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As climate change intensifies, surface vegetation productivity and carbon exchange between terrestrial ecosystems and the atmosphere are significantly affected by the variation of climatic factors. Due to the sensitivity of grasslands to these climatic factors, it is crucial to understand the response of vegetation greenness, or carbon exchange within grasslands, to environment factor dynamics. In this study, we used solar-induced chlorophyll fluorescence (SIF), precipitation (P), vapor pressure deficit (VPD), evaporative stress (ES), and root zone soil moisture (RSM) derived from remote sensing, reanalysis, and assimilation datasets to explore the response of vegetation greenness within Eurasian Steppe to climatic factors. Our results indicated deseasonlization based on the Seasonal-Trend decomposition using Loess (STL) method, which was an effective means to remove the seasonality disturbances that affect the qualification of the relationship between SIF and the four climatic factors. The response of SIF had a time lag effect on these climatic factors, and the longer the response period, the greater the impact on the correlation of SIF with P, VPD, ES, and RSM. We also found, among the four factors, that the response of SIF to ES was the timeliest. The findings of this study emphasized the impact of the seasonality and time lag effect on the dynamic response between variables, and provided references to the attribution and monitoring of vegetation greenness and ecosystem productivity.
Qi Liu; Quan Liu; Xianglei Meng; Jiahua Zhang; Fengmei Yao; Hairu Zhang. The Impact of Seasonality and Response Period on Qualifying the Relationship between Ecosystem Productivity and Climatic Factors over the Eurasian Steppe. Remote Sensing 2021, 13, 3159 .
AMA StyleQi Liu, Quan Liu, Xianglei Meng, Jiahua Zhang, Fengmei Yao, Hairu Zhang. The Impact of Seasonality and Response Period on Qualifying the Relationship between Ecosystem Productivity and Climatic Factors over the Eurasian Steppe. Remote Sensing. 2021; 13 (16):3159.
Chicago/Turabian StyleQi Liu; Quan Liu; Xianglei Meng; Jiahua Zhang; Fengmei Yao; Hairu Zhang. 2021. "The Impact of Seasonality and Response Period on Qualifying the Relationship between Ecosystem Productivity and Climatic Factors over the Eurasian Steppe." Remote Sensing 13, no. 16: 3159.
Achieving high crop yield with less irrigation is important to improve water productivity (WP) and irrigation water productivity (IWP) in water-limited regions in the North China Plain (NCP). Coupling the impacts of precipitation and irrigation on the crop yield is, therefore, an essential tool for understanding crop response to different water sources and forecasting irrigation water requirements. In this study, the CERES-Wheat model was used to simulate winter wheat yield, WP, and IWP under different irrigation schedules (no, deficit, full, and automatic irrigation) and three precipitation category years (wet, normal, and dry years). Results showed that the amount of precipitation fluctuated significantly over site-years, leading to considerably varied irrigation water requirements. For the wet year, deficit two irrigations at greening and jointing stages (treatment 11) alleviated water stress during key winter wheat growth periods, contributing to increasing biomass, yield, WP, and IWP. However, when reducing the irrigation amount by 50%, a significant increase in WP and a nonsignificant difference in yield were found at Fengqiu and Shangqiu. Two irrigation applications 11 improved pre-anthesis biomass and wheat yield while achieving high WP and IWP in normal and dry years (except for extreme drought conditions). The finding also indicated that the distribution of growing season precipitation exerted a significant impact on irrigation time. If the early season precipitation was low, shifting the irrigation to an earlier time to ensure pre-anthesis water requirements, which can synchronously achieve the goals of increasing WP and maintaining a higher yield. In conclusion, optimizing irrigation strategies to various precipitation conditions will be a promising and effective practice for wheat production and water conservation.
Ruiyun Zeng; Fengmei Yao; Sha Zhang; Shanshan Yang; Yun Bai; Jiahua Zhang; Jingwen Wang; Xin Wang. Assessing the effects of precipitation and irrigation on winter wheat yield and water productivity in North China Plain. Agricultural Water Management 2021, 256, 107063 .
AMA StyleRuiyun Zeng, Fengmei Yao, Sha Zhang, Shanshan Yang, Yun Bai, Jiahua Zhang, Jingwen Wang, Xin Wang. Assessing the effects of precipitation and irrigation on winter wheat yield and water productivity in North China Plain. Agricultural Water Management. 2021; 256 ():107063.
Chicago/Turabian StyleRuiyun Zeng; Fengmei Yao; Sha Zhang; Shanshan Yang; Yun Bai; Jiahua Zhang; Jingwen Wang; Xin Wang. 2021. "Assessing the effects of precipitation and irrigation on winter wheat yield and water productivity in North China Plain." Agricultural Water Management 256, no. : 107063.
Drought is pervasive global hazard and seriously impacts ecology. Particularly, vegetation drought, which is chiefly driven by soil moisture (SM) deficiency, has a direct bearing on grain production and human livelihoods. Various drought indices associated with vegetation and SM conditions have been proposed to monitor and detect vegetation drought. In this study, we evaluated the performance of eight drought indices, including Drought Severity Index (DSI), Evaporation Stress Index (ESI), Normalized Vegetation Supply Water Index (NVSWI), Temperature-Vegetation Dryness Index (TVDI), Temperature Vegetation Precipitation Dryness Index (TVPDI), Vegetation Health Index (VHI), Self-calibrating Palmer Drought Severity Index (SC-PDSI) and Standardized Precipitation Evapotranspiration Index (SPEI), for capturing SM dynamic (derived from Copernicus Climate Change Service) across the six main vegetation coverage types of China. Our results showed DSI and ESI had the best overall performance. When exploring the reasons for the uncertainty of these indices (except SC-PDSI and SPEI) in the evaluation, we found that, in the non-arable regions, the time lag effect of drought indices on SM, the average state and rangeability of corresponding variables and the climatic conditions (precipitation and temperature) all impacted the performance of DSI, ESI, NVSWI, TVPDI and VHI. In the arable region, cropland types (paddy field and non-paddy field) and the uncertainty of SM data mainly caused the uncertainties of the above five indices. With regard to the TVDI, abnormalities of dry and wet edges fitting may be the primary factor affecting its performance. These results demonstrated that these drought indices with reliable and robust performance of capturing SM dynamics can be suggested to characterize the trend of SM. Certainly, this study can provide a reference for the improvement of existing drought indices and the establishment of new drought indices.
Qi Liu; Jiahua Zhang; Hairu Zhang; Fengmei Yao; Yun Bai; Sha Zhang; Xianglei Meng; Quan Liu. Evaluating the performance of eight drought indices for capturing soil moisture dynamics in various vegetation regions over China. Science of The Total Environment 2021, 789, 147803 .
AMA StyleQi Liu, Jiahua Zhang, Hairu Zhang, Fengmei Yao, Yun Bai, Sha Zhang, Xianglei Meng, Quan Liu. Evaluating the performance of eight drought indices for capturing soil moisture dynamics in various vegetation regions over China. Science of The Total Environment. 2021; 789 ():147803.
Chicago/Turabian StyleQi Liu; Jiahua Zhang; Hairu Zhang; Fengmei Yao; Yun Bai; Sha Zhang; Xianglei Meng; Quan Liu. 2021. "Evaluating the performance of eight drought indices for capturing soil moisture dynamics in various vegetation regions over China." Science of The Total Environment 789, no. : 147803.
Drought, a climate-related disaster impacting a variety of sectors, poses challenges for millions of people in South Asia. Accurate and complete drought information with a proper monitoring system is very important in revealing the complex nature of drought and its associated factors. In this regard, deep learning is a very promising approach for delineating the non-linear characteristics of drought factors. Therefore, this study aims to monitor drought by employing a deep learning approach with remote sensing data over South Asia from 2001–2016. We considered the precipitation, vegetation, and soil factors for the deep forwarded neural network (DFNN) as model input parameters. The study evaluated agricultural drought using the soil moisture deficit index (SMDI) as a response variable during three crop phenology stages. For a better comparison of deep learning model performance, we adopted two machine learning models, distributed random forest (DRF) and gradient boosting machine (GBM). Results show that the DFNN model outperformed the other two models for SMDI prediction. Furthermore, the results indicated that DFNN captured the drought pattern with high spatial variability across three penology stages. Additionally, the DFNN model showed good stability with its cross-validated data in the training phase, and the estimated SMDI had high correlation coefficient R2 ranges from 0.57~0.90, 0.52~0.94, and 0.49~0.82 during the start of the season (SOS), length of the season (LOS), and end of the season (EOS) respectively. The comparison between inter-annual variability of estimated SMDI and in-situ SPEI (standardized precipitation evapotranspiration index) showed that the estimated SMDI was almost similar to in-situ SPEI. The DFNN model provides comprehensive drought information by producing a consistent spatial distribution of SMDI which establishes the applicability of the DFNN model for drought monitoring.
Foyez Prodhan; Jiahua Zhang; Fengmei Yao; Lamei Shi; Til Pangali Sharma; Da Zhang; Dan Cao; Minxuan Zheng; Naveed Ahmed; Hasiba Mohana. Deep Learning for Monitoring Agricultural Drought in South Asia Using Remote Sensing Data. Remote Sensing 2021, 13, 1715 .
AMA StyleFoyez Prodhan, Jiahua Zhang, Fengmei Yao, Lamei Shi, Til Pangali Sharma, Da Zhang, Dan Cao, Minxuan Zheng, Naveed Ahmed, Hasiba Mohana. Deep Learning for Monitoring Agricultural Drought in South Asia Using Remote Sensing Data. Remote Sensing. 2021; 13 (9):1715.
Chicago/Turabian StyleFoyez Prodhan; Jiahua Zhang; Fengmei Yao; Lamei Shi; Til Pangali Sharma; Da Zhang; Dan Cao; Minxuan Zheng; Naveed Ahmed; Hasiba Mohana. 2021. "Deep Learning for Monitoring Agricultural Drought in South Asia Using Remote Sensing Data." Remote Sensing 13, no. 9: 1715.
Under the limited cultivated land area and the pursuit of sustainable agricultural development, it is essential for the safety of grain production to study agricultural management approaches on narrowing the winter wheat yield gap and improving nitrogen use efficiency (NUE) in China. In this study, DSSAT-CERES-Wheat Model is used to simulate winter wheat yield under different agricultural treatments, and we analyze yield gaps and NUE with different management scenarios at regional scales and evaluate the suitable approaches for reducing yield gap and increasing NUE. The results show that, the potential of narrowing yield gap ranges 300–900 kg ha–1 with soil nutrients increase, 400–1 200 kg ha–1 with sowing date adjustment and 0–400 kg ha–1 with planting density increase as well as 700–2 200 kg ha–1 with adding nitrogen fertilizer. Contribution rates of management measures of soil nutrients, sowing date adjusting, planting density, and nitrogen fertilizers are 5–15%, 5–15%, 0–4%, and 10–20%, respectively. Difference in nitrogen partial productivity ranges 3–10 kg kg–1 for soil nutrients, 1–10 kg kg–1 for sowing date adjusting, 1–5 kg kg–1 for planting density increase, and –12–0 kg kg–1 for adding nitrogen fertilizers, respectively. It indicates that four treatments can narrow yield gap and improve the NUE in varying degrees, but increasing nitrogen fertilizer leads to the decrease of NUE.
Feng-Mei Yao; Qin-Ying Li; Rui-Yun Zeng; Si-Qi Shi. Effects of different agricultural treatments on narrowing winter wheat yield gap and nitrogen use efficiency in China. Journal of Integrative Agriculture 2021, 20, 383 -394.
AMA StyleFeng-Mei Yao, Qin-Ying Li, Rui-Yun Zeng, Si-Qi Shi. Effects of different agricultural treatments on narrowing winter wheat yield gap and nitrogen use efficiency in China. Journal of Integrative Agriculture. 2021; 20 (2):383-394.
Chicago/Turabian StyleFeng-Mei Yao; Qin-Ying Li; Rui-Yun Zeng; Si-Qi Shi. 2021. "Effects of different agricultural treatments on narrowing winter wheat yield gap and nitrogen use efficiency in China." Journal of Integrative Agriculture 20, no. 2: 383-394.
East African region is susceptible to drought due to high variation in monthly precipitation. Studying drought at regional scale is vital since droughts are considered a ‘creeping’ disaster by nature with devasting and extended impact often requiring long periods to reverse the recorded damages. This study assessed drought exceedance and return years over East Africa from 1920 to 2016 using Climate Research Unit (CRU) precipitation data records. Meteorological drought, where precipitation is the central quantity of interest, was adopted in the work. Standardize Precipitation Index (SPI) was used to study long term meteorological droughts and also to assess drought magnitude, frequency, exceedance probability and return years using Joint Probability Density Function (JPDF). Also, Mann-Kendall trend analysis was applied to precipitation and SPI to investigate the trend changes. Results showed that years with high drought magnitude ranged from 1920−22, 1926−29, 1942−46 and 1947−51 with values corresponding to 2.2, 3.2, 3.4 and 2.6, respectively while years with low drought magnitude ranged from 1930−31, 1988−89 and 2001−02 with values as 0.2, 0.12 and 0.15, respectively. The longest droughts occurred from 1926−29, 1937−41, 1942−46, 1947−51, 1952−56, and 1958−61 with values in years as 3, 4, 4, 4, 4, and 3 years, respectively, while the shortest droughts occurred in time period of 1 year and ranged from 1930−31, 1964−65, 1979−80, 1981−82, 1983−84, 1988−89, 1991−92, 1993−94, 1996−97 and 2001−02. Also, it was demonstrated that probability of drought occurrence is high when severity is low and such droughts occur at short time intervals and not all severest drought took longer periods. The SPI trends indicate high positive (negative) pixels above (below) the zero-trend mark, indicating that drought prevails in both low and high elevation areas up to 2000 m. There was no direct link between ENSO and drought but arguably the association of drought in most El Niño and La Niña years suggests that the impact of ENSO cannot be ruled out since peak ENSO events occur during October to March periods which coincides with the short (SON) and long (MAM) rainy seasons of East Africa. The study is particularly relevant in being able to depict continuous and synoptic drought condition all over East Africa, providing vital information to farmers and policy makers, using very cost-effective method.
Wilson Kalisa; Jiahua Zhang; Tertsea Igbawua; Fanan Ujoh; Obas John Ebohon; Jean Nepomuscene Namugize; Fengmei Yao. Spatio-temporal analysis of drought and return periods over the East African region using Standardized Precipitation Index from 1920 to 2016. Agricultural Water Management 2020, 237, 106195 .
AMA StyleWilson Kalisa, Jiahua Zhang, Tertsea Igbawua, Fanan Ujoh, Obas John Ebohon, Jean Nepomuscene Namugize, Fengmei Yao. Spatio-temporal analysis of drought and return periods over the East African region using Standardized Precipitation Index from 1920 to 2016. Agricultural Water Management. 2020; 237 ():106195.
Chicago/Turabian StyleWilson Kalisa; Jiahua Zhang; Tertsea Igbawua; Fanan Ujoh; Obas John Ebohon; Jean Nepomuscene Namugize; Fengmei Yao. 2020. "Spatio-temporal analysis of drought and return periods over the East African region using Standardized Precipitation Index from 1920 to 2016." Agricultural Water Management 237, no. : 106195.
Ecosystem water-use efficiency (WUE) is a critical indicator to investigate the interaction between the terrestrial ecosystem carbon and water cycles. WUE, estimated from gross primary productivity (GPP) and evapotranspiration (ET) based on remote sensing (RS)-based ecosystem models and algorithms (e.g., MODIS (MODerate resolution Imaging Spectroradiometer), BESS (Breathing Earth System Simulator)), have been used to quantify the spatiotemporal dynamics of WUE and its responses to environmental changes. However, few studies have assessed the ability of RS-based ecosystem models and algorithms on global WUE estimation. In this study, we evaluated 8-day and annual WUE from MODIS and BESS among different sites, land cover types and climate zones using the FLUXNET2015 dataset as reference, and conducted spatial intercomparisons of annual WUE between MODIS, BESS and an upscaled FLUXNET dataset (MTE). The site level evaluation results showed that BESS WUE had better performance than MODIS WUE at both 8-day and annual scales. Among different land cover types and climate zones, MODIS and BESS WUE performed unsatisfactorily, especially for MODIS WUE in open shrublands and savannas and for BESS WUE in closed shrublands. Additionally, both MODIS and BESS WUE performed poorly in the hot semi-arid climate zone. The spatial intercomparisons over 2001-2011 revealed that BESS WUE had similar spatial patterns of annual WUE and linear trends with MTE WUE over the globe, except at the high latitudes. However, the spatiotemporal patterns of MODIS WUE were different from those of MTE and BESS WUE, particularly in the (sub) tropical arid and semi-arid regions. Our evaluations results suggested that coupling carbon and water cycles into RS-based models could improve their performance on global WUE estimation. Moreover, the performance of MODIS and BESS on global WUE estimation should be further improved, especially for their performance on temporal variation and their performance at the (semi) arid areas and the high latitudes.
Shanshan Yang; Jiahua Zhang; Sha Zhang; Jingwen Wang; Yun Bai; Fengmei Yao; Huadong Guo. The potential of remote sensing-based models on global water-use efficiency estimation: An evaluation and intercomparison of an ecosystem model (BESS) and algorithm (MODIS) using site level and upscaled eddy covariance data. Agricultural and Forest Meteorology 2020, 287, 107959 .
AMA StyleShanshan Yang, Jiahua Zhang, Sha Zhang, Jingwen Wang, Yun Bai, Fengmei Yao, Huadong Guo. The potential of remote sensing-based models on global water-use efficiency estimation: An evaluation and intercomparison of an ecosystem model (BESS) and algorithm (MODIS) using site level and upscaled eddy covariance data. Agricultural and Forest Meteorology. 2020; 287 ():107959.
Chicago/Turabian StyleShanshan Yang; Jiahua Zhang; Sha Zhang; Jingwen Wang; Yun Bai; Fengmei Yao; Huadong Guo. 2020. "The potential of remote sensing-based models on global water-use efficiency estimation: An evaluation and intercomparison of an ecosystem model (BESS) and algorithm (MODIS) using site level and upscaled eddy covariance data." Agricultural and Forest Meteorology 287, no. : 107959.
Studying the response of vegetation phenology to climate change at different temporal and spatial scales is important for understanding and predicting future terrestrial ecosystem dynamics and the adaptation of ecosystems to global change. In this study, satellite-derived phenology metrics were used to explore spatial and temporal changes in climatic influences on the start of the vegetation greenup season (SOS) across the Tibetan Plateau during 1982–2012. The results showed that SOS changed to a greater degree in areas where the SOS started earlier. In areas where the spring temperature (T-spring) increased at a faster rate, SOS was more likely to be advanced, and the faster the T-spring increase the more obvious the advance of SOS was. The T-spring had a stronger impact on SOS in relatively moist eco-geographic regions, while spring precipitation (P-spring) had a stronger impact on SOS in relatively warm regions. The results also indicated that P-spring effect on SOS tend to get weaker from west to east, while T-spring effect on SOS would produce no significant variation along both altitude and latitude. The T-spring effect on SOS became gradually stronger from west to east, while the P-spring effect on SOS tended to become weaker. In addition, the thermal impacts on SOS became weaker from 1982 to 2012 which may attribute to the vegetation acclimation to environment changes.
Qing Chang; Jiahua Zhang; Wenzhe Jiao; Fengmei Yao; Siyuan Wang. Spatiotemporal dynamics of the climatic impacts on greenup date in the Tibetan Plateau. Environmental Earth Sciences 2016, 75, 1343 .
AMA StyleQing Chang, Jiahua Zhang, Wenzhe Jiao, Fengmei Yao, Siyuan Wang. Spatiotemporal dynamics of the climatic impacts on greenup date in the Tibetan Plateau. Environmental Earth Sciences. 2016; 75 (20):1343.
Chicago/Turabian StyleQing Chang; Jiahua Zhang; Wenzhe Jiao; Fengmei Yao; Siyuan Wang. 2016. "Spatiotemporal dynamics of the climatic impacts on greenup date in the Tibetan Plateau." Environmental Earth Sciences 75, no. 20: 1343.
Drought over Southwest China occurs frequently and has an obvious seasonal characteristic. Proper management of regional droughts requires knowledge of the expected frequency or probability of specific climate information. This study utilized k-means classification and copulas to demonstrate the regional drought occurrence probability and return period based on trivariate drought properties, i.e., drought duration, severity, and peak. A drought event in this study was defined when 3-month Standardized Precipitation Evapotranspiration Index (SPEI) was less than −0.99 according to the regional climate characteristic. Then, the next step was to classify the region into six clusters by k-means method based on annual and seasonal precipitation and temperature and to establish marginal probabilistic distributions for each drought property in each sub-region. Several copula types were selected to test the best fit distribution, and Student t copula was recognized as the best one to integrate drought duration, severity, and peak. The results indicated that a proper classification was important for a regional drought frequency analysis, and copulas were useful tools in exploring the associations of the correlated drought variables and analyzing drought frequency. Student t copula was a robust and proper function for drought joint probability and return period analysis, which is important for analyzing and predicting the regional drought risks.
Cui Hao; Jiahua Zhang; Fengmei Yao. Multivariate drought frequency estimation using copula method in Southwest China. Theoretical and Applied Climatology 2015, 127, 977 -991.
AMA StyleCui Hao, Jiahua Zhang, Fengmei Yao. Multivariate drought frequency estimation using copula method in Southwest China. Theoretical and Applied Climatology. 2015; 127 (3-4):977-991.
Chicago/Turabian StyleCui Hao; Jiahua Zhang; Fengmei Yao. 2015. "Multivariate drought frequency estimation using copula method in Southwest China." Theoretical and Applied Climatology 127, no. 3-4: 977-991.
In this paper, we intent to use the remotely sensed MODerate resolution Imaging Spectroradiometer (MODIS) data and China’s Environment Satellite (HJ-1) data for extracting the corn cultivated area over a regional scale. The high resolution HJ-1 data was to extract corn distribution at a small scale class with Support Vector Machine (SVM). The mean Enhanced Vegetation Index (EVI) time series curve of corn from MODIS was derived for the reference area and validated in a larger area. The MODIS-EVI time series curve derived from the reference area instead of the MODIS-EVI time series curve derived from the study area after validation, which was taken as the standard MODIS-EVI time series curve in for generating a standard MODIS-EVI image of corn. The mean absolute distance (MAD) between the standard MODIS-EVI image of corn and the MODIS-EVI time series image was used to detect the maximum possible extent of corn distribution in the study area. The results showed that the overall accuracy of the method was 82.17 %, with commission and omission errors of 16.85 and 15.40 %, respectively; at the county level, the satellite-estimated corn area and statistical data were well correlated (R 2 = 0.85, N = 50) for the whole Jilin Province. It indicated that the MODIS data integrated with higher spatial resolution of HJ-1 satellite data could be utilized to enhance the extraction accuracy of corn cultivated area at a larger scale.
Fengmei Yao; Lili Feng; Jiahua Zhang. Corn Area Extraction by the Integration of MODIS-EVI Time Series Data and China’s Environment Satellite (HJ-1) Data. Journal of the Indian Society of Remote Sensing 2014, 42, 859 -867.
AMA StyleFengmei Yao, Lili Feng, Jiahua Zhang. Corn Area Extraction by the Integration of MODIS-EVI Time Series Data and China’s Environment Satellite (HJ-1) Data. Journal of the Indian Society of Remote Sensing. 2014; 42 (4):859-867.
Chicago/Turabian StyleFengmei Yao; Lili Feng; Jiahua Zhang. 2014. "Corn Area Extraction by the Integration of MODIS-EVI Time Series Data and China’s Environment Satellite (HJ-1) Data." Journal of the Indian Society of Remote Sensing 42, no. 4: 859-867.
Soil moisture is one of the most important and direct index for assessing drought. In this study, we performed a monitor of soil moisture in typical North China region using modified perpendicular drought index (MPDI) and the MODIS satellite data, and Henan Province was selected as the study area. Firstly, the parameters of MPDI and fraction of vegetation (f v) were description; Secondly, the validation of MPDI for monitoring the soil moisture from different ranges of depth and time-lagged impact were analyzed; Thirdly, the comparison were carried out for observing the area of the same soil texture and different types. The results showed that the MPDI had a negative correlation with soil moisture in winter wheat growing area of Henan, and the MPDI presented its better feature for estimating soil moisture in 10cm soil depth. When calculated the time-lag effect from 0 to 4 days delay length, the results demonstrated that the MPDI corresponded soil moisture rapidly with no obvious time-lag effect from 0-3days time-lag length, and soil moisture changed obviously at 4-daytime-lagin 10cm depth.
Jiahua Zhang; Zhengming Zhou; Fengmei Yao; Zhenming He. Monitoring Soil Moisture in Typical North China Region Using Modified Perpendicular Drought Index and MODIS Satellite Data. Communications in Computer and Information Science 2013, 348 -358.
AMA StyleJiahua Zhang, Zhengming Zhou, Fengmei Yao, Zhenming He. Monitoring Soil Moisture in Typical North China Region Using Modified Perpendicular Drought Index and MODIS Satellite Data. Communications in Computer and Information Science. 2013; ():348-358.
Chicago/Turabian StyleJiahua Zhang; Zhengming Zhou; Fengmei Yao; Zhenming He. 2013. "Monitoring Soil Moisture in Typical North China Region Using Modified Perpendicular Drought Index and MODIS Satellite Data." Communications in Computer and Information Science , no. : 348-358.