Satellite-Derived Spatiotemporal Variations in Evapotranspiration over Northeast China during 1982–2010

Zhang, Lilin; Yao, Yunjun; Wang, Zhiqiang; Jia, Kun; Zhang, Xiaolin; Zhang, Yuhu; Wang, Xuanyu; Xu, Junzeng; Chen, Xiaowei

doi:10.3390/rs9111140

Cited by 14 publications

(13 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This product has been validated at 16 eddy covariance (EC) flux tower sites, and performed better than MODIS ET products at a regional scale, with a higher squared correlation coefficient (R 2 ) and a lower root mean square error (RMSE) [45]. The modified satellite-based Priestley-Taylor (MS-PT) product has provided more reliable and long-term spatiotemporal variations of the ET estimations of China [46].…”

Section: Et Productmentioning

confidence: 99%

Long-Term Spatiotemporal Dynamics of Terrestrial Biophysical Variables in the Three-River Headwaters Region of China from Satellite and Meteorological Datasets

et al. 2019

Self Cite

View full text Add to dashboard Cite

Terrestrial biophysical variables play an essential role in quantifying the amount of energy budget, water cycle, and carbon sink over the Three-River Headwaters Region of China (TRHR). However, direct field observations are missing in this region, and few studies have focused on the long-term spatiotemporal variations of terrestrial biophysical variables. In this study, we evaluated the spatiotemporal dynamics of biophysical variables including meteorological variables, vegetation, and evapotranspiration (ET) over the TRHR, and analyzed the response of vegetation and ET to climate change in the period from 1982 to 2015. The main input gridded datasets included meteorological reanalysis data, a satellite-based vegetation index dataset, and the ET product developed by a process-based Priestley–Taylor algorithm. Our results illustrate that: (1) The air temperature and precipitation over the TRHR increased by 0.597 °C and 41.1 mm per decade, respectively, while the relative humidity and surface downward shortwave radiation declined at a rate of 0.9% and 1.8 W/m2 per decade during the period 1982–2015, respectively. We also found that a ‘dryer warming’ tendency and a ‘wetter warming’ tendency existed in different areas of the TRHR. (2) Due to the predominant ‘wetter warming’ tendency characterized by the increasing temperature and precipitation, more than 56.8% of areas in the TRHR presented a significant increment in vegetation (0.0051/decade, p < 0.05), particularly in the northern and western meadow areas. When energy was the limiting factor for vegetation growth, temperature was a considerably more important driving factor than precipitation. (3) The annual ET of the TRHR increased by 3.34 mm/decade (p < 0.05) with an annual mean of 230.23 mm/year. More importantly, our analysis noted that ET was governed by terrestrial water supply, e.g., soil moisture and precipitation in the arid region of the western TRHR. By contrast, atmospheric evaporative demand derived by temperature and relative humidity was the primary controlling factor over the humid region of the southeastern TRHR. It was noted that land management activities, e.g., irrigation, also had a nonnegligible impact on the temporal and spatial variation of ET.

show abstract

Section: Et Productmentioning

confidence: 99%

Long-Term Spatiotemporal Dynamics of Terrestrial Biophysical Variables in the Three-River Headwaters Region of China from Satellite and Meteorological Datasets

et al. 2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…Thus, the first operation performed was the preprocessing of Landsat-8 images, that is, the conversion of digital numbers to physical values (PONZONI et al, 2012) and atmospheric correction by means of dark-object subtraction methodology (CHAVEZ, 1988), with the exception of the thermal band. Subsequently, the daily ETa was calculated, following the assumptions of the MS-PT model (YAO et al, 2013;ZHANG et al, 2017a). After the estimation of ETa, the daily biomass of the central pivots was calculated, using the model 2995 Semina: Ciências Agrárias, Londrina, v. 40, n. 6, suplemento 2, p. 2991-3006, 2019…”

Section: Methodsmentioning

confidence: 99%

“…There are numerous algorithms based on satellite images to estimate this parameter, both physical and empirical (ZHANG et al, 2016). Some of the well-known algorithms are: SEBAL -Surface Energy Balance Algorithm for Land (BASTIAANSSEN et al, 1998); METRIC -Mapping EvapoTranspiration at high Resolution with Internalized Calibration (ALLEN et al, 2007); R-SSEB -Regional Simplified Surface Energy Balance (ARAÚJO et al, 2017); two-layer models (KUSTAS et al, 1996); SAFER -Simple Algorithm for Evapotranspiration Retrieving (TEIXEIRA, 2010); SSEBOP -Simplified Surface Energy Balance (SENAY et al, 2016); and MS-PT -Modified Satellite Priestley-Taylor (YAO et al, 2013;ZHANG et al, 2017a).…”

Section: Introductionmentioning

confidence: 99%

Productivity and water demand of maize estimated by the modified satellite Priestley-Taylor algorithm

Filgueiras

Mantovani

Althoff

et al. 2019

SCA

View full text Add to dashboard Cite

The water demand of crops, as well as the relation of this variable to productivity and other important factors related to the sustainable management of agriculture, makes it relevant to estimate parameters that help in the most assertive and efficient decision-making in the agricultural environment. In this context, the work aims to estimate the actual evapotranspiration (ETa), biomass (Bio), water productivity (WP) and crop productivity (P), using the Landsat-8 satellite, through the Modified Satellite Priestley-Taylor Algorithm (MS-PT). For this, ETa was estimated for maize culture irrigated by central pivots, using the MS-PT with six images of Landsat-8, which were free of clouds. The ETa estimate was accurate in the first 60 days after emergence (DAE) of the crop. Subsequently, the variables Bio, P, and WP were estimated using the ETa and the assumptions of the Monteith (1972) model. Therefore, we sequentially calculated the dry biomass, crop productivity and water productivity. ETa presented a high correlation with Bio from the second image (06/10/2015), due to the canopy closure of the crop and, consequently, the predominance of transpiration in the evapotranspiration phenomenon. The water productivity was constant throughout the maximum vegetative stage until the reproductive phase R4 of the crop, verifying in this interval the best efficiency in the conversion of water in biomass. From the obtained results, it is verified that the set of algorithms used in the estimation of the parameters demonstrated the potential to increase the capacity to handle agriculture in a more efficient, assertive and sustainable way.

show abstract

“…Validation for 85 EC flux tower sites also indicated that the three machine learning algorithms used were reliable and robust for major land cover types in North America. Figures 9 and 10 both show that the ANN algorithm had no significant LE bias and yielded the closest LE to tower flux data Several previous studies have shown that spatial scale mismatch among different data sources, model input errors, and the limitations of the machine learning algorithms itself all affect the accuracy of LE estimation [7,10,56]. We used MERRA products with a spatial resolution of 1/2 • × 1/3 • and MODIS products with a resolution of 1 km, their resolution being greater than the footprint for field measurements, which is usually several hundred meters [57,58].…”

Section: Performance Of the Machine Learning Algorithmsmentioning

confidence: 99%

MODIS-Based Estimation of Terrestrial Latent Heat Flux over North America Using Three Machine Learning Algorithms

et al. 2017

Self Cite

View full text Add to dashboard Cite

Terrestrial latent heat flux (LE) is a key component of the global terrestrial water, energy, and carbon exchanges. Accurate estimation of LE from moderate resolution imaging spectroradiometer (MODIS) data remains a major challenge. In this study, we estimated the daily LE for different plant functional types (PFTs) across North America using three machine learning algorithms: artificial neural network (ANN); support vector machines (SVM); and, multivariate adaptive regression spline (MARS) driven by MODIS and Modern Era Retrospective Analysis for Research and Applications (MERRA) meteorology data. These three predictive algorithms, which were trained and validated using observed LE over the period [2000][2001][2002][2003][2004][2005][2006][2007], all proved to be accurate. However, ANN outperformed the other two algorithms for the majority of the tested configurations for most PFTs and was the only method that arrived at 80% precision for LE estimation. We also applied three machine learning algorithms for MODIS data and MERRA meteorology to map the average annual terrestrial LE of North America during 2002-2004 using a spatial resolution of 0.05 • , which proved to be useful for estimating the long-term LE over North America.

show abstract

Satellite-Derived Spatiotemporal Variations in Evapotranspiration over Northeast China during 1982–2010

Cited by 14 publications

References 58 publications

Long-Term Spatiotemporal Dynamics of Terrestrial Biophysical Variables in the Three-River Headwaters Region of China from Satellite and Meteorological Datasets

Long-Term Spatiotemporal Dynamics of Terrestrial Biophysical Variables in the Three-River Headwaters Region of China from Satellite and Meteorological Datasets

Productivity and water demand of maize estimated by the modified satellite Priestley-Taylor algorithm

MODIS-Based Estimation of Terrestrial Latent Heat Flux over North America Using Three Machine Learning Algorithms

Contact Info

Product

Resources

About