Citation: Fathololoumi, S.; Karimi Firozjaei, M.; Biswas, A. Innovative Fusion-Based Strategy for Crop Residue Modeling. Land 2022, 11, 1638. https://doi.org/10.3390/ land11101638 Academic Editors: Carmine Serio, Guido Masiello and Sara Venafra Received: 22 August 2022 Accepted: 20 September 2022 Published: 23 September 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). land Article Innovative Fusion-Based Strategy for Crop Residue Modeling Solmaz Fathololoumi 1 , Mohammad Karimi Firozjaei 2 and Asim Biswas 1, * 1 School of Environmental Sciences, University of Guelph, Guelph, ON N1G 2W1, Canada 2 Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran 1417853933, Iran * Correspondence: biswas@uoguelph.ca; Tel.: +1-519-731-6252 Abstract: The purpose of this study was to present a new strategy based on fusion at the decision level for modeling the crop residue. To this end, a set of satellite imagery and field data, including the Residue Cover Fraction (RCF) of corn, wheat and soybean was used. Firstly, the efficiency of Random Forest Regression (RFR), Support Vector Regression (SVR), Artificial Neural Networks (ANN) and Partial-Least-Squares Regression (PLSR) in RCF modeling was evaluated. Furthermore, to increase the accuracy of RCF modeling, different algorithms results were combined based on their modeling error, which is called the decision-based fusion strategy. The R2 (RMSE) between the actual and modeled RCF based on ANN, RFR, SVR and PLSR algorithms for corn were 0.83 (3.89), 0.86 (3.25), 0.76 (4.56) and 0.75 (4.81%), respectively. These values were 0.81 (4.86), 0.85 (4.22), 0.78 (5.45) and 0.74 (6.20%) for wheat and 0.81 (3.96), 0.83 (3.38), 0.76 (5.01) and 0.72 (5.65%) for soybean, respectively. The error of corn, wheat and soybean RCF estimating decision-based fusion strategy was reduced by 0.90, 0.96 and 0.99%, respectively. The results showed that by implementing the decision-based fusion strategy, the accuracy of the RCF modeling was significantly improved. Keywords: crop residue; fusion; machine learning algorithm; reflective and radar bands 1. Introduction Modern agricultural activities, such as plowing and using heavy machinery known as tillage, can damage soil health [1,2]. In this case, the soil is more easily leaching by rain and loses its top layer, which is crucial for crop growth. The leached soil will flow downstream into the rivers and pollute the water due to elements such as phosphorus [3]. On the other hand, with decreasing soil quality, precipitated carbon is released [4]. The release of carbon from the soil plays an important role in increasing the carbon dioxide in the Earth’s atmosphere [5,6]. Gases are one of the parameters affecting climate change. Therefore, maintaining soil quality in the agricultural process is very important [7,8]. One of the possible, cheap and feasible ways to reduce the damage caused by wind and water erosion and increase water storage to soil productivity is to maintain the remaining vegetation on the soil surface of agricultural lands at harvest time [9–11]. Crop residues consist of various components of the crop, including leaves, seeds, stems, etc., after harvest on agricultural land [12,13]. The presence of these residues on the soil surface can strengthen soil organic matter, better the absorption of nutrients by the plant and increase the efficiency of chemical fertilizers. Crop residues also have a large effect on soil, crop and environmental factors, such as water permeability, evaporation, crop yield and erosion [7,14–16]. They can improve the physical, chemical and biological condition of the soil and ultimately lead to a healthier crop due to its desirable and nutritious composition. Preserving residues at the soil surface by preventing the emission of gases, such as NH 3 , CO 2 and SO 2 , can reduce air pollution, while burning plant residues emits these gases [17,18]. Due to the importance of preserving crop residues on the soil surface, modeling and mapping residues as an indicator of tillage intensity are of great importance in agricultural management and achieving sustainable agricultural goals, including maintaining environ- mental health [13,19]. Mapping crop residues for agricultural areas can be a criterion for Land 2022, 11, 1638. https://doi.org/10.3390/land11101638 https://www.mdpi.com/journal/land