全球0.1度和0.25度日尺度土壤水分产品（含相对和体积土壤含水量，2015-2024年）

土壤水分是干旱监测、洪水预报、作物产量估算、滑坡预警等应用中最重要的参数之一。本数据集基于L波段的SMAP被动亮温（TB, ~0.25°）和增强被动亮温（enhanced TB, ~0.1°）（2015.4.1~2024.12.31）数据，使用发展的新型微波土壤水分指数（Soil Moisture Index, SMI）方法开发了一种全新的全球土壤水分产品。所发展的SMI基于两个关键的物理基础：1）植被和地表粗糙度对微波辐射具有“去极化”效应（随着这两个参量的增加，微波极化差异变小），而土壤水分则增强了微波极化差异；2）植被、地表粗糙度与地表发射率呈正相关（两者值越大，地表发射率越大），而土壤水分与地表发射率为负相关关系（发射率随着土壤水分的增加而减少）。基于以上物理原理，可以在不依赖植被类型和地表粗糙度的条件下，在二维空间中将土壤水分与植被及地表粗糙度的影响进行分离。利用SMAP亮温数据计算的原始SMI土壤水分产品是相对值数据（无量纲，范围为0-1之间，0代表极度干旱，1代表极度湿润），主要用来反映土壤水分的动态变化情况（独立于模型/再分析数据集，可用于数据同化等研究）。为了进一步获取土壤水分的绝对值信息（体积含水量）同时不影响原始SMI值中保留的土壤水分动态变化信息，通过累积分布函数（CDF）匹配方法将原始SMI土壤水分数据匹配到绝对精度最优的MERRA-2模型土壤水分数据上，最终获得SMI体积含水量数据（单位：m³/ m³，可用于农业、林业、生态、环境等领域，或者与其他同类产品的比较与评估工作）。本数据集使用了来自国际土壤水分观测网站点数据进行验证。结果表明，SMI能够很好地捕捉土壤水分的动态变化，相关系数相比同类国际产品最高；使用CDF方法标定后的SMI具有较低的无偏均方根误差（ubRMSE），优于目前国际主流产品。因此，SMI土壤水分产品在全球范围内具有良好的应用潜力。

Soil moisture is one of the most critical parameters for applications such as drought monitoring, flood forecasting, crop yield estimation, and landslide early warning. This dataset develops a novel global soil moisture product using the newly proposed microwave Soil Moisture Index (SMI) method, based on L-band SMAP passive brightness temperature (TB, ~0.25°) and enhanced passive brightness temperature (enhanced TB, ~0.1°) data from April 1, 2015 to December 31, 2024. The developed SMI is grounded on two key physical principles: 1) Vegetation and surface roughness exert a "depolarization effect" on microwave radiation: as these two parameters increase, the microwave polarization difference (MPD) decreases, while soil moisture enhances the MPD; 2) Vegetation and surface roughness are positively correlated with surface emissivity (larger values of the two parameters correspond to higher surface emissivity), whereas soil moisture is negatively correlated with surface emissivity (emissivity decreases with increasing soil moisture). Based on the above physical principles, the impacts of soil moisture and those of vegetation and surface roughness can be separated in a two-dimensional space, without relying on vegetation type and surface roughness data. The raw SMI soil moisture product calculated from SMAP brightness temperature data is a set of relative values (dimensionless, ranging from 0 to 1, where 0 represents extreme drought and 1 represents extreme wetness), which is mainly used to reflect the dynamic changes of soil moisture. It is independent of models and reanalysis datasets, and can be applied to studies such as data assimilation. To further obtain the absolute volumetric soil moisture content without compromising the dynamic change information retained in the raw SMI values, we matched the raw SMI soil moisture data to the MERRA-2 model soil moisture data with the optimal absolute accuracy using the cumulative distribution function (CDF) matching method. Finally, we obtained the SMI volumetric soil moisture data (unit: m³/m³), which can be used in agriculture, forestry, ecology, environmental protection and other fields, as well as for the comparison and evaluation with other similar products. This dataset was validated using in-situ data from the international soil moisture observation network. The results show that SMI can well capture the dynamic changes of soil moisture, with the highest correlation coefficient compared to similar international products; the SMI calibrated via the CDF method has a lower unbiased root mean square error (ubRMSE), outperforming current international mainstream products. Therefore, the SMI soil moisture product has excellent application potential globally.

搜集汇总

背景与挑战

背景概述

该数据集提供全球0.1度和0.25度日尺度的土壤水分产品，时间跨度为2015年至2024年，包含相对和体积土壤含水量两种数据。基于SMAP被动亮温数据，采用新型微波土壤水分指数（SMI）方法开发，具有较高的准确性和广泛的应用潜力。