Journal of Geo-information Science ›› 2022, Vol. 24 ›› Issue (9): 1835-1852.doi: 10.12082/dqxxkx.2022.220015

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Digital SOC Mapping in Croplands Using Agricultural Activity Factors Derived from Time-Series Data in Western Fujian

NIE Xiangqin1,2(), CHEN Hanyue1,2,3,*(), NIU Zheng3, ZHANG Liming1,2, LIU Wei1,2, XING Shihe1,2, FAN Xieyu1,2, LI JiaGuo4   

  1. 1. College of Resources and Environment, Fujian Agriculture and Forestry University, Fuzhou 350002, China
    2. Key Laboratory of Soil Ecosystem Health and Regulation, Fujian Agriculture and Forestry University, Fuzhou 350002, China
    3. State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
    4. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
  • Received:2022-01-10 Revised:2022-04-08 Online:2022-09-25 Published:2022-11-25
  • Contact: CHEN Hanyue E-mail:niexiangqin2022@163.com;Chenhanyue.420@163.com
  • Supported by:
    Open Fund of Key Laboratory of Remote Sensing Science(OFSLRSS202112);Major Project of High Resolution Earth Observation(06-Y30F04-9001-20/22);Natural Science Foundation of Fujian Province(2021J01117);Natural Science Foundation of Fujian Province(2019J01660);Natural Science Foundation of China(41971050)

Abstract:

Human activities significantly affect the amount and spatial variation of top Soil Organic Carbon (SOC) in croplands. However, the spatial distribution of agricultural management practices has not been carefully considered in SOC mapping in croplands, especially for croplands in large-scale complex landforms. A case study was conducted in agricultural area in western Fujian Province. Sentinel-2/MSI NDVI time series data were used to derive two types of variables that contains crop rotation information. One is the Crop Rotation (CR) pattern type, which was regarded as qualitative factors of agricultural activities. The other are variables generated using Harmonic Analysis based on sentinel-2 NDVI time series data (HANTS), which were regarded as quantitative factors of agricultural activities. Two types of agricultural activities factors, as well as natural environmental variables were adopted as predictive environmental variables. Four different combinations of above variables according to different categories were formed respectively (i.e., climate factor + terrain factor, climate factor + terrain factor + crop rotation pattern, climate factor + terrain factor + HANTS variables, and climate factor + terrain factor + crop rotation pattern + HANTS variables). Random Forest (RF) models were developed based on four different combinations of above variables for predicting SOC. These RF models were compared to explore whether incorporating agricultural activity factors could improve the SOC mapping accuracy in croplands. Results showed that the combination of natural environment variables with both crop rotation type and variables derived through HANTS yielded the highest accuracy. Compared with the combination of natural environment variables, the prediction accuracy of the optimal model was significantly improved (R2 increased by 89.47%, RMSE and MAE decreased by 10.66% and 12.05%, respectively). Two types of agricultural activity factors were both adopted in optimal model, especially CR significantly affected the SOC in croplands, ranking fourth in the importance of environmental variables of the optimal model. In all RF models, annual rainfall (Rainfall) ranked first in the importance of environmental variables. This indicated that climate factors play a dominant role in soil organic carbon digital soil mapping. The SOC content in croplands of the region predicted from the optimal model was (18.22±2.99) g/kg on average and varied in the range of 8.25~30.69 g/kg. The SOC content in double cropping rice and tobacco-rice planting area were higher than that in rice-vegetable planting area. The results provide a new vision for updating the environmental variables of SOC mapping in complex landform areas.

Key words: soil organic carbon, harmonic analysis of time series, crop rotation mode, factors of agricultural activities, spatial prediction, Sentinel-2, random forest model, combination of variables