Prediction of total nitrogen in cropland soil at different levels of soil moisture with Vis/NIR spectroscopy

Figures & data

Table 1. Basic properties and statistical descriptions of the soil samples on Honghu region, China.

			SOC (g kg^−1)			TN (g kg^−1)
Texture	Sample number	Crop	Min	Max	Mean	Min	Max	Mean
Clay	36	Winter rape, grass, idle field	15.09	26.23	20.59	1.23	2.18	1.69
Clay loam	7	Wheat, winter rape, sesame	11.71	13.74	12.36	0.87	1.6	1.24
Loam	14	Wheat, winter rape, sesame	7.45	10.25	8.60	0.61	1.03	0.78
Sandy loam	5	Peanut, winter rape	2.35	6.12	4.35	0.43	0.64	0.55

Min, minimum; Max, maximum. SOC, soil organic carbon; TN, total nitrogen.

Figure 1. Maps showing the geographical location of the study area, distribution of sampling sites, and landscape of the study area, indicated by a LANDSAT-7 ETM+ image with band 8 (panchromatic band).

Figure 2. Box plots of soil moisture content (SMC) which are divided into eight levels: 0–50 g kg⁻¹ (air-dried), 50–100 g kg⁻¹, 100–150 g kg⁻¹, 150–200 g kg⁻¹, 200–250 g kg⁻¹, 250–300 g kg⁻¹, 300–350 g kg⁻¹, and 350–400 g kg⁻¹.

Figure 3. Flowchart of the processes of data collection and preparation, model calibration, validation, and evaluation.

Figure 4. The average of soil spectra from different data-sets with four spectral transformations: (a) SG smoothing, (b) SG smoothing followed by FD, (c) SG smoothing followed by OSC, and (d) SG smoothing followed by GLSW.

Figure 5. Transferability of SG-PLSR models for total nitrogen (TN) estimation with soil samples of eight moisture gradients as assessed by (a) determination coefficient for validation () and (b) RPD. It should be noted that the diagonals are (a) determination coefficient for calibration () and (b) standard deviation of prediction set divided by root mean square error of calibration.

Figure 6. Pearson’s correlation coefficient between total nitrogen (TN) content and differently pretreated spectra: (a) spectra with Savitzky–Golay (SG) smoothing, (b) spectra with SG smoothing and FD, (c) spectra with SG smoothing and OSC, and (d) spectra with SG smoothing and GLSW.

Figure 7. RMSEP of total nitrogen (TN) prediction models with the seven moist data-sets as validation sets: (a) OSC-PLSR models with different c values (the number of OSC filter dimensions); (b) GLSW-PLSR models with different α values (the weights of GLSW filter intensity).

Table 2. Prediction of soil total nitrogen (TN) content using Vis/NIR spectroscopy with different preprocessing methods.

				Mean			Standard deviation
Model	NLV				RMSEP	RPD		RMSEP	RPD
SG-PLSR	4	0.686	0.619	0.468	0.357	1.382	0.091	0.031	0.122
FD-PLSR	5	0.897	0.684	0.557	0.33	1.493	0.067	0.028	0.131
OSC-PLSR	4	0.75	0.696	0.695	0.277	1.78	0.033	0.017	0.119
GLSW-PLSR	3	0.842	0.699	0.718	0.262	1.885	0.024	0.012	0.086

Figure 8. Performances of the four PLSR models: SG-PLSR, FD-PLSR, OSC-PLSR, and GLSW-PLSR. The air-dried sets were used for model calibration, whereas the other seven sets with moisture gradients were used for validations.

Figure 9. The VIP scores of (a) SG-PLSR, (b) FD-PLSR, (c) OSC-PLSR, and (d) GLSW-PLSR modeling for TN estimation using the air-dried and moist samples (with SMC = 150–200 g kg⁻¹ as an example), respectively.