Classification of rice cropping systems by empirical mode decomposition and linear mixture model for time-series MODIS 250 m NDVI data in the Mekong Delta,Vietnam

Estimating the area of rice planting is vital for production prediction. This study utilizes time-series MODIS NDVI data from 2002 to 2007 to discriminate rice cropping systems in the Mekong Delta (MD), Vietnam. Data are processed using Empirical Mode Decomposition (EMD) and the Linear Mixture Model (LMM). Various spatial and non-spatial data are also collected for accuracy validation. The results indicate that EMD acts as a well-fitted filter for noise reduction of the time-series NDVI data. The classification results derived from the LMM for 2002 showed an overall classification accuracy of 71.6% and a Kappa coefficient of 0.6. The provincial level area estimates were strongly correlated with the rice statistics. An examination of the change in cropping patterns between 2002 and 2007 showed that 29.0% of the triple irrigated-rice cropping systems had been changed to double irrigated-rice cropping systems and that 12.0% and 9.0% of the double irrigated and rainfed-rice cropping systems, respectively, had been changed to triple rice cropping systems. These changes were verified by visual comparisons with Landsat images.     

Detecting temporal changes in the extent of annual flooding within the Cambodia and the Vietnamese Mekong Delta from MODIS time-series imagery

This paper presents the methodology used to detect temporal changes in the extent of annual flooding within the Cambodia and the Vietnamese Mekong Delta (VMD) based on MODIS time-series imagery (Wavelet-based Filter for detecting spatio-temporal changes in Flood Inundation; WFFI). This methodology involves the use of a wavelet-based filter to interpolate missing information and reduce the noise component in the time-series data, as proposed in a previous study. The smoothed time profiles of Enhanced Vegetation Index (EVI), Land Surface Water Index (LSWI), and the Difference Value between EVI and LSWI (DVEL) are obtained from MOD09 8-day composite time-series data (resolution: 500 m; time period: 2000-2005). The proposed algorithm was applied to produce time-series inundation maps (WFFI products) for the five annual flood seasons over the period from 2000 to 2004. The WFFI products were validated via comparisons with Landsat-derived results and inundation maps based on RADARSAT images, hydrological data, and digital elevation model data. Compared with the RADARSAT-derived inundation maps at the province level, the obtained RMSE range from 364 to 443 km² and the determination coefficients [R²] range from 0.89 to 0.92. Compared with Landsat-derived results at the 10-km grid level, the obtained RMSE range from 6.8 to 15.2 km² and the determination coefficients [R²] range from 0.77 to 0.97. The inundated area of flooded forests/marsh to the northeast of Tonle Sap Lake were underestimated, probably because of extensive vegetation cover in this area. The spatial characteristics of the estimated start dates, end dates, and duration of inundation cycles were also determined for the period from 2000 to 2004. There are clear contrasts in the distribution of the estimated end dates and duration of inundation cycles between large-scale floods (2000-2002) and medium- and small-scale floods (2003 and 2004). At the regional scale, the estimated start dates for the southern part of An Giang Province during 2003 and 2004 was distinctly later than that for surrounding areas. The results indicate that these triple-cropping areas enclosed by dikes increased in extent from 2003 to 2004. In contrast, the estimated end dates of inundation at the Co Do and Song Hau State Farms were clearly earlier than those for surrounding areas, although the estimated start dates were similar. Temporal changes in the inundation area of Flood pixels in the Dong Thap and Long An Provinces are in excellent agreement with daily water-level data recorded at Tan Chau Station. The estimated area of Long-term water body increased in size from 2000 to 2004, especially in coastal areas of the Ca Mau and Bac Lieu Provinces. Statistical data for Vietnam indicate that this trend may reflect the expansion of shrimp-farming areas. The WFFI products enable an understanding of seasonal and annual changes in the water distribution and environment of the Cambodia and the VMD from a global viewpoint.     

Operational multi-sensor monitoring of turbidity for the entire Mekong Delta

An operational satellite-based approach was implemented to monitor turbidity and organic absorption in the Mekong river system. Using physics-based algorithms linked together in a fully automated processing chain, more than 300 Landsat Enhanced Thematic Mapper (ETM) scenes and 1000 MODIS scenes, representing five years of data, were used to produce standardized, quantitative time series of turbidity and organic absorption across Vietnam, Thailand, Cambodia, Laos, and China. To set up this system, the specific inherent optical properties (SIOPs) of the Mekong river system were determined through three separate field campaigns, laboratory analysis, and subsequent optical closure calculations. Following this, a range of satellite data types was tested using the derived Mekong-specific inherent optical properties, including Moderate Resolution Imaging Spectroradiometer (MODIS) 500 m data, Landsat ETM, Medium Resolution Imaging Spectrometer (MERIS), Satellite Pour l’Observation de la Terre (SPOT) 5, RapidEye, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and QuickBird. The satellite-based turbidity estimates were coincident with available field data, and comparisons showed them to be in good agreement. Overall, the derived SIOPs were suitable for water-quality monitoring of the Mekong, and the MODIS, MERIS, Landsat, and RapidEye sensors were found to be the most radiometrically stable and thereby suitable for ongoing operational processing. The implemented system delivers consistent results across the different satellite sensors and over time, but is limited to where the spatial resolution of the sensor is still able to resolve the river width. The system is currently applicable for the entire Mekong river system, both for near-real-time monitoring and for analysis of historical data archive.     

Detecting leaf phenology of seasonally moist tropical forests in South America with multi-temporal MODIS images

Leaf phenology of tropical evergreen forests affects carbon and water fluxes. In an earlier study of a seasonally moist evergreen tropical forest site in the Amazon basin, time series data of Enhanced Vegetation Index (EVI) from the VEGETATION and Moderate Resolution Imaging Spectroradiometer (MODIS) sensors showed an unexpected seasonal pattern, with higher EVI in the late dry season than in the wet season. In this study we conducted a regional-scale analysis of tropical evergreen forests in South America, using time series data of EVI from MODIS in 2002. The results show a large dynamic range and spatial variations of annual maximum EVI for evergreen forest canopies in the region. In tropical evergreen forests, maximum EVI in 2002 typically occurs during the late dry season to early wet season. This suggests that leaf phenology in tropical evergreen forests is not determined by the seasonality of precipitation. Instead, leaf phenological process may be driven by availability of solar radiation and/or avoidance of herbivory.     

Regional scale mapping of fractional rice cropping change using a phenology-based temporal mixture analysis

Monitoring changes of paddy rice is challenging due to its diverse cropping patterns and spectral variation. To investigate the spatio-temporal changes of rice cropping, we used the 10-day composited Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) time series data with a spatial resolution of 250 m to map the sub-pixel rice spatial distributions in the Hunan Province, the top one region in rice planting area in southern of China. A method of improved phenology-based temporal mixture analysis (PTMA) was presented to identify early, middle, and late rice cropping patterns. The results show that the PTMA is effective to extract rice cropping. The nine rice cropping patterns were classified as early, middle, and late rice cropping, and fractional rice cropping within 250 m pixels was obtained to analyse the internal changes. Both the local planting conditions and different forms of rice cultivation were compared with statistical data. Overall, MODIS-estimated fractional rice agreed well with field samples at the pixel level and statistical data at the county level, which demonstrates the effectiveness of the PTMA method for mapping rice in these hilly regions with small-size paddy rice field. The changes show that single-cropping rice and double-cropping rice have been frequently transferred in space, which could be important information to support agricultural decision-making.     

Remote sensing techniques to predict salinity intrusion: application for a data-poor area of the coastal Mekong Delta,Vietnam

ABSTRACT

Salinity intrusion is a complex issue in coastal and estuarine areas. Currently, remote sensing techniques have been widely used to monitor water quality changes, ranging from inland river networks to deep oceans. The Vietnamese Mekong Delta is an important rice-growing area, and intrusion of saline water into irrigated freshwater-based agriculture areas is one of the most crucial constraints for agriculture development. This study aimed at building a numerical model to realize the salinity intrusion through the relationship between reflectance from the Landsat-8 Operational Land Imager images and salinity levels measured in situ. A total of 103 observed samples were divided into 50% training and 50% test. Multiple Linear Regression, Decision Trees and Random Forest (RF) approaches were applied in the study. The result showed that the RF approach was the best model to estimate salinity along the coastal river network in the study area. However, the large samples size needed was a significant challenge to circumscribe predicting ability of the RF model. The reflectance has a good correlation with salinity when locations (latitude–longitude) of salinity measured stations were added as a parameter of the Step-wise model with R-square 77.48% in training and 74.16% in test while Root Mean Square Error was smaller than 3.     

Multi-year monitoring of rice crop phenology through time series analysis of MODIS images

Precise phenological calendars, for each cultivated species and variety, are necessary both to highlight anomalous agronomic situations and to feed crop models. This study, conducted in the Italian rice area, focuses on the evaluation of the contribution of remote sensing satellite data to providing phenological information on rice cropping systems. A time series of 5 years (2001–2005) of the Moderate Resolution Imaging Spectrometer (MODIS) Normalized Difference Vegetation Index (NDVI) 16-day composites was analysed with the TIMESAT program in order to automatically retrieve key phenological information such as the start of season (emergence), peak (heading) and end of season (maturity). The procedure involved two steps: (1) interpolation and smoothing of MODIS NDVI temporal profile and (2) the analysis of a temporal signal for the extraction of the phenological metrics. The remote sensing estimates were evaluated using information regarding cultivated variety, sowing dates, management and production directly acquired from rice farmers. A good correlation (R ²?=?0.92, n?=?24) has been observed between estimates derived from satellites and estimates produced with the traditional Growing Degree Days (GDD) method based on thermal unit accumulation. Improved estimates of the maturity stage were obtained using a procedure that integrates satellite and GDD methods; however its application requires spatially distributed information on the cultivated varieties. Satellite derived maps of the retrieved phenological parameters showed an intra-seasonal pattern related to different cultivated varieties. Inter-seasonal analysis allowed the anomalous behaviour of the year 2003 to be highlighted, characterized by rapid growth at the beginning of the spring and an early senescence. The results confirm the potential of remotely sensed data for the monitoring of crop status and for the forcing of crop models in a spatially distributed way.     

Modelling gross primary production of a tropical semi-deciduous forest in the southern Amazon Basin

Semi-deciduous forest in the Amazon Basin is sensitive to temporal variation in surface water availability that can limit seasonal rates of leaf and canopy gas exchange. We estimated the seasonal dynamics of gross primary production (GPP) over 3 years (2005–2008) using eddy covariance and assessed canopy spectral reflectance using MODIS imagery for a mature tropical semi-deciduous forest located near Sinop, Mato Grosso, Brazil. A light-use efficiency model, known as the Vegetation Photosynthesis Model (VPM), was used to estimate seasonal and inter-annual variations in GPP as a function of the enhanced vegetation index (EVI), the land surface water index (LSWI), and local meteorology. Our results indicate that the standard VPM was incapable of reproducing the seasonal variation in GPP, primarily because the model overestimated dry-season GPP. In the standard model, the scalar function that alters light-use efficiency (ε_g) as a function of water availability (W_scalar) is calculated as a linear function of the LSWI derived from MODIS; however, the LSWI is negatively correlated with several measures of water availability including precipitation, soil water content, and relative humidity (RH). Thus, during the dry season, when rainfall, soil water content, and RH are low, LSWI, and therefore, W_scalar, are at a seasonal maximum. Using previous research, we derived new functions for W_scalar based on time series of RH and photosynthetic photon flux density (PPFD) that significantly improved the performance of the VPM. Whether these new functions perform equally well in water stressed and unstressed tropical forests needs to be determined, but presumably unstressed ecosystems would have high cloud cover and humidity, which would minimize variations in W_scalar and GPP to spatial and/or temporal variation in water availability.     

Land-surface temperature dynamics in the Upper Mekong Basin derived from MODIS time series

Land surface temperature (LST) is an important indicator for climate variability and can be sensed remotely by satellites with a high temporal resolution on a broad spatial scale. In this research, Moderate Resolution Imaging Spectroradiometer (MODIS) LST is used to derive a 13 year time series on the Upper Mekong Basin (UMB), belonging to the People’s Republic of China and the Republic of the Union of Myanmar, to analyse the spatial pattern and temporal development of LST. The data set shows the regular annual curve of surface temperatures with maximum values in summer and minimum values in winter. Average temperatures in the southern parts of the basin are higher than in the northern part. Spatial gradients between maximum and minimum LST as well as gradients between daytime and night-time LST are much lower in the southern parts than in the northern parts, which are characterized by a strong topography. The pixel-wise variability of monthly means was found to be in the range of ±4°C for most pixels in the daytime scenes, whereas the night-time scenes show a lower variability with most pixels in the range of ±1°C. The variability of LST in the northern areas clearly exceeds that in the southern areas. Some inter-annual variations occur, mainly during summer: in some years a two-peak distribution is found, which is explained by the generally low number of observations in the respective months. A primary challenge of optical satellite data in the UMB is cloud contamination in the summer months, where peak rainfall occurs. In the Mekong Highlands for instance, the average number of available daytime observations of MODIS LST in July is one observation per month only. It can be assumed that climate statistics calculated from such data is biased. In this context, two gap-filling algorithms were applied to two test areas for the year 2002 and results are discussed in the article. Another issue with MODIS LST data are day-to-day differences in the acquisition time. A temporal homogenization was applied to selected LST data, converting them to one fixed acquisition time. The converted data were compared to the original data set. No significant influence could be found.     

Assessment from space of mangroves evolution in the Mekong Delta,in relation to extensive shrimp farming

This study is an attempt to produce an assessment of the impact of shrimp aquaculture in the Mekong Delta (Viet Nam) on mangrove ecosystems. For this exercise we selected two sub-areas (Ca Mau and Tra Vinh provinces) encompassing a variety of land uses and ecological conditions. Twenty stations in Tra Vinh and 15 stations in Ca Mau have been surveyed several times from September 2000 to March 2002. Field investigations included mangrove soils studies, measurements of pH and salinity of the water, analysis of mangrove flora, and density and structure of the vegetation. Four Syst@me Probatoire de l'Observation de la Terre (SPOT) scenes were used for the discrimination of mangrove types and for the delineation of landscape units. For the first time, five ecologically distinct landscape classes were identified and delineated. Their possible links with the farming and yields of high valued species of shrimps, especially the giant tiger shrimp (Penaeus monodon) destined for export markets, need further studies. Since 1965, about 30% of mangrove ecosystems have been lost in Ca Mau Province and more than 30% of present mangroves are replanted monospecific stands. To the best of our knowledge, this is the first study which demonstrates that, in spite of deep and ancient man interactions in the Mekong Delta, five ecologically distinct classes of land use can be defined. Satellite surveys confirm a clear distribution of landscape units with possible links with shrimp aquaculture potentialities.     

Assessment of the SeaWinds scatterometer for vegetation phenology monitoring across China

Vegetation phenology tracks plants' lifecycle events, revealing the response of vegetation to global climate changes. Changes in vegetation phenology also influence fluxes of carbon, water, and energy at local and global scales. In this study, we analysed a time series of Ku-band radar backscatter measurements from the SeaWinds scatterometer on board the Quick Scatterometer (QuickSCAT) to examine canopy phenology from 2003 to 2005 across China. The thaw season SeaWinds backscatter and Moderate Resolution Imaging Spectroradiometer (MODIS) leaf area index (LAI) time series were significantly correlated in 20 of the 22 sites (p < 0.05). A weighted curve-fitting method was applied to detect the start of season and end of season from both data sets. The SeaWinds scatterometer generally detected earlier timing of spring leaf-out and later fall senescence than the MODIS LAI data sets. The SeaWinds backscatter detected phenological metrics in 75.85% of mainland China. Similar spatial patterns were observed from the SeaWinds backscatter and MODIS LAI time series; however, the average standard deviation of the scatterometer-detected metrics was lower than that of MODIS LAI products. Overall, the phenological information from the SeaWinds scatterometer could provide an alternative view on the growth dynamics of land-surface vegetation.     

Rice heading date retrieval based on multi-temporal MODIS data and polynomial fitting

The objective of the study is to identify the rice heading date and analyse its spatial characteristics on a regional scale using multi-temporal Moderate Resolution Imaging Spectroradiometer (MODIS) normalized differential vegetation index (NDVI) data and a new approach: quadratic polynomial fitting. The cloud-contaminated NDVI value was identified by reliability data and linearly interpolated with values before and after the cloudy one. The discrete Fourier transformation (DFT) and quadratic polynomial fitting were implemented to generate new time series curves. Rice heading date was retrieved by calculating the day for maximum NDVI. Comparing with DFT, the proposed quadratic polynomial fitting significantly improves the computation efficiency, while providing approximate precision of estimation. In regional analysis, the rice heading date retrieved from polynomial fitting is more consistent than that from DFT. The study also suggests that multi-temporal MODIS NDVI data combined with different methods can retrieve crop phenology information on a large scale.     

Relationships between MODIS phenological metrics,topographic shade,and anomalous temperature patterns in seasonal deciduous forests of south Brazil

We investigated the relationships between 11 phenological metrics, topographic shade, and anomalous temperature patterns detected using wavelet analysis in seasonal deciduous forests of south Brazil. To obtain the metrics, we applied the TIMESAT algorithm to the enhanced vegetation index (EVI) from Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra. MODIS acquires data from the study area under a large seasonal amplitude in the solar zenith angle (SZA). We evaluated the effect of topography on phenological metrics by correlating the metrics with shaded relief values. To analyse the inter-annual phenological metric variations with anomalous and regular temperature patterns, we calculated standard anomalies for each metric. Finally, we established relationships between the metrics and the minimum, maximum, and mean temperatures from growing seasons that spanned over 10 seasonal cycles between 2002 and 2012. The correlation results with shaded relief showed that the left (LD) and right derivative (RD), small integral (SInt), seasonal amplitude (SA), base level (BL), and maximum VI value (MV) were sensitive to topographic effects. The seasonal cycles with the highest temperatures in the growing season (2006/2007 and 2009/2010) exhibited a delay at the end of the cycle and a higher interval of duration and productivity, which was indicated by the positive standard anomalies for end of season (EOS), length of season (LOS), large integral (LInt), and SInt. We observed a different result for the lowest temperature cycle (2003/2004). The means for these metrics in anomalous seasons differed significantly from the metrics of other regular cycles at the 0.05 significance level using paired t-tests. Statistically significant correlations were observed between the metrics and minimum and mean temperature values of the 10 seasonal cycles.     

Mapping paddy rice agriculture in southern China using multi-temporal MODIS images

Information on the area and spatial distribution of paddy rice fields is needed for trace gas emission estimates, management of water resources, and food security. Paddy rice fields are characterized by an initial period of flooding and transplanting, during which period open canopy (a mixture of surface water and rice crops) exists. The Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the NASA EOS Terra satellite has visible, near infrared and shortwave infrared bands; and therefore, a number of vegetation indices can be calculated, including Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Land Surface Water Index (LSWI) that is sensitive to leaf water and soil moisture. In this study, we developed a paddy rice mapping algorithm that uses time series of three vegetation indices (LSWI, EVI, and NDVI) derived from MODIS images to identify that initial period of flooding and transplanting in paddy rice fields, based on the sensitivity of LSWI to the increased surface moisture during the period of flooding and rice transplanting. We ran the algorithm to map paddy rice fields in 13 provinces of southern China, using the 8-day composite MODIS Surface Reflectance products (500-m spatial resolution) in 2002. The resultant MODIS-derived paddy rice map was evaluated, using the National Land Cover Dataset (1:100,000 scale) derived from analysis of Landsat ETM+ images in 1999/2000. There were reasonable agreements in area estimates of paddy rice fields between the MODIS-derived map and the Landsat-based dataset at the provincial and county levels. The results of this study indicated that the MODIS-based paddy rice mapping algorithm could potentially be applied at large spatial scales to monitor paddy rice agriculture on a timely and frequent basis.     

Changes in agricultural cropland areas between a water-surplus year and a water-deficit year impacting food security,determined using MODIS 250 m time-series data and spectral matching techniques,in the Krishna River basin (India)

The objective of this study was to investigate the changes in cropland areas as a result of water availability using Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m time-series data and spectral matching techniques (SMTs). The study was conducted in the Krishna River basin in India, a very large river basin with an area of 265 752 km² (26 575 200 ha), comparing a water-surplus year (2000–2001) and a water-deficit year (2002–2003). The MODIS 250 m time-series data and SMTs were found ideal for agricultural cropland change detection over large areas and provided fuzzy classification accuracies of 61–100% for various land‐use classes and 61–81% for the rain-fed and irrigated classes. The most mixing change occurred between rain-fed cropland areas and informally irrigated (e.g. groundwater and small reservoir) areas. Hence separation of these two classes was the most difficult. The MODIS 250 m-derived irrigated cropland areas for the districts were highly correlated with the Indian Bureau of Statistics data, with R ²-values between 0.82 and 0.86.

The change in the net area irrigated was modest, with an irrigated area of 8 669 881 ha during the water-surplus year, as compared with 7 718 900 ha during the water-deficit year. However, this is quite misleading as most of the major changes occurred in cropping intensity, such as changing from higher intensity to lower intensity (e.g. from double crop to single crop). The changes in cropping intensity of the agricultural cropland areas that took place in the water-deficit year (2002–2003) when compared with the water-surplus year (2000–2001) in the Krishna basin were: (a) 1 078 564 ha changed from double crop to single crop, (b) 1 461 177 ha changed from continuous crop to single crop, (c) 704 172 ha changed from irrigated single crop to fallow and (d) 1 314 522 ha changed from minor irrigation (e.g. tanks, small reservoirs) to rain-fed. These are highly significant changes that will have strong impact on food security. Such changes may be expected all over the world in a changing climate.     

Mapping paddy rice agriculture in South and Southeast Asia using multi-temporal MODIS images

In this paper, we developed a new geospatial database of paddy rice agriculture for 13 countries in South and Southeast Asia. These countries have ∼ 30% of the world population and ∼ 2/3 of the total rice land area in the world. We used 8-day composite images (500-m spatial resolution) in 2002 from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the NASA EOS Terra satellite. Paddy rice fields are characterized by an initial period of flooding and transplanting, during which period a mixture of surface water and rice seedlings exists. We applied a paddy rice mapping algorithm that uses a time series of MODIS-derived vegetation indices to identify the initial period of flooding and transplanting in paddy rice fields, based on the increased surface moisture. The resultant MODIS-derived paddy rice map was compared to national agricultural statistical data at national and subnational levels. Area estimates of paddy rice were highly correlated at the national level and positively correlated at the subnational levels, although the agreement at the national level was much stronger. Discrepancies in rice area between the MODIS-derived and statistical datasets in some countries can be largely attributed to: (1) the statistical dataset is a sown area estimate (includes multiple cropping practices); (2) failure of the 500-m resolution MODIS-based algorithm in identifying small patches of paddy rice fields, primarily in areas where topography restricts field sizes; and (3) contamination by cloud. While further testing is needed, these results demonstrate the potential of the MODIS-based algorithm to generate updated datasets of paddy rice agriculture on a timely basis. The resultant geospatial database on the area and spatial distribution of paddy rice is useful for irrigation, food security, and trace gas emission estimates in those countries.     

Tropical forest backscatter anomaly evident in SeaWinds scatterometer morning overpass data during 2005 drought in Amazonia

A severe drought occurred in southwestern Amazonia in the dry season (June-September) of 2005. We analyzed 10 years (7/99-10/09) of SeaWinds active microwave Ku-band backscatter data collected over the Amazon Basin, developing monthly means and anomalies from those means in an effort to detect landscape responses to this drought. We compared these to seasonal accumulating water deficit anomalies generated using Tropical Rainfall Monitoring Mission (TRMM) precipitation data (1999-2009) and 100 mm mo^− 1 evapotranspiration demand as a water deficit threshold. There was significant interannual variability in dry-season monthly mean backscatter only for morning (c. 06:00 LST) overpass data, and little interannual variability in dry-season monthly mean backscatter for afternoon (c. 18:00 LST) overpass data. Strong negative anomalies in both morning-overpass backscatter and accumulating water deficit developed during July-October 2005, centered on the southwestern Amazon Basin, with a strong spatial correlation between morning-overpass backscatter anomalies and water deficit anomalies in September. This is the first reporting of tropical forest seasonal drought detection by active microwave scatterometry. Based on the differences between early-morning and late-afternoon backscatter variability, we hypothesize that as the drought persisted over several months, the forest canopy was increasingly unable to recover full leaf moisture content over night, resulting in anomalously low early-morning overpass backscatter.     

Temporal and Spatial Variation and Influencing Factors of Water Storage on the Loess Plateau

In recent years,the conditions of the underlying surface of the Loess Plateau have changed greatly.We researched the changes of water storage by using multi-source data to further reveal the region’s water cycle process.GRACE data were used to study the temporal and spatial characteristics of Terrestrial Water Storage Changes (TWSC) in the Loess Plateau for 2003~2015 years,combined with the atmospheric circulation data,TRMM (3B43) precipitation,GLDAS evaporation and MODIS surface temperature data to analyze the impact of climate change and human activities on TWSC.The results shown that：①in the 2003~2015 years,the TWSC of Loess Plateau showed a decreasing trend with the rate of -5.16±1.51 mm/a,and the seasonal variation shown autumn＞winter＞summer＞spring.②in the past 13 years,the TWSC of Loess Plateau were decreasing from west to east,and the whole were in the state of loss,the minimum value was up to -4.5 cm.③Precipitation has a greater influence on the TWSC in the southwest and south of Loess Plateau,but the surface temperature plays dominated role in the southeast and east.④Human activities have a greater impact on TWSC in Shanxi province and the border zone of Shaanxi,Shanxi and Henan.The comparative study of multi-source data can more accurately reflect the spatial and temporal distribution of water storage changes in the region,and it also have great significant for further research of water cycle process.     

A comparison of TerraSAR-X Quadpol backscattering with RapidEye multispectral vegetation indices over rice fields in the Mekong Delta,Vietnam

Satellite-based multispectral imagery and/or synthetic aperture radar (SAR) data have been widely used for vegetation characterization, plant physiological parameter estimation, crop monitoring or even yield prediction. However, the potential use of satellite-based X-band SAR data for these purposes is not fully understood. A new generation of X-band radar satellite sensors offers high spatial resolution images with different polarizations and, therefore, constitutes a valuable information source. In this study, we utilized a TerraSAR-X satellite scene recorded during a short experimental phase when the sensor was running in full polarimetric ‘Quadpol’ mode. The radar backscatter signals were compared with a RapidEye reference data set to investigate the potential relationship of TerraSAR-X backscatter signals to multispectral vegetation indices and to quantify the benefits of TerraSAR-X Quadpol data over standard dual- or single-polarization modes. The satellite scenes used cover parts of the Mekong Delta, the rice bowl of Vietnam, one of the major rice exporters in the world and one of the regions most vulnerable to climate change. The use of radar imagery is especially advantageous over optical data in tropical regions because the availability of cloudless optical data sets may be limited to only a few days per year. We found no significant correlations between radar backscatter and optical vegetation indices in pixel-based comparisons. VV and cross-polarized images showed significant correlations with combined spectral indices, the modified chlorophyll absorption ratio index/second modified triangular vegetation index (MCARI/MTVI2) and transformed chlorophyll absorption in reflectance index/optimized soil-adjusted vegetation index (TCARI/OSAVI), when compared on an object basis. No correlations between radar backscattering at any polarization and the normalized difference vegetation index (NDVI) were observed.