scholarly journals A Model-Downscaling Method for Fine-Resolution LAI Estimation

2020 ◽  
Vol 12 (24) ◽  
pp. 4147
Author(s):  
Jingyu Zhang ◽  
Jindi Wang ◽  
Rui Sun ◽  
Hongmin Zhou ◽  
Helin Zhang
Keyword(s):  
Spatial Resolution ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Model Parameters ◽  
Forest Site ◽  
Area Index ◽  
Downscaling Method ◽  
Resolution Model ◽  
Fine Resolution

The leaf area index (LAI) is a critical parameter for characterizing the structure and function of vegetation in ecosystems. Currently, operational LAI products always have coarse spatial resolution, and fine-resolution LAI maps are urgently needed for ecological environment assessment and the precise monitoring of cropland growth. LAI downscaling methods are efficient at improving the spatial resolution of LAI products but often ignore the scaling effect of the model. In this study, a novel model-downscaling method is proposed for fine-resolution LAI estimation. It uses scaling equations of model parameters (SEMPs) to describe the scaling relations of models at different spatial resolutions and construct a downscaled model from a coarse-resolution model. Landsat Normalized Difference Vegetation Index (NDVI) at 30 m and Global LAnd Surface Satellite (GLASS) LAI at 1 km spatial resolutions are used because they are readily available. The downscaled model is evaluated by a fine-resolution model directly constructed with fine-resolution data. The fine-resolution LAI values estimated by this model-downscaling method are evaluated with field LAI measurements. The validation results show that the proposed method can generate highly accurate LAIs, with an RMSE of 0.821 at the Pshenichne cropland site in Ukraine and an RMSE of 0.515 at the Camerons forest site in Australia when compared with field LAI measurements. The results are also better than those of Ovakoglou’s downscaling method. These results demonstrate that the model-downscaling method for fine-resolution LAI estimation is viable and referable for related studies.

scholarly journals Toward High-Resolution Soil Moisture Monitoring by Combining Active-Passive Microwave and Optical Vegetation Remote Sensing Products with Land Surface Model

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3924 ◽  
Author(s):  
Toride ◽  
Sawada ◽  
Aida ◽  
Koike
Keyword(s):  
Soil Moisture ◽  
Spatial Resolution ◽  
Land Surface ◽  
Low Cost ◽  
Land Surface Model ◽  
Surface Model ◽  
Model Parameters ◽  
Area Index ◽  
Coarse Resolution ◽  
Fine Resolution

The assimilation of radiometer and synthetic aperture radar (SAR) data is a promising recent technique to downscale soil moisture products, yet it requires land surface parameters and meteorological forcing data at a high spatial resolution. In this study, we propose a new downscaling approach, named integrated passive and active downscaling (I-PAD), to achieve high spatial and temporal resolution soil moisture datasets over regions without detailed soil data. The Advanced Microwave Scanning Radiometer (AMSR-E) and Phased Array-type L-band SAR (PALSAR) data are combined through a dual-pass land data assimilation system to obtain soil moisture at 1 km resolution. In the first step, fine resolution model parameters are optimized based on fine resolution PALSAR soil moisture and moderate-resolution imaging spectroradiometer (MODIS) leaf area index data, and coarse resolution AMSR-E brightness temperature data. Then, the 25 km AMSR-E observations are assimilated into a land surface model at 1 km resolution with a simple but computationally low-cost algorithm that considers the spatial resolution difference. Precipitation data are used as the only inputs from ground measurements. The evaluations at the two lightly vegetated sites in Mongolia and the Little Washita basin show that the time series of soil moisture are improved at most of the observation by the assimilation scheme. The analyses reveal that I-PAD can capture overall spatial trends of soil moisture within the coarse resolution radiometer footprints, demonstrating the potential of the algorithm to be applied over data-sparse regions. The capability and limitation are discussed based on the simple optimization and assimilation schemes used in the algorithm.

scholarly journals Time Varying Spatial Downscaling of Satellite-Based Drought Index

2021 ◽  
Vol 13 (18) ◽  
pp. 3693
Author(s):  
Hone-Jay Chu ◽  
Regita Faridatunisa Wijayanti ◽  
Lalu Muhamad Jaelani ◽  
Hui-Ping Tsai
Keyword(s):  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Standardized Precipitation Index ◽  
Environmental Data ◽  
Drought Monitoring ◽  
Drought Severity ◽  
Time Varying ◽  
Fine Resolution ◽  
Spatial Downscaling

Drought monitoring is essential to detect the presence of drought, and the comprehensive change of drought conditions on a regional or global scale. This study used satellite precipitation data from the Tropical Rainfall Measuring Mission (TRMM), but refined the data for drought monitoring in Java, Indonesia. Firstly, drought analysis was conducted to establish the standardized precipitation index (SPI) of TRMM data for different durations. Time varying SPI spatial downscaling was conducted by selecting the environmental variables, normalized difference vegetation index (NDVI), and land surface temperature (LST) that were highly correlated with precipitation because meteorological drought was associated with vegetation and land drought. This study used time-dependent spatial regression to build the relation among original SPI, auxiliary variables, i.e., NDVI and LST. Results indicated that spatial downscaling was better than nonspatial downscaling (overall RMSEs: 0.25 and 0.46 in spatial and nonspatial downscaling). Spatial downscaling was more suitable for heterogeneous SPI, particularly in the transition time (R: 0.863 and 0.137 in June 2019 for spatial and nonspatial models). The fine resolution (1 km) SPI can be composed of the environmental data. The fine-resolution SPI captured a similar trend of the original SPI. Furthermore, the detailed SPI maps can be used to understand the spatio-temporal pattern of drought severity.

scholarly journals The Impact of Non-Photosynthetic Vegetation on LAI Estimation by NDVI in Mixed Grassland

2020 ◽  
Vol 12 (12) ◽  
pp. 1979
Author(s):  
Dandan Xu ◽  
Deshuai An ◽  
Xulin Guo
Keyword(s):  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Time Series Data ◽  
Vegetation Indices ◽  
Series Data ◽  
Area Index ◽  
Plant Area Index ◽  
The Impact

Leaf area index (LAI) is widely used for algorithms and modelling in the field of ecology and land surface processes. At a global scale, normalized difference vegetation index (NDVI) products generated by different remote sensing satellites, have provided more than 40 years of time series data for LAI estimation. NDVI saturation issues are reported in agriculture and forest ecosystems at high LAI values, creating a challenge when using NDVI to estimate LAI. However, NDVI saturation is not reported on LAI estimation in grasslands. Previous research implies that non-photosynthetic vegetation (NPV) reduces the accuracy of LAI estimation from NDVI and other vegetation indices. A question arises: is the absence of NDVI saturation in grasslands a result of low LAI value, or is it caused by NPV? This study aims to explore whether there is an NDVI saturation issue in mixed grassland, and how NPV may influence LAI estimation by NDVI. In addition, in-situ measured plant area index (PAI) by sensors that detect light interception through the vegetation canopy (e.g., Li-cor LAI-2000), the most widely used field LAI collection method, might create bias in LAI estimation or validation using NDVI. Thus, this study also aims to quantify the contribution of green vegetation (GV) and NPV on in-situ measured PAI. The results indicate that NDVI saturation (using the portion of NDVI only contributed by GV) exists in grassland at high LAI (LAI threshold is much lower than that reported for other ecosystems in the literature), and that the presence of NPV can override the saturation effects of NDVI used to estimate green LAI. The results also show that GV and NPV in mixed grassland explain, respectively, the 60.33% and 39.67% variation of in-situ measured PAI by LAI-2000.

scholarly journals Assessing the Effect of Real Spatial Resolution of In Situ UAV Multispectral Images on Seedling Rapeseed Growth Monitoring

2020 ◽  
Vol 12 (7) ◽  
pp. 1207 ◽  
Author(s):  
Jian Zhang ◽  
Chufeng Wang ◽  
Chenghai Yang ◽  
Tianjin Xie ◽  
Zhao Jiang ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Crop Growth ◽  
Growth Monitoring ◽  
Multispectral Images ◽  
Area Index ◽  
Uav Images

The spatial resolution of in situ unmanned aerial vehicle (UAV) multispectral images has a crucial effect on crop growth monitoring and image acquisition efficiency. However, existing studies about optimal spatial resolution for crop monitoring are mainly based on resampled images. Therefore, the resampled spatial resolution in these studies might not be applicable to in situ UAV images. In order to obtain optimal spatial resolution of in situ UAV multispectral images for crop growth monitoring, a RedEdge Micasense 3 camera was installed onto a DJI M600 UAV flying at different heights of 22, 29, 44, 88, and 176m to capture images of seedling rapeseed with ground sampling distances (GSD) of 1.35, 1.69, 2.61, 5.73, and 11.61 cm, respectively. Meanwhile, the normalized difference vegetation index (NDVI) measured by a GreenSeeker (GS-NDVI) and leaf area index (LAI) were collected to evaluate the performance of nine vegetation indices (VIs) and VI*plant height (PH) at different GSDs for rapeseed growth monitoring. The results showed that the normalized difference red edge index (NDRE) had a better performance for estimating GS-NDVI (R2 = 0.812) and LAI (R2 = 0.717), compared with other VIs. Moreover, when GSD was less than 2.61 cm, the NDRE*PH derived from in situ UAV images outperformed the NDRE for LAI estimation (R2 = 0.757). At oversized GSD (≥5.73 cm), imprecise PH information and a large heterogeneity within the pixel (revealed by semi-variogram analysis) resulted in a large random error for LAI estimation by NDRE*PH. Furthermore, the image collection and processing time at 1.35 cm GSD was about three times as long as that at 2.61 cm. The result of this study suggested that NDRE*PH from UAV multispectral images with a spatial resolution around 2.61 cm could be a preferential selection for seedling rapeseed growth monitoring, while NDRE alone might have a better performance for low spatial resolution images.

scholarly journals Integrating Optical and Microwave Satellite Observations for High Resolution Soil Moisture Estimate and Applications in CONUS Drought Analyses

2019 ◽  
Vol 7 (1) ◽  
Author(s):  
Donglian Sun ◽  
Yu Li ◽  
Xiwu Zhan ◽  
Chaowei Yang ◽  
Ruixin Yang
Keyword(s):  
Soil Moisture ◽  
Spatial Resolution ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
High Spatial Resolution ◽  
Weather Conditions ◽  
Daily Basis ◽  
Satellite Observations ◽  
Drought Analysis

<strong>In this study, optical and microwave satellite observations are integrated to estimate soil moisture at high spatial resolution and applied for drought analysis in the continental United States.  To estimate soil moisture, a new refined model is proposed to estimate soil moisture (SM) with auxiliary data like soil texture, topography, surface types, accumulated precipitation, in addition to Normalized Difference Vegetation Index and Land Surface Temperature (LST) used in the traditional universal triangle method. It is found the new proposed SM model using accumulated precipitation demonstrated close agreements with the </strong><strong>U.S. Drought Monitor (USDM) spatial patterns.  Currently, the USDM is providing a weekly map.  Recently, “flash” drought concept appears. To obtain drought map on daily basis, LST is derived from microwave observations and downscaled to the same resolution as the thermal infrared LST product and used to fill the gaps due to clouds in optical LST data. With the integrated daily LST available under nearly all weather conditions, daily soil moisture can be estimated at relatively high spatial resolution, thus drought maps based on soil moisture anomalies can be obtained at high spatial resolution on daily basis and made the flash drought analysis and monitoring become possible.</strong>

scholarly journals Mapping Biophysical Parameters for Land Surface Modeling over the Continental US Using MODIS and Landsat

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Lahouari Bounoua ◽  
Ping Zhang ◽  
Kurtis Thome ◽  
Jeffrey Masek ◽  
Abdelmounaime Safia ◽  
...  
Keyword(s):  
Land Cover ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Climate Modeling ◽  
Surface Fluxes ◽  
Biophysical Parameters ◽  
Area Index ◽  
Spatial And Temporal Scales ◽  
The North

In terms of the space cities occupy, urbanization appears as a minor land transformation. However, it permanently modifies land’s ecological functions, altering its carbon, energy, and water fluxes. It is therefore necessary to develop a land cover characterization at fine spatial and temporal scales to capture urbanization’s effects on surface fluxes. We develop a series of biophysical vegetation parameters such as the fraction of photosynthetically active radiation, leaf area index, vegetation greenness fraction, and roughness length over the continental US using MODIS and Landsat products for 2001. A 13-class land cover map was developed at a climate modeling grid (CMG) merging the 500 m MODIS land cover and the 30 m impervious surface area from the National Land Cover Database. The landscape subgrid heterogeneity was preserved using fractions of each class from the 500 m and 30 m into the CMG. Biophysical parameters were computed using the 8-day composite Normalized Difference Vegetation Index produced by the North American Carbon Program. In addition to urban impact assessments, this dataset is useful for the computation of surface fluxes in land, vegetation, and urban models and is expected to be widely used in different land cover and land use change applications.

scholarly journals Integrating optical and microwave satellite observations for high res-olution soil moisture estimate and applications in CONUS drought analyses

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Donglian Sun ◽  
Yu Li ◽  
Xiwu Zhan ◽  
Chaowei Yang ◽  
Ruixin Yang
Keyword(s):  
Soil Moisture ◽  
Spatial Resolution ◽  
Optical Sensors ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Weather Conditions ◽  
Daily Basis ◽  
Satellite Observations ◽  
Drought Analysis

In this study, optical and microwave satellite observations are integrated to estimate soil moisture at the same spatial resolution as the optical sensors (5km here) and applied for drought analysis in the continental United States. A new refined model is proposed to include auxiliary data like soil texture, topography, surface types, accumulated precipitation, in addition to Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) used in the traditional universal triangle method. It is found the new proposed soil moisture model using accumulated precipitation demonstrated close agreements with the U.S. Drought Monitor (USDM) spatial patterns. Currently, the USDM is providing a weekly map. Recently, “flash” drought concept appears. To obtain drought map on daily basis, LST is derived from microwave observations and downscaled to the same resolution as the thermal infrared LST product and used to fill the gaps due to clouds in optical LST data. With the integrated daily LST available under nearly all weather conditions, daily soil moisture can be estimated at relatively higher spatial resolution than those traditionally derived from passive microwave sensors, thus drought maps based on soil moisture anomalies can be obtained on daily basis and made the flash drought analysis and monitoring become possible.

scholarly journals Testing gridded land precipitation data and precipitation and runoff reanalyses (1982–2010) between 45° S and 45° N with Normalized Difference Vegetation Index data

2014 ◽  
Vol 11 (12) ◽  
pp. 13175-13205 ◽  
Author(s):  
S. O. Los
Keyword(s):  
Spatial Resolution ◽  
Land Surface ◽  
General Circulation ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Hydrological Cycle ◽  
General Circulation Models ◽  
Precipitation Data ◽  
Index Data ◽  
Adverse Weather

Abstract. The realistic simulation of key components of the land-surface hydrological cycle – precipitation, runoff, evaporation and transpiration – in general circulation models of the atmosphere is crucial to assess adverse weather impacts on environment and society. Here, gridded precipitation data from observations and precipitation and runoff fields from reanalyses were tested with satellite-derived global vegetation index data for 1982–2010 and latitudes between 45° S and 45° N. Data were obtained from the Climate Research Unit (CRU), the Global Precipitation Climatology Project (GPCP) and Tropical Rainfall Monitoring Mission (TRMM; analysed for 1998–2010 only) and (precipitation and runoff) reanalyses were obtained from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR), the European Centre for Medium-Range Weather Forecasts (ECMWF) and the NASA Global Modelling and Assimilation Office (GMAO). Annual land-surface precipitation was converted to annual potential vegetation net primary productivity (NPP) and was compared to mean annual Normalized Difference Vegetation Index data measured by the Advanced Very High Resolution Radiometer (1982–1999) and MODIS (2001–2010). The effect of spatial resolution on the agreement between NPP and NDVI was investigated as well. The CRU and TRMM derived NPP agreed most closely with the NDVI data. The GPCP data showed weaker spatial agreement, largely because of their lower spatial resolution, but similar temporal agreement. MERRA Land and ERA Interim precipitation reanalyses showed similar spatial agreement as the GPCP data and good temporal agreement in semi-arid regions of the Americas, Asia, Australia and southern Africa. The NCEP/NCAR reanalysis showed the lowest spatial agreement which could only in part be explained by its lower spatial resolution. No reanalysis showed realistic interannual precipitation variations for northern tropical Africa. Inclusion of runoff in the NPP prediction resulted only in (marginally) better agreement for the MERRA Land reanalysis and worse agreement for the NCEP/NCAR and ERA Interim reanalyses.

scholarly journals A Fused Variable by Night Light Images and MODIS Products for Improving Urban Built-Up Area Extraction

Technologies ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 40
Author(s):  
Guang Yang ◽  
Yuntao Ma ◽  
Jiaqi Hu
Keyword(s):  
Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Extraction Methods ◽  
Average Error ◽  
Geographic Coordinate System ◽  
Optimal Thresholds ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Spatial Locations

The boundary of urban built-up areas is the baseline data of a city. Rapid and accurate monitoring of urban built-up areas is the prerequisite for the boundary control and the layout of urban spaces. In recent years, the night light satellite sensors have been employed in urban built-up area extraction. However, the existing extraction methods have not fully considered the properties that directly reflect the urban built-up areas, like the land surface temperature. This research first converted multi-source data into a uniform projection, geographic coordinate system and resampling size. Then, a fused variable that integrated the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) night light images, the Moderate-resolution Imaging Spectroradiometer (MODIS) surface temperature product and the normalized difference vegetation index (NDVI) product was designed to extract the built-up areas. The fusion results showed that the values of the proposed index presented a sharper gradient within a smaller spatial range, compared with the only night light images. The extraction results were tested in both the area sizes and the spatial locations. The proposed index performed better in both accuracies (average error rate 1.10%) and visual perspective. We further discussed the regularity of the optimal thresholds in the final boundary determination. The optimal thresholds of the proposed index were more stable in different cases on the premise of higher accuracies.

scholarly journals Modeling the Radial Stem Growth of the Pine (Pinus sylvestris L.) Forests Using the Satellite-Derived NDVI and LST (MODIS/AQUA) Data

Atmosphere ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 12
Author(s):  
Yulia Ivanova ◽  
Anton Kovalev ◽  
Vlad Soukhovolsky
Keyword(s):  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Principal Component ◽  
Stem Growth ◽  
Forest Growth ◽  
Parabolic Approximation ◽  
New Approach ◽  
The Relationship ◽  
Tree Increment

The paper considers a new approach to modeling the relationship between the increase in woody phytomass in the pine forest and satellite-derived Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) (MODIS/AQUA) data. The developed model combines the phenological and forest growth processes. For the analysis, NDVI and LST (MODIS) satellite data were used together with the measurements of tree-ring widths (TRW). NDVI data contain features of each growing season. The models include parameters of parabolic approximation of NDVI and LST time series transformed using principal component analysis. The study shows that the current rate of TRW is determined by the total values of principal components of the satellite indices over the season and the rate of tree increment in the preceding year.

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