## 4 Discussion  
The inadequate efficacy of remote sensing indices in forecasting the green water footprint can be attributed to the reliance on climatic data for the computation of green water footprints, which principally involves the estimation of evapotranspiration during the vegetative period, alongside another climatic factor, namely effective rainfall in conjunction with agricultural productivity. Consequently, models incorporating climatic data demonstrate a robust and efficacious capacity in predicting the green water footprints associated with sugarcane. Conversely, remote sensing indices exhibited a lack of correlation with methodologies employed for the estimation of water footprints. The diminished performance of Sc3, representing remote sensing indices, may be improved by augmenting predictive accuracy through advanced remote sensing techniques. This enhancement can be realized firstly by utilizing higher-resolution remote sensing  
Figure 15: Correlation matrix between effective precipitation, maximum temperature, minimum temperature, relative humidity, solar radiation, wind speed, crop evapotranspiration, yield, sown area, EVI, NDVI, SAVI, NDWI, and green water footprint. datasets, such as Sentinel 1 and 2, which are anticipated to better capture the nuanced variability of fields in contrast to lower-resolution datasets. Secondly, an increase in temporal resolution may elevate the frequency of data acquisition, thus facilitating more accurate monitoring of phenological changes, particularly during critical developmental stages of wheat. Finally, the incorporation of additional remote sensing indices, such as the Normalized Difference Temperature Index (NDTI), could be advantageous in clarifying plant stress, whereas Gross Primary Productivity (GPP) provides a direct link to crop water consumption, and Soil Moisture along with Land Surface Temperature (LST) aids in comprehending the hydrological processes relevant to sugarcane agriculture. [PERSON], [PERSON] [62] predicted the green and blue water footprints (GWFPs and BWFPs) of cassava in Nanning, Guangxi, China, using an Artificial Intelligence-Seasonal ARIMA (AI-SARIMA) integrated model. In addition to the SARIMA model for time series forecasting, they used three supervised learning algorithms: random forests (RFs), support vector machines (SVMs), and artificial neural networks (ANNs). To simulate WFs under various climate scenarios, meteorological data from 1994 to 2019--including Tmin, Tmax, P, SH, WS, and H--were employed. According to the results, the optimum ANN for estimating BWFPs was an ANN with hidden layers (8, 6) and input variables Tmax, Tmin, P, Kc, SH, and H; for estimating GWFP, an ANN with hidden layers (7, 5) and input variables Tmax, Tmin, P, WS, and SH was ideal. These models had coefficients of determination that were almost one and exceptional accuracy. Our results are in line with those of [PERSON], [PERSON] [62], where the research results revealed that the single model followed by hybrid models obtained the highest R\({}^{2}\) where the SVR and Sc2 had a coefficient of determination of 0.9846 followed by the XGB-SVR hybrid model and Sc2 with R\({}^{2}\) value of 0.9809, while the lowest R\({}^{2}\) value was detected under XGB and Sc3 (remote sensing indices) at 0.2322. In the same context, [PERSON], [PERSON] [63] investigated lettuce yield (fresh weight) prediction using four machine learning models, namely, support vector regressor (SVR), extreme gradient boosting (XGB), random forest (RF), and deep neural network (DNN). Three scenarios consisting of the combinations of input variables (i.e., leaf number, water consumption, dry weight, stem length, and stem diameter) were assessed.