**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 NH3, CO2 and SO2, 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 grea<sup>t</sup> importance in agricultural managemen<sup>t</sup> and achieving sustainable agricultural goals, including maintaining environmental health [13,19]. Mapping crop residues for agricultural areas can be a criterion for

**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 affiliations.

**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/).

evaluating the efficiency and quality of methods and tools used in harvesting. It is practically impossible to use traditional methods, such as field visits and sampling, to determine the amount of residues on a large scale and in a short time [12,20]. Utilizing the capabilities of remote sensing techniques and data can be useful in quantifying crop residues on a large spatial and temporal scales and higher accuracy [18,21]. Previous studies have used various satellite imagery to model the amount of residue cover fraction (RCF), including reflective multispectral imagery, such as Landsat; Sentinel 2 [16,22,23]; radar imagery, such as RADARSAT [13,24]; and reflective hyper Spectral imagery, such as Probe-1 [25]. Each of these types of images has advantages and disadvantages [13].

In previous studies, several remote sensing-based indices, such as Normalized Difference Senescent Vegetation Index (NDSVI) [26], Normalized Difference Residue Index (NDRI) [27], Normalized Difference Tillage Index (NDTI) [28], Shortwave Green Normalized Difference Index (SGNDI) [29], Shortwave Infrared Normalized Difference Residue Index (SINDRI) [30], Broadband spectral Angle Index (BAI) [8], Dead Fuel Index (DFI) [31], Normalized Difference Index (NDI) [32], Normalized Difference Vegetation Index (NDVI) [8], Simulated crop residue cover (MCRC) [29], Simple tillage index (STI) [28], Simulated cellulose absorption index (3BI1) [33], Simulated lignin Cellulose Absorption Index (3BI2) [33], Simulated NDRI (3BI3) [33] and Short-wave near-infrared Normalized Difference residue Index (SRNDI) [34], etc., have been proposed to identify and quantify the RCF [14,34]. In some studies, the efficiency of different spectral indices was compared [29,33]. The results showed that each of these indicators can have different performances, some of them are suitable for dry areas and some for wet areas. A number of indicators do not perform well in areas with high vegetation. Each of the developed indicators has advantages and disadvantages. Yue, Tian, Dong, Xu and Zhou [29] showed that single indices are not highly capable of modeling RCF in the complex surface conditions of agricultural areas. Hence, in some studies, multivariate regression based methods, such as Random Forest Regression (RFR), Support Vector Regression (SVR), Artificial Neural Networks (ANN), etc., were proposed to model RCF [33]. The researchers used experimental regression methods to examine the linear or nonlinear relationship between actual RCF and remote sensing-based indices related to RCF [7,14,33], the spectral angle [8] or spectral unmixing [35] used to estimate the amount of RCF. Raoufat, Dehghani, Abdolabbas, Kazemeini and Nazemossadat [9] utilized Landsat 8 and drone data for RCF mapping and found that Landsat 8 data was more accurate than drone data although the drone data had its own advantages. Yue and Tian [36] used the spectral and laboratory data for RCF mapping. They evaluated RS data and triangle technique using RFR method in their study. They concluded that their proposed method was very effective in the accurate modeling of RCF and decreased the negative effect of soil moisture on it. Wang et al. [37] used MODIS and ground data to quantify some crop-related indices in a large-scale area using ground data and building a linear regression relationship between them. They showed that their used method was successful in monitoring soil conditions, including soil erosion.

Although the research in RCF modeling is limited, summarizing previous studies show that several models have been developed over the years to estimate the RCF, each with its own advantages and disadvantages. Selecting the appropriate model to estimate the amount of RCF has a high impact on the modeling accuracy of this parameter. Therefore, providing an integrated model based on using the capabilities of different models and indicators in estimating RCF can be useful in improving the modeling accuracy of this parameter. Sentinel 1 satellite imagery is one of the well-known and widely used radar data in various agricultural applications. However, our knowledge shows that the capability of this radar image's bands in estimating the amount of RCF has not been evaluated. Therefore, evaluating the performance of the satellites and combining the capabilities of these bands with the indicators presented in previous studies to improve the accuracy of modeling RCF can be useful and crucial.

The purpose of this study was to present a new strategy based on fusion at the decision level for modeling the RCF. In this study, (1) the efficiency of spectral indices based on reflective multispectral images presented in previous studies in modeling RCF were compared. (2) The importance of using Sentinel 1 radar satellite imagery bands in improving the accuracy of RCF modeling was assessed. (3) The efficiency of RFR, SVR, ANN and Partial-Least-Squares Regression (PLSR) algorithms in modeling RCF was evaluated and compared. A new strategy was proposed to integrate the results obtained from different algorithms at the decision level to improve the accuracy of modeling RCF.
