كليدواژه :
توزيع مكاني , طيف مرئي مادون قرمز , كربن آلي خاك , مدلسازي , سنجنده لندست 8
چكيده لاتين :
Introduction: Understanding the spatial distribution of soil organic carbon (SOC) is one of the practical
tools in determining sustainable land management strategies. Over the past two decades, the use of data mining
approaches in spatial modeling of soil organic carbon using machine learning techniques to investigate the
amount of carbon to soil using remote sensing data has been widely considered. Accordingly, the aim of this
study was to investigate the feasibility of estimating soil organic matter using satellite imagery and to assess the
ability of spectral and terrestrial data to model the amount of soil organic matter.
Materials and Methods: The study area is located in Lorestan province, and Sarab Changai area. This area
has hot and dry summers and cold and wet winters and the wet season starts in November and ends in May. A
total of 156 samples of surface soil (0-30 cm) were collected using random sampling pattern. Data were
categorized into two categories: 80% (117 points) for training and 20% (29 points) for validation. Three machine
learning algorithms including Random Forest (RF), Cubist, and Partial least squares regression (PLSR) were
used to prepare the organic soil carbon map. In the present study, auxiliary variables for predicting SOC included
bands related to Lands 8 OLI measurement images, and in order to reduce the volume of data, the principle
component analysis method (PCA) was used to select the features that have the greatest impact on quality.
Results and Discussion: The results of descriptive statistics showed that soil organic carbon from 0.02 to
2.34% with an average of 0.56 and a coefficient of variation of 69.64% according to the Wilding standard was
located in a high variability class (0.35). According to the average amount of soil organic carbon, it can be said
that the amount of soil organic carbon in the region is low. At the same time, the high value of organic carbon
change coefficient confirms its high spatial variability in the study area. These drastic changes can be attributed
to land use change, land management, and other environmental elements in the study area. In other words, the
low level of soil organic carbon can be attributed to the collection of plant debris and their non-return to the soil.
Another factor in reducing the amount of organic carbon is land use change, which mainly has a negative impact
on soil quality and yield. In general, land use, tillage operations, intensity and frequency of cultivation, plowing,
fertilizing, type of crop, are effective in reducing and increasing the amount of soil organic carbon. Based on the
analysis of effective auxiliary variables in predicting soil organic carbon, based on the principle component
analysis for remote sensing data, it led to the selection of 4 auxiliary variables TSAVI, RVI, Band10, and
Band11 as the most effective environmental factors. Comparison of different estimation approaches showed that
the random forest model with the values of coefficient of determination (R2), root mean square error (RMSE)
and mean square error (MSE) of 0.74, 0.17, and 0.02, respectively, was the best performance ratio another study
used to estimate the organic carbon content of surface soil in the study area.
Conclusion: In this study, considering the importance of soil organic carbon, the efficiency of three different
digital mapping models to prepare soil organic carbon map in Khorramabad plain soils was evaluated. The
results showed that auxiliary variables such as TSAVI, RVI, Band 10, and Band11 are the most important
variables in estimating soil organic carbon in this area. The wide range of soil organic carbon changes can be
affected by land use and farmers' managerial behaviors. Also, the results indicated that different models had
different accuracy in estimating soil organic carbon and the random forest model was superior to the other
models. On the other hand, it can be said that the use of remote sensing and satellite imagery can overcome the
limitations of traditional methods and be used as a suitable alternative to study carbon to soil changes with the
possibility of displaying results at different time and space scales. Due to the determination of soil organic
carbon content and their spatial distribution throughout the region, the present results can be a scientific basis as
well as a suitable database and inputs, and any study in sustainable agriculture with soil properties in this area. In general, the results of this
study indicated the ability of remote sensing techniques and random forest learning model in simultaneous
estimation of soil organic carbon location. Therefore, this method can be used as an alternative to conventional
laboratory methods in determining some soil characteristics, including organic carbon.data for the implementation of any field operations, management of agricultural
