Monitoring Perennial Sub-Surface Waterlogged Croplands Based on MODIS in Jianghan Plain, Middle Reaches of the Yangtze River

Journal of Integrative Agriculture 2014, 13(8): 1791-1801

August 2014

RESEARCH ARTICLE

Monitoring Perennial Sub-Surface Waterlogged Croplands Based on MODIS in Jianghan Plain, Middle Reaches of the Yangtze River XIAO Fei, LI Yuan-zheng, DU Yun, LING Feng, YAN Yi, FENG Qi and BAN Xuan Institute of Geodesy and Geophysics, Chinese Academy of Sciences, Wuhan 430077, P.R.China

Abstract Perennial waterlogged soil (PWS) is induced by the high level of groundwater, and has a persistent impact on natural ecosystems and agricultural production. Traditionally, distribution information regarding PWS is mainly collected from in situ measurements through groundwater level surveys and physicochemical property analyses. However, in situ measurements of PWS are costly and time-consuming, only rough estimates of PWS areas are available in some regions. In this paper, we developed a method to monitor the perennial waterlogged cropland using time-series moderate resolution imaging spectroradiometer (MODIS) data. The Jianghan Plain, a floodplain located in the middle reaches of the Yangtze River, was selected as the study area. Temporal variations of the enhanced vegetation index (EVI), night land surface temperature (LST), diurnal LST differences (∆LST), albedo, and the apparent thermal inertia (ATI) were used to analyze the ecological and thermodynamic characteristics of the waterlogged croplands. To obtain pure remote sensing signatures of the waterlogged cropland from mixed pixels, the croplands were classified into different types according to soil and land cover types in this paper, and a linear mixing model was developed by fitting the signatures using the multiple linear regression approach. Afterwards, another linear spectral mixing model was used to get the proportions of waterlogged croplands in each 1 km×1 km pixel. The result showed an acceptable accuracy with a root-mean-square error of 0.093. As a tentative method, the procedure described in this paper works efficiently as a method to monitor the spatial patterns of perennial sub-surface waterlogged croplands at a wide scale. Key words: perennial waterlogged soil, waterlogging, MODIS, enhanced vegetation index

INTRODUCTION Waterlogging is a worldwide agricultural disaster, especially in floodplain cropping regions (Cai et al. 1996; McFarlane and Williamson 2002; Pandey et al. 2010). It affects the survival, growth, and development of numerous plant species (Datta and Jong 2002; Dat et al. 2004; Parent et al. 2008), and thus poses serious threats to agricultural land production (Yu 1992; Fan 1997). Long-term water saturation induces changes in

the physicochemical properties of soil, forming a kind of perennial waterlogged soil (PWS). In PWS environment, soil water is blocked off and gas diffusion is reduced. Decrease in oxygen levels leads to anoxic conditions, which critically affect the availability and concentration of different plant nutrients (Pezeshki 2001). Phytotoxic compounds in the soil also accumulate as anaerobic conditions prevail (Parent et al. 2008). PWS causes vast expanses of land to become completely unsuitable for cultivation. In some developing countries that depend heavily on agriculture, the menace of PWS is a grave concern.

Received 1 March, 2013 Accepted 20 June, 2013 Correspondence XIAO Fei, Tel: +86-27-68881901, E-mail: [email protected]

© 2014, CAAS. All rights reserved. Published by Elsevier Ltd. doi: 10.1016/S2095-3119(13)60563-8

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At present, detecting and rapidly mapping the spatial distribution of PWS over wide areas are difficult to conduct because of the sub-surface characteristics of soil. Distribution information of PWS is traditionally collected from in situ measurements through groundwater level surveys, physicochemical property analyses, and observations of seepage losses in soil. However, in situ measurements are costly and time consuming. Only a few in situ groundwater measurements are available at suitable locations and time, and data scarcity is often the main limiting factor for the mapping of waterlogged area. Thus far, only rough estimates about the area of PWS can be found in some regions. More accurate distribution information of PWS areas is always in demand to improve many soil applications, such as agricultural planning and crop loss assessment. To date, most studies of waterlogging remote sensing focus on short-term surface waterlogging phenomena. Well-established practices of surface waterlogging monitoring have been carried out using visual analysis (Dwivedi et al. 2007; Mandal and Sharma 2011), normalized difference water index (NDWI) (Chowdary et al. 2008) and unsupervised and supervised classification techniques (Choubey 1997) according to differences in image such as color/tone between waterlogged and non-waterlogged areas (Blavet et al. 2000; Bastawesy and Ali 2013). However, these methods cannot be used to monitor sub-surface waterlogged areas, and the accuracy of which is largely relevant to the prior knowledge of researchers of specific study areas. PWS is essentially induced by high levels of groundwater (Cox and McFarlane 1995; Liu et al. 2003). Sub-surface waterlogged areas can be directly assessed under geographic information system (GIS) environments using field-based groundwater level data (Chowdary et al. 2008; Singh 2012; Pandey et al. 2013). According to some field surveys (Yu 1992; Liu et al. 2003), waterlogging disasters occur when the water table of the groundwater is less than 0.6 m. When the groundwater level ranges from 0.1 to 0.2 m, distinct waterlogging disasters could occur. However, few direct applications of satellite data are available for the measurement of groundwater parameters. Most of the data from available resources, including microwave remote sensing data with certain penetration depths, are limited to the upper few centimeters (about 0 to 10 cm) of the

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soil profile (Schmugge 1998; Scott et al. 2003) and are usually relatively expensive. Little attention has been given to the use of surface characteristics for the remote sensing of subsurface waterlogging. According to field surveys, the characteristics of PWS not only show sub-surface physicochemical features, such as a high level of groundwater, low water permeability, saturation of soil, high organic contents, and anoxic conditions, but also show ecological and thermodynamic appearances (Yu 1993). Under ordinary conditions, the growth status and productivity of crops in waterlogged areas are not as favorable as those in normal croplands. Moreover, the thermal conductivity, thermal capacity, and thermal inertia of waterlogged soil are different from those of non-waterlogged soil because of higher levels of groundwater and soil moisture in waterlogged soil. Perennial sub-surface waterlogging can be assumed to result in persistent exceptional performance in terms of ecological and thermodynamic characteristics, which can be detected by time-series remote sensing data. In this paper, we monitored PWS in terms of several surface characteristics using moderate resolution imaging spectroradiometer (MODIS) data in consideration of the crucial requirements of high temporal and multi-spectral resolution. MODIS data can provide a time-series of synoptic land observation data with appropriate spectral and temporal resolutions. However, due to the spatial variability of land cover and waterlogged soil types, heterogeneous conditions result in a high percentage of mixed pixels in moderate resolution imagery. In our field survey, locating a land area with purely waterlogged soil exceeding 1 km×1 km was difficult. Therefore, this paper aims to develop a methodology for mapping croplands with perennial waterlogged soil at a sub-pixel level using time-series moderate-resolution remote sensing imagery. Jianghan Plain, a typical PWS area in the middle reaches of the Yangtze River in South China was selected as the study area. Comparable with flood, waterlogging is one of the most serious disasters in the middle and lower reaches of the Yangtze River (Cai et al. 1996; Fan 1997). According to estimates from field surveys, areas of PWS amount to almost 30% of the total cropland in some provinces in the lower reaches of the Yangtze River, including Jiangsu, Jiangxi, and Zhejiang (Yu 1993;

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Monitoring Perennial Sub-Surface Waterlogged Croplands Based on MODIS in Jianghan Plain, Middle Reaches of the Yangtze River

Li et al. 2003). In such situations, information on PWS distribution will be helpful for the optimization of land use patterns and the agricultural development.

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RESULTS AND DISCUSSION

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Seasonal cyclical characteristics of the PWS cropland

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A total of 883 pixels were extracted from the sampling area via random sampling to calculate the seasonal cyclical characteristics of different land classes. Fig. 1-A shows the calculated pure albedo of mixed pixel signatures using the linear spectral mixing model for croplands, including waterlogged paddy fields, normal paddy fields, waterlogged dry-farming fields, and normal dry-farming fields. From January to May and from November to December, the albedo values of waterlogged fields were significantly lower than those of normal fields. To our knowledge, vegetation cover for these months is relatively lower than those for other months in this area, and there will be more information about the soil background in remote sensing signatures. Therefore, the differences in albedo values between waterlogged and normal fields in the aforementioned months may indicate that the soil color of waterlogged areas is darker than that of normal soil. In fact, soil color differences between waterlogged and normal fields have also been observed in our field surveys. From May to August, the albedos increased rapidly, and the albedos of the waterlogged and normal fields all reached maximum values in August. Except from June to September, the values of normal paddy fields were generally lower than those of normal dry-farming fields. The albedo values of waterlogged paddy fields were higher than those of waterlogged dry-farming fields for the whole-year cycle. All of the croplands showed different characteristics compared with other land classes, such as water body, wetland, built-up area, and woodland, as shown in Fig. 1-B. All of the enhanced vegetation index (EVI) values of the cropland classes increased from January to April and decreased from April to May. Afterwards, the EVI values rapidly increased from May to August (Fig. 2). A step-by-step decrease was observed in all EVI values

0.12 0.10 0.08 Water body Built-up area

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Fig. 1 Ten-year average monthly albedo values of different land classes from July 2002 to July 2011. A, ten-year average monthly albedo values of different croplands. B, ten-year average monthly albedo values of water body, wetland, built-up area and woodland, respectively.

of cropland fields from August to November. From January to May, the EVI values of the waterlogged croplands were significantly lower than those of the normal croplands. By contrast, differences between waterlogged paddy fields and waterlogged dry-farming fields were not remarkably significant during this period. Except for the months from June to August, EVI values of the waterlogged fields were all lower than those of the normal fields. The above phenomenon is in accordance with the ecological characters of waterlogged field observed by field survey (Yu 1993). Before May, the waterlogged field is easy to suffer from sparse rooting, runt seedling, stifle, and slow growth because of the effects of waterlogging. All these will cause the weaker crops and thus the lower values of EVI for the waterlogged fields during this stage. From June to August, EVI values of the waterlogged

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that the night LST values of waterlogged dry-farming fields were higher than those of waterlogged paddy fields. The night LST difference between waterlogged fields and normal fields observed by MODIS data was in accordance with the field survey results by Yu (1993). Diurnal LST difference signatures of cropland classes increased from January to February and then decreased from March to July as shown in Fig. 4. The diurnal LST differences reached the lowest in July and August for all classes. From August to October, the diurnal LST difference signatures all increased. The signatures of cropland classes decreased from October to December, whereas the signatures of some other land classes, such as those of water and wetlands, with the exception of the class of built-up areas and woodland, increased during the same period. From March to August, the diurnal LST difference values of waterlogged fields were all lower than the values of the normal fields. The diurnal

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paddy fields increased rapidly and exceeded those of the normal paddy fields. According to some field experiments, the organic content is higher in the waterlogged paddy field than the normal paddy field (Yu 1993), and the higher organic content may cause the faster growth in some conditions. Presumptively, the higher EVI values of waterlogged paddy field from June to August were related to the higher organic content in the soil of waterlogged paddy filed. In addition, significant differences were observed among EVI signatures of water body, wetland, built-up area, and woodlands, apparent contrasts with the trends of cropland fields were found too. All of the night LST values increased from January to July and decreased from August to December (Fig. 3). Among the water body, wetland, built-up area and woodland, built-up area had the relative lower night LST vaules all the year cycle. Among the cropland classes, waterlogged fields had relatively higher night LST values than normal fields each month. It can also be found

Night LST

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Fig. 2 Ten-year average monthly enhanced vegetation index (EVI) of different land classes from July 2002 to July 2011. A, ten-year average monthly EVIs of different croplands. B, ten-year average monthly EVIs of water body, wetland, built-up area and woodland.

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Fig. 3 Ten-year average monthly night land surface temperature (LST) of different land classes from July 2002 to July 2011. A, tenyear average monthly night LSTs of different croplands. B, ten-year average monthly night LSTs of water body, wetland, built-up area and woodland.

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Monitoring Perennial Sub-Surface Waterlogged Croplands Based on MODIS in Jianghan Plain, Middle Reaches of the Yangtze River

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Fig. 4 Ten-year average monthly diurnal LST differences of different land classes from July 2002 to July 2011. A, ten-year average monthly diurnal LST differences of different croplands. B, ten-year average monthly diurnal LST differences of water body, wetland, built-up area and woodland.

LST difference values of waterlogged paddy fields were lower than those of the normal paddy fields except the months of September and October, and the values of waterlogged dry-farming fields were lower than those of the normal dry-farming field except the months of January, February, September, and December. In comparison, wetlands and water bodies had the lowest diurnal LST differences among the eight classes except in May and June, and the built-up areas had the highest diurnal LST differences among the eight classes from March to October. Differences in the characteristics of night LST and diurnal LST difference among the eight classes reflect the different thermal inertias essentially. It can be seen that the patterns of night LST were in accordance with the characteristics of apparent thermal inertia (ATI), whereas the diurnal LST differences were in contrast with the characteristics of ATI to a certain extent, as shown in Fig. 5. Among the classes of croplands, waterlogged fields exhibited higher ATI than normal

0.05 0.00

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Fig. 5 Ten-year average monthly apparent thermal inertia (ATI) of different land classes from July 2002 to July 2011. A, ten-year average monthly ATIs of different croplands. B, ten-year average monthly ATIs of water body, wetland, built-up area and woodland.

fields except in January, September, and October. In comparison, waterlogged dry-farming fields had higher values than waterlogged paddy fields except in August and September. Among the other land classes, water bodies had the highest ATI values for each month, followed by wetlands on the whole. By contrast, built-up area exhibited the lowest ATI values for each month. Thermal inertia denotes the ability to conduct and store heat. In most months of the year, waterlogged fields had higher ATI values than normal fields. Accordingly, waterlogged fields had higher night LST and lower diurnal LST difference. Similarly, water bodies and wetlands had higher ATI values, and thus had higher night LST and lower diurnal LST differences. Although fluctuations occurred in the values of night LST, diurnal LST difference, and ATI values for the eight land classes in one year, the relative relationships among them were stable in most months. Based on the fluctuations, the thermal characteristics of the eight land classes were speculated to vary as the soil moisture, conditions of surface water and groundwater, vegetation growth and © 2014, CAAS. All rights reserved. Published by Elsevier Ltd.

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etc. change. In general, all classes of croplands, including normal paddy fields, waterlogged paddy fields, normal dry-farming fields, and waterlogged dry-farming fields, showed similar but distinguishable seasonal signatures in terms of ecological and thermodynamic characteristics. Furthermore, the classes of croplands also demonstrated distinct differences compared with the signatures of the other land classes such as water bodies, wetlands, built-up areas, and woodlands. Differences in the ecological and thermodynamic characteristics among croplands were the consequences of variations in soil physicochemical property, thus providing the basis for identifying waterlogged soil using remotely sensed data. The advantage of using linear spectral mixing model to fit the pure signatures of different classes from mixed pixels is that more pixels can be used for evaluating the signatures, which enables the use of lower spatial resolution images in heterogeneous areas. Although the general characteristics of PWS were in accordance with some field surveys, the accuracy was determined by the accuracies of all the classes’ proportions in mixed pixels anyway. In addition, there was still an uncertainty in the assumption that the mixed pixel is a linear combination of materials with relative proportional to the area.

Distribution of the perennial waterlogged cropland The proportions of the area of the dry-farming fields and the paddy fields in each of the 1 km×1 km pixels in 2011 were calculated based on the land-use map, as shown in Fig. 6-A and B, respectively. According to statistics, the total area of cropland was approximately 24 248.20 km2, which comprised 73.38% the total area of the Jianghan Plain. Among the cropland classes, the area of the dry-farming fields was approximately 9 670.46 km2, and the area of the paddy fields was approximately 14 577.74 km2. Fig. 7 shows the proportion of waterlogged croplands during the study period, including waterlogged dry-farming fields and waterlogged paddy fields, in each of the pixels. The area of waterlogged dry-farming fields was 698.30 km2, and that of waterlogged paddy fields was 1 457.01

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Fig. 6 Proportion of the area of the croplands in each of the 1 km×1 km pixels in 2011. A, proportion of the area of the dry-farming fields in each of the 1 km×1 km pixels. B, proportion of the area of the paddy fields in each of the 1 km×1 km pixels.

km2. The total area of waterlogged croplands was 2 155.31 km2, which comprised 8.89% the total area of croplands in the Jianghan Plain. Given the concentration of lakes and towns in the southeastern parts of the Jianghan Plain, the cropland areas were relatively smaller in these regions. Although the spatial patterns of waterlogged croplands were generally defined by the distribution of cropland, it showed some concentrated characteristics in Jianghan Plain. In the northern and northwestern parts of the plain, relatively less waterlogged croplands were observed. Although the dry-farming fields were mainly distributed in the northern part of the Jianghan Plain, most of the waterlogged dry-farming fields were located on the southern part of the Jianghan Plain. Overall, the distribution of waterlogged paddy fields was more dispersed than the waterlogged dry-farming fields. The waterlogged paddy fields were largely distributed in the central part of Ji© 2014, CAAS. All rights reserved. Published by Elsevier Ltd.

Monitoring Perennial Sub-Surface Waterlogged Croplands Based on MODIS in Jianghan Plain, Middle Reaches of the Yangtze River

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Fig. 7 Proportion of the waterlogged croplands in each of the 1 km×1 km pixels from 2002 to 2011. A, proportion of the waterlogged dry-farming fields in each of the 1 km×1 km pixels. B, proportion of the waterlogged paddy fields in each of the 1 km×1 km pixels.

anghan Plain and close to the concentration areas of the lakes. The surrounding regions of the Honghu Lake were the clustered areas for both the waterlogged dry-farming fields and the waterlogged paddy fields. According to historical records, large areas of the croplands around the lakes in the Jianghan Plain were reclaimed from wetlands and lakes. Because of the low-lying terrain and poor drainage in the reclaimed croplands, the groundwater level remained high in the long term. Therefore, PWS was much more likely to occur around lakes. The root mean square error (RMSE) was approximately 0.091 for the waterlogged dry-farming fields when the values calculated from soil and land-use maps were compared with the values calculated from the model in the sampling pixels. The RMSE was 0.094 for the waterlogged paddy fields, and 0.093 for the total waterlogged croplands. To date, no recent open information that is derived directly from field surveys is available

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for accurately determining waterlogged cropland areas. Nevertheless, several researchers have estimated the approximate area of the waterlogged croplands based on groundwater data for parts of the Jianghan Plain or the entire plain. The total area of the waterlogged cropland in the Jianghan Plain was estimated to be about 2 900 km2 in 2000 (Xiang et al. 2000). This estimate is larger than the area calculated using the model in the present study, which is 2 155.31 km2. According to land-use maps between 2000 and 2010, recent China-Brazil earth resources satellite (CBERS-02) images in 2011, and our field investigation, large areas of croplands in the Jianghan Plain have been transformed into fishponds or lakes from 2000 to 2011. The area of croplands which was transformed into fishponds was approximately 585 km2. This type of land-use change occurred primarily in low-lying places or regions near the lakes. Such areas are most likely to suffer from waterlogging disasters. Better agreement between the waterlogged cropland areas obtained from the current model and Xiang et al. (2000) may be achieved by considering the effect of land-use changes.

CONCLUSION In this paper, we presented a new method for monitoring perennial sub-surface waterlogged croplands at the sub-pixel level using time-series moderate-resolution remote sensing imagery. Several indices derived from MODIS, including EVI, night LST, diurnal LST difference (∆LST), albedo, and ATI, were used to analyze the ecological and thermodynamic characteristics of the waterlogged cropland. Time-variation information for the remote sensing characteristics of PWS and non-waterlogged soil was calculated separately according to the land-use type. To obtain pure remote sensing signatures of the waterlogged croplands from mixed pixels, a linear mixing model was used in this paper by fitting the signatures through a multiple linear regression approach. It was found that all of the classes of croplands showed similar but distinguishable seasonal signatures in terms of ecological and thermodynamic characteristics. Based on the ecological and thermodynamic characteristics of the waterlogged cropland and its differences compared with other land classes, we calculated the pro© 2014, CAAS. All rights reserved. Published by Elsevier Ltd.

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portions of waterlogged croplands, including waterlogged paddy fields and waterlogged dry-farming fields, separately in each of the 1 km×1 km pixels using the linear spectral mixing model. The results exhibited general agreement with Xiang et al. (2000) in which the waterlogged area was estimated using groundwater data. In conclusion, as a tentative method, the procedure described in this paper works efficiently as a means for monitoring the spatial patterns of perennial sub-surface waterlogged croplands in a broad scale.

provides abundant water and heat resources that are highly important for agriculture. Jianghan Plain has long been one of the most important sources for commodity grain, cotton, and aquatic products in China. However, low-lying fluvial landforms, abundant rainfall, vast runoff from rivers, and inappropriate land use patterns in some regions of the plain often cause soil saturation and surface flooding of high frequency and long duration. In Jianghan Plain, the water level of the Yangtze River is often higher than the elevation of the adjacent plain, especially in the Jingjiang reaches of the Yangtze River. Jingjiang is the most dangerous reach to flooding with a length of 360 km. Its riverbed is 2 m higher than the nearby plain on average. During the flood season, the water level of the river is often over 10 m above the elevation of the adjacent plain. As a result, the groundwater level in Jianghan Plain remains high all year round. The average annual precipitation ranges from 926 to 1 275 mm in different parts of Jianghan Plain. Abundant rainfall also contributes to the high level of groundwater in the plain. Many lakes in the plain have been reclaimed over the last 60 years. Reclamation decreases the storage capacity of lakes and adds to the difficulty of draining the inundated water out the plain. These factors increase and intensify waterlogging and flooding disasters in Jianghan Plain. At present, large areas of perennially waterlogged croplands are distributed in the plain, seriously affecting the local agricultural production.

MATERIALS AND METHODS Study area Jianghan Plain is located in the middle reaches of the Yangtze River at approximately 111°30´-114°45´E and 29°25´-31°20´N (Fig. 8). It extends from the Yangtze River in the south to the Jingshan Mountains in the north. Its western boundary lies near the mountains northwest of Hubei Province and its eastern border lies near the Dabieshan Mountains. The total area of Jianghan Plain is about 33 045 km2. Jianghan Plain is an alluvial plaindominated lowland formed by the deposition of the Yangtze and Hanjiang Rivers over a long period. The elevation of most areas in Jianghan Plain is below 50 m. A number of different scales of lakes are present in the plain, such as Honghu, which is a shallow lake with an area exceeding 300 km2. According to our investigation, there are over 950 lakes in Jianghan Plain with areas exceeding 0.1 km2, the total area of which is about 2 438 km2 at present. The hydrological regimes of the surface water and groundwater in the plain are strongly controlled by close interactions between lakes and rivers. The northern sub-tropical monsoon climate in this area 112°0´0´´E

Data source In this paper, we primarily used time-series MODIS data sets from July 2002 to July 2011 to obtain the ecological and thermodynamic characteristics of the perennial sub-surface waterlogged cropland. The data sets used in this paper included the level-3 MODIS/Aqua global land surface temperature and emissivity (LST/E) 8-day product (MYD11A2), the MODIS

113°0´0´´E

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Jingmen

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Yichang the Hanjiang River

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the Yangtze River Hubei Province

Jianghan Plain Rivers and lakes

Hunan Province

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Fig. 8 Location of the Jianghan Plain.

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Monitoring Perennial Sub-Surface Waterlogged Croplands Based on MODIS in Jianghan Plain, Middle Reaches of the Yangtze River

albedo product (MCD43B3), and the MODIS vegetation indices 16-day L3 global product (MYD13A2). All of the aforementioned MODIS data sets were stored at 1 km spatial resolution on Sinusoidal projection grid. The data were obtained from the NASA Land Processes Distributed Active Archive Center. The MYD11A2 datasets provide the average values of clear-sky LSTs during an 8-day period in each grid, which are available from the July 4, 2002 to present. The datasets are retrieved from the calibrated radiance data of bands 31 and 32 with the generalized split-window algorithm (Wan et al. 2004). The passing time of the satellite is at approximately 12:35 p.m.-14:35 p.m. and 1:25 a.m.-3:20 a.m. local time, which is near the moments of the highest daily temperature and the lowest daily temperature in the study area. MCD43B3 product provides gridded data describing both the bihemispherical reflectance and the directional hemispherical reflectance for bands 1-7 as well as for three broad bands every 16 days. The shortwave (0.3-5.0 µm) broadband albedo data were used in this paper. Quality information of the MCD43B3 product was stored as another product (MCD43B2). Temporal coverage of MCD43B3 and MCD43B2 datasets are from February 24, 2000 to present. To obtain vegetation information, we used the MYD13A2 collection 5 product. MYD13A2 data provides both the normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI) information every 16 days. The product is computed from atmospherically corrected bi-directional surface reflectance using band 1, band 2 and band 3. MYD13A2 data are available from July 4, 2002 to present. Considering that the EVI included in the products can minimize canopy background variations and remove residual atmosphere contamination caused by smoke and sub-pixel thin clouds, we adopted the EVI instead of the NDVI in our analysis. Besides remote sensing images, soil (at 1:50 000 scale) and land-use (at 1:100 000 scale) maps were used in this paper. The soil map was drawn by the Institute of Geodesy & Geophysics, Chinese Academy of Sciences, and Soil Survey Office of Honghu in the 1990s and covers Honghu County and part of Jianli County in the study area. The land-use map was obtained from the Chinese Data Sharing Infrastructure of Earth System Science. These maps were interpreted using remote sensing images obtained in 2000 and 2010, respectively, and covered all the study area. In addition, CBERS-02 19.5 m resolution images from 2011 were used to estimate land-use changes during the study period.

Methodology Data preprocessing All of the downloaded spatially adjacent MODIS data, including MYD11A2, MCD43B3, MCD43B2, and MYD13A2, were mosaicked separately using the MODIS reprojection tool. Regions that covered the entire study area were extracted and reprojected to the Albers equal-area

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projection at the same time. Quality control items in the LST scientific data sets, which consist of mandatory quality assurance flag, data quality flags, emissivity error flag, and average LST error flags, were applied to evaluate pixel availability of all LST images. These items aim to remove cloud-contaminated pixels and other questionable pixels. Pixels that were not produced or poorly calibrated were eliminated. Moreover, pixels with average emissivity error greater than 0.02 and the pixels with average LST error greater than 2 K were also eliminated. The night LST and the diurnal LST differences (∆LST) were extracted from the preprocessed MYD11A2 data sets. For the MODIS Albedo product (MCD43B3), we used the band-wise albedo quality data contained in MCD43B2 to filter pixels with 75% or more fill values. Soil maps, land-use maps, and CBERS-02 images were geometrically corrected and also reprojected to the Albers equal-area projection. We calculated the apparent thermal inertia (ATI) using the MODIS albedo and LST data. ATI can be used to indicate the relative impedance to temperature change of materials and can be derived directly from multi-spectral remote sensing imagery (Minacapilli et al. 2009; Doninck et al. 2011). We adopted the approach of Claps and Laguardia (2004) to calculate the ATI of the study area in consideration of its easily implemented operation. The constraints can be expressed as follows: ATI=

1-A ∆T

(1)

Where, A represents the albedo of a surface, and ∆T is the difference between the maximum and the minimum value of the surface temperature during the diurnal cycle. Analysis of the seasonal cyclical characteristics of PWS croplands To monitor a perennial sub-surface waterlogged cropland, differences in the ecological and thermodynamic characteristics between normal and waterlogged croplands should be estimated. The region with a soil map was selected as the sampling area. Although the soil type that directly describes the PWS was not found in the soil map, some of the soil types, such as gleyed paddy soil, swamp paddy soil, and blue clay soil, can indicate the PWS according to our field survey. Thus, in the sampling area, we could mark off perennial sub-surface waterlogged croplands based on the classification system of the soil map. The ecological and thermodynamic characteristics of land are not only affected by soil types but also by land-use patterns. For the same PWS area, different land-use types will result in different spectra in the remote sensing images. Therefore, we counted the time-variation information of the remote sensing characteristics of PWS and non-waterlogged soil separately according to the land-use type. We classified the study area into eight classes according to the land-use and soil maps: waterlogged paddy field, normal paddy field, waterlogged dry-farming field, normal dry-farming field, water body, wetland, built-up area, and woodland. Based on the remote sensing interpretation and field survey, land-use changes mainly occurred in the transition from croplands to © 2014, CAAS. All rights reserved. Published by Elsevier Ltd.

XIAO Fei et al.

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water surfaces, such as fishpond and lakes, during the study period. To avoid the effects of land-use changes in the analysis of remote sensing characteristics for each class, we updated the land-use map using CBERS-02 19.5 m resolution images in 2011. In the subsequent analysis, we only estimated the pixels of the cropland and excluded the pixels from the areas where the land-use had changed. Remote sensing characteristic analysis commonly requires pure pixels for counting. However, pure pixels are often limited for some ground objects in the MODIS images. In this study area, the length and width of the perennial waterlogged cropland within the same land use type seldom exceeded 1 km simultaneously. Obtaining a sufficient number of representative samples to accurately estimate the ecological and thermodynamic time-variation characteristics of each of the eight classes was difficult. However, when impure pixels are used, the results may be influenced by the degree of occurrence of other classesand thus be inaccurate. In this paper, we obtained the pure ecological and thermodynamic characteristics out of mixed pixels by using the linear spectral mixing model for reference. In the linear spectral mixing model, a mixed pixel is represented as a linear combination of materials with relative concentrations (Heinz and Chang 2001). In principle, the relative concentrations are proportional to the area (Cárdenas and Wang 2010). The linear spectral mixing model has been widely used and shown to be effective in remote sensing for the quantification of the abundance of materials (Garcia-Haro et al. 1996; Defries et al. 2000). The linear spectral mixing model expresses the linear relationship for the signature mixture of the different classes for a given pixel by the following equation. N

Pi = ∑ f ki rk +ei k =1

(2)

Where, Pi is the spectral signature of pixel i, N is the number of components in the pixel, fki is the spectral signature of the kth component, rk is the proportion of the kth component, and ei is the residual term, which indicates the disagreement between the measured and modeled spectrum signatures. In this work, we took each of the ecological and thermodynamic indices as a type of spectrum signature, and used the linear mixing model as a reference to calculate the pure ecological and thermodynamic characteristics out of mixed pixels. For each mixed pixel in the selected sampling area, we counted the proportion of each class using land-use and soil maps. We used the multiple linear regression approach without constant terms to calculate the linear mixing model. We further calculated the monthly averaged signature values of EVI, night LST, ∆LST, and albedo for all eight classes; these signatures were relatively unbiased pure signatures of mixed pixels. There were 1 178 pixels in the sampling area. Three-fourths of the pixels extracted from the sampling area using random sampling algorithm was used to estimate the time-variation information of the classes, and the remaining one-fourth of the pixels was prepared for verification. Remote sensing of the perennial waterlogged cropland After

calculating the time-series variation of the ecological and thermodynamic indices of monthly averaged EVI, night LST, ∆LST, and albedo for every class, we adopted a same linear mixing model to evaluate the proportion of perennial waterlogged croplands at the sub-pixel level. Dissimilarly, the model was calculated by applying a constrained least-squares approach. The constraint condition was that the proportion of each class within each pixel should fall between 0 and 1, the summation of the proportions of the component classes should be equal to 1, and the summation of the proportions of waterlogged paddy field, normal paddy field, waterlogged dry-farming field, and normal dry-farming field should be equal to the total proportion of the cropland calculated from soil and land-use maps in each pixel. The constraints can be expressed as follows: N

∑ r =1 , 0 k =1

k

rk 1 and rtc = rwp+rnp+rwd +rnd

(3)

Where, rtc, rwp, rnp, rwd, and rnd is the proportion of total cropland, waterlogged paddy field, normal paddy field, waterlogged dry-farming field, and normal dry-farming field respectively. The constrained least-squares approach was then applied to calculate the components’ proportions by finding a set of values for rk such that the sum of the root mean square error (RMSE) of the residuals becomes the minimum for each pixel. Given that we adopted five ecological and thermodynamic indices, all of which were monthly averaged data, 48 values were used to calculate the rk for each pixel. The final result of the analysis was a fractional area map for perennial waterlogged croplands within each pixel. Accuracy assessment A total of 295 random pixels remained in the sampling area for accuracy assessment. Among these pixels, 201 pixels with cropland information were selected to assess the accuracy of extracted waterlogged croplands. In this paper, we adopted the RMSE to measure the difference between the percentage values of waterlogged croplands calculated by the linear spectral mixing model and the actual percentage values calculated from soil map and land-use maps.

Acknowledgements

The work was supported by the National Basic Research Program of China (2012CB417001) and the National Natural Science Foundation of China (41271125).

References

Bastawesy M E, Ali R R. 2013. The use of GIS and remote sensing for the assessment of waterlogging in the dryland irrigated catchments of Farafra Oasis, Egypt. Hydrological Processes, 27, 206-216. Blavet D, Mathe E, Leprun J C. 2000. Relations between soil colour and waterlogging duration in a representative hillside of the West African granite-gneissic bedrock. Catena, 39, 187-210. Cai S, Wang X, Huang J. 1996. Exploitation and the

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Monitoring Perennial Sub-Surface Waterlogged Croplands Based on MODIS in Jianghan Plain, Middle Reaches of the Yangtze River

Development of Agriculture in Sihu Basin, Jianghan Plain. Science Press, Beijing, China. (in Chinese) Cárdenas J L S, Wang L. 2010. Fully constrained linear spectral unmixing: analytic solution using fuzzy sets. IEEE Transactions on Geoscience and Remote Sensing, 48, 3992-4002. Choubey V K. 1997. Detection and delineation of waterlogging by remote sensing techniques. Journal of the Indian Society of Remote Sensing, 25, 123-135. Chowdary V M, Chandran R V, Neeti N, Bothale R V, Srivastava Y K, Ingle P, Ramakrishnan D, Dutta D, Jeyaram A, Sharma J R, Singh R. 2008. Assessment of surface and sub-surface waterlogged areas in irrigation command areas of Bihar state using remote sensing and GIS. Agricultural Water Management, 95, 754-766. Claps P, Laguardia G. 2004. Assessing spatial variability of soil water content through thermal inertia and NDVI. In: Manfred O, Guido D, eds., Remote Sensing for Agriculture, Ecosystems, and Hydrology. SPIE, Bellingham. pp. 378387. Cox J W, McFarlane D J. 1995. The causes of waterlogging in shallow soils and their drainage in southwestern Australia. Journal of Hydrology, 167, 175-194. Dat J, Capelli N, Folzer H, Bourgeade P, Badot P M. 2004. Sensing and signaling during plant flooding. Plant Physiology and Biochemistry, 42, 273-282. Datta K K, Jong C. 2002. Adverse effect of waterlogging and soil salinity on crop and land productivity in northwest region of Haryana, India. Agricultural Water Management, 57, 223-238. Defries R S, Hansen M C, Townshend J R G. 2000. Global continuous fields of vegetation characteristics: A linear mixture model applied to multi-year 8 km AVHRR data. International Journal of Remote Sensing, 21, 1389-1414. Doninck J V, Peters J, Baets B D, Clercq E M D, Ducheyne E, Verhoest N E C. 2011. The potential of multitemporal Aqua and Terra MODIS apparent thermal inertia as a soil moisture indicator. International Journal of Applied Earth Observation and Geoinformation, 13, 934-941. Dwivedi R S, Ramana K V, Sreenivas K. 2007. Temporal behavior of surface waterlogged areas using spaceborne multispectral multitemporal measurements. Journal of the Indian Society of Remote Sensing, 35, 173-184. Fan W. 1997. Wetland Agriculture and the Ecological Economy in Jianghan Plain. Hubei Science and Technology Press, Wuhan. (in Chinese) Garcia-Haro F J, Gilabert M A, Melia J. 1996. Linear spectral mixture modelling to estimate vegetation amount from optical spectral data. International Journal of Remote Sensing, 17, 3373-3400. Heinz D C, Chang C I. 2001. Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing, 39, 529-545. Li B, Liu B, Li Z. 2003. Differences of waterlogged regions

1801

in Jianghan Plain and the development strategy for the districts. Resources and Environment in the Yangtze Basin, 12, 285-288. (in Chinese) Liu Z, Liu B, Li B. 2003. Forming reason, succession and variation regularity of waterlogging land in Jianghan Plain. Research of Agricultural Modernization, 24, 24-28. (in Chinese) Mandal A K, Sharma R C. 2011. Delineation and characterization of waterlogged salt affected soils in IGNP using remote sensing and GIS. Journal of the Indian Society of Soil Science, 39, 39-50. McFarlane D J, Williamson D R. 2002. An overview of water logging and salinity in southwestern Australia as related to the “Ucarro” experimental catchment. Agricultural Water Management, 53, 5-29. Minacapilli M, Iovino M, Blanda F. 2009. High resolution remote estimation of soil surface water content by a thermal inertia approach. Journal of Hydrology, 379, 229-238. Pandey A C, Singh S K, Nathawat M S, Saha D. 2013. Assessment of surface and subsurface waterlogging, water level fluctuations, and lithological variations for evaluating groundwater resources in Ganga Plains. International Journal of Digital Earth, 6, 276-296. Pandey A C, Singh S K, Nathawat M S. 2010. Waterlogging and flood hazards vulnerability and risk assessment in Indo Gangetic plain. Natural Hazards, 55, 273-289. Parent C, Capelli N, Berger A, Crevecoeur M, Dat J F. 2008. An overview of plant responses to soil waterlogging. Plant Stress, 2, 20-27. Pezeshki S R. 2001. Wetland plant responses to soil flooding. Environmental and Experimental Botany, 46, 299-312. Schmugge T. 1998. Applications of passive microwave observations of surface soil moisture. Journal of Hydrology, 212, 188-197. Scott C A, Bastiaanssen W G M, Ahmad M. 2003. Mapping root zone soil moisture using remotely sensed optical imagery. Journal of Irrigation and Drainage Engineering, 129, 326-335. Singh A. 2012. Development and application of a watertable model for the assessment of waterlogging in irrigated semiarid regions. Water Resources Management, 26, 4435-4448. Wan Z, Zhang Y, Zhang Q, Li Z L. 2004. Quality assessment and validation of the MODIS global land surface temperature. International Journal of Remote Sensing, 25, 261-274. Xiang W, Li W, Tong C. 2000. Current status of waterlogging and soil gleyization of farmland in Jianghan Plain and the strategy for its management. Soil and Environmental Sciences, 9, 214-217. (in Chinese) Yu G. 1992. Mechanism of waterlogging damage in Jianghan Plain. Geographical Research, 12, 37-44. (in Chinese) Yu G. 1993. Study on the ecological characteristics of waterlogging field in Jianghan Plain. Acta Ecologica Sinica, 13, 252-260. (in Chinese) (Managing editor SUN Lu-juan)

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