ESA CCI SM FREEZE/THAW Long-term Climate Data Record of surface conditions from merged multi-satellite observations

This dataset was produced with funding from the European Space Agency (ESA) Climate Change Initiative (CCI) Plus Soil Moisture Project (CCN 3 to ESRIN Contract No: 4000126684/19/I-NB "ESA CCI+ Phase 1 New R&D on CCI ECVS Soil Moisture").  Project website: https://climate.esa.int/en/projects/soil-moisture/

This dataset contains information on the Surface Soil Moisture (SM) state derived from satellite observations in the microwave domain.

The operational (ACTIVE, PASSIVE, COMBINED) ESA CCI SM products are available at https://catalogue.ceda.ac.uk/uuid/c256fcfeef24460ca6eb14bf0fe09572/ 

Abstract

Understanding whether the soil surface is frozen or thawed is crucial for interpreting satellite-based soil moisture measurements and for many Earth system applications. The physical state of water in the soil strongly affects its dielectric properties, which in turn determine how satellites sense moisture content. Current ESA CCI Soil Moisture products exclude data when the surface is likely frozen, as reliable retrievals are not possible under such conditions. Yet, the freeze/thaw state itself carries valuable environmental information: it reflects the changing energy and water exchange between land and atmosphere, shapes seasonal hydrological cycles, and influences agriculture, ecosystems, and climate feedbacks across much of the Northern Hemisphere.

This dataset provides global estimates of the soil moisture freeze/thaw state for the period from 11-1978 to 12-2024 derived from PASSIVE (radiometer) and ACTIVE (scatterometer) satellite observations within the ESA CCI Soil Moisture framework. These sensors, operating in the K- and C-band frequency range, are sensitive to surface temperature, enabling the detection of frozen versus thawed conditions at daily temporal and ~25 km spatial sampling. Data from L-band missions (e.g., SMAP, SMOS) are not included, resulting in a total number of 12 satellites.

The classification algorithm, described in Van der Vliet et al. (2020), was originally developed to flag frozen conditions in passive soil moisture retrievals and has since evolved into a dedicated data product. It applies a decision-tree approach using multi-frequency satellite measurements to classify the surface state for each sensor. Similarly, Naeimi et al. (2012) have developed an algorithm based on ASCAT backscatter for freeze/thaw classification in C-band scatterometer retrievals. Individual classifications are then merged into a single spatiotemporal record using a conservative unanimity rule—if any contributing satellite detects a frozen surface, the merged product is classified as “frozen.”

While this approach ensures reliability, it may lead to some over-flagging, which could be refined in future versions. The current product achieves an estimated accuracy of 75% against in situ surface temperature observations and 92% compared to ERA5 reanalysis data.

Summary

Daily binary (true/false) freeze/thaw surface soil moisture state classification dataset (~25 km spatial sampling) for the period November 1978 to December 2024.

Based on a satellite brightness temperature (K-band) classification algorithm (Van der Vliet et al., 2020) from 12 satellite radiometers and a satellite backscatter (C-band) classification algorithm (Naeimi et al., 2012).

A pixel is classified as "frozen" if it was classified accordingly for at least one satellite. This can lead to potential over-flagging in the current version.

 Approximately 75% agreement with in situ surface temperature measurements (Dorigo et al., 2021) and 92% with ERA5-Land reanalysis temperature fields (Muñoz-Sabater et al., 2021)

Programmatic (bulk) download

You can use command-line tools such as wget or curl to download (and extract) data for multiple years. The following command will download and extract the complete data set to the local directory ~/Download on Linux or macOS systems.

#!/bin/bash# Set download directoryDOWNLOAD_DIR=~/Downloadsbase_url="https://researchdata.tuwien.at/records/m3g2x-a6958/files"# Loop through years 1978 to 2024 and download & extract datafor year in {1978..2024}; do echo "Downloading $year.zip..." wget -q -P "$DOWNLOAD_DIR" "$base_url/$year.zip" unzip -o "$DOWNLOAD_DIR/$year.zip" -d $DOWNLOAD_DIR rm "$DOWNLOAD_DIR/$year.zip"done

Data details

Filename template

The dataset provides global daily estimates for the 1978-2024 period at 0.25° (~25 km) horizontal grid resolution. Daily images are grouped by year (YYYY), each subdirectory containing one netCDF image file for a specific day (DD) and month (MM) of that year in a 2-dimensional (longitude, latitude) grid system (CRS: WGS84). The file name follows the convention:

ESACCI-SOILMOISTURE-L3S-FT-YYYYMMDD000000-fv09.2.nc

Data Variables

Each netCDF file contains 3 coordinate variables

lon: longitude (WGS84), [-180,180] degree W/E

lat: latitude (WGS84), [-90,90] degree N/S

time: datetime, encoded as "number of days since 1970-01-01 00:00:00 UTC"

 and the following data variables

ft: (int) Soil moisture freeze-thaw state binary indicator (0=not frozen, 1=frozen, -1=missing data)

ft_agreement (float): Classification agreement between available sensors. 1 means that the frozen/unfrozen classification was the same for all merged sensors. The number decreases as the classification results between available satellites contradict.

sensor_count (int): Total number of merged sensors/overpasses

sensor_count_frozen (int): Total number of measuring sensors/overpasses that detected frozen soils

mode: (int) Indicator for satellite orbit(s) used in the retrieval (1=ascending, 2=descending, 3=both, 0=missing data)

sensor: (int) Indicator for satellite sensor(s) used in the retrieval. For more details, see netcdf attributes.

Additional information for each variable is given in the netCDF attributes.

Version Changelog

Changes in v9.2 (first released version):

This version applies the classification algorithms described by Van der Vliet et al. (2020) and Naeimi et al. (2012) to 17 sensors and a unanimous merging approach. Covers the period from 11-1978 to 12-2024.

Software to open netCDF files

These data can be read by any software that supports Climate and Forecast (CF) conform metadata standards for netCDF files, such as:

Xarray (Python)

netCDF4 (Python)

esa_cci_sm (Python)

Similar tools exist for other programming languages (Matlab, R, etc.)

Software packages and GIS tools can open netCDF files, e.g. CDO, NCO, QGIS, ArcGIS

You can also use the GUI software Panoply to view the contents of each file

Related Records

This record and all related records are part of the ESA CCI Soil Moisture science data records community.

本数据集由欧洲空间局（European Space Agency, ESA）气候变化倡议（Climate Change Initiative, CCI）Plus土壤湿度项目资助产生（项目合同编号：ESRIN 合同号4000126684/19/I-NB "ESA CCI+ Phase 1 New R&D on CCI ECVS Soil Moisture"，项目识别码CCN 3）。项目官网：https://climate.esa.int/en/projects/soil-moisture/

本数据集包含基于微波频段卫星观测反演得到的地表土壤湿度（Surface Soil Moisture, SM）状态信息。欧洲空间局CCI土壤湿度业务化产品（主动式ACTIVE、被动式PASSIVE、联合式COMBINED）可在以下链接获取：https://catalogue.ceda.ac.uk/uuid/c256fcfeef24460ca6eb14bf0fe09572/

## 摘要
理解地表土壤的冻融状态，对于解读星载土壤湿度观测结果以及众多地球系统应用场景均至关重要。土壤中水的物理状态会显著改变其介电特性，进而决定卫星如何感知土壤含水量。当前欧洲空间局CCI土壤湿度产品会在地表大概率处于冻结状态时剔除对应数据，因为此类条件下无法开展可靠的反演。然而，冻融状态本身携带了极具价值的环境信息：它反映了陆地与大气之间动态变化的能量与水分交换，塑造了季节性水文循环，并对北半球大部分区域的农业、生态系统以及气候反馈产生影响。

本数据集提供了1978年11月至2024年12月期间的全球土壤湿度冻融状态估算结果，数据源自欧洲空间局CCI土壤湿度框架下的被动式（radiometer）与主动式（scatterometer）卫星观测。这些工作在K波段（K-band）与C波段（C-band）的传感器对地表温度敏感，能够以每日的时间分辨率与约25公里的空间采样分辨率识别冻融状态。由于未纳入L波段（L-band）任务（如SMAP、SMOS）的观测数据，本数据集共涵盖12颗卫星。

本数据集采用的分类算法由Van der Vliet等人（2020）提出，最初仅用于标记被动式土壤湿度反演中的冻结状态，后续已发展为专属的数据产品。该算法采用决策树（decision-tree）方法，结合多频卫星观测对每台传感器的地表状态进行分类。同理，Naeimi等人（2012）开发了基于ASCAT后向散射信号的算法，用于C波段散射计反演结果的冻融分类。随后，通过保守的一致表决规则（unanimity rule）将各传感器的单独分类结果合并为单一时空数据集：若任意一颗参与观测的卫星检测到地表冻结，则合并后的产品将被归类为“冻结”。

尽管该方法保障了结果的可靠性，但可能会导致一定程度的过度标记，这一问题可在未来版本中优化。当前产品的估算精度为：与原位（in situ）地表温度观测数据相比吻合度约75%，与ERA5再分析（reanalysis）数据相比吻合度达92%。

## 总结
本数据集为1978年11月至2024年12月期间的每日二元（是/否）地表土壤湿度冻融状态分类产品，空间采样分辨率约25公里。
数据集基于12台卫星辐射计的K波段亮温分类算法（Van der Vliet等人，2020），以及C波段卫星后向散射分类算法（Naeimi等人，2012）生成。
若任意一颗参与观测的卫星将某像素分类为“冻结”，则该像素将被标记为冻结状态，当前版本可能因此存在潜在的过度标记问题。
本产品与原位地表温度观测数据（Dorigo等人，2021）的吻合度约为75%，与ERA5-Land再分析温度场（Muñoz-Sabater等人，2021）的吻合度达92%。

## 批量程序下载
可使用wget、curl等命令行工具批量下载并解压数据。以下命令可将完整数据集下载并解压至Linux或macOS系统的~/Downloads本地目录：
bash
#!/bin/bash
# 设置下载目录
DOWNLOAD_DIR=~/Downloads
base_url="https://researchdata.tuwien.at/records/m3g2x-a6958/files"
# 循环遍历1978至2024年，下载并解压数据
for year in {1978..2024}; do
echo "Downloading $year.zip..."
wget -q -P "$DOWNLOAD_DIR" "$base_url/$year.zip"
unzip -o "$DOWNLOAD_DIR/$year.zip" -d $DOWNLOAD_DIR
rm "$DOWNLOAD_DIR/$year.zip"
done

## 数据细节
### 文件名模板
本数据集提供1978-2024年全球每日估算数据，水平网格分辨率为0.25°（约25公里）。每日影像按年份（YYYY）分组，每个子目录包含对应年份某月（MM）某日（DD）的单幅netCDF影像文件，采用二维（经度、纬度）网格系统（坐标参考系统Coordinate Reference System, CRS：WGS84）。文件名遵循以下约定：
`ESACCI-SOILMOISTURE-L3S-FT-YYYYMMDD000000-fv09.2.nc`

### 数据变量
每个netCDF文件包含3个坐标变量：
- `lon`：经度（WGS84），范围[-180,180]度，东/西经
- `lat`：纬度（WGS84），范围[-90,90]度，北/南纬
- `time`：时间，编码为“距1970-01-01 00:00:00 UTC的天数”

以及以下数据变量：
- `ft`：（整数型）土壤湿度冻融状态二元指示符，0=未冻结，1=冻结，-1=数据缺失
- `ft_agreement`：（浮点型）可用传感器间的分类吻合度。值为1表示所有合并传感器的冻融分类结果完全一致；当各卫星分类结果存在分歧时，该数值会降低。
- `sensor_count`：（整数型）合并的传感器/过境总数量
- `sensor_count_frozen`：（整数型）检测到冻结土壤的测量传感器/过境总数量
- `mode`：（整数型）反演所用卫星轨道类型指示符，1=升轨ascending，2=降轨descending，3=两者兼有，0=数据缺失
- `sensor`：（整数型）反演所用卫星传感器指示符，详细信息请参考netCDF属性说明

各变量的附加信息已在netCDF属性中给出。

### 版本更新日志
v9.2（首个发布版本）更新内容：
本版本将Van der Vliet等人（2020）与Naeimi等人（2012）提出的分类算法应用于17台传感器，并采用一致表决合并方法。数据覆盖时段为1978年11月至2024年12月。

### 打开netCDF文件的软件
支持netCDF文件及气候与预报（Climate and Forecast, CF）兼容元数据标准的软件均可读取本数据集，例如：
- Python库：Xarray、netCDF4、esa_cci_sm
- 其他编程语言对应工具：Matlab、R等
- 软件包与GIS工具：CDO、NCO、QGIS、ArcGIS
- 亦可使用GUI软件Panoply查看单文件内容

## 相关记录
本数据集及所有相关记录均隶属于欧洲空间局CCI土壤湿度科学数据记录共同体。