Xingming Zheng

Temporal dynamics of spatial heterogeneity over cropland quantified by time-series NDVI, near infrared and red reflectance of Landsat 8 OLI imagery

Abstract Spatial heterogeneity is an important characteristic of the land surface. Because multi-spectral bands are used to describe the land surface, an approach has to be established to characterize the surface spatial heterogeneity from multi-spectral remote-sensing observations. This work aims at quantifying the spatial heterogeneity of cropland using varigorams for multi-temporal NDVI, near infrared (NIR) and red reflectance. A concept of mean length variability is proposed to compare the difference in spatial heterogeneity detected by variables with different magnitudes. The important temporal changes in spatial heterogeneity observed by NDVI, NIR and red bands over cropland are a result of changes in the fraction of vegetation cover. The results indicate the following: (1) the NIR and red variables detect a similar spatial heterogeneity of the cropland with similar values of the mean length variability before the sowing of crops; (2) the NDVI, NIR and red values capture different degrees of spatial heterogeneity when vegetation cover is low; (3) over medium vegetation cover, the NDVI and NIR values capture similar spatial heterogeneity, which is low compared to the red band due to the homogeneity of soil; and (4) the spatial heterogeneity quantified by the NIR value is more heterogeneous than those of the NDVI and red values when vegetation cover is high. The red reflectance is sensitive to soil properties while the NIR reflectance responds to vegetation. The spatial heterogeneity of red reflectance decreases and that of the NIR reflectance increases with the growth of vegetation. The NDVI value shows the greatest heterogeneity in the early stage of crop growth. With an increase in the image pixel size, the spatial heterogeneity quantified by the mean length variability of the NDVI, NIR and red variables tends to be the same.

Quantifying the Impact of NDVI soil Determination Methods and NDVI soil Variability on the Estimation of Fractional Vegetation Cover in Northeast China

Fractional vegetation cover (FVC) is one of the most critical parameters in monitoring vegetation status. Accurate estimates of FVC are crucial to the use in land surface models. The dimidiate pixel model is the most widely used method for retrieval of FVC. The normalized difference vegetation index (NDVI) of bare soil endmember (NDVIsoil) is usually assumed to be invariant without taking into account the spatial variability of soil backgrounds. Two NDVIsoil determining methods were compared for estimating FVC. The first method used an invariant NDVIsoil for the Northeast China. The second method used the historical minimum NDVI along with information on soil types to estimate NDVIsoil for each soil type. We quantified the influence of variations of NDVIsoil derived from the second method on FVC estimation for each soil type and compared the differences in FVC estimated by these two methods. Analysis shows that the uncertainty in FVC estimation introduced by NDVIsoil variability can exceed 0.1 (root mean square error—RMSE), with the largest errors occurring in vegetation types with low NDVI. NDVIsoil with higher variation causes greater uncertainty on FVC. The difference between the two versions of FVC in Northeast China, is about 0.07 with an RMSE of 0.07. Validation using fine-resolution FVC reference maps shows that the second approach yields better estimates of FVC than using an invariant NDVIsoil value. The accuracy of FVC estimates is improved from 0.1 to 0.07 (RMSE), on average, in the croplands and from 0.04 to 0.03 in the grasslands. Soil backgrounds have impacts not only on NDVIsoil but also on other VIsoil. Further focus will be the selection of optimal vegetation indices and the modeling of the relationships between VIsoil and soil properties for predicting VIsoil.

Evaluation and Improvement of SMOS and SMAP Soil Moisture Products for Soils with High Organic Matter over a Forested Area in Northeast China

Soil moisture (SM) retrieval from SMOS (the Soil Moisture and Ocean Salinity mission) and SMAP (the Soil Moisture Active/Passive mission) passive microwave data over forested areas with required accuracy is of great significance and poses some challenges. In this paper, we used Ground Wireless Sensor Network (GWSN) SM measurements from 9 September to 5 November 2015 to validate SMOS and SMAP Level 3 (L3) SM products over forested areas in northeastern China. Our results found that neither SMOS nor SMAP L3 SM products were ideal, with respective RMSE (root mean square error) values of 0.31 cm3/cm3 and 0.17 cm3/cm3. Nevertheless, some improvements in SM retrieval might be achievable through refinements of the soil dielectric model with respect to high percentage of soil organic matter (SOM) in the forested area. To that end, the potential of the semi-empirical soil dielectric model proposed by Jun Liu (Liu’s model) in improving SM retrieval results over forested areas was investigated. Introducing Liu’s model into the retrieval algorithms of both SMOS and SMAP missions produced promising results. For SMAP, the RMSE of L3 SM products improved from 0.16 cm3/cm3 to 0.07 cm3/cm3 for AM (local solar time around 06:00 am) data, and from 0.17 cm3/cm3 to 0.05 cm3/cm3 for PM (local solar time around 06:00 pm) data. For SMOS ascending orbit products, the accuracy was improved by 56%, while descending orbit products improved by 45%.

Comparison and Validation of Long Time Serial Global GEOV1 and Regional Australian MODIS Fractional Vegetation Cover Products Over the Australian Continent

Fractional vegetation cover (FVC) is one of the most critical parameters in monitoring vegetation status. Comprehensive assessment of the FVC products is critical for their improvement and use in land surface models. This study investigates the performances of two major long time serial FVC products: GEOV1 and Australian MODIS. The spatial and temporal consistencies of these products were compared during the 2000–2012 period over the main biome types across the Australian continent. Their accuracies were validated by 443 FVC in-situ measurements during the 2011–2012 period. Our results show that there are strong correlations between the GEOV1 and Australian MODIS FVC products over the main Australian continent while they exhibit large differences and uncertainties in the coastal regions covered by dense forests. GEOV1 and Australian MODIS describe similar seasonal variations over the main biome types with differences in magnitude, while Australian MODIS exhibit unstable temporal variations over grasslands and shifted seasonal variations over evergreen broadleaf forests. The GEOV1 and Australian MODIS products overestimate FVC values over the biome types with high vegetation density and underestimate FVC in sparsely vegetated areas and grasslands. Overall, the GEOV1 and Australian MODIS FVC products agree with in-situ FVC values with a RMSE around 0.10 over the Australian continent.

Spatiotemporal analysis of snow depth inversion based on the FengYun-3B MicroWave Radiation Imager: a case study in Heilongjiang Province, China

Abstract To improve snow depth (SD) inversion algorithms using passive microwave data, it is important to objectively assess their accuracy and to analyze their uncertainty. Some previous studies validated the inversion algorithms only using spatial data at a fixed time node, which is not objective or convincing. The spatiotemporal analysis of the SD inversion based on the FengYun-3B MicroWave Radiation Imager is performed in Heilongjiang Province, China. Based on the temporal analysis, the results show that the accuracy of SD inversion algorithms is different at different time phases throughout the winter. In cropland areas, the variation in snow properties, particularly the increase in snow grain and the presence of depth hoar, leads to underestimation and overestimation at the earlier and later phases, respectively. The spatial analysis shows that the SD in the high forest coverage regions is seriously overestimated due to the addition of a forest correction factor using the Chang algorithm. In addition, the complex underlying surfaces and hilly terrain are also influencing factors that result in the low accuracy for several regions. Therefore, the analysis and identification of these uncertainties are benefits not only in understanding the influential factors of SD inversion algorithms but also in developing better algorithms for the next generation of SD retrieval.

Identification of saline-alkali soil based on target decomposition of full-polarization radar data

Abstract The potential of C-band polarimetric synthetic aperture radar data for the discrimination of saline-alkali soils in the western Jilin Province, China, is shown. This area is one of the three saline-alkali landscapes in the world; the presence of saline-alkali soils severely restricts the development of local farming and limits the land use. It is extremely important to identify saline-alkali landscapes accurately and effectively. Radar remote sensing is one of the most promising approaches for saline-alkali soil identification due to the sensitivity of radar data to the dielectric and geometric characteristics of objects, its weather-independent imaging capability, and its potential to acquire subsurface information, independent of the frequency band. Full polarimetric radar data from the RADARSAT-2 satellite were used. We focused on target decomposition theory and the statistical classification approach using a Wishart distribution to identify saline-alkali soils. The precise validation of the classification results is based on 129 ground sampling points. The results indicate that the polarimetric classifications using the H - α ¯ method performed poorly, with Kappa values of approximately 0.29. The classification method based on Freeman-Durden decomposition showed better results, with Kappa values of approximately 0.54 and an overall accuracy of 68.22%. The best result was achieved using an input of anisotropy, with Kappa values of approximately 0.62 and an overall accuracy of 74.42%. The validity of the anisotropy approach implies that the scattering randomness of saline-alkali soil is very strong, which reflects the complex scattering characteristics of saline-alkali landscapes. Further study of the scattering characteristics of saline-alkali soil is necessary.

Investigating the accuracy of vegetation index-based models for estimating the fractional vegetation cover and the effects of varying soil backgrounds using in situ measurements and the PROSAIL model

ABSTRACT Fractional vegetation cover (FVC) is an important variable for describing the quality and changes of vegetation in terrestrial ecosystems. The simplest and most widely used model for the estimation of FVC is the dimidiate pixel model. The normalized difference vegetation index (NDVI) is commonly used as a vegetation index (VI) in this model. A range of VIs is possible alternative to the use of NDVI in the dimidiate pixel model. In this article, six VI-based dimidiate pixel models were compared using in situ measurements and canopy reflectances simulated by the PROSAIL model over nine different soil backgrounds. A comparison with in situ measurements showed that the Gutman–Ignatov method overestimated FVC, with a mean root mean square error (RMSE) of 0.14. The mean RMSE had an intermediate value of 0.08 in the Carlson–Ripley method and was further reduced to 0.05 in the method proposed by Baret et al. The use of both modified soil-adjusted vegetation index (MSAVI) and a mixture of NDVI and the ratio vegetation index (RVI) to replace NDVI in the Gutman–Ignatov model reduced the RMSE to 0.06. The mean RMSE in the difference vegetation index (DVI)-based model was 0.08. The simulated results indicated that soil backgrounds have significant effects on these VI-based models. The sensitivity of the first three models and the NDVI plus RVI-based model to soil backgrounds decreased with an increase in soil reflectance. In contrast, the DVI-based model is sensitive to soil backgrounds with high reflectances. MSAVI, which is less sensitive to soil backgrounds, represents a feasible alternative to the use of NDVI in the Gutman–Ignatov model.

Comparison of fractional vegetation cover estimations using dimidiate pixel models and look-up table inversions of the PROSAIL model from Landsat 8 OLI data

Abstract. Fractional vegetation cover (FVC) is an important variable for describing the quality and changes of vegetation in terrestrial ecosystems. Dimidiate pixel models and physical models are widely used to estimate FVC. Six dimidiate pixel models based on different vegetation indices (VI) and four look-up table (LUT) methods were compared to estimate FVC from Landsat 8 OLI data. Comparisons with in situ FVC of steppe and corn showed that the model proposed by Baret et al., which is based on the normalized difference vegetation index (NDVI), predicted FVC most accurately followed by Carlson and Ripley’s method. Gutman and Ignatov’s method overestimated FVC. Modified soil adjusted vegetation index (MSAVI) and the mixture of NDVI and RVI showed potential to replace NDVI in Gutman and Ignatov’s model, whereas the difference vegetation index (DVI) performed less well. At low vegetation cover, the LUT using reflectances to constrain the cost function performed better than LUTs using VI to constrain the cost function, whereas at high vegetation cover, the LUT based on NDVI estimated FVC most accurately. The applications of DVI and MSAVI to constrain the cost function also obtained improvement at high vegetation cover. Overall, the accuracies of LUT methods were a little lower than those of dimidiate pixel models.

Effects of snow cover on ground thermal regime: A case study in Heilongjiang Province of China

The important effects of snow cover to ground thermal regime has received much attention of scholars during the past few decades. In the most of previous research, the effects were usually evaluated through the numerical models and many important results are found. However, less examples and insufficient data based on field measurements are available to show natural cases. In the present work, a typical case study in Mohe and Beijicun meteorological stations, which both are located in the most northern tip of China, is given to show the effects of snow cover on the ground thermal regime. The spatial (the ground profile) and time series analysis in the extremely snowy winter of 2012–2013 in Heilongjiang Province are also performed by contrast with those in the winter of 2011–2012 based on the measured data collected by 63 meteorological stations. Our results illustrate the positive (warmer) effect of snow cover on the ground temperature (GT) on the daily basis, the highest difference between GT and daily mean air temperature (DGAT) is as high as 32.35°C. Moreover, by the lag time analysis method it is found that the response time of GT from 0 cm to 20 cm ground depth to the alternate change of snow depth has 10 days lag, while at 40 cm depth the response of DGAT is not significant. This result is different from the previous research by modeling, in which the response depth of ground to the alteration of snow depth is far more than 40 cm.

An error analysis method for snow depth inversion using snow emission model

The snow depth and the amount of regional snow play an important role in the climate change and also affect the management of water resources, and the estimate of the runoff and flood forecasting. At present, snow depth is generally thought to be linearly dependent on the difference of the brightness temperature (i.e. NASA algorithm) in passive microwave remote sensing. But some authors argued according to results of snow emission model simulation that it is not simply linear relationship between them due to the influence of many factors (snow grains radius, land use/cover type). If snow depth is retrieved using the linearly dependent relationship, the error will easily come out. In this paper the graphical representation of probability error is constructed based on the simulated multi-solution curve by snow emission model, which reveals quantitatively and visually the snow depth inversion error due to multi-solution inversion. It will make a significant contribution to reduce the influence of uncertainty and increase the accuracy of snow depth inversion.