Land parcel-based digital soil mapping of soil nutrient properties in an alluvial-diluvia plain agricultural area in China

Dong, Wen; Wu, Tianjun; Luo, Jiancheng; Sun, Yingwei; Xia, Liegang

doi:10.1016/j.geoderma.2019.01.018

Cited by 66 publications

(22 citation statements)

References 52 publications

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“…The basis of this study is the geo-parcel [34,35,36], which is defined as the smallest visually perceivable spatial entity in geography. The features of geo-parcels can be represented by the spatial form and spectral information.…”

Section: Methodsmentioning

confidence: 99%

Synchronous Response Analysis of Features for Remote Sensing Crop Classification Based on Optical and SAR Time-Series Data

Sun

Luo

et al. 2019

Sensors

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Accurate crop classification is the basis of agricultural research, and remote sensing is the only effective measuring technique to classify crops over large areas. Optical remote sensing is effective in regions with good illumination; however, it usually fails to meet requirements for highly accurate crop classification in cloud-covered areas and rainy regions. Synthetic aperture radar (SAR) can achieve active data acquisition by transmitting signals; thus, it has strong resistance to cloud and rain interference. In this study, we designed an improved crop planting structure mapping framework for cloudy and rainy regions by combining optical data and SAR data, and we revealed the synchronous-response relationship of these two data types. First, we extracted geo-parcels from optical images with high spatial resolution. Second, we built a recurrent neural network (RNN)-based classifier suitable for remote sensing images on the geo-parcel scale. Third, we classified crops based on the two datasets and established the network. Fourth, we analyzed the synchronous response relationships of crops based on the results of the two classification schemes. This work is the basis for the application of remote sensing data for the fine mapping and growth monitoring of crop planting structures in cloudy and rainy areas in the future.

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Section: Methodsmentioning

confidence: 99%

Synchronous Response Analysis of Features for Remote Sensing Crop Classification Based on Optical and SAR Time-Series Data

Sun

Luo

et al. 2019

Sensors

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“…Chen et al [40] used MODIS data (500 m) to predict soil organic matter with an accuracy of 0.61. Xu et al [24] used Landsat data (30 m) to predict soil total nitrogen and available potassium with an accuracy of 0.65 and 0.50. Zhang et al [38] used Sentinel-2A data (10 m) to predict soil organic matter with an accuracy of 0.8.…”

Section: Advantages and Limitations Of The Proposed Approachmentioning

confidence: 99%

“…Numerous algorithms have been applied to predict the spatial distribution of STN, including random forest (RF) [18], multiple linear regression, Cubist [19], partial least squares regression (PLSR) [20], least squares support vector machines (LS-SVM) [14]. The RF approach is widely used to map soil types [21], soil moisture [22,23], soil potassium [24], soil pH [18], soil phosphorus [25], soil texture [26], and soil pollutants [27]. The RF algorithm shows higher accuracy than traditional tree models due to the robust performance by tree integration.…”

Section: Introductionmentioning

confidence: 99%

Multi-Temporal Mapping of Soil Total Nitrogen Using Google Earth Engine across the Shandong Province of China

Xiao

Chen

et al. 2020

Sustainability

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Nitrogen plays an important role in improving soil productivity and maintaining ecosystem stability. Mapping and monitoring the soil total nitrogen (STN) content is the basis for modern soil management. The Google Earth Engine (GEE) platform covers a wide range of available satellite remote sensing datasets and can process massive data calculations. We collected 6823 soil samples in Shandong Province, China. The random forest (RF) algorithm predicted the STN content in croplands from 2002 to 2016 in Shandong Province, China on the GEE platform. Our results showed that RF had the coefficient of determination (R2) (0.57), which can predict the spatial distribution of the STN and analyze the trend of STN changes. The remote sensing spectral reflectance is more important in model building according to the variable importance analysis. From 2002 to 2016, the STN content of cropland in the province had an upward trend of 35.6%, which increased before 2010 and then decreased slightly. The GEE platform provides an opportunity to map dynamic changes of the STN content effectively, which can be used to evaluate soil properties in the future long-term agricultural management.

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“…Recently, supervised segmentation methods using CNN have been proven feasible for extracting the edge information of objects via self-learning features from images [36]. Hence, in [37], we proposed a CNN-based method for extracting geo-objects from GF-2 fusion images, which is designed as follows (see Figure 4). First, the polygons of roads and rivers in a historical LC map are used to zone the target HSR image into several sub-regions for subsequent processing.…”

Section: Geo-object Extractionmentioning

confidence: 99%

Geo-Object-Based Land Cover Map Update for High-Spatial-Resolution Remote Sensing Images via Change Detection and Label Transfer

Luo

Yinqing

et al. 2020

Remote Sensing

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Land cover (LC) information plays an important role in different geoscience applications such as land resources and ecological environment monitoring. Enhancing the automation degree of LC classification and updating at a fine scale by remote sensing has become a key problem, as the capability of remote sensing data acquisition is constantly being improved in terms of spatial and temporal resolution. However, the present methods of generating LC information are relatively inefficient, in terms of manually selecting training samples among multitemporal observations, which is becoming the bottleneck of application-oriented LC mapping. Thus, the objectives of this study are to speed up the efficiency of LC information acquisition and update. This study proposes a rapid LC map updating approach at a geo-object scale for high-spatial-resolution (HSR) remote sensing. The challenge is to develop methodologies for quickly sampling. Hence, the core step of our proposed methodology is an automatic method of collecting samples from historical LC maps through combining change detection and label transfer. A data set with Chinese Gaofen-2 (GF-2) HSR satellite images is utilized to evaluate the effectiveness of our method for multitemporal updating of LC maps. Prior labels in a historical LC map are certified to be effective in a LC updating task, which contributes to improve the effectiveness of the LC map update by automatically generating a number of training samples for supervised classification. The experimental outcomes demonstrate that the proposed method enhances the automation degree of LC map updating and allows for geo-object-based up-to-date LC mapping with high accuracy. The results indicate that the proposed method boosts the ability of automatic update of LC map, and greatly reduces the complexity of visual sample acquisition. Furthermore, the accuracy of LC type and the fineness of polygon boundaries in the updated LC maps effectively reflect the characteristics of geo-object changes on the ground surface, which makes the proposed method suitable for many applications requiring refined LC maps.

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Land parcel-based digital soil mapping of soil nutrient properties in an alluvial-diluvia plain agricultural area in China

Cited by 66 publications

References 52 publications

Synchronous Response Analysis of Features for Remote Sensing Crop Classification Based on Optical and SAR Time-Series Data

Synchronous Response Analysis of Features for Remote Sensing Crop Classification Based on Optical and SAR Time-Series Data

Multi-Temporal Mapping of Soil Total Nitrogen Using Google Earth Engine across the Shandong Province of China

Geo-Object-Based Land Cover Map Update for High-Spatial-Resolution Remote Sensing Images via Change Detection and Label Transfer

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