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一个新的框架,通过整合土壤,植被和人类活动信息来提高耕地中的土壤有机物预测准确度

Jiawen Wang¹, Chunhui Feng², Bifeng Hu³

¹College of Agriculture, Tarim University, Alar 843300, China; College of Life Sciences and Technology, Tarim University, Alar 843300, China.

The Science of the total environment

|August 11, 2023

概括

在植被地区准确地估计土壤有机物 (SOM) 是通过一个新的框架改进. 这种方法将时间序列的植被生物量 (AMBAI) 和种植年 (PY) 与土壤和人类活动数据相结合,用于更好的遥感模型.

科学领域:

环境科学环境科学
土壤科学土壤科学
遥感遥感遥感遥感

背景情况:

使用遥感对土壤有机物 (SOM) 进行准确的空间建模,在植被覆盖面较高的农业生态系统中具有挑战性.
现有的研究往往忽视了植被动态,土壤特性和人类活动对SOM的综合影响.

研究的目的:

通过整合植被,土壤和人类活动数据,开发和验证一个新的框架来评估干旱/半干旱农业地区的SOM.
探索时间序列植被生物质指数和历史土地利用信息对SOM估计的有用性.

主要方法:

从Landsat时间序列数据 (1990-2019) 开发了一种年度最大生物质积累指数 (AMBAI).
使用光谱指数值量化耕地种植年 (PY).
集成的AMBAI,PY,近距离土壤传感数据 (高光谱,明显导电性) 和土壤颗粒大小分数到预测模型中 (PLSR,RF,CNN).

主要成果:

在AMBAI中,与SOM的相关性最高 (r=0.76).
AMBAI,土壤超谱数据和PY被确定为SOM的关键预测因素.
卷积神经网络 (CNN) 模型获得了最佳性能 (R2=0.83,RPD=2.38,RMSE=1.38 g kg−1).

结论:

拟议的框架包括AMBAI和PY,显著提高了干旱和半干旱地区的SOM特征.
整合植被,土壤和人类活动数据为SOM空间建模提供了更全面的方法.
这项研究为类似环境中基于遥感的土壤监测提供了有价值的见解.

关键词:

年度最大生物质积累指数.干旱和半干旱地区.卷积神经网络是一种卷积神经网络.农田是农作物种植的土地.种植年份种植年份

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