Ƶ

Skip to main content
Site navigation
Xiaoyang Zhang Lab

Xiaoyang Zhang Lab Research Publications

Latest Publications

Fangjun Li, 2022, publication

Biomass burning influences atmospheric composition and regional air quality. The hourly biomass-burning emissions are usually required by air quality models, yet most available emission inventories provide daily or monthly estimates in 0.1° or coarser grids, limiting the prediction accuracy. The Advanced Baseline Imager (ABI) on the Geostationary Operational Environmental Satellites – R Series (GOES-R) observes fires across the conterminous United States (CONUS) every 5 min at a spatial resolution of 2 km, which allows for characterizing fires and emissions on diurnal scale. In this study, we developed a new operational algorithm to generate regional hourly 3 km fire emission across the CONUS by fusing temporally resolved ABI fire radiative power (FRP) and fine spatial-resolution (375 m) FRP from the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Joint Polar Satellite System (JPSS) satellites. To do this, ABI FRP was first calibrated against and fused with VIIRS FRP in 3 km grids. Then, FRP diurnal cycles at an interval of 5 min were reconstructed using the fused ABI-VIIRS FRP and the land cover-ecoregion-specific FRP diurnal climatologies. The reconstructed FRP diurnal cycles were applied to estimate hourly emissions of eight species (e.g., carbon monoxide (CO) and fine particulate matter with diameters <2.5 μm (PM2.5)). The accuracy was verified by comparing with CO observations from the TROPOspheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor satellite, and with PM2.5 emissions from eight other inventories. The results of the ABI-VIIRS estimates during one year from April 2020 to March 2021 indicate that fires burned 221 Tg dry matter and emit 2.25 Tg PM2.5 emissions across the CONUS. The seasonal and diurnal patterns of emissions vary with land cover types. The largest and smallest seasonal variations are shown in forest and agriculture fire emissions, respectively. The diurnal emission patterns of different land cover types share similar shapes but differ largely in magnitude. Moreover, the diurnal pattern of forest fire emissions suggests that emissions are dominated during daytime in the eastern U.S. but strong during both daytime and nighttime in the western U.S. The evaluation shows that the fused ABI-VIIRS based CO agrees well with the TROPOMI CO, with a difference of 11%. However, the agreement between fused ABI-VIIRS emissions and other inventories varies for different fire events.

Yongchang Ye, 2022, publication

Satellite-based land surface phenology (LSP) products play an important role in understanding atmosphere-vegetation carbon and energy exchanges. These products have been widely calculated from various satellite observations from local to global scales. However, the quality and accuracy of LSP products are often poorly quantified due to spatial mismatch between satellite observed pixels and in-situ observations. In the present study, we demonstrate an optimal algorithm leveraging the scalability, consistency, and representativeness of rich in-situ observations from national phenology networks to validate LSP products. Specifically, we demonstrate two approaches for validating the phenological timing of greenup onset in the operational Visible Infrared Imaging Radiometer Suite (VIIRS) LSP product developed at NASA using in-situ observations collected from the Pan European Phenological database (PEP725, 9664 site-years) and the USA National Phenology Network (USA-NPN, 3144 site-years) spanning 2013–2020. The first approach assumes that in-situ data contain observations of phenological transitions (e.g., leaf-out) that are directly comparable with satellite detections. Accordingly, in-situ data were aggregated using four upscaling methods (mean, median, 30th percentile, and minimum bias) to directly compare with VIIRS LSP. The second approach assumes that species-specific phenological timing in in-situ data is basically impossible to spatially reconcile VIIRS LSP, but phenological events in a local area are driven by the same or very similar weather conditions. Therefore, interannual variations and long-term trends were applied to compare VIIRS LSP with in-situ data. The result shows first that the 30th percentile method is more promising in aggregating in-situ observations than the commonly used mean method. Second, direct comparison indicates that VIIRS greenup onset has a mean absolute difference of 13.9 ± 9.8 days with PEP725 in-situ observations and 12.3 ± 10.9 days with USA-NPN observations in well-selected deciduous forest sites. Third, the interannual comparison reveals that VIIRS greenup onset exhibits the same directions of multi-year anomalies and long-term trends as those of both PEP725 and USA-NPN observations in over 70% of sample sites. These findings improve our understanding of the scale mismatch and sample representativeness of species-specific phenology and the uncertainties of long-term LSP detections from remote sensing data.

Xiaoyang Zhang, 2022, publication

Satellite-derived vegetation greenness and seasonal dynamics in the Amazon have generated considerable academic debate over the past two decades. Despite this, the phenological timing of Amazon forests and, in particular their responses to dry periods, remain poorly understood. Here we explicitly identify the diverse timing of vegetation canopy greenup onsets from 10-min geostationary satellite observations, and compute the timing of both the start and end of dry periods from daily precipitation data. We, for the first time, reveal that the Amazon vegetation canopy regularly experiences two cycles of greenup onsets during a year. The occurrence of greenup onset varies diversely from the start to end of the dry periods, but demonstrates regular shifts in local areas, although irregular shifts across the region. The multiple greenup onsets show complex spatial shifts, which closely follow the spatial movement of dry periods. The results provide a new insight into our understanding of the complexity of Amazonian vegetation canopy dynamics during dry periods, which could significantly improve the simulation of carbon and water cycles.

K.Through, 2022

Vegetation phenology is one of the most sensitive indicators to environmental and climate changes. In order to characterize the seasonal variation in relatively pure or homogenous vegetation types, fine spatial resolution satellite data (≤ 30 m), such as Landsat, Sentinel-2, PlanetScope, or Harmonized Landsat and Sentinel-2 (HLS), have been increasingly applied to detect land surface phenology (LSP). However, the most critical challenge in LSP detections is the gaps in temporal satellite observations caused by noise and persistent cloud/snow cover. Therefore, this study presented a novel algorithm for generating synthetic gap-free time series at the field scale (30 m) for LSP detections. Specifically, we first developed a framework to establish a large collection of temporal shapes of vegetation growth with as many as 100 grid-based Green Chromatic Coordinate (GCC) time series in a single PhenoCam site. For a given HLS pixel, the two-band Enhanced Vegetation Index (EVI2) time series was matched and fused with the most comparable temporal GCC shape selected from the collection of PhenoCam GCC time series to generate a synthetic gap-free HLS-PhenoCam EVI2 time series, which was used to detect the 30 m phenometrics. The detected phenometrics were evaluated using manually selected and spatially matched GCC observations as well as phenology detections from HLS alone. The result indicates that the HLS-PhenoCam phenometrics are very close to the observations from PhenoCam network with a correlation coefficient (R) of 0.82–0.97, a mean absolute difference (MAD) of 2.8–3.5 days, a root mean squared error (RMSE) of 3.5–4.0 days, and a mean systematic bias (MSB) of 0.1–2.2 days. The HLS-PhenoCam detections are significantly improved relative to the HLS phenometrics that have a statistic accuracy of R = 0.57–0.78, MAD = 6.4–9.3 days, RMSE = 8.8–13.9 days, MSB = -5.2–5.9 days. The difference between HLS-PhenoCam and HLS alone LSP detections over a HLS tile could be on average larger than two weeks if high-quality observation (HQO) proportion in the annual HLS time series is <10%, which exponentially reduces with the increase of HQO in HLS observations. The analyses in this study suggest that the gap-free HLS-PhenoCam time series is able to be generated for producing high-quality phenology datasets across a local and regional scale, to bridge near-surface PhenoCam observations with satellite observations data at various scales, and to be used as a scalable phenology dataset for the validation of global MODIS and VIIRS LSP products.

Khuong, 2023

Land surface phenology (LSP) products are currently of large uncertainties due to cloud contaminations and other impacts in temporal satellite observations and they have been poorly validated because of the lack of spatially comparable ground measurements. This study provided a reference dataset of gap-free time series and phenological dates by fusing the Harmonized Landsat 8 and Sentinel-2 (HLS) observations with near-surface PhenoCam time series for 78 regions of 10 × 10 km2 across ecosystems in North America during 2019 and 2020. The HLS-PhenoCam LSP (HP-LSP) reference dataset at 30 m pixels is composed of: (1) 3-day synthetic gap-free EVI2 (two-band Enhanced Vegetation Index) time series that are physically meaningful to monitor the vegetation development across heterogeneous levels, train models (e.g., machine learning) for land surface mapping, and extract phenometrics from various methods; and (2) four key phenological dates (accuracy ≤5 days) that are spatially continuous and scalable, which are applicable to validate various satellite-based phenology products (e.g., global MODIS/VIIRS LSP), develop phenological models, and analyze climate impacts on terrestrial ecosystems.

Liu, 2024, publications

Phenology detection from remotely sensed data remains challenging in semi-arid ecosystems due to the unique spatial heterogeneity and irregular temporal growth in plants. PlanetScope imagery, with fine spatial and temporal resolutions, is revolutionizing the earth observation sector. It has demonstrated its effectiveness in monitoring phenology dynamics across various terrestrial ecosystems. However, the quality and accuracy of PlanetScope data for depicting plant growth development and detecting phenological metrics (phenometrics) in semi-arid environments have not been systematically examined. In this study, we evaluated the capability of PlanetScope for monitoring plant-specific phenology across the semi-arid western United States, by comparing phenometrics (onsets of greenup, maturity, senescence, and dormancy) retrieved from time series of two PlanetScope vegetation indices (VI), which are EVI2 (two-band Enhanced Vegetation Index) and NDVI (Normalized Difference Vegetation Index), with a set of PhenoCam observations at 15 sites. To conduct a comprehensive comparison, PhenoCam time series and phenometrics were extracted from infrared-enabled PhenoCam EVI2 and NDVI, as well as commonly used PhenoCam GCC (Green Chromatic Coordinate). Our results show that (1) time series of PlanetScope VI were consistent with PhenoCam GCC and VI time series during greenup phase but moderately comparable during senescence phase, with an average R2 of 0.67 and 0.57 for greenup and senescence phases, respectively; (2) phenometrics derived from PlanetScope VI exhibited better agreement with those from PhenoCam GCC and VI in greenup phase (greenup and maturity onsets) than in senescence phase (senescence and dormancy onsets), with an average R2 of 0.81, 0.84, 0.72, and 0.53 for greenup, maturity, senescence, and dormancy onsets, respectively; (3) PlanetScope-detected senescence onset and dormancy onset were systematically later than PhenoCam-based retrievals with a mean systematic bias of 12.6 days and 18.2 days, respectively; (4) phenometrics derived from PlanetScope VI were more comparable with PhenoCam VI retrievals than with PhenoCam GCC retrievals, which are reflected in better correlations and smaller bias between phenometrics, especially during senescence phase; and (5) PlanetScope and PhenoCam EVI2 time series produced the most comparable phenometrics, suggesting that EVI2 is an optimal index for vegetation phenology detections from different sensors. In summary, this study suggests PlanetScope has the ability to detect plant-specific phenology and to improve our understanding of phenology dynamics in heterogeneous semi-arid ecosystems at fine scales.

Xiaoman Lu, 2022, publication

Smoke aerosol emissions from Indonesian fires frequently cause adverse environmental consequences across southeast Asia. Satellite observations provide us with a great opportunity to monitor such emissions at large scales. However, existing satellite-based estimates of Indonesian fire emissions vary considerably in magnitude, differing by a factor of four. Here, we aim to improve Indonesian fire emissions estimates through improved calculations of fire radiative energy (FRE: time-integrated fire radiative power (FRP)) and smoke aerosol emission coefficients (Ce) using multiple new-generation satellite observations. Specifically, peatland and non-peatland Ce values were derived from FRP and emission rates of smoke aerosols based on Visible Infrared Imaging Radiometer Suite (VIIRS) active fire and aerosol products. FRE was calculated from the diurnal FRP cycle that was reconstructed by fusing cloud-corrected FRP retrievals from the high temporal-resolution Himawari-8 Advanced Himawari Imager (AHI) with those from high spatial-resolution VIIRS. Then, fuel type-specific Ce values and fused AHI-VIIRS FRE were used to produce hourly and daily fire emission data from 2015 to 2020 across Indonesia. To evaluate AHI-VIIRS estimates, we generated a reference dataset of total particulate matter (TPM) by computing Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol observation differences from successive Terra and Aqua satellites overpasses over a set of manually-selected smoke plumes. AHI-VIIRS-based fire emissions correlated significantly with the MODIS reference data (r = 0.84; p < 0.001). The interannual time series analysis of AHI-VIIRS emissions showed extreme variability (~26 fold), with the greatest amount in 2015 (6.09 Tg) and the least in 2020 (0.23 Tg), during the study period. Mean annual fire emissions distributed across Indonesia's islands were primarily from Kalimantan (43.8%) and Sumatra (41.7%), followed by Sulawesi (5.6%), Java (4.5%), and Papua (4.4%). Additionally, contributions of Indonesian fire emissions from peatland, forest, cropland, and savanna/grassland fuel types were 51.4%, 37.8%, 7.0%, and 3.8%, respectively, during the fire season of the strong 2015 El Niño event. The analysis suggests that the majority of Indonesian fire emissions are very likely associated with large-scale land use conversion from peatland to agriculture, as well as prolonged droughts induced by El Niño events.

Oliveira, 2023, publication

The vertical structure of forests provides important parameters for estimating aboveground biomass (AGB) and it can be measured by lidar sensors. The Global Ecosystem Dynamics Investigation (GEDI) full-waveform lidar sensor collects data systematically over the Earth's surface from the International Space Station. Since GEDI became operational, it has collected billions of ∼25-m diameter footprints. This massive dataset has been used to create higher level gridded and non-grided products. However, GEDI's ∼25-m footprints can be subject to errors associated with effects of geolocation, terrain slope, and beam sensitivity, among others, which are likely transferred to the downstream products. This study aims to (1) evaluate the effect of beam sensitivity and terrain slope on the accuracy of relative heights (RH) of GEDI product L2A version 2 through comparison with discrete-return airborne lidar data collected over transects in the Brazilian Amazon Forest biome, (2) assess GEDI's geolocation uncertainty and investigate its combined effects with beam sensitivity and terrain slope, and (3) re-evaluate beam sensitivity and terrain slope effects on the GEDI L2A RHs using footprints that were geolocation-adjusted through a simple novel approach. The analysis separates GEDI footprints by acquisition time, i.e., daytime, nighttime, and combined (all-data). The discrete-return airborne lidar point clouds are used to derive terrain slope within the GEDI footprints and to simulate GEDI waveforms and derive RHs comparable to the GEDI L2A product. Results indicate that terrain slope only causes significant effects on GEDI data collected during daytime because solar radiation affects waveform signal-to-noise ratio. Beam sensitivity causes significant effects on nighttime and all-data GEDI L2A RHs. If geolocation uncertainty is considered, the effects of beam sensitivity and terrain slope have just minor changes. Geolocation-adjusted data continue showing significant effects on nighttime and all-data RH differences caused by beam sensitivity but produce unbiased results. This study improves the understanding of how beam sensitivity, terrain slope, and their combined effect with geolocation uncertainty may affect the GEDI L2A RH collected over the Brazilian Amazon forest during daytime and nighttime.

Shen Yu, 2023, publication_1

The Advanced Baseline Imager (ABI) instruments onboard the Geostationary Operational Environmental Satellite-R series (GOES-R) provide new opportunities to expand their applications in global terrestrial ecology and environmental sciences. It offers a 5–15 min temporal resolution and 0.5–1 km spatial resolution that greatly benefit those environmental applications where spatial resolution can be compromised in favor of more frequent imagery. However, ABI captures reflected radiation at varying sun positions throughout the day, causing significant diurnal variations. The varying off-nadir view across ABI full disk presents further challenges for generating comparable and consistent vegetation observations, such as the two-band Enhanced Vegetation Index (EVI2). To address these challenges, three Bidirectional Reflectance Distribution Function (BRDF) models (RossThick-LiSparse-Reciprocal (RTLSR), Walthall, and Tian models) are examined to generate high-quality BRDF-adjusted EVI2 based on GeoNEX GOES-16 ABI surface reflectance. The ABI BRDF retrievals are then systematically compared to the corresponding VIIRS (Visible Infrared Imaging Radiometer Suite) Nadir BRDF-Adjusted Reflectance (NBAR) products (VNP43 V001) retrieved from RTLSR model. The viewing geometry effects are further analyzed over various land cover types using time series analysis, Pearson Correlation Coefficients (PCC), and Relative Difference (RD) between ABI and VIIRS high-quality time series inversions. The results indicate that the RTLSR model fits well the diurnal ABI observed reflectance and EVI2 values (even for the hot-spot phenomena), and outperforms the Walthall and Tian models for generating ABI high-quality BRDF retrievals with consistently low Root Mean Square Error (RMSE<0.02) and Weight of Determination (WoD < 0.5) across the CONUS. The annual proportion of ABI high-quality BRDF retrievals with a 3-day moving window using the RTLSR model is spatially and statistically comparable to that of VIIRS NBAR with a 16-day retrieval period. Compared to the time series of high-quality VIIRS NBAR EVI2, the BRDF adjusted ABI EVI2 time series still show considerable impacts of view zenith angles over evergreen forest, shrubland, and savanna pixels. However, such impacts are not significant over spatially homogeneous pixels such as cropland and deciduous forests. These results emphasize the need for BRDF correction when considering the impact of off-nadir observations from geostationary satellites on vegetation monitoring. They also demonstrate the advantages of geostationary satellites in capturing rapid changes in greenness trajectories.

Shen Yu, 2023, publication_2

Crop phenology has been widely detected from multiple historical satellite observations. Conversely, Near-Real-Time (NRT) monitoring of crop progress from timely available remote sensing data is barely investigated because of the lack of high-frequency cloud-free satellite observations and future potential crop development. To address the challenge, this study proposes a novel algorithm for operational NRT monitoring of crop progress at the field scale. This algorithm first fuses the high spatial resolution (30 m) Harmonized Landsat and Sentinel-2 (HLS) data and the high temporal frequent (10 min) Advanced Baseline Imager (ABI) observations to generate cloud-free time series of HLS-ABI EVI2 (two-band Enhanced Vegetation Index) with a Spatiotemporal Shape-Matching Model (SSMM). It then predicts future potential EVI2 values at a given pixel using a reference EVI2 time series obtained from the neighboring pixels in the preceding year. Integrating the currently available HLS-ABI observations and the predicted future EVI2 values to generate annual EVI2 time series, the algorithm finally detects six crop phenometrics including greenup onset, mid-greenup phase, maturity onset, senescence onset, mid-senescence phase, and dormancy onset. The NRT monitoring, which are separated as near-real-time prediction (phenological event detected after the occurrence), real-time prediction (phenological event detected around the occurrence), and short-term prediction (phenological event detected before the occurrence), are continuously updated and improved with new HLS and ABI observations at a weekly basis throughout the growing season. We evaluate the NRT monitoring against standard phenology products, PhenoCam observations, as well as the weekly Crop Progress Reports (CPRs) released from the National Agricultural Statistics Service (NASS) of the United States Department of Agriculture (USDA) in 2020 across Iowa. The evaluation demonstrates the robustness of the developed algorithm in NRT monitoring of crop phenology. Although the uncertainties are relatively large for short-term prediction compared with standard detections, the real-time prediction shows that the Mean Absolute Difference (MAD) is <10 days for greenup and dormancy onset, and ∼ 5 days in the other four phenometrics. Further, the real-time prediction aligns well with PhenoCam observations (R2 = 0.96, P < 0.001) with a MAD of 7.8 days. Moreover, the HLS-ABI real-time prediction of crop phenometrics is capable of tracking NASS crop progress closely with small time shifts (≤ 5 days) and significant correlations (R2 > 0.85, P < 0.001) for various phenological stages of corn and soybean. These results prove that the algorithm could be implemented for NRT monitoring of various crop phenometrics from field, state, to national scales.

Yu Shen, publication, 2022

Accurate and timely crop phenology is essential for efficient crop management and precise modelling of crop yield and productivity. It has been commonly mapped from moderate or coarse resolution (≥500 m) satellite data at a national or global scale, which generally represents the mixture of multiple crops or between crops and natural vegetation. Crop phenological progress at field scales (≤30 m) is critical for monitoring the growth of specie-specific crop types, but it is very challenging at large scales because of limited cloud-free observations in the satellite time series. To map corn and soybean phenometrics at a field scale (30 m) over entire Corn Belt in the United States, this study for the first time develops a new approach that fuses high temporal resolution Visible Infrared Imaging Radiometer Suite (VIIRS) observations (500 m) and high spatial resolution Harmonized Landsat and Sentinel-2 (HLS) data (30 m). The fused HLS-VIIRS time series (daily 30 m) is applied to map crop phenometrics of greenup onset, mid-greenup phase, maturity onset, senescence onset, mid-senescence phase, and dormancy onset. These six phenometrics are correlated to crop progress (CP) of seven corn and six soybean growth stages from planting to harvest that are observed by the National Agricultural Statistics Service (NASS) of the United States Department of Agriculture (USDA) and are further validated using PhenoCam near-surface time series imagery. The result indicates that the HLS-VIIRS crop phenometrics are capable of tracking the CP very well. Although their biophysical definitions of CP stages and sample coverage were not the same in our comparisons, the HLS-VIIRS crop phenometrics and NASS CP at a state level are closely comparable with a difference<4 days for corn and soybean emergence, 1 day for soybean dropping leaves, and 6–10 days for other corn and soybean growth stages. Their significant correlations (R2 ranging from 0.81 to 0.99, p < 0.01) suggest that the HLS-VIIRS crop phenometrics are robust proxies for predicting NASS CP with a high accuracy. Moreover, the HLS-VIIRS phenometrics aligns closely with PhenoCam phenological timings at various crop growth stages with an absolute average difference from 3 days to 5.3 days and bias from 1.5 to 3.5 days. The result demonstrates that the proposed approach could be implemented for monitoring field scale crop progress at a national or global scale.

Other publications

Refereed Journal Publications

(* first author is Dr. Xiaoyang Zhang's Ph.D. students or Postdocs, or Dr. Zhang is the corresponding author but not the first author)

  1. *Liu, Y., Zhang, X., Shen, Y., Ye, Y., Gao, S., Tran, K.h., 2024, , ISPRS Journal of Photogrammetry and Remote Sensing, 210:242-259.
  2. *Shen, Y., Zhang, X., Gao, S., Zhang, H., Schaaf C., Wang, W., Ye, Y., Liu, Y., Tran K.H., 2024, , Remote Sensing of Environment, 302:113972, DOI:10.1016/j.rse.2023.113972.
  3. *Shen, Y., Zhang, X., Yang, Z., Ye, Y., Wang, J., Gao, S., Liu, Y., Wang, W., Tran, K.H., Ju, J., 2023, , Remote Sensing of Environment, 296:113729, DOI:10.1016/j.rse.2023.113729.
  4. Yang, J., Dong, J., Liu, L., Zhao, M., Zhang, X., Li, X., Dai, J., Wang, H., Wu, C., You, N., Fang, S., Pang, Y., He, Y., Zhao, G., Xiao, X., Ge, G., 2023, , ISPRS Journal of Photogrammetry and Remote Sensing, 202:610-636, DOI:10.1016/j.isprsjprs.2023.07.017.
  5. *Oliveira, P. VC., Zhang, X., Peterson, B., Ometto, J.P., 2023, , Science of Remote Sensing, 7,100083, DOI:10.1016/j.srs.2023.100083.
  6. *Tran. K.H., Zhang, X., Ye, Y., Shen, Y., Gao, S., Liu, Y., Richardson A., 2023, , Scientific Data, 10:691, DOI:10.1038/s41597-023-02605-1.
  7. Ingty, T., Erb, A., Zhang, X., Schaaf, C., Bawa, K.S., 2023, , International Journal of Biometeorology, 67(5):913-925, DOI:10.1007/s00484-023-02465-9.
  8. Pan, L., Bhattacharjee, P. S., Zhang, L., Montuoro, R., Baker, B., McQueen, J., Grell, G. A., McKeen, S. A., Kondragunta, S., Zhang, X., Frost, G. J., Yang, F., and Stajner, I.: , Geosci. Model Dev. Discuss., DOI:10.5194/gmd-2023-61.
  9. Li, Y., Tong, D., Ma, S., Freitas, S.R., Ahmadov, R., Sofiev, M., Zhang, X., Kondragunta, S., Kahn, R., Tang, Y., Baker, B., Campbell, P., Saylor, R., Grell, G., Li, F., 2023, , Atmospheric Chemistry and Physics, 23 (5): 3083-3101. DOI:10.5194/acp-23-3083-2023.
  10. Pan, Y., Peng, D., Chen, J.M., Myneni, R. B., Zhang, X., Huete, A.R., Fu, Y.H., Zheng, S., Yan, K., Yu, L., Zhu, P., Shen, M., Ju, W., Zhu, W., Xie, Q., Huang, W., Chen, Z., Huang, J., Wu, C., 2023, , Environmental Research Letters, 18(4):044045. DOI:10.1088/1748-9326/acca34.
  11. Yang, F., He, B., Zhou, Y., Li, W., Zhang, X., Feng Q., 2023, , Ecological Indicators, 146:109898. DOI:10.1016/j.ecolind.2023.109898.
  12. Tang, Y., Campbell, P. C., Lee, P., Saylor, R., Yang, F., Baker, B., Tong, D., Stein, A., Huang, J., Huang, H.-C., Pan, L., McQueen, J., Stajner, I., Tirado-Delgado, J., Jung, Y., Yang, M., Bourgeois, I., Peischl, J., Ryerson, T., Blake, D., Schwarz, J., Jimenez, J.-L., Crawford, J., Diskin, G., Moore, R., Hair, J., Huey, G., Rollins, A., Dibb, J., and Zhang, X., 2022, , Geoscientific Model Development, 15, 7977–7999, DOI:10.5194/gmd-15-7977-2022.
  13. Zhang, X., Shen, Y., Gao, S., Wang, W., & Schaaf, C., 2022, , Geophysical Research Letters, 49, e2022GL098662. DOI:10.1029/2022GL098662.
  14. *Ye, Y., Zhang, X., Shen, Y., Wang, J., Crimmins, T., Scheifinger, H., 2022, , ISPRS Journal of Photogrammetry and Remote Sensing, 194: 74-90, DOI:10.1016/j.isprsjprs.2022.09.018.
  15. *Tran, K.H., Zhang, X., Ketchpaw, A.R., Wang, J., Ye, Y., Shen, Y., 2022, , Remote Sensing of Environment, 282, 113275, DOI:10.1016/j.rse.2022.113275.
  16. *Lu, X., Zhang, X., Li, F., Cochrane, M.A., 2022, , Remote Sensing of Environment, 281,113238, DOI:10.1016/j.rse.2022.113238.
  17. *Li, F., Zhang, X., Kondragunta, S., Lu, X., Csiszar, I., Schmidt, C.C., 2022, , Remote Sensing of Environment, 281, 113237, DOI:10.1016/j.rse.2022.113237.
  18. Liu, Y., Wu, C., Tian, F., Wang, X., Gamon, J.A., Wong, C. Zhang, X., Gonsamo, A., Jassal R.S., 2022, , Agricultural and Forest Meteorology, 324, 109095, DOI:10.1016/j.agrformet.2022.109095.
  19. Campbell, P.C., Tong, D., Saylor, R., Li, Y., Ma, S., Zhang, X., Kondragunta, S., Li, F., 2022, , Science of The Total Environment, 839, 156130, DOI:10.1016/j.scitotenv.2022.156130.
  20. Wu, C., Peng, J., Ciais, P., Peñuelas, J., Wang, H., Beguería, S., Black, T.A., Jassal, R.S., Zhang, X., Yuan, W., Liang, E., Wang, X., Hua, H., Liu, R., Ju, W., Fu, Y.H., Ge, Q., 2022, , Nature Climate Change, 1-7, DOI:10.1038/s41558-022-01464-9.
  21. Li, Y., Tong, D., Ma, S., Freitas, S. R., Ahmadov, R., Sofiev, M., Zhang, X., Kondragunta, S., Kahn, R., Tang, Y., Baker, B., Campbell, P., Saylor, R., Grell, G., and Li, F., 2022, , EGUsphere, DOI:10.5194/egusphere-2022-713.
  22. Zhang, L., Montuoro, R., McKeen, S. A., Baker, B., Bhattacharjee, P. S., Grell, G. A., Henderson, J., Pan, L., Frost, G. J., McQueen, J., Saylor, R., Li, H., Ahmadov, R., Wang, J., Stajner, I., Kondragunta, S., Zhang, X., and Li, F., 2022, , Geoscientific Model Development, 15, 5337–5369, DOI:10.5194/gmd-15-5337-2022.
  23. Wu, J., Kong, S., Yan, Y., Yao, L., Yan, Q., Liu, D., Shen, G., Zhang, X., Qi, S., 2022, , Atmospheric Environment, 278, 119082, DOI:10.1016/j.atmosenv.2022.119082.
  24. Wu, J., Kong, S., Yan, Y., Yao, L., Yan, Q., Liu, D., Shen, G., Zhang, X., Qi, S., 2022, , Atmospheric Environment, 284, 119178, DOI:10.1016/j.atmosenv.2022.119178.
  25. Pan, Y., Wang, Y., Zheng, S., Huete, A.R., Shen, M., Zhang, X., Huang, J., He, G., Yu, L., Xu, X., Xie, Q., Peng, D., 2022, , Remote Sensing, 14(10), 2408, DOI:10.3390/rs14102408.
  26. Lou, Z., Peng, D., Zhang, X., Yu, L., Wang, F., Pan, Y., Zheng, S., Hu, J., Yang, S., Chen, Y., Liu, S., 2022, , Remote Sensing, 14 (8), 1867, DOI:10.3390/rs14081867.
  27. An, S., Chen, X.Q., Shen, M.G., Zhang, X., Lang, W.G., and Liu, G.H., 2022, . Front. Plant Sci. 13:844971, DOI:10.3389/fpls.2022.844971.
  28. An, S., Zhang, X., Ren, S., 2022, , Remote Sens., 14(5), 1273, DOI:10.3390/rs14051273.
  29. Bela, M.M., Kille, N., McKeen, S.A., Romero‐Alvarez, J., Ahmadov, R., James, E., Pereira, G., Schmidt, C., Pierce, R.B., O’Neill, S.M., Zhang, X., Kondragunta, S., Wiedinmyer, C., Volkamer, R., 2022, , Geophysical Research Letters, 49 (3), DOI:10.1029/2021GL095831.
  30. Donnelly, A., Yu, R., Jones, K., Belitz, M., Li, B., Duffy, K., Zhang, X., Wang, J., Seyednasrollah, B., Gerst, K.L., Li, D., Kaddoura, Y., Zhu, K., Morisette, J., Ramey, C., Smith, K., 2022, , Ecosphere 13 (1), e3912, DOI:10.1002/ecs2.3912.
  31. *Shen, Y., Zhang, X., Yang, Z., 2022, , ISPRS Journal of Photogrammetry and Remote Sensing, 186, 55-69, DOI:10.1016/j.isprsjprs.2022.01.023.
  32. Zhang, X., Gao, F., Wang, J., Ye, Y., 2021, , International Journal of Applied Earth Observation and Geoinformation, 104, DOI:10.1016/j.jag.2021.102545.
  33. Li, Y., Tong, D., Ma, S., Zhang, X., Kondragunta, S., Li, F., Saylor, R. 2021, , Geophysical Research Letters, 48 (21), e2021GL094908, DOI:10.1029/2021GL094908.
  34. *Shen, Y., Zhang, X., Wang, W., Nemani, R., Ye, Y., and Wang, J., 2021, , Remote Sensing, 13(21), 4465; DOI:10.3390/rs13214465.
  35. *Ye, Y., Zhang, X., 2021, , Science of Remote Sensing, 4, 100030, DOI:10.1016/j.srs.2021.100030.
  36. Lu, X., Zhang, X., Li, F., Gao, L., Graham, L., Vetrita, Y., Saharjo, B., Cochran, M., 2021, , Environmental Research Letters, 16(9), 095008, DOI:10.1088/1748-9326/ac2011.
  37. Liu, Y, Mackenzie, C.M., Primack, R.B., Hill, M.J., Zhang, X., Wang, Z., and Schaaf, C.B., 2021, , Ecosphere, 12(12), DOI:10.1002/ecs2.3888.
  38. Peng, J., Wu, C., Zhang, X., Ju, W., Wang, X., Lu, L., Liu, Y., 2021, , Environmental Research Letters,16(9), DOI:10.1088/1748-9326/ac1a3b.
  39. Liu, H., He, B., Zhou, Y., Yang, X., Zhang, X., Xiao, F., Feng, Q., Liang, S., Zhou, X., Fu, C., 2021, , GIScience & Remote Sensing, 58(5):1-23, DOI:10.1080/15481603.2021.1940738.
  40. Gao, F., Zhang, X., 2021, , Journal of Remote Sensing, 8379391, DOI:10.34133/2021/8379391.
  41. Lu, X., Zhang, X., Li, F., Cochrane, M.A., Ciren, P., 2021, , Remote Sensing, 13 (2), 196, DOI:10.3390/rs13020196.
  42. *Wang, J., Zhang, X., Rodman, K., 2021, , Agricultural and Forest Meteorology, DOI:10.1016/j.agrformet.2021.108432.
  43. Wu, C., Wang, J., Ciais, P., Peñuelas, J., Zhang, X., Sonnentag, O.,Tian, F., Wang, X., Wang, H., Liu, R., Fu, Y., and Ge, Q., 2021, , PNAS, 118 (16), DOI:10.1073/pnas.2015821118.
  44. Jia, W., Zhao, S., Zhang, X., S Liu, S., Henebry, G.M., Liu, L., 2021, , Global Change Biology, DOI:10.1111/gcb.15602.
  45. Liang, L., Henebry, G.M., Liu, L., Zhang, X., Hsu, LC, 2021, , Ecological Applications, DOI:10.1002/eap.2323.
  46. Wu, J., Kong, S., Zeng, X., Cheng, Y., Yan, Q., Zheng, H., Yan, Y., Zheng, S., Liu, D., Zhang, X., Fu, P., Wang, S., Qi, S., 2021, , Environ Sci Technol, DOI:10.1021/acs.est.0c06675.
  47. *Peng, D., Wang, Y., Xian, G., Huete, A.R., Huang, W., Shen, M., Wang, F., Yu, L., Liu., L., Xie, Q., Liu, L., Zhang, X., 2021, , Remote Sensing of Environment, 252, DOI:10.1016/j.rse.2020.112133.
  48. *Liu, L., Zhang, X., 2020, , Scientific Reports (10), 17952, DOI:10.1038/s41598-020-74804-4.
  49. Wang, X., Wu, C., Zhang, X., Li, Z., Liu, Z., Gonsamo, A., and Ge, Q., 2020, , Environmental Research Letters, 15, 094055, DOI:10.1088/1748-9326/aba57f.
  50. Li, Y., Tong, D.Q., Ngan, F., Cohen, M.D., Stein, A.F., Kondragunta, S., Zhang, X., Ichoku, C., Hyer, E.J., Kahn, R.A., 2020, , Journal of Geophysical Research: Atmospheres, 125 (15), e2020JD032768, DOI:10.1029/2020JD032768.
  51. *Li, F ., Zhang, X., Kondragunta, S., Lu, X., 2020, , Environmental Research Letters, 15, 094049, DOI:10.1088/1748-9326/ab9d3a.
  52. *Mfuka, C., Byamukama, E., Zhang, X., 2020, , Geo-Spatial Information Science, 23 (2), 182-193, DOI:10.1080/10095020.2020.1712265.
  53. Zhang, X., Wang, J., Henebry, G.M., Gao, F., 2020, , ISPRS Journal of Photogrammetry and Remote Sensing, 161, 37-51; DOI:10.1016/j.isprsjprs.2020.01.012.
  54. *Li, F ., Zhang, X., Kondragunta, S., Schmidt, C.C., Holmes, C.D., 2020, , Remote Sensing of Environment, 237: 111600; DOI:10.1016/j.rse.2019.111600.
  55. *Wang, J. and Zhang, X., 2020, , ISPRS Journal of Photogrammetry and Remote Sensing, 159, 281-295; DOI:10.1016/j.isprsjprs.2019.11.027.
  56. Qian, Y., Yang, Z., Di, L., Rahman, Md. S., Tan, Z., Xue, L., Gao, F., Yu, E.G., and Zhang, X., 2019, , Remote Sens. 2019, 11(20), 2439; DOI:10.3390/rs11202439.
  57. Peng, D., Zhang, H., Liu, L., Huang, W., Huete, A., Zhang, X., Wang, F., Yu, L., Xie, Q., Wang, C., Luo, S., Li, C., and Zhang, B., 2019, , Remote Sensing 11, 2270; DOI:10.3390/rs11192270.
  58. Xiao, J., Chevallier, F., Gomez, C., Guanter, L., Hicke, J.A., Huete, A.R., Ichii, K., Ni, W., Pang, Y., Rahman, A.F., Sun, G., Yuan, W., Zhang, L., Zhang, X., 2019, , Remote Sensing of Environment, 233, DOI:10.1016/j.rse.2019.111383.
  59. Liu, L., Cao, R., Shen, M., Chen, J., Wang, J., Zhang, X. 2019, Remote Sensing 11 (18), 2137; DOI:10.3390/rs11182137.
  60. *Mfuka, C., Zhang, X., E Byamukama, E., 2019, , Journal of Geographic Information System, 11, 331-346, DOI:10.4236/jgis.2019.113020.
  61. *Li, F., Zhang, X., Roy, D.P., Kondragunta, S. 2019, , Atmospheric Environment, 211, 274-287, DOI:10.1016/j.atmosenv.2019.05.017.
  62. *Lu, X., Zhang, X., Li, F., Cochrane, M.A., 2019, , Journal of Geophysical Research: Biogeosciences, 124 (6): 1413-1429, DOI:10.1029/2018JG004974.
  63. Moon, M., Zhang, X., Henebry, G.M., Liu, L., Gray, J.M., Melaas, E.K., Friedl, M.A., 2019, , Remote Sensing of Environment, 226, 74-92, DOI:10.1016/j.rse.2019.03.034.
  64. Peng, J., Wu, C., Zhang, X., Wang, X., Gonsamo, A., 2019, , Global change biology, 25(6): 2174-2188. DOI:10.1111/gcb.14627.
  65. Zhang, X., Liu, L., Henebry, G., 2019, , Environmental Research Letters, 14: 044020.
  66. *Yan, D., Zhang, X., Nagai, S., Yu, Y., Akitsu, T., Nasahara, K., Ide, R., Maeda, T., 2019, , International Journal of Applied Earth Observation and Geoinformation, 79: 71-83. DOI:10.1016/j.jag.2019.02.011.
  67. Wang, J., Wu, C., Wang, X., Zhang, X., 2019, , ISPRS Journal of Photogrammetry and Remote Sensing, 149:77-90. DOI:10.1016/j.isprsjprs.2019.01.017.
  68. *Liu, L., Zhang, X., Yu, Y., Gao, F., Yang, Z., 2018, , Remote Sensing, 10(10), 1540; DOI:10.3390/rs10101540.
  69. Zhang, X., Liu, L., Liu, Y., Jayavelu, S., Wang, J., Moon, M., Henebry, G.M., Friedl, M.A., Schaaf, C.B., 2018, , Remote Sensing of Environment, 216, 212-229, DOI:10.1016/j.rse.2018.06.047.
  70. Wang, J., Bhattacharjee, P.S., Tallapragada, V., Lu, C., Kondragunta, S., da Silva, A., Zhang, X., Chen, S., 2018, , Geoscientific Model Development, 11, 2315–2332, DOI:10.5194/gmd-11-2315-2018.
  71. Donnelly, A., Liu, L., Zhang, X., and Wingler, A., 2018, , International Journal of Remote Sensing, 39 (22), 8129-8150. DOI:10.1080/01431161.2018.1482021.
  72. *An, S., Zhang, X., Chen, X., Dong Yan, D., and Henebry, G.M., 2018, , Remote Sensing, 10, 1069; DOI:10.3390/rs10071069.
  73. *Li, F., Zhang, X., Kondragunta, S., Csiszar, I., 2018, . Journal of Geophysical Research-Atmosphere, 123(9): 4545-4563. DOI:10.1029/2017JD027823.
  74. *Li, F., Zhang, X., Kondragunta, S., Roy, D.P., 2018, . Journal of Geophysical Research-Biogeoscience, 132, 722-739. DOI:10.1002/2017JG004279.
  75. Huang, R., Zhang, X., Chan, D., Kondragunta, S., Russell, A.G., Odman, M.T., 2018, . Journal of Geophysical Research-Atmosphere, 123(9): 4746-4757. DOI:10.1029/2017JD028217.
  76. Peng, D., Wu, C., Zhang, X., Yu, L., Huete, A.R., Wang, F., Luo, S., Liu, X., Zhang, H., 2018, . Agricultural and Forest Meteorology, 256, 207-219. DOI:10.1016/j.agrformet.2018.03.010.
  77. Zhang, X., Jayavelu, S., Liu, L., Friedl, M.A., Henebry, G.M., Liu, Y., Schaaf, C.B., Richardson, A.D., and Gray, J., 2018, , Agricultural and Forest Meteorology, 256–257, 137-149. DOI:10.1016/j.agrformet.2018.03.003.
  78. *Liu, Y., Wang, Z., Sun, Q., Erb, A.M., Li, Z., Schaaf, C.B., Zhang, X., Román, M.O., Scott, R.L., Zhang, Q., Novick, K.A., Bret-Harte, S., Petroy, S., SanClements, M., 2017, , Remote Sensing of Environment, 201, 256-274. DOI:10.1016/j.rse.2017.09.020.
  79. Peng, D., Zhang, X., Zhang, B., Liu, L., Liu, X., Huete, A.R., Huang, W., Wang, S., Luo, S., Zhang, X., Zhang, H., 2017, , ISPRS Journal of Photogrammetry and Remote Sensing, 132:185-198. DOI:10.1016/j.isprsjprs.2017.09.002.
  80. *Wang, J., Zhang, X., 2017, , Environmental Research Letters, 12: 05400. DOI:10.1088/1748-9326/aa6ad9 (news http://environmentalresearchweb.org/cws/article/news/69988).
  81. Zhang, X., Liu, L., and Yan, D., 2017, , Journal of Geophysical Research-Biogeoscience, 122, DOI:10.1002/2017JG003811. (Highlighted by the Journal).
  82. *Krehbiel, C., Zhang, X., and Henebry, G.M., 2017, , Remote Sensing, 9, 499, DOI:10.3390/rs9050499.
  83. *Yan, D., Zhang, X., Yu, Y., and Guo, W., 2017, , Remote Sensing, 9(5), 461; DOI:10.3390/rs9050461.
  84. Peng, D., Zhang, X., Wu, C., Huang, W., Gonsamo, A., Huete, A.R. Didan, K., Tang, B.,Liu, X., Zhang, B., 2017, , Agricultural and Forest Meteorology, 242: 33–46. DOI:10.1016/j.agrformet.2017.04.009.
  85. Wang, Z., Schaaf, C.B., Sun, Q., Kim, J., Erb, A.M., Gao, F., Román, M.O., Yang, Y., Petroy, S., Taylor, J.R., Masek, J.G., Morisette, J.T., Zhang, X., Papuga, S.A., 2017, , International Journal of Applied Earth Observation and Geoinformation, 59, 104–117. DOI:10.1016/j.jag.2017.03.008.
  86. *Liu, L., Zhang, X., Yu, Y., Guo, W., 2017, , Remote Sensing of Environment, 194, 89–99, DOI:10.1016/j.rse.2017.03.009.
  87. Peng, D., Wu, C., Li, C., Zhang, X., Liu, Z., Ye, H., Luo, S., Liu, X., Hu, Y. Fang, B., 2017, , Ecological Indictors, 77, 323-336, DOI:10.1016/j.ecolind.2017.02.024.
  88. Liu , Y., Hill, M.J., Zhang, X., Wang, Z., Richardson, A., Hufkens, K., Filippa, G., Baldocchi, D. D., Ma, S., Verfaillie, J., Schaaf, C.B., 2017, , Agricultural and Forest Meteorology, 237–238, 311–325, DOI:10.1016/j.agrformet.2017.02.026.
  89. Zhang, X., Wang, J., Gao, F., Liu, Y., Schaaf, C.B., Friedl, M.A., Yu, Y., Jayavelu, S., Gray, J., Liu, L., Yan, D., and Henebry, G.M., 2017, , Remote Sensing of Environment, 190, 318-330, DOI:10.1016/j.rse.2017.01.001.
  90. *Liu, L. Zhang, X., Yu, Y., and Donnelly, A., 2017. , Environmental Research Letters, 12 024013, DOI:10.1088/1748-9326/aa5b3a (news http://environmentalresearchweb.org/cws/article/news/68993).
  91. Gao F., Anderson, M.C., Zhang, X., Yang, Z., Alfieri, J.G., Kustas, W.P., Mueller, R., Johnson, D.M., Prueger, J.H., 2017, , Remote Sensing of Environment, 188, 9–25, DOI:10.1016/j.rse.2016.11.004.
  92. *Yan, D., Zhang, X., Yu, Y., Guo, W. and Hanan, N. P., 2016, , Journal of Geophysical Research- Biogeosciences, 121, DOI:10.1002/2016JG003441.
  93. Peng, D., Wu, C., Zhang, B., Huete, A., Zhang, X., Sun, R., Lei, L., Huang, W., Liu, L., Liu, X., Li, J., Luo, S., Fang, B., 2016, . PLOS ONE, 11(6), DOI:10.1371/journal.pone.0158173.
  94. Liang, L., Schwartz, M., Zhang, X., 2016, , Climate, 4(2), 24, http://dx.doi.org/10.3390/cli4020024.
  95. *Yan, D., Zhang, X., Yu, Y., and Guo, W., 2016, , IEEE Transactions On Geoscience and Remote Sensing, 54(8): 4867 – 4881, DOI:10.1109/TGRS.2016.2552462.
  96. Zhang, X., and Zhang, Q. 2016, , ISPRS Journal of Photogrammetry and Remote Sensing 114, 191-205, DOI:10.1016/j.isprsjprs.2016.02.010.
  97. *Liu, L., Zhang, X., Donnelly, A. and Liu, X. ,2016, I, Int. J. Biometeorol., DOI:10.1007/s00484-016-1147-6.
  98. Wu, M., Zhang, X, Huang, W., Niu, Z., Wang,C., Li, W. and Hao, P., 2015, , Remote Sensing, DIO:10.3390/rs71215826.
  99. Liang, L., Zhang, X., 2015. , International Journal of Climatology, DOI:10.1002/joc.4456.
  100. Yue, X., Unger, N., Keenan, T. F., Zhang, X., and Vogel, C. S. 2015. . Biogeosciences, 12, 4693–4709, DOI:10.5194/bg-12-4693-2015.
  101. Senthilnath, J., Kumar, D., Benediktsson, J.A., Zhang, X., 2015. . International Journal of Image and Data Fusion, DOI:10.1080/19479832.2015.1053995.
  102. Zhang, X., 2015. . Remote Sensing of Environment, 156, 457-472, DOI:10.1016/j.rse.2014.10.012.
  103. Zhang, Q., Cheng, Y.B., Lyapustin, A.I., Wang, Y., Zhang, X., Suyker, A., Verma, S., Shuai, Y., Middleton, E.M., 2015. Agricultural and Forest Meteorology, 200, 1–8, DOI:10.1016/j.agrformet.2014.09.003.
  104. Fan, B., Guo, L., Li, N., Chen, J., Lin, H., Zhang, X., Shen, M., Rao, Y., Wang, C., Ma, L., 2014. . Scientific Reports, 4 : 6749, DOI:10.1038/srep06749.
  105. Xiao J., Ollinger, S.V., Frolking, S., Hurtt, G.C., Hollinger, D.Y., Davis, K.J., Pan, Y., Zhang, X., Deng, F., Chen, J., Baldocchi, D.D., Law, B.E., Arain, M.A., Desai, A.R., Richardson, A.D., Sun, G., Amiro, B., Margolis, H., Gu, L., Scott, R.L., Blanken, P.S., Suyker, A.E., 2014. . Agricultural and Forest Meteorology, 197, 142–157, DOI:10.1016/j.agrformet.2014.06.013.
  106. Zhang, X., Kondragunta, S., and Roy, D.P., 2014. . Journal of Geophysical Research-Biogeosciences, DOI:10.1002/2013JG002518.
  107. Zhang, F., Wang, J. Ichoku, C., Hyer, E., Yang, Z., Ge, C., Su, S., Zhang, X., Kondragunta, S., Kaiser, J., Wiedinmyer, C., and da Silva, A., 2014. . Environmental Research Letters, 9, 075002, DOI:10.1088/1748-9326/9/7/075002.
  108. Zhang, X., Tan, B., and Yu, Y. 2014. . International Journal of Biometeorology, 58(4), 547-564, DOI:10.1007/s00484-014-0802-z.
  109. Liang, L., Schwartz, M.D., Wang, Z., Gao, F., Schaaf, C.B., Tan, B., Morisette, J.T., and Zhang, X., 2014. . IEEE Transactions On Geoscience and Remote Sensing, DOI:10.1109/TGRS.2014.2313558.
  110. Shuai, Y., Schaaf, C., Zhang, X., Strahler, A., Roy, D., Morisette, J., Wang, Z., Nightingale, J., Nickeson, J., Richardson, A.D., Xie, D., Wang, J., Li, X., Strabala, K., Davies, J.E., 2013. . International Journal of Remote Sensing, 34(16): 5997-6016, DOI:10.1080/01431161.2013.803169.
  111. Zhang, X., Kondragunta, S., Ram, J., Schmidt, C., Huang,H-C, 2012. . Journal of Geophysical Research-Atmosphere, DOI:10.1029/2012JD017459.
  112. Zhang, X., 2012. Impacts of global climate change on the plant seasonality of our planet. Overseas Scholars, 1:35-45.
  113. Zhang, X., Goldberg, M.D., Yu, Y., 2012. . Agricultural and Forest Meteorology, 158: 21-29, DOI:10.1016/j.agrformet.2012.01.013.
  114. Kovalskyy, V., Roy, D. P., Zhang, X., Ju, J., 2012. . Remote Sensing Letters, 3(4): 325–334, DOI:10.1080/01431161.2011.593581.
  115. Zhang, X. Kondragunta, S., and Quayle, B., 2011. . IEEE Transactions on Geosciences and Remote Sensing, 49: 4469-4482, DOI:10.1109/TGRS.2011.2149535.
  116. Zhang, X. and Goldberg, M, 2011. . Remote Sensing of Environment, 115 (2): 382-391, DOI:10.1016/j.rse.2010.09.009.
  117. Yang, E.S., Christopher, S.A., Kondragunta, S., and Zhang, X., 2010. . Journal of Geophysical Research, 116, D04303, DOI:10.1029/2010JD014482.
  118. Zhang, X., Goldberg, M., Tarpley, D., Friedl, M., Morisette, J., Kogan, F., Yu, Y., 2010. . Environmental Research Letters, 5 (2010) 024008, DOI:10.1088/1748-9326/5/2/024008.
  119. Ganguly, S., Friedl, M.A., Tan, B., Zhang, X., and Verma, M., 2010. . Remote Sensing of Environment, 114(8), 1805-1816, DOI:10.1016/j.rse.2010.04.005.
  120. Christopher, S.A., Gupta, P., Nair, U., Jones, T.A., Kondragunta, S., Wu, Y.L, Hand, J., Zhang, X, 2009. . Journal of Selected Topics in Earth Observations and Remote Sensing, 2:163 – 175, DOI:10.1109/JSTARS.2009.2026626.
  121. Zhang, X., Friedl, M.A., Schaaf, C.B., 2009. . International Journal of Remote Sensing, 30(8): 2061 – 2074, DOI:10.1080/01431160802549237.
  122. Zhang, X., Kondragunta, S., Schmidt, C., Kogan, F., 2008. . Atmospheric Environment, 42 (29), 6959-6972, DOI:10.1016/j.atmosenv.2008.04.060.
  123. Al-Saadi, J., Soja, A., Pierce, B., Kittaka, C., Emmons, L., Kondragunta, S., Zhang, X., Wiedinmyer, C., Schaack, T. Szykman, J., 2008. . Journal of Applied Remote Sensing, v2, DOI:10.1117/1.2948785.
  124. Zhang, X., Kondragunta, S., 2008. . Remote Sensing of Environment, 112 (6), 2886-2897. DOI:10.1016/j.rse.2008.02.006.
  125. Zhang, X., Tarpley, D., Sullivan, J. 2007. . Geophysical Research Letters, 34, L19405, DOI:10.1029/2007GL031447. (This paper was reported in more than 60 different news sites worldwide, including New Scientist Magazine, Wired Magazine, Natural History Magazine, AGU EOS, and Live Science).
  126. Zhang, X., Friedl, M.A., Schaaf, C.B., 2006. . Journal of Geophysical Research, Vol. 111, G04017, DOI:10.1029/2006JG000217.
  127. Zhang X., Kondragunta, S., 2006. . Geophysical Research Letters, 33: L09402, DOI:10.1029/2006GL025879.
  128. Wiedinmyer, C., Quayle, B., Geron, C., Belote, A., McKenzie, Zhang, X., O’Neil, S., and Wynne, K.K., 2006. . Atmospheric Environment, 40: 3419-3432, DOI:10.1016/j.atmosenv.2006.02.010.
  129. Zhang, X., Friedl, M.A., Schaaf, C.B., and Strahler, A.H., Liu, Z., 2005. . Journal of Geophysical Research-Atmospheres, 110, D12103. DOI:10.1029/2004JD005263.
  130. Zhang, X., Friedl, M.A., Schaaf, C. B., Strahler, A.H., and Schneider, A., 2004. . Geophysical Research Letter, Vol. 31, L12209, DOI:10.1029/2004GL020137. (This paper was reported in more than 100 different news sites, such as Science News (newsmagazine), NASA press release, and The Associated Press).
  131. Zhang, X., Friedl, M.A., Schaaf, C.B., Strahler, A.H., 2004. . Global Change Biology, 10:1133-1145, DOI:/10.1111/j.1529-8817.2003.00784.x.
  132. Tian Y, Dickinson, R.E., Zhou, L., Zeng, X., Dai, Y., Myneni, R.B., Knyazikhin, Y., Zhang, X., Friedl, M., Yu, II., Wu, W., Shaikh, M. 2004. . Journal of Geophysical Research-Atmospheres, 109 (D1): Art. No. D01103, DOI:10.1029/2003JD003777.
  133. Penuelas, J., Filella, I., Zhang, X., LLorens, L., Ogaya, R., Lloret, F., Comas, P., Estiarte, M., Terradas, J., 2004. . New Phytologist, 161(3): 837-846, DOI:10.1111/j.1469-8137.2004.01003.x.
  134. Zhang, X., Schaaf, C. B., Friedl, M. A., Strahler, A. H., Gao F., Hodges, J. F., Reed, B. C., Huete, A., 2003. . Remote Sensing of Environment, 84(3), 471-475, DOI:10.1016/S0034-4257(02)00135-9.
  135. Zhang, X., Drake, N. A., and Wainwright, J. 2002. . Water Resources Research, 38(9), 191-199, DOI:10.1029/2001WR000356. (This paper was highlighted by EOS, 83(43), Oct., 2002).
  136. Schaaf, C. B., Gao, F., Strahler, A. H., Lucht, W., Li, X., Tsang, T., Strugnell, N. C., Zhang, X., Jin, Y., Muller, J. P. et al. 2002. . Remote Sensing of Environment, 83(1-2), 135-148, DOI:10.1016/S0034-4257(02)00091-3.
  137. Friedl, M. A, McIver, D. K, Hodges, J. C., Zhang, X. Y., Muchoney, D., Strahler, A. H., Woodcock, C. E., Gopal, S., Schnieder, A., Cooper, A., Baccini, A., Gao, F., and Schaaf, C. B. 2003. . Remote Sensing of Environment, 83(1-2), 287-302, DOI:10.1016/S0034-4257(02)00078-0.
  138. Yun Du, Y., Cai, S., Zhang, X. and Zhao, Y. 2001. . Geomorphology, 41(2-3), 171-181, DOI:10.1016/S0169-555X(01)00114-3.
  139. Zhang, X., Drake, N. A., Wainwright, J. and Mulligan, M. 1999. . Earth Surface Processes and Landforms, 24(9), 763-779, DOI:10.1002/(SICI)1096-9837(199908)24:9<763::AID-ESP9>3.0.CO;2-J.
  140. Zhang, X. 1998. . International Journal of Remote Sensing, 19(1), 11-20, DOI:10.1080/014311698216396.
  141. Liu, L., Pang, Y., Zhang, X., Solberg, S., Fan,W., Li, Z., Li, M., 2012. Monitoring Forest Growth Disturbance Using Time Series MODIS EVI Data. Forest Science, China, 28: 54-62. (Chinese)
  142. Zhang, X., Li, J., 1995. The derivation of a reflectance model for the estimation of leaf area index using perpendicular vegetation index. Remote Sensing Technology and Application, 10(3):13-18. (Chinese)
  143. Zhang, X., Du, Y. and Cai, S. 1995. An analysis on evolutional tendency of Dongting Lake. Resources and Environment in the Yangtze Valley, China, 4(1), 64-69. (Chinese)
  144. Zhang, X., Cai, S. and Sun, S. 1994. Evolution of Dongting Lake since Holocene, Limnology Science, China, 16(1).
  145. Yu, L., Xu, Y, Cai, S. and Zhang, X. 1993. The application of GIS to a lake environmental change study. Limnology Science, China, 15(4). (Chinese)
Edited Books
  1. Zhang, X. (Ed.), 2012. , ISBN: 978-953-51-0336-3, InTech.
Refereed Book Chapters
  1. Zhang, X., 2018. . In Liang, S. (ed), Comprehensive Remote sensing: Terrestrial ecosystems, ELSE, Vol 3: 35-52. DOI: 10.1016/B978-0-12-409548-9.10351-3
  2. Zhang, X., Ni-Meister, W., 2014. . In Hanes, J. (ed), Biophysical Applications of Satellite Remote Sensing, Springer, New York, pp 63-98.
  3. Zhang, X., Friedl, M.A., Tan, B., Goldberg, M.D. and Yu, Y., 2012. Long-Term Detection of Global Vegetation Phenology from Satellite Instruments. In X. Zhang (Ed.), Phenology and Climate Change, ISBN: 978-953-51-0336-3, InTech.
  4. Zhang, X., Drake, N. A., Wainwright, J., 2013. Spatial Modelling and Scaling Issues. In Wainwright, J. and Mulligan, M. (eds.), Environmental Modeling: Finding Simplicity in Complexity (Second Edition), John Wiley and Sons, Chichester.
  5. Friedl, M.A., Zhang, X., Strahler, A.H, 2011. Characterizing global land cover type and seasonal land cover dynamics at moderate spatial resolution using MODIS. In Ramachandran, B., Justice, C., and Abrams, M. (Eds), Land Remote Sensing and Global Environmental Change: NASA’s Earth Observing System and the Science of ASTER and MODIS, Springer, New York, pp. 709-721.
  6. Zhang, X., Drake, N. A., and Wainwright, J. 2004. Scaling issues in environmental modeling. In Wainwright, J. and Mulligan, M. (eds.), Environmental Modeling: Finding Simplicity in Complexity, John Wiley and Sons, Chichester, pp. 319-334.
  7. Drake, N.A., Zhang, X., Symeonakis, E., Patterson, G., Bryant, A.R. 2004. Near Real-time Modeling of Regional scale soil erosion using AVHRR and METEOSAT data: a tool for monitoring the impact of sediment yield on the biodiversity of Lake Tanganyika. In Kelly, R., Drake, N., and Barr, S. (eds.), Spatial Modelling of the Terrestrial Environment. John Wiley and Sons, Chichester, pp. 157-174.
  8. Drake, N. A., Zhang, X., Berkhout, E., Bonifacio, R., Grimes, D., Wainwright, J. and Mulligan, M. 1999. Modeling soil erosion at global and regional scales using remote sensing and GIS techniques. In Atkinson, P. M. and Tate, N. J. (eds.), Advances in Remote Sensing and GIS Analysis, John Wiley and Sons, Chichester, pp. 241-261.
  9. Zhang, X. 1992. Study on the swamping of lakes and lowland in Jianghan and Dongting plain by using remote sensing techniques. In Embleton, C. (ed.), Geo-hazards and their Reduction, Science Press, Beijing, pp. 61-69.
  10. Zhang, X. and Cai, S. 1994. Study on wetland and its dynamic changes in Jianhan plain by using remote sensing. In Wetland Environment and Peatland Utilization: Wetland Environment and Peatland Utilization, Changchun, China, Jilin People's Publishing House, Changchun, China, pp. 296-302.
  11. Zhang, X., Huang, J., Li, J., Chen, S. and Liu, K. 1995. Remote sensing for modeling rice yield in Hubei province, PRC. In Zhou, R. et al (ed.), Rice Yield Estimation Using Remote Sensing in China, Science Press, Beijing. (Chinese)
  12. Zhang, X. 1995. The relationship between biomass of submerged vegetation and spectral properties. In Chen, Y. and Xu Y. (eds.), Hydrobiology and Resource Exploitation in the Honghu Lake, Sciences Press, Beijing. (Chinese)
  13. Zhang, X. 1995. Investigating biomass of submerged vegetation using PCA analysis. In Chen, Y. and Xu Y. (eds.), Hydrobiology and Resource Exploitation in the Honghu Lake, Sciences Press, Beijing. (Chinese)
  14. Zhang, X. and Cai, S. 1994. The effect of the Three Gorge Project on Dongting Lake. In Pu, P. (ed.), The Effect of Three Gorge Project on The Environment of Lakes And Wetland In The Middle Reaches of Yangtze River, Sciences Press, Beijing. (Chinese)
  15. Zhang, X., Li, R., Chen, S. and Liu, K. 1993. Exploring a remote sensing model of rice yield estimation. In Chen, S. (ed.), Estimation of Wheat, Maize and Rice Yield Using Remote Sensing Techniques, Chinese Science and Technology Press, Beijing. (Chinese)
  16. Zhang, X., Li, R. and Du, Y. 1993. Sampling frame for rice yield estimation based on the remote sensing techniques in Jiangli County. In Chen, S. (ed.), Estimation of Wheat, Maize and Rice Yield Using Remote Sensing Techniques, Chinese Science and Technology Press, Beijing. (Chinese)
  17. Liu, K., Yang, B. and Zhang, X. 1993. Numerical simulation of rice yield. In Chen, S. (ed.), Estimation of Wheat, Maize and Rice Yield Using Remote Sensing Techniques, Chinese Science and Technology Press, Beijing. (Chinese)
  18. Zhang, X. and Cai, S. 1991. Recent change of Dongting Lake. In Chinese Association of Geomorphology and Quaternary (ed.), Research Progress of Geomorphology and Quaternary, Survey and Drawing Press, Beijing. (Chinese)
  19. Zhang, X. and Cai, S. 1991. Analysis of the swamping process and the dynamic change in emergent vegetation on the basis of remote sensing data. In Honghu Research Group, Institute of Hydrobiology, Academia Sinica (ed.), Studies on Comprehensive Exploitation of Aquatic Biological Productivity and Improvement of Ecological Environment in Lake Honghu, China Ocean Press, Beijing. (Chinese)
  20. Zhang, X. and Cai, S. 1991. Estimation of the emergent vegetation biomass in Lake Honghu by means of remote sensing. In Honghu Research Group, Institute of Hydrobiology, Academia Sinica (ed.), Studies on Comprehensive Exploitation of Aquatic Biological Productivity and Improvement of Ecological Environment in Lake Honghu, China Ocean Press, Beijing. (Chinese)
  21. Cai, S. and Zhang, X. 1991. Co-ordinate development of fishery and agriculture in the Honghu basin. In Honghu Research Group, Institute of Hydrobiology, Academia Sinica (ed.), Studies on Comprehensive Exploitation of Aquatic Biological Productivity and Improvement of Ecological Environment in Lake Honghu, China Ocean Press, Beijing. (Chinese)
  22. Cai, S., Yi, C., and Zhang, X. 1991. Process of swamping and pedogenesis in Honghu Lake and utilization. In Honghu Research Group, Institute of Hydrobiology, Academia Sinica (ed.), Studies on Comprehensive Exploitation of Aquatic Biological Productivity and Improvement of Ecological Environment in Lake Honghu, China Ocean Press, Beijing. (Chinese)
  23. Cai, S., Zhang, X., Zhou, S. and Wang, K. 1989. Map of the change of lakes in Sihu district. In Atlas of Ecosystems and Environments in the Three Gorges of the Yangtze River, Science Press, Beijing. (Chinese)
  24. Cai, S. and Zhang, X. 1989. Map of the change of Dongting Lake. In Atlas of Ecosystems and Environments in the Three Gorges of the Yangtze River, Science Press, Beijing. (Chinese)
  25. Cai, S., Guan, Z., Zou, J. Zhang, X., Yi, L. and Yang H. 1987. Effects of the Three Gorge project on lake environmental evolution and potential gleization and creation of marshes in the north and south of Jingjiang River. In Impacts of the Three Gorges Project on Ecosystems and Environment and Possible Countermeasures, Science Press, Beijing. (Chinese)