JOURNAL OF NATURAL RESOURCES ›› 2015, Vol. 30 ›› Issue (6): 1035-1046.

• Resource Research Method •

Arable Land Fertility Inversion Based on Vegetation Index from TM Image

WU Jie1, LI Zeng-bing2, LI Yu-huan1, ZHAO Geng-xing1, LI Chun-guang1

1. 1. National Engineering Laboratory for Efficient Utilization of Soil and Fertilizer Resources, College of Resources and Environment Shandong Agricultural University, Tai'an 271018, China;
2. Land Reserve Center of Changyi City, Changyi 261300, China
• Received:2014-02-28 Revised:2014-06-18 Online:2015-06-20 Published:2015-06-20

Abstract:

The establishment of the arable land fertility inversion model based on vegetation index from TM remote sensing image provides a scientific basis for resource management and sustainable use of regional farmland. The study used the field survey of the arable land fertility and TM remote sensing data to screen vegetation index which can better reflect the arable land fertility. We chose the counties of Tancheng and Dongping in Shandong Province as study area, where the arable land fertilities are similar. Regression analysis was used to establish the model of arable land fertility-vegetation index with data of Tancheng, and the data of Dongping were used to validate the inversion model. The results showed that the positive correlation between enhanced vegetation index (EVI) and evaluation results of cultivated land is the most significant one, and the correlation coefficient was 0.82. The best fitted model was the Quadratic model with EVI as independent variable whose decisive factor was 0.69. The conformity degree between the result of inversion model and the result of original evaluation were tested by use of four indicators which include the decisive coefficient (R2), root mean square error (RMSE), precision and accuracy. The results showed that the Quadratic model built by EVI was the best inversion model of arable land fertility. The accuracy of it was the highest which is 95.84%, and the RMSE and precision was the lowest which are 5.21 and 0.04 respectively. Through the comparison of the result of inversion model and conventional evaluation of arable land fertility in Dongping, we can see that the fertility levels obtained by the inversion model agree with the actual farmland productivity levels in space. Classifying the arable land fertility levels into three grades of high, medium and low, it was found that the inconsistent areas of the three grades all took less than 3.3% of the area of the grade. The inversion effect was good and accorded with the actual situation. This study proved the feasibility of estimating farmland productivity by quantitative remote sensing, and provided an effective tool for monitoring and utilizing farmland resources.

CLC Number:

• S127