Reconstructing in-situ vegetation dynamics using carbon isotopic composition of biopolymeric residues in the central Chinese Loess Plateau

We developed and tested a new method to separate CO2 and CH4 from bulk gas samples for radiocarbon and stable-carbon analysis that utilizes a zeolite molecular sieve. To validate the technique, tests were performed using a suite of standard gases, composed of CO2 and CH4 of distinctly different isotopic composition. We employed the method to investigate the carbon isotopic composition of samples of dissolved CO2 and CH4 collected in situ from the near surface to deep layers of an ombrotrophic raised peat bog. Results showed that the age of both the CO2 and CH4 components of the dissolved gases increased with depth from ≃0–300 BP at 0.25 m to ≃4000 BP at 4 m. CH4 was mainly similar or slightly older in age compared to CO2, with the greatest difference in ages occurring at 1 m depth where CH4 was older by 430–615 yr. The δ13C values of CO2 increased with depth from −12.4‰ and −8.0‰ at 0.25 m to +6.9‰ and +8.3‰ at 4 m, whereas the δ13C of CH4 stayed in the range −58.4‰ to −70.6‰. The 14C results from the deepest layers are consistent with a similar source for both gases. 14C ages for the CO2 component were younger compared to CH4, within the shallower depths of the peat bog (≤1 m) and demonstrate the incorporation of acrotelm-derived respired CO2 into the catotelm.

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Soil Moisture Estimation for the Chinese Loess Plateau Using MODIS-derived ATI and TVDI

Remote Sensing ◽

10.3390/rs12183040 ◽

2020 ◽

Vol 12 (18) ◽

pp. 3040

Author(s):

Lina Yuan ◽

Long Li ◽

Ting Zhang ◽

Longqian Chen ◽

Jianlin Zhao ◽

...

Keyword(s):

Soil Moisture ◽

Loess Plateau ◽

Vegetation Index ◽

Joint Model ◽

Iteration Procedure ◽

Chinese Loess Plateau ◽

Vegetation Coverage ◽

Chinese Loess ◽

The Chinese Loess Plateau

Timely and effective estimation and monitoring of soil moisture (SM) provides not only an understanding of regional SM status for agricultural management or potential drought but also a basis for characterizing water and energy exchange. The apparent thermal inertia (ATI) and Temperature Vegetation Dryness Index (TVDI) are two widely used indices to reflect SM from remote sensing data. While the ATI-based model is routinely used to estimate the SM of bare soil and sparsely vegetated areas, the TVDI-based model is more suitable for areas with dense vegetation coverage. In this study, we present an iteration procedure that allows us to identify optimal Normalized Difference Vegetation Index (NDVI) thresholds for subregions and estimate their relative soil moisture (RSM) using three models (the ATI-based model, the TVDI-based model, and the ATI/TVDI joint model) from 1 January to 31 December 2017, in the Chinese Loess Plateau. The initial NDVI (NDVI0) was first introduced to obtain TVDI value and two other thresholds of NDVIATI and NDVITVDI were designed for dividing the whole area into three subregions (the ATI subregion, the TVDI subregion, and the ATI/TVDI subregion). The NDVI values corresponding to maximum R-values (correlation coefficient) between estimated RSM and in situ RSM measurements were chosen as optimal NDVI thresholds after performing as high as 48,620 iterations with 10 rounds of 10-fold cross-calibration and validation for each period. An RSM map of the whole study area was produced by merging the RSM of each of the three subregions. The spatiotemporal and comparative analysis further indicated that the ATI/TVDI joint model has higher applicability (accounting for 36/38 periods) and accuracy than the ATI-based and TVDI-based models. The highest average R-value between the estimated RSM and in situ RSM measurements was 0.73 ± 0.011 (RMSE—root mean square error, 3.43 ± 0.071% and MAE—mean absolute error, 0.05 ± 0.025) on the 137th day of 2017 (DOY—day of the year, 137). Although there is potential for improved mapping of RSM for the entire Chinese Loess Plateau, the iteration procedure of identifying optimal thresholds determination offers a promising method for achieving finer-resolution and robust RSM estimation in large heterogeneous areas.

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