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Article

Remote Sensing Prediction Model of Cultivated Land Soil Organic Matter Considering the Best Time Window

by
Yiang Wang
1,2,
Chong Luo
2,
Wenqi Zhang
1,*,
Xiangtian Meng
2,
Qiong Liu
3,
Xinle Zhang
4 and
Huanjun Liu
2,3
1
School of Economics and Management, Jilin Agricultural University, Changchun 130118, China
2
Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
3
School of Pubilc Adminstration and Law, Northeast Agricultural University, Harbin 150030, China
4
College of Information Technology, Jilin Agricultural University, Changchun 130118, China
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(1), 469; https://doi.org/10.3390/su15010469
Submission received: 26 November 2022 / Revised: 25 December 2022 / Accepted: 26 December 2022 / Published: 27 December 2022
Browse Figures
Versions Notes

Abstract

:
Soil organic matter (SOM) is very important to the quality evaluation of cultivated land, especially in fertile black soil areas. Many studies use remote sensing images combined with different machine learning algorithms to predict the regional SOM content. However, the information provided by remote sensing images in different time windows is very different. Taking Youyi Farm, a typical black soil area in Northeast China, as the research area, this study obtains all available Sentinel-2 images covering the research area from 2019 to 2021, calculates the spectral index of single-phase and multi-temporal synthesis images, takes the spectral index and band of each image as the input, and employs the random forest regression algorithm to evaluate the performance of SOM prediction using remote sensing images with different time windows. The results show that: (1) the accuracy of SOM prediction using image band and spectral index is generally improved compared to using only the band; (2) when using single-phase images, the R2 range of SOM prediction using image band and spectral index is from 0.16 to 0.59 and the RMSE ranges from 0.82% to 1.23%; When using multi-temporal synthesis images, the R2 range of SOM prediction using image band and spectral index is from 0.18 to 0.56 and the RMSE ranges from 0.85% to 1.19%; (3) the highest accuracy of SOM prediction using synthetic images is lower than that of single-phase images; (4) the best time window of the bare soil period in the study area is May. This study emphasizes the importance of the time window to SOM prediction. In subsequent SOM prediction research, remote sensing images with appropriate time windows should be selected first, and then the model should be optimized.

1. Introduction

Soil organic matter (SOM) is usually produced by vegetation activity in soil and is an important index of soil fertility [1,2]. Under the influence of human farming and crop growth, agroecosystems are an active and renewable soil carbon pool [3,4]. On one hand, SOM can support the growth and development of crops [5,6]. On the other hand, SOM stores a large amount of carbon [7,8]. SOM contributes to the maintenance and turnover of nutrients, the maintenance and utilization of water, and to agricultural production [9]. Monitoring the spatial distribution of SOM is helpful in analyzing the trend of carbon sequestration to make reasonable decisions on future environmental changes; it can provide valuable information for government measures and farmland activities [10,11,12].
The traditional SOM monitoring method is mainly through field sampling combined with laboratory analysis [13]. This method has high accuracy but can only obtain point data. Furthermore, it is difficult to obtain the overall regional spatial distribution data. At present, there are two mainstream methods to obtain SOM spatial distribution data. The first is the spatial interpolation method, and the second is the remote sensing prediction method [14]. Compared with the spatial interpolation method, the remote sensing prediction method can better avoid SOM prediction error caused by soil spatial heterogeneity [15,16]. In recent years, many studies have relied on the relationship between SOM and the soil reflection spectrum and have used bare soil images to map the spatial distribution of SOM [17,18,19]. However, most studies usually improve the SOM prediction accuracy from model improvement and input optimization and rarely consider the impact of the bare soil time window on the accuracy of SOM prediction. Because bare soil pixels are easily disturbed by environmental factors, the impact of a bare soil window on the accuracy of SOM prediction may be higher than that of other factors.
Many studies have attempted to improve the accuracy of digital soil mapping (DSM) through various methods [20,21]. There are two main ways to select the input variables. First, the common methods include extracting a large number of secondary variables from traditional environmental factors (topography, temperature, and precipitation) and obtaining valuable information from these variables [22,23,24]. Second, the spectral bands and derived spectral indexes based on remote sensing images are also increasingly used in DSM. The use of remote sensing data can directly observe bare soil surface, which has been proved to have a good effect in DSM [25,26]. As the bare soil surface is very vulnerable to the impact of the surrounding environment, selecting a suitable remote sensing image is crucial to providing the accuracy of DSM [27,28].
At present, commonly used prediction methods mainly include linear models, machine learning models, and deep learning models [29,30]. The random forest (RF) model in machine learning models is widely used in remote sensing prediction because of its good performance and model interpretability [31,32,33]. Many studies have used RF regression in grain yield estimation, surface temperature prediction, water quality parameter prediction, and soil physical and chemical property prediction and have achieved good results [34,35,36,37,38]. The prediction of soil organic carbon and/or organic matter using the RF algorithm has been proven to be simple and effective [39].
Sentinel-2 is a high-resolution multispectral imaging satellite equipped with a multispectral imager (MSI) and composed of “twin” Sentinel-2A and Sentinel-2B satellites. It has good spatial, temporal, and spectral resolutions and has great potential in soil mapping [40,41]. There have been many studies on soil mapping using Sentinel-2 images, including soil salinity mapping, soil organic matter mapping, and soil texture mapping, which have achieved good results [42,43]. Recently, research has been conducted to mask unsuitable pixels based on NDVI and NBR2 thresholds, compared with the regions studied (tropical Brazil and Belgium, where conservation tillage is very common). There is a long period of bare soil in Northeast China. When the research area has a precise range of cultivated land, simple median synthesis can also be used to obtain suitable pixels for SOM mapping [25,44].
Existing research on SOM prediction generally uses remote sensing images and different environmental covariates as inputs to explore their impact on the accuracy of SOM prediction. However, even the remote sensing images obtained in the same study area will produce different prediction accuracies. This is mainly because the image may be affected by precipitation, straw mulching, surface morphology, and other factors [45]. Some studies have proved that the accuracy difference caused by SOM mapping using bare soil images in different periods is much higher than that caused by the model method [17,18,19]. It has also been proved that the accuracy of SOM mapping using multi-temporal remote sensing images is higher than that using single-temporal remote sensing images. In a word, screening suitable remote sensing images is the first step of SOM remote sensing mapping [27].
Therefore, it is necessary to determine a specific time window in which relatively good spectral data can be obtained, and the performance of the spectral model is also very high for predicting SOM. The Sentinel-2 satellite remote sensing image with high spatial resolution was used as the data source, and the band and the constructed spectral index were used as the input variables based on the RF algorithm. The specific aims of this study are as follows: (i) to determine the best time window for SOM prediction according to the prediction accuracy; (ii) to compare the accuracy difference of SOM prediction using single-phase image and multi-phase image, and finally; (iii) to map the spatial distribution of SOM content.

2. Materials and Methods

2.1. Study Area

Youyi Farm is located in the hinterland of Sanjiang Plain, Heilongjiang Province, Northeast China [46]. The coordinate range is 131°27′~132°15′ E, 46°28′~46°59′ N (Figure 1). The farm area is 56 km long from east to west and 44 km wide from north to south. The total control area in the area is 1888 km2. It is adjacent to Baoqing County in the east, Jixian County in the west, Shuangyashan city in the south, and Fujin city in the north. Youyi Farm, with its flat terrain and contiguous land, is the largest mechanized state-owned farm in China, with a comprehensive degree of agricultural mechanization of more than 90%. The main soil types include Phaeozems, GleysolS, and Cambisols. The crop planting mode is one season a year, and there is no crop cover from March to May and October to November every year, which is the window period of bare soil. The depth of regular tillage is 20 cm. Due to the early development and high intensity of cultivated land use and serious soil degradation, it is urgent to map the spatial distribution of SOM and other indicators to clarify the key areas and directions of cultivated land protection.

2.2. Data Acquisition and Processing

2.2.1. Soil Data Acquisition

A total of 188 topsoil soil samples (0–20 cm) were collected in the study area from 1 April to 7 April 2021. The SOM concentration will remain stable for several years, so we used the images of nearly three years for SOM prediction [27]. Based on the accessibility of soil sampling, we used the layered sampling method to design the soil sample points, which covered the main soil types, landform types, and cultivated land use types in the study area; using these sampling points ensured the high universality of the SOM prediction model established. During sampling, we first ensured that the sample points were more than 50 m away from roads, forest belts, ditches, and other ground objects to avoid the influence of mixed pixels, and then we collected 5–6 (0–20 cm) soil samples within a 30 m × 30 m square for mixing to avoid the influence of random factors. A portable global positioning system (GPS, g350, Unistrong, Beijing, China) was used to record the longitude and latitude information and acquisition time of the sample points. Finally, the soil samples were ground, air dried, and screened through a 2 mm sieve. The SOC content of the samples was measured by the potassium dichromate heating method, and then the SOC content was multiplied by 1.724 according to the “van Bemmelen factor” to convert it into the SOM content [47].

2.2.2. Sentinel-2 Remote Sensing Image Acquisition and Preprocessing

(1) Acquisition and preprocessing of single-phase Sentinel-2 image
In this study, the L2a products of Sentinel-2 (6 in 2019, 7 in 2020, and 10 in 2021, a total of 23) used in the study area from 2019 to 2021 (cloud cover less than 1% and no snow cover) were screened and downloaded in the Google Earth engine for SOM mapping (Table 1). The L2a product of Sentinel-2 was the surface reflectance data after geometric correction and atmospheric treatment [48]. Among them, band 1 was the aerosol band, band 9 was the water vapor band, and band 10 was the reaction atmosphere band. Therefore, these three bands were removed in the study, and 10 bands remained. Among them, band 2, band 3, band 4, and band 8 had a 10 m spatial resolution, and band 5, band 6, band 7, band 8a, band 11, and band 12 had a 20 m spatial resolution. Then, the remote sensing images were cut and mosaicked according to the boundary of the study area; all bands were spatially resampled to a 10 m resolution.
(2) Acquisition and preprocessing of multi-temporal Sentinel-2 synthesis images
The L2a products of Sentinel-2 used in the study area from 2019 to 2021 (March to November) were screened and downloaded in the Google Earth engine for SOM mapping. The official cloud removal algorithm provided in the GEE platform for cloud mask processing was used on all images. Finally, monthly median synthesis was carried out, and some studies have proved that the median is robust to outliers. The strategy of using median synthesis to select the best pixel has been proved to have good results and high computational efficiency [49]. However, the results obtained by the average value synthesis method may not be the actual physical observation results, and are more vulnerable to the impact of extreme outliers [50].
According to the agricultural planting habits in the study area, the bare soil period was in March, April, May, and November, and crop growth period was in June, July, August, September, and October.

2.3. Construction of the Spectral Index

Because there are few bands in multispectral images, the construction of a spectral index can make full use of the useful information in multispectral images. The construction of the difference index (DI), ratio index (RI), normalized difference index (NDI), and other spectral indices can reduce the effects of moisture, roughness, and atmosphere. Therefore, the spectral index set was established by performing band operation on the band reflectance of Sentinel-2 image data. The formula is as follows:
D I i j = ρ i − ρ j
R I i j = ρ i / ρ j
N D I i j = ρ i − ρ j / ρ i + ρ j
where ρ i and ρ j are the reflectivity of the i and j bands, respectively, where i > j. A total of 135 spectral indices (45 each for difference indices, ratio indices, normalized difference indices) were generated in this study.

2.4. RF Prediction Model

A RF model is a nonlinear machine learning model based on decision trees. In this study, RF modeling was implemented through the RF package in the R language. There were three key parameters involved in the RF model: the number of decision trees (ntree), the number of random variables that split nodes (mtry), and the importance of the independent variable on the SOM (importance). The RF package ranked the importance of the independent variables that affect the SOM prediction during the operation. The larger the value, the greater the influence of the independent variable on the SOM prediction, and the stronger the correlation. In addition, the RF model can effectively avoid the multicollinearity problem between features. In the actual RF modeling operation in this study, ntree was set to 500, and ntry was set to 1/3 of the number of inputs. At this time, not only could a relatively stable out-of-bag error rate be generated, but the value was also small, and the model was relatively stable.

2.5. Model Validation and Establishment

In this study, the RF algorithm model was used. Ten bands and 135 spectral indices of Sentinel-2 image data were used as inputs to carry out SOM inversion modeling. Among the 188 samples, the modeling set and the validation set were divided according to the ratio of 3:1 by Kennard stone algorithm [51], including 141 training samples and 47 validation samples. The coefficient of determination (R2) was used to check the model stability. The root mean square error (RMSE) was used to measure the accuracy of the model.

2.6. Technology Roadmap

The technical route of this study is shown in Figure 2.

3. Results

3.1. Descriptive Statistics of SOM Content

The maximum value of the entire dataset was 9.91%, the minimum value was 0.48%, and the SOM content range was very wide, with a mean value of 3.92%, a standard deviation of 1.40%, and a coefficient of variation of 35.84%, showing strong variation (Table 2). The training and validation sets were similar to the descriptive statistics of the entire dataset.

3.2. Analysis of Spectral Characteristics of Different SOM Contents

The spectral reflectance of soils with different SOM contents is shown in Figure 3. The spectral curves of soils with different organic matter contents were generally similar. Similar to previous studies, the higher the SOM content, the lower the soil spectral reflectance. For the Sentinel-2 image, the soil spectral reflectance gradually increased and showed a trend of first increasing and then decreasing. Soil samples with an SOM content lower than 3% had one or two peaks in the B8 band and B11 band, and soil samples with an SOM content higher than 3% had one or two peaks. The soil sample point had only one peak of B11.

3.3. SOM Prediction Using Single-Phase Images

We used all available imagery in the study area from 2019 to 2021 for SOM prediction (Table 3). In general, the SOM prediction using the image band and the spectral index generally improved the accuracy compared to using only the image band. The accuracy of SOM prediction using remote sensing images from different periods varied greatly. The R2 range of SOM prediction using only bands ranged from 0.05 to 0.53, and the RMSE range was from 0.89% to 1.30%. The R2 range of SOM prediction using bands and the spectral index ranged from 0.16 to 0.59, and the RMSE ranged from 0.82% to 1.23%.
The prediction accuracy of SOM using remote sensing images in the growing period could not reach the highest value but was still relatively stable. The accuracy of SOM prediction using remote sensing images in the bare soil period varied greatly. The period with the highest SOM prediction accuracy was the beginning of May in the bare soil period, which is the best time window for SOM prediction in the study area.

3.4. SOM Prediction Using Synthetic Images

The precision of SOM prediction using multi-year synthetic images in different months is shown in Table 4. The results show that when only the band was used, the accuracy of SOM prediction using May synthesis image was the highest, R2 was the highest 0.55, RMSE was 0.97%. When both the band and the spectral index was used, the accuracy of SOM prediction using May synthesis image was the highest, R2 was the highest 0.56, and RMSE was 0.85%. The highest accuracy of SOM prediction using synthetic images was lower than that of single-phase images. The results using synthetic images were similar to those of single-phase images. May was the window period for SOM prediction in the study area.

3.5. Spatial Distribution of SOM Content

The image and spectral index with the highest prediction accuracy of SOM in the bare soil period and growing period (17 May 2021 and 1 July 2011, respectively) were used as inputs for the RF algorithm. SOM mapping was carried out on the cultivated land of the Youyi Farm in the study area (Figure 4). The results showed that the SOM content was lower in the cultivated land near the urban area and the western mountainous area of the Youyi Farm, and that the SOM content was higher in the cultivated land near the wetland reserve in the east. The spatial difference in SOM prediction using the optimal bare soil image was more obvious than that using the optimal growing season image.

4. Discussion

4.1. Selection of the Best Window

Compared with the model improvement and input optimization that concerns traditional research, this study analyzed the time window of the key factor that is easy to ignore in SOM prediction using bare soil images. General soil studies usually randomly select the images of the bare soil window period for soil mapping because they believe that the images of the bare soil window period have little difference [10,16,52]. Our research compared the accuracy of SOM prediction of different bare soil window images. The results showed that the accuracy of SOM prediction varied greatly in different time periods, which showed that although there are bare soil images without crop cover, the spectral reflectance difference of different bare soil images greatly affected the accuracy of SOM prediction (Figure 5). The analysis of this study showed that the best time window for SOM prediction in the study area was early May, which is the most stable period in a year after cultivated land plowing and before crop emergence [17]. The SOM mapping accuracy during the peak crop growth period (July to September) was relatively stable, not very high or very low, which may be because the crop growth can reflect the SOM content to a certain extent [53]. October was the time for large-scale harvesting in the region, which was the reason for the poor prediction accuracy [33,54].

4.2. Factors Affecting the Prediction Accuracy of SOM Content

The research showed that the highest accuracy of SOM prediction in different years always appeared in the traditional bare soil period (from the end of March to the end of May and after October). The accuracy of SOM prediction using growth period images was relatively stable (R2 was approximately 0.3), which indicated that crop growth would also reflect the spatial distribution of SOM content to a certain extent. The accuracy of SOM prediction using remote sensing images in the bare soil period varied greatly. We analyzed the soil water content and straw residue of all training samples in images in different periods (Figure 6). We used the normalized tillage index (DNTI) to characterize straw coverage and the normalized differential water index (NDMI) to represent the soil water content [55,56,57]. The results showed that soil water content was negatively correlated with the prediction accuracy of SOM (Table 5); that is, the lower the soil water content, the higher the prediction accuracy of SOM. The correlation between straw coverage and SOM was not obvious, which may be related to the local habit of plowing in autumn. After plowing in autumn, almost all images in the bare soil period would be unaffected by straw images.

4.3. Differences between Single-Temporal and Multi-Temporal Synthetic Images

We compared the accuracy difference of SOM prediction using single-phase and multi-phase synthetic images in the study area. The results showed that the highest accuracy of single-phase synthetic images was higher than that of multi-phase synthetic images. Some studies have compared the accuracy of SOM prediction between single-phase and multi-phase synthetic images in tropical Brazil, and the results show that the effect of SOM prediction using multi-phase images is better [25]. The main reason why the results of this study differ from those of that study is that the characteristics of the study area are different. In the tropical region of Brazil, it is almost impossible to obtain an image with a single period and full bare soil pixels, so a single time phase image often has other impurity pixels, which will in turn affect the prediction of SOM [44]. However, in our study area, the farming mode of one season a year plus no promotion of conservation tillage (even promotion of “black wintering”– a farming measure of plowing after crop harvest in October), pure soil pixels were very common (late March to early June) [58,59]. The single-period image is more consistent with the actual situation than the multi-year synthesis image, so SOM prediction of some single-phase images can obtain higher accuracy than multi-phase synthesis images.
Comparing the importance ranking of SOM prediction using single-phase and synthetic images (Figure 7), it can be found that the spectral index of the bands with wavelengths 490–860 nm (B2 to B8A) and the combination of these bands are of high importance, which is similar to previous studies [16]. Spectral index is a powerful supplement to SOM prediction: five of the top ten inputs in SOM prediction of single-phase images are spectral indexes. When using synthetic images for SOM prediction, the contribution of the spectral index is not so strong, and only three of the top ten inputs are spectral indexes.

4.4. Comparison of Different SOM Content Mapping Results

We compared the spatial distribution map of SOM content in this study with the mapping results of SOM content in other studies (Figure 8). We found that the SOM content predicted by Soil Subcenter (http://soil.geodata.cn, accessed on 15 June 2022) and Poggio, et al. [60] was higher than that in our study, which may be because the results of the Soil Subcenter were produced in 1990, when the soil was not yet greatly degraded [61]. These results were obtained from national and global perspectives. In the mapping results, our mapping results had large spatial differences, which could better reflect the actual situation of the soil surface. In the mapping results of Soil Subcenter and Poggio, et al., the spatial variability of SOM content in local areas was small. It should be emphasized that the mapping results of the Soil Subcenter and previous study were of great significance to the spatial distribution of soil organic carbon content in China and even the world.

4.5. Limitations and Prospects

In this study, the influence of images from different shooting dates on SOM prediction was considered. The relationships amongst soil moisture, straw content, and SOM content were analyzed, and the single-phase and multi-phase synthetic images were compared, which could provide a reference for selecting the best bare soil period image for SOM content prediction at the same latitude. It has been proved that although feature selection can improve the accuracy of SOM prediction, the improvement was very small [18]. Combined with the characteristics of random forest regression, without feature selection the accuracy of SOM prediction would not be affected too much, and the use efficiency of the model would be improved. Since this study only evaluated the highest accuracy that could be obtained from experimental remote sensing data, many studies have proven that adding environmental elements (meteorological data, terrain data) can further improve the accuracy of SOM prediction, which is needed for the next step of research. There are many paddy fields in this study area, which impacted the prediction accuracy of SOM [54,62]. Therefore, in future research, paddy fields and dry land will be modeled separately, and more input variables considering soil forming factors will be added to improve the prediction accuracy of SOM.

5. Conclusions

This study used 2019–2021 to cover all available Sentinel-2 remote sensing images of Youyi Farm in the study area. Combined with a RF algorithm, this study evaluated the performance difference of SOM prediction using remote sensing images in different periods to select the time window of SOM prediction in the study area. Compared with SOM prediction using remote sensing in the growth period, bare soil images can describe the soil surface more directly. However, they are also more vulnerable to changes in the soil environment, and the SOM prediction result was more unstable. The results showed that the image time difference had a great impact on the SOM prediction image. The best time window for SOM prediction in the study area was May; this was mainly because the soil moisture content in May was more appropriate than that in other bare soil periods. The difference in SOM prediction accuracy in the bare soil period was affected by factors such as soil water content and straw coverage. This study emphasized that selecting the correct remote sensing image is the first step for soil mapping; otherwise, the subsequent model optimization may be useless.

Author Contributions

Conceptualization, Y.W. and C.L.; methodology, Q.L. and W.Z.; software, X.M.; validation, X.Z.; formal analysis, H.L.; investigation, C.L.; resources, C.L; data curation, H.L.; writing—original draft preparation, Y.W.; writing—review and editing, C.L.; visualization, H.L.; supervision, W.Z.; project administration, W.Z.; funding acquisition, W.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Key R&D Program of China (Grant No. 2021YFD1500105).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Data are available upon request by email to the author.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Overview of the study area. (a) Distribution map of soil sampling points; (b–d) Photos of bare soil period in the study area. All photos were taken in early April 2021.
Figure 1. Overview of the study area. (a) Distribution map of soil sampling points; (b–d) Photos of bare soil period in the study area. All photos were taken in early April 2021.
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Figure 2. Technology roadmap.
Figure 2. Technology roadmap.
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Figure 3. Sentinel-2 (date: 2021-05-17) spectral curve of different organic matter contents.
Figure 3. Sentinel-2 (date: 2021-05-17) spectral curve of different organic matter contents.
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Figure 4. Spatial distribution of SOM content in Youyi Farm. (a) The bare soil image with the highest precision of SOM inversion on 17 May 2021; (b) the growing season image with the highest precision of SOM inversion on 18 July 2021.
Figure 4. Spatial distribution of SOM content in Youyi Farm. (a) The bare soil image with the highest precision of SOM inversion on 17 May 2021; (b) the growing season image with the highest precision of SOM inversion on 18 July 2021.
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Figure 5. Field photos of the study area in different periods. (a) Shot on 2 April 2021; (b) Shot on 28 May 2021.
Figure 5. Field photos of the study area in different periods. (a) Shot on 2 April 2021; (b) Shot on 28 May 2021.
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Figure 6. NDTI and NDMI values of image sampling points at different times.
Figure 6. NDTI and NDMI values of image sampling points at different times.
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Figure 7. Top 20 input for importance. (a) 17 May 2021; (b) Synthetic image in May.
Figure 7. Top 20 input for importance. (a) 17 May 2021; (b) Synthetic image in May.
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Figure 8. Comparison of mapping results of different SOM contents. (a–c) The mapping results of the study, Soil Subcenter, and Poggio, et al.
Figure 8. Comparison of mapping results of different SOM contents. (a–c) The mapping results of the study, Soil Subcenter, and Poggio, et al.
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Table
Table 1. Properties of images selected for this study.
Table 1. Properties of images selected for this study.
YearDate (Mon/D)YearDate (Mon/D)YearDate (Mon/D)
201903/29202004/12202104/04
05/0304/2904/19
06/2405/0705/17
08/3105/2906/08
09/2707/2306/13
11/0409/2306/23
10/2607/18
08/17
09/06
10/29
Table
Table 2. Descriptive statistical analysis of SOM content.
Table 2. Descriptive statistical analysis of SOM content.
SetNMax (%)Min (%)Mean (%)SD (%)CV (%)
Whole dataset1889.910.483.921.4035.84
Training set1419.010.483.891.3735.26
validation set479.911.754.021.5037.29
Note: SD represents standard deviation; CV stands for coefficient of variation.
Table
Table 3. SOM prediction accuracy of single-phase images at different times.
Table 3. SOM prediction accuracy of single-phase images at different times.
TimeYearImage TimeOnly BandsBands + Spectral Indices
R2RMSE (%)R2RMSE (%)
growing season2019201906240.450.950.470.94
201908310.301.080.411.00
201909270.241.140.261.14
2020202007230.271.120.251.13
202009210.421.100.281.01
2021202106080.381.030.470.95
202106130.361.040.391.01
202106230.371.020.361.03
202107180.500.920.510.92
202108170.231.140.261.15
202109060.351.040.271.10
bare soil period2019201903290.231.140.281.11
201905030.480.940.520.90
201911040.251.130.281.11
2020202004120.051.300.171.18
202004290.301.100.411.00
202005070.301.100.460.95
202005290.421.000.430.99
202010260.261.120.410.10
2021202104040.181.170.191.17
202104190.530.890.590.83
202105170.420.980.590.82
202110290.101.270.161.23
Table
Table 4. SOM prediction accuracy of multi-year synthetic images at different times.
Table 4. SOM prediction accuracy of multi-year synthetic images at different times.
YearImage TimeOnly BandsBands + Spectral Indices
R2RMSE (%)R2RMSE (%)
2019–2021Apr.0.321.060.351.04
May0.550.870.560.85
Jun.0.061.270.221.14
Jul.0.321.070.391.01
Aug.0.371.040.401.10
Sep.0.371.020.401.00
Oct.0.111.240.181.19
Table
Table 5. Correlation analysis between RMSE of SOM prediction using different time images and NDTI and NDMI values of sampling points.
Table 5. Correlation analysis between RMSE of SOM prediction using different time images and NDTI and NDMI values of sampling points.
CorrelationNDTINDMIRMSE
NDTI1 **
NDMI−0.22 **1 **
RMSE0.18 **−0.60 **1 **
Note: ** Represents significance at p < 0.01 level.
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Wang, Y.; Luo, C.; Zhang, W.; Meng, X.; Liu, Q.; Zhang, X.; Liu, H. Remote Sensing Prediction Model of Cultivated Land Soil Organic Matter Considering the Best Time Window. Sustainability 2023, 15, 469. https://doi.org/10.3390/su15010469

AMA Style

Wang Y, Luo C, Zhang W, Meng X, Liu Q, Zhang X, Liu H. Remote Sensing Prediction Model of Cultivated Land Soil Organic Matter Considering the Best Time Window. Sustainability. 2023; 15(1):469. https://doi.org/10.3390/su15010469

Chicago/Turabian Style

Wang, Yiang, Chong Luo, Wenqi Zhang, Xiangtian Meng, Qiong Liu, Xinle Zhang, and Huanjun Liu. 2023. "Remote Sensing Prediction Model of Cultivated Land Soil Organic Matter Considering the Best Time Window" Sustainability 15, no. 1: 469. https://doi.org/10.3390/su15010469

APA Style

Wang, Y., Luo, C., Zhang, W., Meng, X., Liu, Q., Zhang, X., & Liu, H. (2023). Remote Sensing Prediction Model of Cultivated Land Soil Organic Matter Considering the Best Time Window. Sustainability, 15(1), 469. https://doi.org/10.3390/su15010469

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