Soil quality assessment is essential to improve the understanding of soil quality and make proper agricultural practices. However, soil quality assessments are extremely difficult to implement in a large-scale area, since they are time and labor consuming. Remote sensing technique gained more attention in plant and soil information monitoring recently for its high effi-ciency and convenience. But seldom studies tested the applicability of remote sensing techniques before implementing. This study conducted the soil quality assessment in a typical agricultural county in the Yellow River delta (Kenli). We found the soil quality in Kenli was dominantly in the low grade (71.85%), with deficient nutrient (SOM and TN), poor structure (high BD) and high EC. Salinity is the primary limiting factor for soil quality in Kenli, and adjustment of soil salinization through suitable farming practices such as organic fertilizers application, irrigation for leaching, and salt-tolerant crop planting is the key point for soil quality improvement. We obtained the normalized difference vegetation index (NDVI) of the study area by remote sensing technique, and found the high correlation between NDVI and soil quality indicator (SOM, TN and EC) and yield. The NDVI can help to study the soil conditions as a soil quality assessment indicator. More studies about the ap-plication of remote sensing technique on soil quality detecting are expected.
Soil quality can be defined as “the capacity of soil to function to sustain plant and animal productivities, to maintain or enhance water and air quality and to support human health and habitation” [
Improved understanding of soil quality comes from a reliable and accurate soil quality assessment, which is a decision-making tool that effectively combines a variety of soil information to analyze quantitatively the soil conditions. Soil quality indexing is the most commonly used method, as it is easy and flexible to use [
Yellow River delta is one of the primary growing regions in China with Fluvisol being the main agricultural soil type. However, limiting factors such as nutrient deficiency, structural degeneration, and land desertification have been reported for soils in Yellow River delta [
The present study assessed the soil quality of a typical agricultural county (Kenli) in the Yellow River delta, and analyzed the limiting factors of soil quality based on the assessment result. In addition, the applicability of remote sensing technique on soil quality assessment studies was tested.
The experiment was conducted in the typical agricultural county in Yellow Riverdelta, Kenli (118˚24' - 119˚10'E, 37˚21' - 38˚9'N) (
Soil samples were collected after harvest and before the next cropping season. The sampling points in Kenli are shown in
The moderate-resolution imaging spectroradiometer (MODIS) is a payload scientific instrument that is on board the NASA’s Terra and Aqua satellites. This study used the MOD09 data which comes from the land surface reflectance product developed by the NASA MODIS. MOD09 data is the level 2 dataset, with the spatial resolution of 250 m and time resolution of 1 day. After the atmospheric, geometric and radiation corrections, the normalized vegetation index (NDVI) can be calculated based on the MODIS data of 1 - 7 bands (620 - 670, 841 - 876, 459 - 479, 455 - 565,1230 - 1250, 1628 - 1652, 2105 - 2155). This study calculated the NDVI of Kenli with a scene remote sensing image on September 11before harvest. NDVI mainly reflects plant canopy status, and usually be used to detect vegetation growth and vegetation coverage. The calculation equation is
NDVI = ( Band 2 − Band 1 ) / ( Band 2 + Band 1 ) (1)
where, the Band 1 is the first band, Band 2 is the second band, and the range of NDVI was between −1 to 1.
Soil quality indexing normally includes three steps: 1) choosing appropriate indicators, 2) scoring the indicators, and 3) combining the indicator scores into an
Soil quality indicator | Method |
---|---|
Soil organic matter (SOM) | Walkley-Black method |
Total nitrogen (TN) | Kjeldahl digestion method |
Available phosphorus (AP) | Extracting-spectrophotometer detection |
Available phosphorus (AK) | Extracting-flame photometry detection |
pH | Electrometric method |
Dry bulk density (BD) | Core method |
Clay % | Pipette method |
Electrical conductivity (EC) | Conductivity meter |
index. In the present study, eight soil characteristics (SOM, TN, AP, AK, BD, EC, Clay%, and pH), which can influence the functions of carbon transformations, soil nutrient cycles, structure maintenance and buffer capacity, were considered as indicators.
During the scoring of indicators, the values of indicators were transformed into appropriate scores (0 - 1) through the linear scoring methods, since indicators are expressed with different numerical scales. Based on the indicator sensitivity, three types of functions were developed: 1) a “more is better” function (M), 2) a “less is better” function (L), and 3) an “optimal range” function (R) (
After indicator scoring, indictor scores were transformed to a soil quality index. To calculate the soil quality index (SQI), soil quality indicators should be weighted. In the present study, the weight value of each indicator was assigned by the communality value through factor analysis (IBM, SPSS Statistics 20.0) (
SQI = ∑ i n W i N i (2)
where, SQI is the soil quality index; Wi is the weight of the indicator; and Ni is the score of the indicator.
Soil quality was divided into five grades: very high (SQI ≥ 0.85), high (0.85 > SQI ≥ 0.7), moderate (0.7 > SQI ≥ 0.55), low (0.55 > SQI ≥ 0.4), and very low (SQI < 0.4), according to the classification criteria [
Eight representative soil characteristics were functioned as indicators to evaluate
Indicator | Type | x1 | x2 | Function |
---|---|---|---|---|
SOM (g∙kg−1) | M (x) | 6 | 20 | M ( x ) = { 0.1 x < x 1 0.9 × x − x 1 x 2 − x 1 + 0.1 x 1 < x < x 2 1 x > x 2 L ( x ) = { 1 x < x 1 1 − 0.9 × x − x 1 x 2 − x 1 x 1 < x < x 2 0.1 x > x 2 R ( x ) = { 0.1 x < x 1 0.9 × x − x 1 r 1 − x 1 + 0.1 x 1 < x < r 1 1 r 1 < x < r 2 1 − 0.9 × x − r 2 x 2 − r 2 r 2 < x < x 2 0.1 x > x 2 |
TN (g∙kg−1) | M (x) | 0.3 | 1.2 | |
AP (mg∙kg−1) | M (x) | 5 | 15 | |
AK (mg∙kg−1) | M (x) | 40 | 200 | |
EC (mS∙cm−1) | L (x) | 0.2 | 4 | |
BD (g∙cm−3) | L (x) | 1.25 | 1.55 | |
Clay % | R (x) | 5 | 40 | |
pH | R (x) | 5.5 | 9.5 |
Where, x is the measured value of the indicator; M(x), L(x), and R(x), are “More is better”, “Less is better”, and “Optimal range” scoring functions; x1 and x2 are the lower and the upper threshold values, respectively; and r1 and r2 are the lower and the upper values of the optimal range, respectively.SOM, soil organic matter; TN, total nitrogen; AP, available phosphorus; AK, available potassium; EC, electrical conductivity; BD, bulk density.
Indicator | Weight |
---|---|
SOM (g∙kg−1) | 0.14 |
TN (g∙kg−1) | 0.15 |
AP (mg∙kg−1) | 0.07 |
AK (mg∙kg−1) | 0.10 |
EC (mS∙cm−1) | 0.16 |
BD (g∙cm−3) | 0.12 |
Clay % | 0.10 |
pH | 0.16 |
SOM, soil organic matter; TN, total nitrogen; AP, available phosphorus; AK, available potassium; EC, electrical conductivity; BD, bulk density.
the soil quality in Kenli.
Indicator | Mean | Range |
---|---|---|
SOM (g∙kg−1) | 9.97 ± 3.90 | 3.25 - 21.73 |
TN (g∙kg−1) | 0.63 ± 0.23 | 0.22 - 1.28 |
AP (mg∙kg−1) | 19.04 ± 16.13 | 3.31 - 86.63 |
AK (mg∙kg−1) | 159.13 ± 61.93 | 76.84 - 379.12 |
EC (mS∙cm−1) | 1.37 ± 1.41 | 0.11 - 7.01 |
BD (g∙cm−3) | 1.48 ± 0.13 | 1.05 - 1.69 |
Clay % | 17.53± 8.82 | 2.26 - 44.92 |
pH | 8.78 ± 0.23 | 8.13 - 9.20 |
SOM, soil organic matter; TN, total nitrogen; AP, available phosphorus; AK, available potassium; EC, electrical conductivity; BD, bulk density.
that borders the Bohai Sea, and the high soil salinity responded to a high EC. Excess salt in soils causes clay particles to disperse or swell, and consequently, these soils have poor structure with low aggregate stability, aeration, and water infiltration. Moreover, saline soils are a poor rooting medium for nutrients providing and plant growth, which leads to low quality level [
The soil quality in the studied areas was classified into five grades―very high, high, moderate, low, or very low. Soil areas in the low grade were dominant, with the area of 71.85% (
The main factors that influenced soil quality included climate, topography, soil type, plant species, and agricultural management. In Kenli, both agricultural management and geographical position were the major influencing factors, since salinization is an important determinant of soil quality. Adjustment of soil salinization through suitable farming practices is the key point for soil quality improvement. The improvement management may include: 1) increasing organic fertilizers application, 2) lands leveling, 3) residue covering, 4) irrigation for salinity leaching, 5) chemical conditioner application, and, 6) salt-tolerant crop planting [
The normalized difference vegetation index (NDVI) of the study area was obtained by remote sensing technique (
To understand the soil quality in Yellow River delta, soil quality assessment was conducted in a typical agricultural county of Yellow River delta, Kenli. The assessment result showed the soil quality in Kenli was dominantly in the low grade, because of nutrient deficiency (especially of SOM and TN), poor structure and salinity. Salinity is the primary limiting factor for soil quality in Kenli, which was influenced by agricultural management and geographical position.
Applicability of the normalized difference vegetation index (NDVI) obtained by remote sensing technique was tested as a soil quality indicator. We found
NDVI | SOM (g∙kg−1) | TN (g∙kg−1) | AP (mg∙kg−1) | AK (mg∙kg−1) | EC (mS∙cm−1) | BD (g∙cm−3) | Clay % | pH | SQI | Yield (kg∙ha−1) | |
---|---|---|---|---|---|---|---|---|---|---|---|
NDVI | 1.00 | 0.38** | 0.37** | 0.11 | 0.01 | −0.41** | 0.02 | 0.02 | 0.24 | 0.39** | 0.36** |
SOM (g∙kg−1) | 1.00 | 0.81** | 0.20 | 0.36** | −0.22 | 0.02 | 0.22 | −0.15 | 0.81** | 0.44** | |
TN (g∙kg−1) | 1.00 | 0.26* | 0.45** | −0.24* | 0.03 | 0.34** | −0.21 | 0.86** | 0.43** | ||
AP (mg∙kg−1) | 1.00 | 0.16 | −0.15 | −0.05 | 0.10 | −0.12 | 0.34** | 0.20 | |||
AK (mg∙kg−1) | 1.00 | 0.21 | −0.03 | 0.12 | −0.20 | 0.44** | 0.45** | ||||
EC (mS∙cm−1) | 1.00 | −0.18 | −0.19 | −0.61** | −0.48** | −0.20 | |||||
BD (g∙cm−3) | 1.00 | −0.11 | 0.09 | 0.17 | −0.01 | ||||||
Clay % | 1.00 | −0.07 | 0.33** | 0.06 | |||||||
pH | 1.00 | −0.04 | 0.08 | ||||||||
SQI | 1.00 | 0.53** | |||||||||
Yield(kg∙ha−1) | 1.00 |
SOM, soil organic matter; TN, total nitrogen; AP, available phosphorus; AK, available potassium; EC, electrical conductivity; BD, bulk density; NDVI, the normalized difference vegetation index; SQI, soil quality index.
NDVI can reflect the soil conditions and explain the crop production well. Considering the remote sensing technique can also obtained information efficiently and conveniently, application of remote sensing techniques on more soil quality studies is expected.
Guo, L.L., Hao, H.J., Liu, Y.H., Ma, H.B., An, J.B., Sun, Q. and Yang, Z. (2017) The Assessment of Soil Quality on the Arable Land in Yellow River Delta Combined with Remote Sensing Technology. World Journal of Engineering and Technology, 5, 18-26. https://doi.org/10.4236/wjet.2017.55B003