The Global Long-term Microwave Vegetation Optical Depth Climate Archive VODCA

Abstract

Abstract. Since the late 1970s, spaceborne microwave sensors have been providing measurements of radiation emitted by the Earth's surface. From these measurements it is possible to derive vegetation optical depth (VOD), a model-based indicator related to vegetation density and its relative water content. Because of its high temporal resolution and long availability, VOD can be used to monitor short- to long-term changes in vegetation. However, studying long-term VOD dynamics is generally hampered by the relatively short time span covered by the individual microwave sensors. This can potentially be overcome by merging multiple VOD products into a single climate data record. But, combining multiple sensors into a single product is challenging as systematic differences between input products, e.g. biases, different temporal and spatial resolutions and coverage, need to be overcome. Here, we present a new series of long-term VOD products, which combine multiple VOD data sets derived from several sensors (SSM/I, TMI, AMSR-E, Windsat, and AMSR-2) using the Land Parameter Retrieval Model. We produce separate VOD products for microwave observations in different spectral bands, namely Ku-band (period 1987–2017), X-band (1997–2018), and C-band (2002–2018). In this way, our multi-band VOD products preserve the unique characteristics of each frequency with respect to the structural elements of the canopy. Our approach to merge the single-sensor VOD products is similar to the one of the ESA CCI Soil Moisture products (Liu et al., 2012; Dorigo et al., 2017): First, the data sets are co-calibrated via cumulative distribution function matching using AMSR-E as scaling reference. We apply a new matching technique that scales outliers more robustly than ordinary piece-wise linear interpolation. Second, we aggregate the data sets by taking the arithmetic mean between temporally overlapping observations of the scaled data, generating a VOD Climate Archive (VODCA). The characteristics of VODCA are assessed for self-consistency and against other products: spatio-temporal patterns and anomalies of the merged products show consistency between frequencies and both with observations of Leaf Area Index derived from the MODIS instrument as well as Vegetation Continuous Fields from AVHRR instruments. Trend analysis shows that since 1987 there has been a decline in VOD in the tropics and in large parts parts of east-central and north Asia along with a strong increase in India, large parts of Australia, south Africa, southeastern China and central north America. Using an autocorrelation analysis, we show that the merging of the multiple data sets successfully reduces the random error compared to the input data sets. In summary, VODCA shows vast potential for monitoring spatio-temporal ecosystem behaviour complementary to existing long-term vegetation products from optical remote sensing. The VODCA products (Moesinger et al., 2019) are open access and available under Attribution 4.0 International at https://doi.org/10.5281/zenodo.2575599.

Conflicting Changes of Vegetation Greenness Interannual Variability on Half of the Global Vegetated Surface

“…We used six long‐term and three short‐term satellite remote sensing datasets in this study, covering four types of vegetation indicators ‐ NDVI, LAI, SIF and VOD (Table 1). These remote sensing products include: (a) VIP15 NDVI product (1981–2014), which has 0.05° and 15‐day resolutions and is developed by harmonizing the observations of Advanced Very High Resolution Radiometer (AVHRR) from 1981 to 1999 and Moderate Resolution Imaging Spectroradiometer (MODIS) C5 from 2000 to 2014 (Didan et al., 2015); (b) GIMMS NDVI3g (1981–2015), which has 15‐day and 1/12° resolutions, and was produced by aggregating daily AVHRR surface reflectance (Pinzon & Tucker, 2014); (c) GIMMS LAI3g product (1981–2015), which was further produced by GIMMS NDVI3g product using a neural network algorithm (Zhu et al., 2013); (d) PKU GIMMS NDVI (1982–2020), which is a new version of GIMMS NDVI product produced by a machine learning model incorporating Landsat images (Li et al., 2023); (e) GLASS LAI product (1981–2018), which has 8‐day temporal resolution and 0.05° spatial resolution, and was reconstructed by combing AVHRR LAI from 1981 to 1999 and MODIS LAI from 2000 to 2018 using a bidirectional long short‐term memory (Bi‐LSTM) model (Ma & Liang, 2022); (f) GLOBMAP LAI (1982–2019) dataset at a spatial resolution of ∼0.07°, covering the period from 1982 to 2019, has half‐month (1982–2000) and 8‐day (2001–2019) temporal resolutions, and was produced by establishing a pixel‐level AVHRR Simple Ratio (SR)‐MODIS LAI relationship (Liu et al., 2012); (g) MOD13C1 NDVI (2000–2020) product (C61), which has a 0.05° spatial resolution and a 16‐day temporal resolution (Didan & Munoz, 2019); (h) OCO2 SIF product (2000–2020) at resolutions of 0.05° and 4 days, which was generated from MODIS surface reflectance and OCO2 SIF data using a neural network approach (Zhang et al., 2018); and (i) VOD (1987–2017) dataset, which was produced by merging observations from multiple microwave sensors at daily temporal resolution and 0.25° spatial resolution (Moesinger et al., 2020). We standardized these satellite remote sensing products to a 0.5° spatial resolution by using pixel aggregation (PA) method and to a monthly temporal interval by using maximum value composite (MVC) method (Ma et al., 2022; Tian et al., 2021).…”

Enhanced Drought Exposure Increasingly Threatens More Forests Than Observed

“…Global Inventory Modeling and Mapping Studies NDVI was from the NOAA/AVHRR satellites (Pinzon & Tucker, 2014). Vegetation optical depth was obtained from the VOD Climate Archive (Moesinger et al., 2019). Tree ring width indices were obtained from the International Tree‐Ring Data Bank (Grissino‐Mayer & Fritts, 1997).…”

Satellite‐Observed Vegetation Responses to Intraseasonal Precipitation Variability

“…IMERG precipitation: https://doi.org/10.5067/GPM/IMERGDF/DAY/06 (Huffman et al., 2019). VODCA: https://doi.org/10.5281/zenodo.2575599 (Moesinger et al., 2019). ESA CCI soil moisture: https://www.esa-soilmoisture-cci.org combined product v06.1 (accessed 23 July 2021).…”