is computationally costly. Several heuristics or formulas have been proposed to avoid this cumbersome work, and success depends on the type of data, the complexity of the network architecture, etc. [31].

Last but not least, ANN forecasting models can be separated into two broad groups, namely, the recursive multistep neural network (RMSNN) and the direct multistep neural network (DMSNN) (Figure 2). In RMSNN, the model forecast one time-step ahead, and the network is applied recursively, using previous predictions as inputs for subsequent forecasts—that is, a forecast horizon of 3 months will have, as inputs, the outputs of forecasts with lead times of 1 and 2 months.

Similar to the RMSNN model, the DMSNN approach has a single or multiple neurons in both the input and hidden layers. However, it can have several neurons in the output layer representing multiple-month lead time forecasts. Similar to the RMSNN model, the DMSNN model is designed to forecast drought conditions using the present index value and several months of past index values as inputs.

2.3 Deep belief networks (DBN)

ANNs are suitable for complex time series forecasting but have several weaknesses: (1) selection of the initial values of the weights (normally at random) can affect the learning process, leading to slower convergence or to different forecast results for each training process and (2) the training process may get stuck at local optima, especially in networks with several hidden layers. Hinton et al. [32] proposed a probabilistic generative model with multiple hidden layers that uses layer-wise unsupervised learning to pre-train the initial weights of the network and then fine-tune the whole network using standard supervised methods such as the back-propagation algorithm.

Classically, a DBN is constructed by stacking multiple restricted Boltzmann machines (RBMs) on top of each other (Figure 3). The layers are trained by using the feature activations of one layer as the training data for the next layer. Better initial values of weights in all layers are obtained by greedy layer-wise unsupervised training, and the entire network is fine-tuned using an SL algorithm. Pre-training can be done with principal component analysis or nonlinear generalization [33].

Figure 3.

Basic deep belief network (DBN) structure with three hidden layers.

An RBM [34] is a neural network model used for unsupervised learning. Typically, it consists of a single layer of hidden units (the outputs) with undirected and symmetrical connections to a layer of visible units (the data) (Figure 3). The configuration (bipartite graph) defines the state of each unit. Only connections between a hidden unit and a visible unit are permitted—that is, no connections between two visible units or between two hidden units are allowed. An RBM is a special type of generative energy-based model that is defined in terms of the energies of the configurations between visible and hidden units.

The standard type of RBM has binary-valued (Boolean/Bernoulli) hidden and visible units.

2.4 Bagging

Bootstrap aggregating, or bagging, is an ML ensemble meta-algorithm designed to increase the stability and accuracy of unstable procedures, for example, artificial neural networks or decision trees [35]. Given a standard training set T of size n, the algorithm sample is taken from T uniformly and with replacement m new training sets, T’, each of size n′ (some observations may be repeated in each D). This process is known as a bootstrap sampling [36]. The basic idea is that the samples are de-correlated, and this reduces the expected error as m increases.

The mmodels are fitted using the above mbootstrap samples, and results of an unknown instance are obtained by averaging the output (for regression) or by voting (for classification) (Figure 4).

Figure 4.

Structure of bootstrap aggregating, or bagging.

This method may slightly degrade the performance of stable algorithms (e.g., k-nearest neighbor) because smaller training sets are used to train each algorithm.

Bagging does not necessarily improve forecast accuracy in all cases. Nevertheless, this method and its derivatives tend to outperform traditional forecasting procedures [37].

2.5 Random forest regression (RFR)

A random forest (RF) [38] is a collection of Kbinary recursive partitioning trees, where each tree is grown on a subset of n instances extracted with replacement from the original training data. It is an instance of bagging where the individual learners are de-correlated trees. Each tree is grown in a top-down recursive manner, from the root node to terminal nodes or leaves (Figure 5). In each node, a random sample of m(m ≈ p/3) predictors is chosen as candidates from the full set of ppredictors. The data are partitioned into the two descendant branches by choosing the variable that minimized:


Figure 5.

Architecture of the random forest model.

The advantage of selecting a random subset of predictors is that two trees generated on same training data will be de-correlated (independent of each other) because randomly different variables were selected at each split. Each internal (non-leaf) node is signed with a predictor determined by the RSStest, and each one of the two possible subsets of this variable labels the arcs connecting to the subordinate decision node. Each tree extends as much as possible until all the terminal nodes are maximally homogeneous (a minimum of five examples in each leaf is recommended).

Once the random forest is generated, the output of new data is obtained by averaging the predictions of the K trees.

The number of trees influences the error of prediction; it decreases as the number of trees (ntree) grows, but there is a threshold beyond which there is no significant gain [38, 39]. In general, ntree≈500 gives good results [40].RF can successfully handle high dimensionality and multicollinearity, because it is both fast and insensitive to overfitting. It is, however, sensitive to the sampling design.

2.6 Adaptive neuro-fuzzy inference system or adaptive network-based fuzzy inference system (ANFIS)

ANFIS is a hybrid learning procedure which employs the linguistic concept of fuzzy systems (human knowledge) and the training power of the ANN to solve a regression problem [41]. All ANFIS works reported here are based on the Takagi-Sugeno fuzzy inference system [42], where the fuzzy rule applied has the form: if x is A and y is B then z=fxy. Other fuzzy methods are Mamdani-type or Tsukamoto-type [42].

Figure 6 depicts a typical ANFIS architecture. Square nodes (adaptive nodes) have parameters, while circle nodes (fixed nodes) do not. The first and the fourth layers contain the parameters that can be modified over time. A particular learning method was required to update these parameters.

Figure 6.

Architecture of an adaptive network-based fuzzy inference system (ANFIS).

In layer 1, every node is adaptive and associated with an appropriate continuous and piecewise differentiable function such as Gaussian, generalized bell-shaped, trapezoidal-shaped, and triangular-shaped functions.

In layer 2, every node is fixed and represents the firing strength of each rule. This is calculated by the fuzzy and connective method of the “product” of the incoming signals, that is, Oi2=wi=μAixμBix,i_1,2.

In layer 3, every node is also fixed, showing the normalized firing strength of each rule. The ith node calculates the ratio of the ith rule’s firing strength to the summation of two rules’ firing strengths.

In every adaptive node of layer 4 (consequent nodes) is a function indicating the contribution of the ith rule to the overall output: O4,i=wi¯f=w¯ipi+qi+ri,where wiis the output of layer 3 and pi,qi,riis the parameter set. Finally, layer 5 (output node) is a single node that computes the overall output of the ANFIS as: O5,1=w¯ifi=iwifiiwi.

One of the most important steps in developing a satisfactory forecasting model is the selection of the input variables. These variables determine the structure of the forecasting model and affect the weighted coefficients and the results of the model function in layer 2. As the number of parameters increases with the fuzzy rule increment, the model structure becomes more complicated. A very good description of ANFIS is presented in [43, 44].

2.7 Boosting

Boosting attempts to increase the performance of a given learning algorithm by iteratively adjusting the weight of an observation based on the last training/testing process. In other words, the meta-algorithm produces a sequence of models by adaptive reweighting of the training set [45].

AdaBoost, the first boosting algorithm, is definitely beaten by noisy data; its performance is highly affected by outliers, as the algorithm tries to fit every point perfectly. Friedman [46] extended the concept to present gradient boosting, which constructs additive regression models by sequentially fitting a simple parameterized function (base learner) to current “pseudo”-residuals by least squares at each iteration. The pseudo-residuals are the gradient of the loss function being minimized with respect to the model values at each training data point evaluated in the current step. This reduces the loss of the loss function. We iteratively added each model and computed the loss. The loss represents the difference between the actual value and the predicted value (the error residual), and using this loss value, the predictions are updated to minimize these residuals.

A regularization method that penalizes various parts of the boosting algorithm is necessary to avoid overfitting. This generally improves the performance of the algorithm by reducing overfitting.

2.8 Hybrid models

The time series that characterize the evolution of meteorological events (drought, precipitation) in the temporal domain have localized high- and low-frequency components with dynamic nonlinearity and non-stationary features. MM models have not always proven to be good at capturing the behavior of the time series. Hybrid models can perform superbly when forecasting hydrological and climatological time series. Different combination techniques have been proposed in order to overcome the deficiencies of single models and improve forecasting performance [47]. Many combined models have been introduced in the literature, for example, ANN-ARIMA [48], SVR-ARIMA [49], etc.

Here we will only focus on WT-ML hybrids, where ML is a machine learning method (e.g., ANN or SVR) and WT is a discrete wavelet transform [50].

2.8.1 Wavelet transform (WT)

WT is a time-dependent spectral analysis that decomposes time series in the time-frequency space and provides a timescale illustration of processes and their relationships. In this method, the data series are broken down by transforming them into “wavelets,” which are scaled and shifted versions of a mother wavelet [50]. This allows the use of long time intervals for low-frequency information and shorter intervals for high-frequency information and can reveal aspects of data such as tendencies, breakdown points, and discontinuities that other signal analysis techniques might miss, for example, Fourier transform.

There are two main alternatives for WT: discrete wavelet transform (DWT) and continuous wavelet transform (CWT). For DWT, the WT is applied using a discrete set of the wavelet scaling and shifting, whereas in the case of CWT, this scaling and shifting is continuous—that is, CWT is computationally expensive and most researchers use DWT. For more information about CWT, the reader should refer to [51].

DWT operates two sets of functions (scaling and wavelets) viewed as high-pass (HPF) and low-pass (LPF) filters. The signal is convolved with the pair of HPF and LPF followed by subband downsampling producing two components. The first component, which is obtained by passing the signal through the low-pass filter, is called an approximation component (or series), and the other component (fast events) is called a detailed component (Figure 7). This process is iterated ntimes with successive approximation series being decomposed in turn, so that the original time series is broken down into the minimum number of components needed to reflect the time series according to the mother wavelet.

Figure 7.

Time series wavelet-ANN conjunction model. (A) Three-level wavelet decomposition tree (DWT). (B) Example of the decomposition of a precipitation signal.

The filterbank implementation of wavelets can be interpreted as computing the wavelet coefficients of a discrete set of child wavelets for a given mother. This mother wavelet function was defined at scale a and location b as


ψ0,0tis a mother wavelet prototype and a, b are scaling and shifting parameters, respectively.

Several wavelet families have proven useful for forecasting various hydrological time series. As an example, we can mention Haar, which is also known as daubechies1 or db1 [50]. It is defined as


A full description of DWT can be found in [50, 52].

3. Meteorological indices

Drought can be defined as a period of unusually arid conditions (usually due to rainfall deficiency) that have lasted long enough to cause non-balance in a region’s hydrological situation. Based on its intensity and persistence, drought can be classified into four categories [53]: (1) meteorological drought, which occurs when precipitation is less than usual, is characterized by changes in weather patterns; (2) agricultural (vegetation) drought refers to water deficits in plants; it occurs after meteorological drought and before hydrological drought; (3) hydrological drought ensues when the level of surface water and the groundwater table are less than the long-term average; and finally, (4) socioeconomic drought materializes when water resources required for industrial, agricultural, and household consumption are less than required and thus cause socioeconomic anomalies.

A drought index is an indicator or measure derived from a series of observations that reveals some of the cumulative effects of a prolonged and abnormal water deficit. It integrates pertinent meteorological and/or hydrological parameters (accumulated precipitation, temperature, and evapotranspiration) into a single numerical value or formula and gives a comprehensive picture of the situation [53]. Such an index is more readily usable and comprehensible than the raw data and, if presented as a numerical value, makes it easier for planners and policymakers to make decisions. Authorities and public and private committees evaluate the impact of drought using these indices and take measures to prevent its effects [54].

More than 100 drought indices have so far been proposed, and each one has been formulated for a specific condition [55]. The reclamation drought index (RDI), for example, was developed in the USA to activate drought emergency relief funds associated with public lands affected by drought; the crop moisture index (CMI) was designed to show the effects of water conditions on growing crops in the short term and is not a good instrument for displaying long-term conditions. Here we will only describe the standardized precipitation index, which those indices used in case studies.

3.1 Standardized precipitation index (SPI)

Most of the forecasting works reviewed here are based on SPI [56]. It is perhaps the most popular index for forecasting meteorological drought and has been recommended by the World Meteorological Organization [57]. It can be defined as the number of standard deviations that the observed cumulative rainfall at a given time scale (1,3,6 month) would deviate from the long-term mean for that same time scale over the entire length of the record (z-score).

More specifically, SPI is calculated by building a frequency distribution from historical precipitation data (at least 30 years) at a specific location for the precipitation accumulated during a specified period, for example, 1 month (SPI1), 3 months, (SPI3), 24 months (SPI24), and so on. A theoretical probability density function (usually the gamma distribution) is fitted to the empirical distribution for the selected time scale.

SPI1 to SPI6 are considered indices for short-term or seasonal variation (soil moisture), whereas SPI12 is considered a long-term drought index (groundwater and reservoir storage).

The “drought” part of the SPI range is arbitrarily split into “near normal” (0.99 > SPI > −0.99), “moderately dry” (−1.0 > SPI > −1.49), “severely dry” (−1.5 > SPI > −1.99), and “extremely dry” (SPI < −2.0) conditions [56]. A drought event starts when SPI becomes negative and ends when it becomes positive again.

SPI is easy to calculate (using precipitation only) and can characterize drought or abnormal wetness on different time scales. Its standardization ensures independence from geographical position, and it is thus more comparable across regions with different climates. The index can be computed using several packages of the R project [58], for example, the SPEI package [59] or the SPI package [60]. Limitations of SPI include the following: (1) it does not account for evapotranspiration; (2) it is sensitive to the quantity and reliability of the data used to fit the distribution; and (3) it does not consider the intensity of precipitation and its potential impacts on runoff, streamflow, and water availability within the system. A more detailed explanation of how SPI is calculated can be found at [43].

3.2 Other indices

Other indices including only precipitation data are EDI [61], SIAP[62], deciles index (DI), percent of normal (PN), standard precipitation index (SPI), China-Z index (CZI), modified CZI (MCZI), and z-score [55].

4. Forecasting meteorological drought

Forecasting meteorological drought using historical data is not a trivial task. The time series that characterize the evolution of meteorological events (drought, precipitation) in the temporal domain have localized high- and low-frequency components with dynamic nonlinearity and non-stationary features. Several statistical indicators have been proposed to evaluate the success of prediction. Most of these metrics are not independent; for example, MSE can be decomposed in many ways to link it with the bias and the correlation coefficient [63]. A standard practice of model corroboration is to compute a common set of performance metrics, typically more than three. Most important is that at least three critical components, that is, one dimensionless statistic, one absolute error index statistic, and one graphical technique, should be represented in the corroboration [64].

Regarding the dimensionless statistic, we must mention:

  • Pearson’s correlation coefficient (R) is used to evaluate how well the estimates correspond to the observed values. Due to the standardization of many indices, the robustness of R can be limited [64].

  • Coefficient of determination (R2) measures the degree of association among the observed (oi)and predicted values (pi).

  • Nash-Sutcliffe efficiency (NSE) or MSE skill [65].

  • Willmott’s index (WI) represents the ratio of the mean square error and the potential error [66].


Among one absolute error index statistic most used are

  • Mean ErrorMSEestimates the average estimate error.

  • Mean absolute error (MAE).


In all the formulas presented above, oi, pirepresent the observed and estimated values, nis the number of records, and o¯,p¯indicate the means of the observed and predicted values, respectively.

Here we included R and R2, two standard regression criteria, in the group of dimensionless statistics.

Finally, we present just one example of the graphical technique, mainly to show how a training and evaluation process is executed with a ML algorithm (Figure 8).

Figure 8.

Generic example of time series forecasting using two different ML methods. The green dotted line indicates a “bad” forecast method. The red dashed line indicates an appropriate method for the data, that is, the curve is closer to the observed time series. Both methods were trained using 80% of the data and tested on the remaining 20%.

4.1 Case studies

Some inconsistencies in the observations and the duration of satellite records introduce difficulties and uncertainties when applying forecast methods. At least 30 years of data record are required to SPI forecast; therefore, some of the examples we present here are based exclusively on ground gauge data. This situation is very close to reverting since satellite observations are reaching the minimum number of years required and the data are calibrated with ground observations (Table 1).


Table 1.

Coefficient of determination (R2) of 10 ML methods to predict 3, 12, and 24 months SPI. Extracted from [71].

Abbreviations: SVR, support vector regression; BSVR, bootstrap SVR ensemble; BS-SVR, boosting-SVR; ANN, artificial neural networks; BANN, bootstrap ANN ensemble; BS-ANN, boosting-ANN; WBANN, wavelet coupled bootstrap ANN ensemble; WBS-ANN, wavelet boosting-ANN; WBSVR, wavelet coupled bootstrap SVR ensemble; WBS-SVR, wavelet boosting-SVR.

Shirmohammadi et al. [67] evaluated the performance of two ANN architectures (feedforward neural network and Elman or recurrent neural network), different kinds of ANFIS (four different membership functions: Gaussian, bell-shaped, triangular, and Piduetoits shape), WT-ANFIS, and WT-ANN. The wavelets families used here were db4, bior1.1, bior1.5, rboi1.1, rboi1.5, coif2, and coif4.

Training data came from 1952 to 1992 rain records from East Azerbaijan province (Iran). More than 1000 model structures were tested to predict SPI6 for 1, 2, and 3 months’ lead-time over the test period covering from 1992 to 2011. R2, NSE, and RMSE evaluated the performance of the models.

ANFIS models provided more accurate predictions than ANN models, and the inclusion of WT could improve meteorological drought modeling: WT-ANFIS (best RMSE = 0.097), WT-ANN (best RMSE = 0.227), ANFIS (best RMSE = 0.089), and ANN (best RMSE = 1.81).

Belayneh et al. [68] used precipitation records (1970 to 2005) to generate SPI3 and SPI6 time series from 12 stations in the Awash River Basin of Ethiopia (that is, 12 x 2 independent time series). The forecast was performed with ANN (RMSNN trained with the Levenberg-Marquardt back propagation), SVR, and the coupled models: WA-ANN and WA-SVR. About 80% of the data was used for training, 10% for validation, and 10% for testing, and ARIMA forecasting was used as a benchmark [69]. Regarding wavelet decomposition, each time series was decomposed between one and nine levels, and the appropriate level was selected by comparing results among all decomposition levels. The results of all the methods were compared by RMSE, MAE, and R2. Overall, the WA-ANN and WA-SVR models were effective in forecasting SPI3 although most WA-ANN models had more accurate estimates (1- or 3-month lead). The WA-ANN model seemed to be more effective in anticipating extreme SPI values (severe drought or heavy precipitation), whereas WA-SVR closely reflected the observed SPI trends but underestimated the extreme events.

For SPI3 (1 month lead time) forecast, the best results in terms of RMSE (0.407) and MAE (0.391) were obtained by the WA-ANN model at the Ziquala station, whereas in terms of R2 (0.881), the Ginchi station had the best WA-ANN model.

When the lead-time was raised to 3 months, WA-ANN remained the best model. One station (Bantu Liben) had the model with the lowest RMSE and MAE values (0.510 and 0.4941), whereas a second station (Sebeta) had the best results in terms of R2 (0.7304).

Regarding SPI6 forecasts, the WA-ANN and WA-SVR models provided the best SPI6 forecasts. Neither method was meaningfully better than the other. The predictions for SPI6 were significantly better than SPI3 predictions according to three performance measures. As the forecast lead time increased, the forecast accuracy of all the models declined. This drop was most evident in the ARIMA, ANN, and SVR models.

These results were similar to [70]. Authors used precipitation records (1970–2005) from 20 stations in the same basin of Ethiopia (three different sub-basins) to generate SPI3 and SPI12 series. ANN, SVR, and WA-ANN were evaluated for 1 and 6 months lead time prediction. The comparison was made using RMSE, MAE, and R2. Forecasting of SPI 12, for all the models, had better performance results than predicting SPI 3, regardless of the lead time (best R2 = 0.953, WA-ANN). The performance of all the models declined when the lead time increased.

Belayneh et al. [71] modeled ANN and SVR as in [68] to forecast SPI 3, SPI12, and SPI24, but they included bootstrap (BANN and BSVR), boosting (BS-ANN, BS-SVR), wavelet coupled bootstrap ensemble (WBANN and WBSVR), and wavelet coupled boosting (WBS-ANN and WBS-SVR) in the analysis.

In general, the performances of SVR and ANN were comparable, although ANN performance was slightly higher. The inclusion of wavelets improved both techniques (wavelet decomposition denoises the time series). All models were more effective at forecasting SPI12 and SPI24 than SPI3 (Table 1). All boosting ensemble models were developed in MATLAB (“fitensemble” function).

The WBS-ANN and WBS-SVR models provided better prediction results than all the other types of models evaluated.

Ali, Deo, et al. [43] evaluated the performance of three models (ANFIS, M5Tree, and MPMR) to forecast SPI3, SPI6, and SPI12 calculated from a 35-year rainfall data set (1981–2015) from three (3) stations in Pakistan. SPI data were partitioned into 70% (training) and 30% (testing) periods. M5Tree is a kind of decision tree with linear regression functions on the leaves [72], whereas MPMR stands for minimax probability machine regression [73] and was also applied to benchmark the ensemble-ANFIS model. Regarding SPI3 forecast, ANFIS (R = 0.889 to 0.946) outperformed MPMR (R = 0.843 to 0.935) and M5Tree (R = 0.831 to 0.916). Similarly, SPI6 ANFIS (R = 0.968 to 0.974) outperformed M5Tree (R = 0.950 to 0.967) and MPMR (R = 0.952 to 0.970). For SPI12, ANFIS (R = 0.987 to 0.993) overcome M5Tree (R = 0.950 to 0.967) and MPMR (R = 0.984 to 0.986). The other statistics (e.g., RMSE, WI) corroborated the superior performance of ANFIS. Just as important, the ensemble-ANFIS model achieved the highest accuracy at the three stations when predicting moderate, severe, and extreme droughts.

Khosravi et al. [74] used rainfall data from the Tropical Rainfall Measuring Mission (TRMM) during 2000–2014 in the eastern district of Isfahan to generate 12-month SPI. The first 85% of the data was used to train a single-hidden layer feedforward ANN, an SVR with RBF kernel, an LS-SVR with RBF kernel, and an ANFIS method. Optimum values of SVR and LS-SVR were obtained by a grid search within the range of [10−3, 10+3] and [2−3, 2+3] for C and γ (SVR) or (10, 100 and 1000) for g and (1, 0.5 and 1) for γ (LS-SVR).

For SPI12, SVR achieved the highest accuracy (RMSE = 0.21), followed by LS-SVR (RMSE = 0.38), ANN (RMSE = 1.24), and ANFIS (RMSE = 1.36). The best ANN model consisted of three layers (input, hidden, and output) with 30, 8, and 1 neuron, respectively.

Chen et al. [75] evaluated RF and ARIMA to forecast SPI3 (short-term drought) with a 1-month lead time and SPI12 (long-term drought). Both models were developed based on data from 1966 to 1995 (four stations in China), and predictions (1 month or 6 months ahead) were made from 1996 to 2004. Overall, RF performed consistently better than ARIMA. Results also suggested that RF is more robust in predicting dry events. Finally, ARIMA lost the capacity to predict SPI12, whereas the accuracy of RF was less affected by the longer lead time.

Agana and Homaifar [76] developed a hybrid model using a denoised empirical mode decomposition [77] and DBN. The proposed method was applied to predict a standardized streamflow index (SSI) across the Colorado River basin (ten stations). The new model was compared with MLP and SVR in predicting SSI12 (1, 6, and 12 months lead time). DBN, SVR, and their hybrid versions displayed rather similar prediction errors. However, DBN and EMD-DBN outperformed all other models for two-step predictions at almost all stations. As in wavelets, the empirical mode decomposition significantly improves the quality of prediction.

Finally, we want to mention two examples where ML was directly applied to rainfall prediction.

El Shafie et al. [77] evaluated a radial basis function neural network (RBFNN) to forecast rainfall in Alexandria City, Egypt. The model was trained using rainfall data from 1960 to 2001 (four stations) and tested with data from 2002 to 2009 to predict yearly and monthly (January and December) precipitation. Regarding yearly model efficiency, R2 = 0.94 for RBFNN, whereas the control (a multiple linear regression MR model) only reached R2 = 0.21. Regarding monthly precipitation, RBFNN was very successful (R2 = 0.899 for January and R2 = 0.997 for December) as compared to the control (R2 = 0.997 and 0.34, respectively).

Sumi et al. [78] compared ANNs, multivariate adaptive regression splines or MARS [79], k-nearest neighbor [80], and SVR with RBF kernel to predict daily and monthly rainfall in Fukuoka, Japan. A preprocessed training set (1975–2004) was used to train the algorithms with extensive parameter optimization, whereas the test set covered from 2005 to 2009. For monthly rainfall, SVR produced the most accurate forecast (lowest RMSE) and the best rainfall mapping (R2 = 0.93), whereas for the daily rainfall series, the MARS method produced the best R2 value (0.99). All the metrics were calculated based on single-step ahead forecasting.

5. Satellite precipitation products (SPPs)

There is no satellite that can reliably quantify rainfall under all circumstances. However, ground observations, although reliable and with long-term records, do not provide a consistent spatial representation of precipitation, particularly on certain world regions. Therefore, satellite data become necessary, as they provide more homogeneous data quality compared to ground observations [81, 82]. To our knowledge, merged satellite-gauge products are becoming indispensable.

Precipitation data sets may be classified into one of four categories: gauge data sets (e.g., CRU TS [83], APHRODITE [84]), satellite-exclusive (e.g., CHOMPS [85]), merged satellite-gauge products (e.g., GPCP [86], TRMM3B42), and reanalysis (e.g., NCEP1/NCEP2 [87], ERA-Interim [88]). Reanalysis implies integrating irregular observations with models encompassing physical and dynamic processes in order to generate an estimate of the state of the system across a uniform grid and with temporal continuity [89].

Many studies show that satellite precipitation algorithms show different biases, detection probabilities, and missing rainfall ratios in summer and winter. Sources of error include the satellite sensor itself, the retrieval error [90], and spatial and temporal sampling [91, 92].

Algorithms that estimate rainfall from satellite observations are based on either thermal infrared (TIR) bands (inferring cloud-top temperature), passive microwave sensors (PMW), or active microwave sensors (AMW). The TIR-based approach takes into account cold cloud duration or CCD, that is, the time that a cloud has a temperature below the threshold at a given pixel [93]. The PMW-based approach takes advantage of the fact that microwaves can penetrate clouds to explore their internal properties through the interaction of raindrops [94]. AMW is what usually known as precipitation radar [95].

There is a plethora of validation studies of satellite-based rainfall estimates (SREs). Normally, these SREs are compared against ground rainfall estimates [91, 96].

Sun et al. [97] reviewed 30 currently available global precipitation (gauge-based, satellite-related, or reanalysis) data sets. The degree of variability of the precipitation estimates varies by region. Large differences in annual and seasonal estimates were found in tropical oceans, complex mountain areas, Northern Africa, and some high-latitude regions. Systematic errors are the main sources of errors over large parts of Africa, northern South America, and Greenland. Random errors are the dominant kinds of error in large regions of global land, especially at high latitudes. Regarding satellite assessments, PERSIANN-CCS has larger systematic errors than CMORPH, TRMM 3B42, and PERSIANN-CDR. The spatial distribution of systematic errors is similar for all reanalysis products [97].

Table 2 presents a comparison of several representative satellite rainfall products. More information regarding these and other products can be found in [97, 98].

ProductSpatial coverageTemporal coverageInputsAccess
CHIRPSv2.0 Funk et al. [99]0.05° × 0.05°
Daily, pentadal and monthly
1981 to near present
Nguyen et al. [100]
0.25° × 0.25°
1,3,6 hours
March 2000 to present
(GOES-8, GOES-10, GMS-5, Metsat-6, and Metsat-7) corrected with MW(DMSP 7, 8, and 9 and NOAA-15, 16, and 17)
Ashouri et al. [101]
0.25° × 0.25°
Daily, monthly
1983 to 2017
+GPCP correction
Joyce et al. [102]
0.25° × 0.25°
30 min
2002 to present
SSM/I (DMSP 13, 14, and 15) AMSU-B (NOAA-15, 16, 17, and 18) AMSU-E (Aqua), TMI (TRMM)
Geostationary satellite IR
Xie and Arkin [103]
0.1° × 0.1°
2001 to present
Huffman et al. [104]
0.25° × 0.25°
3 hourly/daily
1998 to 2015.

Table 2.

Representative satellite rainfall products.

Abbreviations: (IR) infrared satellite imagery, (MW) microwave estimates, (GG) ground gauges, (AMSU) Advanced Microwave Sounding Unit, (AMSU-B) Advanced Microwave Sounding Unit-B, (SSM/I) Special Sensor Microwave/Imager; (AMSR-E) Advanced Microwave Scanning Radiometer for the Earth Observing System, (MHS) Microwave Humidity Sounder, (GPCP) Global Precipitation Climatology Centre, (GOES) Geostationary Operational Environmental Satellite, (Metsat) meteorological satellite, (NOAA) NASA-provided TIROS series of weather forecasting satellite run by the National Oceanic and Atmospheric Administration, (DMSP) Defense Meteorological Satellite Program, (GRIDSAT-B1) geostationary IR channel brightness temperature.


6. Accessing and processing the data

The capacity to acquire information from remote sensing data has been improved to an unprecedented level, accumulating overwhelming amounts of information. For example, the Google Earth Engine (GEE) [105] is updated at a rate of nearly 6000 scenes per day from active missions (a typical image 10 km by 10 km requires 50–200 million bytes of memory). Such a large amount of data requires not only vast amounts of memory data but also higher-level services with high-performance computing systems [106]. Successful experiences have already been recorded [97], but the GEE is worth mentioning [105]. GGE stores a multi-petabyte catalog of satellite imagery and geospatial data sets collected from different resources and provides high-performance computing systems that can be accessed and controlled through an Internet-accessible application programming interface (API) and an associated web-based interactive development environment (IDE). It also possess a library with more than 800 functions, ranging from simple mathematical functions to powerful geostatistical, machine learning, and image processing operations [105].

In many situations, intense computing resources are required for image processing operations [105], but more friendly solutions can be suggested to those who do have not the necessary skills.

As mentioned before, many satellite precipitation products are freely available (Table 1). Most of them are in network Common Data Form (netCDF) format [95]. R users can access this format using the “ncdf4” [96] or “raster” [97] packages. These data were already processed and can be used to forecast and perform complementary analyses [98]. We have already mentioned the SPEI [59] or the SPI [60] packages used to generate, for example, the SPI index.

Regarding the ML methods discussed here, almost all of them are available in packages deposited at the CRAN or CRAN-like repositories, for example, “Random Forest” package [43], “rminer” [99] that implements ANN, SVR and boosting [99], etc. A full list of packages implementing ML algorithms is available at

Finally, also available at the repositories are plenty of packages that are really helpful for visualizing and interpreting the results [107, 108].

7. Conclusion

Climate change is shifting global rainfall patterns and will increase the intensity and duration of drought around the world; this produces the need to take contingency actions to prevent the impact of famine. ML models, an evolving research area, are a valuable complement to methods previously proposed for forecasting drought. Results obtained so far for predicting meteorological indices are very satisfactory, especially with hybrid models such as WT-ANN or WT-SVR.

Most of the work that we reported here is based on the standardized precipitation index or SPI, which is a reliable measure of drought used in more than 60 countries. The leading month or the number of months over which SPI is calculated significantly influences the prediction values.

Unfortunately, many of the examples were based on ground gauge data. The brevity (and noise) of the records obstructs the use of many satellite products. However, as time progresses and data retrieval improves, satellite products will be long and accurate enough to generate reliable results.

The exponential growth of public and free satellite imagery sources and of open-source software, as well as cheaper access to cloud-based technology, will provide powerful forecasting tools to a greater number of researchers, allowing them to forecast drought before it occurs.

Conflict of interest

The authors declare that there is no conflict of interest regarding the publication of this article.

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution 3.0 License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Leonardo Ornella, Gideon Kruseman and Jose Crossa (June 6th 2019). Satellite Data and Supervised Learning to Prevent Impact of Drought on Crop Production: Meteorological Drought, Drought - Detection and Solutions, Gabrijel Ondrasek, IntechOpen, DOI: 10.5772/intechopen.85471. Available from:

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