An LSWI-Based Method for Mapping Irrigated Areas in China Using Moderate-Resolution Satellite Data

Accurate spatial information about irrigation is crucial to a variety of applications, such as water resources management, water exchange between the land surface and atmosphere, climate change, hydrological cycle, food security, and agricultural planning. Our study proposes a new method for extracting cropland irrigation information using statistical data, mean annual precipitation and Moderate Resolution Imaging Spectroradiometer (MODIS) land cover type data and surface reflectance data. The approach is based on comparing the land surface water index (LSWI) of cropland pixels to that of adjacent forest pixels with similar normalized difference vegetation index (NDVI). In our study, we validated the approach over mainland China with 612 reference samples (231 irrigated and 381 non-irrigated) and found the accuracy of 62.09%. Validation with statistical data also showed that our method explained 86.67 and 58.87% of the spatial variation in irrigated area at the provincial and prefecture levels, respectively. We further compared our new map to existing datasets of FAO/UF, IWMI, Zhu and statistical data, and found a good agreement with the irrigated area distribution from Zhu’s dataset. Results show that our method is an effective method apply to mapping irrigated regions and monitoring their yearly changes. Because the method does not depend on training samples, it can be easily repeated to other regions.

Download Full-text

Investigation of Spatial and Temporal Changes in the Land Surface Albedo for the Entire Chinese Territory

Geosciences ◽

10.3390/geosciences10090362 ◽

2020 ◽

Vol 10 (9) ◽

pp. 362

Author(s):

Jihui Yuan

Keyword(s):

Land Surface ◽

Surface Albedo ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Global Climate ◽

Temporal Changes ◽

Spatial And Temporal Changes ◽

Moderate Resolution ◽

Moderate Resolution Imaging Spectroradiometer ◽

Terra Satellite

Currently, global climate change (GCC) and the urban heat island (UHI) phenomena are becoming serious problems, partly due to the artificial construction of the land surface. When sunlight reaches the land surface, some of it is absorbed and some is reflected. The state of the land surface directly affects the surface albedo, which determines the magnitude of solar radiation reflected by the land surface in the daytime. In order to better understand the spatial and temporal changes in surface albedo, this study investigated and analyzed the surface albedo from 2000 to 2016 (2000, 2008, and 2016) in the entire Chinese territory, based on the measurement database obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument, aboard NASA’s Terra satellite. It was shown that the Northeast China exhibited the largest decline in surface albedo and North China showed the largest rising trend of surface albedo from 2000 to 2016. The correlation between changes in surface albedo and the Normalized Difference Vegetation Index (NDVI) indicated that the change trend of surface albedo was opposite to that of NDVI. In addition, in order to better understand the distribution of surface albedo in the entire Chinese territory, the classifications of surface albedo in three years (2000, 2008, and 2016) were implemented using five classification methods in this study.

Download Full-text

Simplified Priestley–Taylor Model to Estimate Land-Surface Latent Heat of Evapotranspiration from Incident Shortwave Radiation, Satellite Vegetation Index, and Air Relative Humidity

Remote Sensing ◽

10.3390/rs13050902 ◽

2021 ◽

Vol 13 (5) ◽

pp. 902

Author(s):

Yunjun Yao ◽

Zhenhua Di ◽

Zijing Xie ◽

Zhiqiang Xiao ◽

Kun Jia ◽

...

Keyword(s):

Relative Humidity ◽

Land Cover ◽

Latent Heat ◽

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Mainland China ◽

Shortwave Radiation ◽

Land Cover Types

An operational and accurate model for estimating global or regional terrestrial latent heat of evapotranspiration (ET) across different land-cover types from satellite data is crucial. Here, a simplified Priestley–Taylor (SPT) model was developed without surface net radiation (Rn) by combining incident shortwave radiation (Rs), satellite vegetation index, and air relative humidity (RH). Ground-measured ET for 2000–2009 collected by 100 global FLUXNET eddy covariance (EC) sites was used to calibrate and evaluate the SPT model. A series of cross-validations demonstrated the reasonable performance of the SPT model to estimate seasonal and spatial ET variability. The coefficients of determination (R2) of the estimated versus observed daily (monthly) ET ranged from 0.42 (0.58) (p < 0.01) at shrubland (SHR) flux sites to 0.81 (0.86) (p < 0.01) at evergreen broadleaf forest (EBF) flux sites. The SPT model was applied to estimate agricultural ET at high spatial resolution (16 m) from Chinese Gaofen (GF)-1 data and monitor long-term (1982–2018) ET variations in the Three-River Headwaters Region (TRHR) of mainland China using the Global LAnd-Surface Satellite (GLASS) normalized difference vegetation index (NDVI) product. The proposed SPT model without Rn provides an alternative model for estimating regional terrestrial ET across different land-cover types.

Download Full-text

Algorithms and Applications in Grass Growth Monitoring

Abstract and Applied Analysis ◽

10.1155/2013/508315 ◽

2013 ◽

Vol 2013 ◽

pp. 1-7 ◽

Cited By ~ 4

Author(s):

Jun Liu ◽

Xi Yang ◽

Hao Long Liu ◽

Zhi Qiao

Keyword(s):

Land Surface ◽

Large Scale ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Logistic Function ◽

Growth Monitoring ◽

Function Fitting ◽

Fitting Algorithm ◽

Moderate Resolution ◽

Grass Growth

Monitoring vegetation phonology using satellite data has been an area of growing research interest in recent decades. Validation is an essential issue in land surface phenology study at large scale. In this paper, double logistic function-fitting algorithm was used to retrieve phenophases for grassland in North China from a consistently processed Moderate Resolution Spectrodiometer (MODIS) dataset. Then, the accuracy of the satellite-based estimates was assessed using field phenology observations. Results show that the method is valid to identify vegetation phenology with good success. The phenophases derived from satellite and observed on ground are generally similar. Greenup onset dates identified by Normalized Difference Vegetation Index (NDVI) and in situ observed dates showed general agreement. There is an excellent agreement between the dates of maturity onset determined by MODIS and the field observations. The satellite-derived length of vegetation growing season is generally consistent with the surface observation.

Download Full-text

A global approach to estimate irrigated areas – a comparison between different data and statistics

Hydrology and Earth System Sciences ◽

10.5194/hess-22-1119-2018 ◽

2018 ◽

Vol 22 (2) ◽

pp. 1119-1133 ◽

Cited By ~ 28

Author(s):

Jonas Meier ◽

Florian Zabel ◽

Wolfram Mauser

Keyword(s):

Spatial Resolution ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Spatial Information ◽

Decision Makers ◽

Data Sets ◽

Ancillary Data ◽

Irrigated Area ◽

Time Period ◽

Multi Temporal

Abstract. Agriculture is the largest global consumer of water. Irrigated areas constitute 40 % of the total area used for agricultural production (FAO, 2014a) Information on their spatial distribution is highly relevant for regional water management and food security. Spatial information on irrigation is highly important for policy and decision makers, who are facing the transition towards more efficient sustainable agriculture. However, the mapping of irrigated areas still represents a challenge for land use classifications, and existing global data sets differ strongly in their results. The following study tests an existing irrigation map based on statistics and extends the irrigated area using ancillary data. The approach processes and analyzes multi-temporal normalized difference vegetation index (NDVI) SPOT-VGT data and agricultural suitability data – both at a spatial resolution of 30 arcsec – incrementally in a multiple decision tree. It covers the period from 1999 to 2012. The results globally show a 18 % larger irrigated area than existing approaches based on statistical data. The largest differences compared to the official national statistics are found in Asia and particularly in China and India. The additional areas are mainly identified within already known irrigated regions where irrigation is more dense than previously estimated. The validation with global and regional products shows the large divergence of existing data sets with respect to size and distribution of irrigated areas caused by spatial resolution, the considered time period and the input data and assumption made.

Download Full-text

Reconstruction of Spatiotemporally Continuous MODIS-Band Reflectance in East and South Asia from 2012 to 2015

Remote Sensing ◽

10.3390/rs12213674 ◽

2020 ◽

Vol 12 (21) ◽

pp. 3674

Author(s):

Bo Gao ◽

Huili Gong ◽

Jie Zhou ◽

Tianxing Wang ◽

Yuanyuan Liu ◽

...

Keyword(s):

South Asia ◽

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Basic Model ◽

Moderate Resolution ◽

Resolution Imaging ◽

Moderate Resolution Imaging Spectroradiometer ◽

Intermediate Variables ◽

Bidirectional Reflectance Distribution

To reconstruct Moderate Resolution Imaging Spectroradiometer (MODIS) band reflectance with optimal spatiotemporal continuity, three bidirectional reflectance distribution function (BRDF) models—the Ross-Thick-Li-Sparse Reciprocal (RTLSR) model, Gao model, and adjusted BF model—were used to retrieve MODIS-band reflectance for cloudy MODIS pixels according to different inversion conditions with a proposed filling algorithm. Then, a spatiotemporally continuous MODIS-band reflectance dataset for most of Asia with more than 98% spatiotemporal coverage was reconstructed from 2012 to 2015. The validation highlighted an evident improvement in filling cloudy MODIS observations; a reasonable spatial distribution, such as in South Asia and Southeast Asia; and acceptable precision for the filled MODIS pixels, with the root mean square error percentage (RMSE%) at 9.7–9.8% and 12–16% for the Gao and adjusted BF models, respectively. In the course of reconstructing the spatiotemporal continuous MODIS-band reflectance, the differences among the three models were discussed further. For a 16-day period with a stable and unchanged land surface, the RTLSR model, as a basic model, accurately derived land surface reflectance (no more than 10% RMSE% for MCD43C1 V006 band 1) and outperformed the other two models. When the inversion period is sufficiently long (e.g., 108 days, 188 days, 268 days, or a full year), the Gao/adjusted BF model provides better precision than the RTLSR model by considering the normalized difference vegetation index (NDVI) and soil moisture/NDVI as intermediate variables used to adjust the BRDF parameters in real time. The Gao model is optimal when the inversion period is sufficiently long. Based on combining the RTLSR model and Gao/adjusted BF model, we proposed a filling algorithm to derive a dataset of MODIS-band reflectance with optimal spatiotemporal continuity.

Download Full-text

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

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-11-13175-2014 ◽

2014 ◽

Vol 11 (12) ◽

pp. 13175-13205 ◽

Cited By ~ 1

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.

Download Full-text

MAPPING RICE CROPPING SYSTEM IN THE LOWER GANGETIC PLAIN USING LANDASAT 8 (OLI) AND MODIS IMAGERY

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-5-271-2018 ◽

2018 ◽

Vol XLII-5 ◽

pp. 271-274

Author(s):

A. Maiti ◽

P. Acharya

Keyword(s):

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Cropping System ◽

Rice Fields ◽

Gangetic Plain ◽

Water Index ◽

Modis Imagery ◽

Moderate Resolution ◽

Moderate Resolution Imaging Spectroradiometer

Abstract. The Indo-Gangetic basin is one of the productive rice growing areas in South-East Asia. Within this extensive flat fertile land, lower Gangetic basin, especially the south Bengal, is most intensively cultivated. In this study we map the rice growing areas using Moderate Resolution Imaging Spectroradiometer (MODIS) derived 8-day surface reflectance product from 2014 to 2015. The time series vegetation and wetness indices such as Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Land Surface Water Index (LSWI) were used in the decision tree (DT) approach to detect the rice fields. The extracted rice pixels were compared with Landsat OLI derived rice pixels. The accuracy of the derived rice fields were computed with 163 field locations, and further compared with statistics derived from Directorate of Economics and Statistics (DES). The results of the estimation shows a high degree of correlation (r=0.9) with DES reported area statistics. The estimated error of the area statistics while compared with the Landsat OLI was &plusmn;15%. The method, however, shows its efficiency in tracing the periodic changes in rice cropping area in this part of Gangetic basin and its neighboring areas.

Download Full-text

Land Surface Temperature Assessment in Central Sumatra, Indonesia

Indonesian Journal of Geography ◽

10.22146/ijg.51327 ◽

2020 ◽

Vol 52 (2) ◽

pp. 239

Author(s):

Tofan Agung Eka Prasetya ◽

Munawar Munawar ◽

Muhammad Rifki Taufik ◽

Sarawuth Chesoh ◽

Apiradee Lim ◽

...

Keyword(s):

Land Cover ◽

Surface Temperature ◽

Regression Model ◽

Land Surface Temperature ◽

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Multiple Linear Regression Model ◽

Moderate Resolution ◽

Moderate Resolution Imaging Spectroradiometer

Land Surface Temperature (LST) assessment can explain temperature variation, which may be influenced by factors such as elevation, land cover, and the normalized difference vegetation index (NDVI). In this study, a multiple linear regression model of LST variation was constructed based on data from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard NASA’s Terra satellite, relating to the period, 2000-2018. The highest LST variation of nearly 1.3 °C/decade was found in savanna areas while the lowest variation was in the evergreen broadleaf forest and woody savanna, which experienced a decrease of 2.1 °C/decade. The overall mean change of LST was -0.4 °C/decade and the regression model with LST as the dependent variable and elevation, land cover type, and NVDI as independent variables produced an R square of 0.376. The variation in LST was different depending upon the NDVI.

Download Full-text

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

Technologies ◽

10.3390/technologies9020040 ◽

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.

Download Full-text

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

Atmosphere ◽

10.3390/atmos12010012 ◽

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.

Download Full-text