An Innovative Fusion-Based Scenario for Improving Land Crop Mapping Accuracy.

The accuracy of land crop maps obtained from satellite images depends on the type of feature selection algorithm and classifier. Each of these algorithms have different efficiency in different conditions; therefore, developing a suitable strategy for combining the capabilities of different algorithms in preparing a land crop map with higher accuracy can be very useful. The objective of this study was to develop a fusion-based framework for improving land crop mapping accuracy. First, the features were retrieved using the Sentinel 1, Sentinel 2, and Landsat-8 imagery. Then, training data and various feature selection algorithms including recursive feature elimination (RFE), random forest (RF), and Boruta were used for optimal feature selection. Various classifiers, including artificial neural network (ANN), support vector machine (SVM), and RF, were implemented to create maps of land crops relying on optimal features and training data. After that, in order to increase the result accuracy, maps of land crops derived from several scenarios were fused using a fusion-based voting strategy at the level of decision, and new maps of land crops and classification uncertainty maps were prepared. Subsequently, the performance of different scenarios was evaluated and compared. Among the feature selection algorithms, RF accuracy was higher than RFE and Boruta. Moreover, the efficiency of RF was higher than SVM and ANN. The overall accuracy of the voting scenario was higher than all other scenarios. The finding of this research demonstrated that combining the features' capabilities extracted from sensors in different spectral ranges, different feature selection algorithms, and classifiers improved the land crop classification accuracy.

Surface biophysical features fusion in remote sensing for improving land crop/cover classification accuracy.

Preparing up-to-date land crop/cover maps is important to study in order to achieve food security. Therefore, the aim of this study was to evaluate the impact of surface biophysical features in the land crop/cover classification accuracy and introduce a new fusion-based method with more accurate results for land crop/cover classification. For this purpose, multi-temporal images from Sentinel 1 and 2, and an actual land crop map prepared by Agriculture and Agri-Food Canada (AAFC) in 2019 were used for 3 test sites in Ontario, Canada. Firstly, surface biophysical features maps were prepared based on spectral indices from Sentinel 2 including Normalized Difference Vegetation Index (NDVI), Index-based Built-up Index (IBI), Wetness, Albedo, and Brightness and co-polarization (VV) and cross-polarization (VH) from Sentinel 1 for different dates. Then, different scenarios were generated; these included single surface biophysical features as well as a combination of several surface biophysical features. Secondly, land crop/cover maps were prepared for each scenario based on the Random Forest (RF). In the third step, based on the voting strategy, classification maps from different scenarios were combined. Finally, the accuracy of the land crop/cover maps obtained from each of the scenario was evaluated. The results showed that the average overall accuracy of land crop/cover maps obtained from individual scenario (one feature) including NDVI, IBI, Wetness, Albedo, Brightness, VV and VH were 66%, 68%, 63%, 60%, 57%, 62% and 58%, respectively, which by the surface biophysical features fusion, the overall accuracy of land crop/cover maps increased to 83%. Also, by combining the classification results obtained from different scenarios based on voting strategy, the overall accuracy increased to 89%. The results of this study indicate that the feature level-based fusion of surface biophysical features and decision level based fusion of land crop/cover maps obtained from various scenarios increases the accuracy of land crop/cover classification.

Comparison of spectral and spatial-based approaches for mapping the local variation of soil moisture in a semi-arid mountainous area.

Accurate information on soil moisture (SM) is critical in various applications including agriculture, climate, hydrology, soil and drought. In this paper, various predictive relationships including regression (Multiple Linear Regression, MLR), machine learning (Random Forest, RF; Triangular regression, Tr) and spatial modeling (Inverse Distance Weighing, IDW and Ordinary kriging, OK) approaches were compared to estimate SM in a semi-arid mountainous watershed. In developing predictive relationship, Remote Sensing datasets including Landsat 8 satellite imagery derived surface biophysical characteristic, ASTER digital elevation model (DEM) derived surface topographical characteristic, climatic data recorded at the synoptic station and in situ SM data measured at Landsat 8 overpass time were utilized, while in spatial modeling, point-based SM measurements were interpolated. While 70%(calibration set) of the measured SM data were used for modeling, 30%(validation set) were used to evaluate modeling accuracy. Finally, the SM uncertainty maps were created for different models based on a bootstrapping approach. Among the environmental parameter sets, land surface temperature (LST) showed the highest impact on the spatial distribution of SM in the region at all dates. Mean R2(RMSE) between measured and modeled SM on three dates obtained from the MLR, RF, IDW, OK, and Tr models were 0.70(1.97%), 0.72(1.92%), 0.59(2.38%), 0.59(2.27%) and 0.71(1.99%), respectively. The results showed that RF and IDW produced the highest and lowest performance in SM modeling, respectively. Generally, the performance of RS-based models was higher than interpolation models for estimating SM due to the influence from combination of topographic parameters and surface biophysical characteristics. Modeled SM uncertainty with different models varies in the study area. The highest uncertainty in SM modeling was observed at the north part of the study area where the surface heterogeneity is high. Using RS data increased the accuracy of SM modeling because they can capture the surface biophysical characteristics and topographical properties heterogeneity.

Improved digital soil mapping with multitemporal remotely sensed satellite data fusion: A case study in Iran.

Modeling and mapping of soil properties are critical in many environmental, climatic, ecological and hydrological applications. Digital soil mapping (DSM) techniques are now commonly applied to predict soil properties with limited data by developing predictive relationships with environmental covariates. Most studies derive covariates from a digital elevation model (named static covariates). Many works also include single-day remotely sensed satellite imagery. However, multitemporal satellite images can capture information about soil properties over time and bring additional information in predicting soil properties in DSM. We refer to covariates derived from multitemporal satellite images as dynamic covariates. The objective of this study was to assess the performance of DSM when using terrain derivatives (static covariates), single-date remotely sensed satellite indices (limited dynamic covariates), multitemporal satellite indices (dynamic covariates), and combinations of terrain derivatives and satellite indices (covariate fusion) as covariates in predicting soil properties and estimating uncertainty. Three soil properties are considered in this study: organic carbon (OC), sand content, and calcium carbonate equivalent (CCE). Inclusion of single and/or multitemporal remotely sensed satellite indices improved the prediction of soil properties over traditionally used terrain indices. Significant improvements were observed in the prediction of soil properties using two models, Cubist and random forest (RF). The increase in the R2 values for Cubist and RF were 126% and 78% for OC, 110% and 54% for sand, and 87% and 32% for CCE. The RMSE decreased by 34% and 27% for OC, 25% and 12% for sand, and 39% and 19% for CCE, when compared to the terrain indices only model. This also reduced the uncertainty of estimation and mapping. These clearly showed the advantage of using multitemporal satellite data fusion rather than simply using static terrain indices for DSM of soil properties to deliver a great potential in improving soil modeling and mapping for many applications.