Abstract
Plastic mulching on farmland has been increasing worldwide for decades due to its superior advantages for improving crop yields. Monitoring Plastic-Mulched Land-cover (PML) can provide essential information for making agricultural management decisions and reducing PML’s eco-environmental impacts. However, mapping PML with remote sensing data is still challenging and problematic due to its complicated and mixed characteristics. In this study, a new Object-Based Image Analysis (OBIA) approach has been proposed to investigate the potential for combined use of Sentinel-1 (S1) SAR and Sentinel-2 (S2) Multi-spectral data to extract PML. Based on the ESP2 tool (Estimation of Scale Parameter 2) and ED2 index (Euclidean Distance 2), the optimal Multi-Resolution Segmentation (MRS) result is chosen as the basis of following object-based classification. Spectral and backscattering features, index features and texture features from S1 and S2 are adopted in classifying PML and other land-cover types. Three machine-learning classifiers known as the—Classification and Regression Tree (CART), the Random Forest (RF) and the Support Vector Machine (SVM) are carried out and compared in this study. The best classification result with an overall accuracy of 94.34% is achieved by using spectral, backscattering, index and textural information from integrated S1 and S2 data with the SVM classifier. Texture information is demonstrated to contribute positively to PML classifications with SVM and RF classifiers. PML mapping using SAR information alone has been greatly improved by the object-based approach to an overall accuracy of 87.72%. By adding SAR data into optical data, the accuracy of object-based PML classifications has also been improved by 1–3%.
Highlights
In the past decades, plasticulture has been widely utilized in agriculture around the world due to its notable advantages for improving crop yields by shielding crops from adverse conditions, such as coldness, heat, strong rainfall, wind, drought, harmful insects, and crop diseases, conserving water resources, and increasing the soil temperature [1]
It was important to highlight that the Object-Based Image Analysis (OBIA) approach we proposed could improve the classification performance greatly in Synthetic Aperture Radar (SAR) images alone with the best overall accuracy of 87.72%
The first stage of the OBIA was to obtain an optimal segmentation for plastic-mulched landcover (PML) extraction by Multi-Resolution Segmentation (MRS) based on the ESP2 tool by, taking both optical and SAR data into consideration
Summary
Plasticulture has been widely utilized in agriculture around the world due to its notable advantages for improving crop yields by shielding crops from adverse conditions, such as coldness, heat, strong rainfall, wind, drought, harmful insects, and crop diseases, conserving water resources, and increasing the soil temperature [1]. Plasticulture has some negative impacts on local or regional eco-environment because it reduces the biodiversity by changing the pollination of plants and deteriorates the soil structure by leaving plastic film residue [2,3]. Monitoring and mapping plastic-mulched landcover (PML) which is one of the three dominant types of plasticultrue [3] and covers 95% area of overall plasticulture farmland in China [4] can provide prerequisites for improving agricultural management and lessening PML’s eco-environmental impacts. The conventional methods for PML mapping such as field-surveying and photogrammetric measurement are time-consuming, expensive and labor-intensive. In this context, remote sensing techniques are the only promising approaches to acquiring PML information for a large geographic area regardless of time and locations. Mapping PML automatically with remote sensing data is far from being solved due to PML’s special and mixed characteristics
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