scholarly journals Exploring the Applicability and Scaling Effects of Satellite-Observed Spring and Autumn Phenology in Complex Terrain Regions Using Four Different Spatial Resolution Products

2021 ◽  
Vol 13 (22) ◽  
pp. 4582
Author(s):  
Fangxin Chen ◽  
Zhengjia Liu ◽  
Huimin Zhong ◽  
Sisi Wang
Keyword(s):  
Correlation Coefficient ◽  
Loess Plateau ◽  
Spatial Resolution ◽  
Land Surface ◽  
Remote Sensing Data ◽  
Growing Season ◽  
Scaling Effects ◽  
Modis Ndvi ◽  
Phenological Changes ◽  
Highly Correlated

The information on land surface phenology (LSP) was extracted from remote sensing data in many studies. However, few studies have evaluated the impacts of satellite products with different spatial resolutions on LSP extraction over regions with a heterogeneous topography. To bridge this knowledge gap, this study took the Loess Plateau as an example region and employed four types of satellite data with different spatial resolutions (250, 500, and 1000 m MODIS NDVI during the period 2001–2020 and ~10 km GIMMS3g during the period 1982–2015) to investigate the LSP changes that took place. We used the correlation coefficient (r) and root mean square error (RMSE) to evaluate the performances of various satellite products and further analyzed the applicability of the four satellite products. Our results showed that the MODIS-based start of the growing season (SOS) and end of the growing season (EOS) were highly correlated with the ground-observed data with r values of 0.82 and 0.79, respectively (p < 0.01), while the GIMMS3g-based phenology signal performed badly (r < 0.50 and p > 0.05). Spatially, the LSP that was derived from the MODIS products produced more reasonable spatial distributions. The inter-annual averaged MODIS SOS and EOS presented overall advanced and delayed trends during the period 2001–2020, respectively. More than two-thirds of the SOS advances and EOS delays occurred in grasslands, which determined the overall phenological changes across the entire Loess Plateau. However, both inter-annual trends of SOS and EOS derived from the GIMMS3g data were opposite to those seen in the MODIS results. There were no significant differences among the three MODIS datasets (250, 500, and 1000 m) with regard to a bias lower than 2 days, RMSE lower than 1 day, and correlation coefficient greater than 0.95 (p < 0.01). Furthermore, it was found that the phenology that was derived from the data with a 1000 m spatial resolution in the heterogeneous topography regions was feasible. Yet, in forest ecosystems and areas with an accumulated temperature ≥10 °C, the differences in phenological phase between the MODIS products could be amplified.

scholarly journals BVOC fluxes above mountain grassland

2010 ◽  
Vol 7 (5) ◽  
pp. 1413-1424 ◽  
Author(s):  
I. Bamberger ◽  
L. Hörtnagl ◽  
R. Schnitzhofer ◽  
M. Graus ◽  
T. M. Ruuskanen ◽  
...  
Keyword(s):  
Land Surface ◽  
Transfer Reaction ◽  
Growing Season ◽  
Proton Transfer Reaction ◽  
Gap Filling ◽  
Eddy Covariance Method ◽  
Mountain Grassland ◽  
Volatile Organic ◽  
Filling Method ◽  
Highly Correlated

Abstract. Grasslands comprise natural tropical savannah over managed temperate fields to tundra and cover one quarter of the Earth's land surface. Plant growth, maintenance and decay result in volatile organic compound (VOCs) emissions to the atmosphere. Furthermore, biogenic VOCs (BVOCs) are emitted as a consequence of various environmental stresses including cutting and drying during harvesting. Fluxes of BVOCs were measured with a proton-transfer-reaction-mass-spectrometer (PTR-MS) over temperate mountain grassland in Stubai Valley (Tyrol, Austria) over one growing season (2008). VOC fluxes were calculated from the disjunct PTR-MS data using the virtual disjunct eddy covariance method and the gap filling method. Methanol fluxes obtained with the two independent flux calculation methods were highly correlated (y = 0.95×−0.12, R2 = 0.92). Methanol showed strong daytime emissions throughout the growing season – with maximal values of 9.7 nmol m−2 s−1, methanol fluxes from the growing grassland were considerably higher at the beginning of the growing season in June compared to those measured during October (2.5 nmol m−2 s−1). Methanol was the only component that exhibited consistent fluxes during the entire growing periods of the grass. The cutting and drying of the grass increased the emissions of methanol to up to 78.4 nmol m−2 s−1. In addition, emissions of acetaldehyde (up to 11.0 nmol m−2 s−1), and hexenal (leaf aldehyde, up to 8.6 nmol m−2 s−1) were detected during/after harvesting.

scholarly journals Monitoring Drought over the Conterminous United States Using MODIS and NCEP Reanalysis-2 Data

2010 ◽  
Vol 49 (8) ◽  
pp. 1665-1680 ◽  
Author(s):  
Yunjun Yao ◽  
Shunlin Liang ◽  
Qiming Qin ◽  
Kaicun Wang
Keyword(s):  
United States ◽  
Spatial Resolution ◽  
Great Plains ◽  
Land Surface ◽  
Remote Sensing Data ◽  
Moisture Stress ◽  
Ncep Reanalysis ◽  
Southern Great Plains ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract Monitoring land surface drought using remote sensing data is a challenge, although a few methods are available. Evapotranspiration (ET) is a valuable indicator linked to land drought status and plays an important role in surface drought detection at continental and global scales. In this study, the evaporative drought index (EDI), based on the estimated actual ET and potential ET (PET), is described to characterize the surface drought conditions. Daily actual ET at 4-km resolution for April–September 2003–05 across the continental United States is estimated using a simple improved ET model with input solar radiation acquired by Moderate-Resolution Imaging Spectroradiometer (MODIS) at a spatial resolution of 4 km and input meteorological parameters from NCEP Reanalysis-2 data at a spatial resolution of 32 km. The PET is also calculated using some of these data. The estimated actual ET has been rigorously validated with ground-measured ET at six Enhanced Facility sites in the Southern Great Plains (SGP) of the Atmosphere Radiation Measurement Program (ARM) and four AmeriFlux sites. The validation results show that the bias varies from −11.35 to 27.62 W m−2 and the correlation coefficient varies from 0.65 to 0.86. The monthly composites of EDI at 4-km resolution during April–September 2003–05 are found to be in good agreement with the Palmer Z index anomalies, but the advantage of EDI is its finer spatial resolution. The EDI described in this paper incorporates information about energy fluxes in response to soil moisture stress without requiring too many meteorological input parameters, and performs well in assessing drought at continental scales.

scholarly journals Mapping evapotranspiration with high-resolution aircraft imagery over vineyards using one- and two-source modeling schemes

2016 ◽  
Vol 20 (4) ◽  
pp. 1523-1545 ◽  
Author(s):  
Ting Xia ◽  
William P. Kustas ◽  
Martha C. Anderson ◽  
Joseph G. Alfieri ◽  
Feng Gao ◽  
...  
Keyword(s):  
High Resolution ◽  
Surface Temperature ◽  
Spatial Resolution ◽  
Land Surface ◽  
Contextual Information ◽  
Turbulent Transport ◽  
Hybrid Approach ◽  
Remote Sensing Data ◽  
Source Modeling ◽  
Physically Based

Abstract. Thermal and multispectral remote sensing data from low-altitude aircraft can provide high spatial resolution necessary for sub-field (≤  10 m) and plant canopy (≤  1 m) scale evapotranspiration (ET) monitoring. In this study, high-resolution (sub-meter-scale) thermal infrared and multispectral shortwave data from aircraft are used to map ET over vineyards in central California with the two-source energy balance (TSEB) model and with a simple model having operational immediate capabilities called DATTUTDUT (Deriving Atmosphere Turbulent Transport Useful To Dummies Using Temperature). The latter uses contextual information within the image to scale between radiometric land surface temperature (TR) values representing hydrologic limits of potential ET and a non-evaporative surface. Imagery from 5 days throughout the growing season is used for mapping ET at the sub-field scale. The performance of the two models is evaluated using tower-based measurements of sensible (H) and latent heat (LE) flux or ET. The comparison indicates that TSEB was able to derive reasonable ET estimates under varying conditions, likely due to the physically based treatment of the energy and the surface temperature partitioning between the soil/cover crop inter-row and vine canopy elements. On the other hand, DATTUTDUT performance was somewhat degraded presumably because the simple scaling scheme does not consider differences in the two sources (vine and inter-row) of heat and temperature contributions or the effect of surface roughness on the efficiency of heat exchange. Maps of the evaporative fraction (EF  =  LE/(H + LE)) from the two models had similar spatial patterns but different magnitudes in some areas within the fields on certain days. Large EF discrepancies between the models were found on 2 of the 5 days (DOY 162 and 219) when there were significant differences with the tower-based ET measurements, particularly using the DATTUTDUT model. These differences in EF between the models translate to significant variations in daily water use estimates for these 2 days for the vineyards. Model sensitivity analysis demonstrated the high degree of sensitivity of the TSEB model to the accuracy of the TR data, while the DATTUTDUT model was insensitive to systematic errors in TR as is the case with contextual-based models. However, it is shown that the study domain and spatial resolution will significantly influence the ET estimation from the DATTUTDUT model. Future work is planned for developing a hybrid approach that leverages the strengths of both modeling schemes and is simple enough to be used operationally with high-resolution imagery.

scholarly journals Mapping evapotranspiration with high resolution aircraft imagery over vineyards using one and two source modeling schemes

2015 ◽  
Vol 12 (11) ◽  
pp. 11905-11957 ◽  
Author(s):  
T. Xia ◽  
W. P. Kustas ◽  
M. C. Anderson ◽  
J. G. Alfieri ◽  
F. Gao ◽  
...  
Keyword(s):  
High Resolution ◽  
Surface Temperature ◽  
Spatial Resolution ◽  
Land Surface ◽  
Contextual Information ◽  
Turbulent Transport ◽  
Hybrid Approach ◽  
Remote Sensing Data ◽  
Source Modeling ◽  
Physically Based

Abstract. Thermal and multispectral remote sensing data from low-altitude aircraft can provide high spatial resolution necessary for sub-field (≤ 10 m) and plant canopy (≤ 1m) scale evapotranspiration (ET) monitoring. In this study, high resolution aircraft sub-meter scale thermal infrared and multispectral shortwave data are used to map ET over vineyards in central California with the Two Source Energy Balance (TSEB) model and with a simple model called DATTUTDUT (Deriving Atmosphere Turbulent Transport Useful To Dummies Using Temperature) which uses contextual information within the image to scale between radiometric land surface temperature (TR) values representing hydrologic limits of potential ET and a non-evaporative surface. Imagery from five days throughout the growing season is used for mapping ET at the sub-field scale. The performance of the two models is evaluated using tower-based energy flux measurements of sensible (H) and latent heat (LE) or ET. The comparison indicates that TSEB was able to derive reasonable ET estimates under varying conditions, likely due to the physically based treatment of the energy and the surface temperature partitioning between the soil/cover crop inter-row and vine canopy elements. On the other hand, DATTUTDUT performance was somewhat degraded presumably because the simple scaling scheme does not consider differences in the two sources (vine and inter-row) of heat and temperature contributions or the effect of surface roughness on the efficiency of heat exchange. Maps of the evaporative fraction (EF = LE/(H + LE)) from the two models had similar spatial patterns but different magnitudes in some areas within the fields on certain days. Large EF discrepancies between the models were found on two of the five days (DOY 162 and 219) when there were significant differences with the tower-based ET measurements, particularly using the DATTUTDUT model. These differences in EF between the models translate to significant variations in daily water use estimates for these two days for the vineyards. Model sensitivity analysis demonstrated the high degree of sensitivity of the TSEB model to the accuracy of the TR data while the DATTUTDUT model was insensitive as is the case with contextual-based models. However, study domain and spatial resolution will significantly influence the ET estimation from the DATTUTDUT model. Future work is planned for developing a hybrid approach that leverages the strengths of both modeling schemes and is simple enough to be used operationally with high resolution imagery.

scholarly journals Stepwise Disaggregation of SMAP Soil Moisture at 100 m Resolution Using Landsat-7/8 Data and a Varying Intermediate Resolution

2019 ◽  
Vol 11 (16) ◽  
pp. 1863 ◽  
Author(s):  
Nitu Ojha ◽  
Olivier Merlin ◽  
Beatriz Molero ◽  
Christophe Suere ◽  
Luis Olivera-Guerra ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Spatial Resolution ◽  
Land Surface ◽  
Vegetation Index ◽  
Moving Average ◽  
Remote Sensing Data ◽  
Global Soil ◽  
Landsat 7 ◽  
Mean Square Difference

Global soil moisture (SM) products are currently available from passive microwave sensors at typically 40 km spatial resolution. Although recent efforts have been made to produce 1 km resolution data from the disaggregation of coarse scale observations, the targeted resolution of available SM data is still far from the requirements of fine-scale hydrological and agricultural studies. To fill the gap, a new disaggregation scheme of Soil Moisture Active and Passive (SMAP) data is proposed at 100 m resolution by using the disaggregation based on physical and theoretical scale change (DISPATCH) algorithm. The main objectives of this paper is (i) to implement DISPATCH algorithm at 100 m resolution using SMAP SM and Landsat land surface temperature and vegetation index data and (ii) to investigate the usefulness of an intermediate spatial resolution (ISR) between the SMAP 36 km resolution and the targeted 100 m resolution. The sequential disaggregation approach from 36 km to ISR (ranging from 1 km to 30 km) and from ISR to 100 m resolution is evaluated over 22 irrigated field crops in central Morocco using in-situ SM measurements collected from January to May 2016. The lowest root mean square difference (RMSD) between the 100 m resolution disaggregated and in-situ SM is obtained when the ISR is around 10 km. Therefore, the two-step disaggregation is more efficient than the direct disaggregation from SMAP to 100 m resolution. Moreover, we propose a moving average window algorithm to increase the accuracy in the 100 m resolution SM as well as to reduce the low-resolution boxy artifacts on disaggregated images. The correlation coefficient between 100 m resolution disaggregated and in situ SM ranges between 0.5–0.9 for four out of the six extensive sampling dates. This methodology relies solely on remote sensing data and can be easily implemented to monitor SM at a high spatial resolution over irrigated regions.

scholarly journals Actual daily evapotranspiration estimated from MERIS and AATSR data over the Chinese Loess Plateau

2009 ◽  
Vol 6 (1) ◽  
pp. 921-942
Author(s):  
R. Liu ◽  
J. Wen ◽  
X. Wang ◽  
L. Wang ◽  
H. Tian ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Heat Flux ◽  
Loess Plateau ◽  
Land Surface ◽  
Sunshine Duration ◽  
Sensible Heat Flux ◽  
Remote Sensing Data ◽  
Chinese Loess Plateau ◽  
Near Surface ◽  
Sensing Data

Abstract. The Loess Plateau is located in north of China and has a significant impact on the climate and ecosystem evolvement over the East Asian continent. Based on the land surface energy balance theory, the potential of using Medium Resolution Imaging Spectrometer (onboard sensor of the Environmental Satellite) remote sensing data on 7, 11 and 27 June 2005 is explored. The "split-window" algorithm is used to retrieve surface temperature from the Advanced the Along-Track Scanning Radiometer, another onboard senor of the Environmental Satellite. Then the near surface net radiation, sensible heat flux and soil heat flux are estimated by using the developed algorithm. We introduce a simple algorithm to predict the heat flux partitioning between the soil and vegetation. Combining the sunshine hours, air temperature, sunshine duration and wind speed measured by weather stations, a model for estimating daily ET is proposed. The instantaneous ET is also converted to daily value. Comparison of latent heats flux retrieved by remote sensing data with ground observation from eddy covariance flux system during Loess Plateau land surface process field Experiment, the maximum and minimum error of this approach are 10.96% and 4.80% respectively, the cause of the bias is also explored and discussed.

scholarly journals ANALYSIS OF WHEAT CROP FORECASTS, IN INDIA, GENERATED USING REMOTE SENSING DATA, UNDER FASAL PROJECT

2019 ◽  
Vol XLII-3/W6 ◽  
pp. 223-228
Author(s):  
S. Kumar ◽  
S. Saxena ◽  
S. K. Dubey ◽  
K. Chaudhary ◽  
S. Sehgal ◽  
...  
Keyword(s):  
Correlation Coefficient ◽  
Spatial Resolution ◽  
Satellite Data ◽  
National Level ◽  
Remote Sensing Data ◽  
State Level ◽  
Ground Truth ◽  
District Level ◽  
Wheat Crop ◽  
Ground Truth Data

<p><strong>Abstract.</strong> Wheat (<i>Triticum aestivum</i> L.) is a major cereal crop of the world, which plays an important role in global food and nutritional security. In India, wheat grown areas are more as compared to other food crops, except for rice. The total area under wheat cultivation is 30.60 million hectares with production of 98.38 million tonnes and the productivity is 3.22 tonnes /hectare (DES, 2017). The main objective of this paper is to highlight the development of satellite-based methodology, compare the relative deviations (%) at national level, RMSE (%) and correlation coefficient at state level and correlation coefficient at district level between DES and FASAL estimates from 2013 to 2017. It was observed that the area and production estimates improved with improvement in the satellite resolution and ground truth data. During the last 10 years of estimation the spatial resolution of the satellite data has gradually improved from 23.5 meter of (Reourcesat-2, LISS-III) and finally 10&amp;thinsp;m of Sentinel-2, MSI, which is being currently used for acreage estimation purpose. Hooda R.S et al (2006) studied that the improvement in the spatial resolution, spectral and temporal resolution of the satellite data has also improved the crop discrimination. Both accuracy as well as precision of the estimates has improved over the years from 2013 to 2017, as reflected by relative deviation, RMSE (%) and Coefficient of correlation values at national, state and district level respectively.</p>

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