Evaluation of temporal variations in soil moisture based on the microwave polarization difference index using in situ data over agricultural areas in China

Soil moisture plays a critical role in the energy exchange and water redistribution of the land-atmosphere system. Knowledge of the temporal variations in soil moisture is vital in agricultural applications. Microwave indices are often used to characterize the temporal variations in soil moisture. In this study, we evaluate the temporal variations in soil moisture based on the microwave polarization difference index (MPDI) using ground-based measurements in China. In situ soil moisture at six test sites during the crop-growing season from 2009 to 2011 is obtained. The consistency of the temporal variations between the MPDI values and the in situ soil moisture is analysed in terms of (1) microwave frequencies, (2) satellite overpass times, and (3) measurement depths of soil moisture. The results show that the accuracies of the consistency vary from approximately 40% to 90%. Compared with the in situ soil moisture at 0–10 cm, the temporal variations in soil moisture are best characterized by the 6.9 GHz MPDI values from the ascending overpasses (MPDI_06A). Furthermore, the accuracies of the consistency between MPDI_06A and the in situ soil moisture at 0–10 cm are greater than those between MPDI_06A and the in situ soil moisture at 10–20 cm.     

Validation of a practical normalized soil moisture model with in situ measurements in humid and semi-arid regions

Soil moisture is an important parameter that influences the exchange of water and energy fluxes between the land surface and the atmosphere. Through the simulation by a Soil–Vegetation–Atmosphere Transfer model, Carlson proposed the universal spatial information-based method to determine soil moisture that is insensitive to the initial atmospheric and surface conditions, net radiation, and atmospheric correction. In this study, a practical normalized soil moisture model is established to describe the relationship among the normalized soil moisture (M), the normalized land surface temperature (T*), and the fractional vegetation cover. The dry and wet points are determined using the surface energy balance principle, which has a robust physical basis. This method is applied to retrieve soil moisture for the Soil Moisture-Atmosphere Coupling Experiment campaign in the Walnut Creek watershed, which has a humid climate, and at the Linzestation, which has a semi-arid climate. The validation data are obtained on days of year (DOYs) 182 and 189 in 2002 in the humid region and on DOYs 148 and 180 in 2008 for the semi-arid region; these data collection days are coincident with the overpass of the Landsat Thematic Mapper/Enhanced Thematic Mapper Plus. When the estimates are compared with the in situ measurements of soil water content, the root mean square error is approximately 0.10 m³ m^?3 with a bias of 0.05 m³ m^?3 for the humid region and 0.08 m³ m^?3 with a bias of 0.03 m³ m^?3 for the semi-arid region. These results demonstrate that the practical normalized soil moisture model is applicable in both humid and semi-arid regions.     

Study of vegetation spectral anomaly behaviour in a porphyry copper mine area based on hyperspectral indices

ABSTRACT

Hyperspectral remote sensing is economical and fast, and it can reveal detailed spectral information of plants. Hence, hyperspectral data are used in this study to analyse the spectral anomaly behaviours of vegetation in porphyry copper mine areas. This analytical method is used to compare the leaf spectra and relative differences among the vegetation indices; then, the correlation coefficients were computed between the soil copper content and vegetation index of Quercus spinosa leaves at both the leaf scale and the canopy scale in the Chundu mine area with different geological backgrounds. Lastly, this study adopts hyperspectral data for the level slicing of vegetation anomalies in the Chundu mine area. The results showed that leaf spectra in the orebody and background area differed greatly, especially in the infrared band (750 nm – 1300 nm); moreover, some indices like the normalized water index (NWI) and normalized difference water index (NDWI) of Quercus spinosa and Lamellosa leaves are sensitive to changes in the geological background. Compared with the canopy, the leaf hyperspectral indices of Quercus spinosa in Chundu can better reflect soil cuprum (Cu) anomaly. In addition, the NWI and NDWI of Quercus spinosa are significantly correlated with the soil Cu content at both the canopy scale and the leaf scale. Consequently, the results of the vegetation anomaly level slicing can adequately reflect the plant anomalies from ore bodies and nearby areas, thereby providing a new ore-finding method for areas with a high degree of vegetation coverage.     

Predicting winter wheat condition,grain yield and protein content using multi‐temporal EnviSat‐ASAR and Landsat TM satellite images

Since optical and microwave sensors respond to very different target characteristics, their role in crop monitoring can be viewed as complementary. In particular, the all‐weather capability of Synthetic Aperture Radar (SAR) sensors can ensure that data gaps that often exist during monitoring with optical sensors are filled. There were three Landsat Thematic Mapper (TM) satellite images and three Envisat Advanced Synthetic Aperture Radar (ASAR) satellite images acquired from reviving stage to milking stage of winter wheat. These data were successfully used to monitor crop condition and forecast grain yield and protein content. Results from this study indicated that both multi‐temporal Envisat ASAR and Landsat TM imagery could provide accurate information about crop conditions. First, bivariate correlation results based on the linear regression of crop variables against backscatter suggested that the sensitivity of ASAR C‐HH backscatter image to crop or soil condition variation depends on growth stage and time of image acquisition. At the reviving stage, crop variables, such as biomass, Leaf Area Index (LAI) and plant water content (PWC), were significantly positively correlated with C‐HH backscatter (r = 0.65, 0.67 and 0.70, respectively), and soil water content at 5 cm, 10 cm and 20 cm depths were correlated significantly with C‐VV backscatter (r = 0.44, 0.49 and 0.46, respectively). At booting stage, only a significant and negative correlation was observed between biomass and C‐HH backscatter (r = ?0.44), and a saturation of the SAR signal to canopy LAI could explain the poor correlation between crop variables and C‐HH backscatter. Furthermore, C‐HH backscatter was correlated significantly with soil water content at booting and milking stage. Compared with ASAR backscatter data, the multi‐spectral Landsat TM images were more sensitive to crop variables. Secondly, a significant and negative correlation between grain yield and ASAR C‐HH & C‐VV backscatter at winter wheat booting stage was observed (r = ?0.73 and ?0.55, respectively) and a yield prediction model with a correlation coefficient of 0.91 was built based on the Normalized Difference Water Index (NDWI) data from Landsat TM on 17 April and ASAR C‐HH backscatter on 27 April. Finally, grain protein content was found to be correlated significantly with ASAR C‐HH backscatter at milking stage (r = ?0.61) and with Structure Insensitive Pigment Index (SIPI) data from Landsat TM at grain‐filling stage (r = 0.53), and a grain protein content prediction model with a correlation coefficient of 0.75 was built based on the C‐HH backscatter and SIPI data.     

Observation of flooding and rice transplanting of paddy rice fields at the site to landscape scales in China using VEGETATION sensor data

A unique physical feature of paddy rice fields is that rice is grown on flooded soil. During the period of flooding and rice transplanting, there is a large proportion of surface water in a land surface consisting of water, vegetation and soils. The VEGETATION (VGT) sensor has four spectral bands that are equivalent to spectral bands of Landsat TM, and its mid-infrared spectral band is very sensitive to soil moisture and plant canopy water content. In this study we evaluated a VGT-derived normalized difference water index (NDWI VGT =(B3-MIR)/ (B3+MIR)) for describing temporal and spatial dynamics of surface moisture. Twenty-seven 10-day composites (VGT- S10) from 1 March to 30 November 1999 were acquired and analysed for a study area (175 km by 165 km) in eastern Jiangsu Province, China, where a winter wheat and paddy rice double cropping system dominates the landscape. We compared the temporal dynamics and spatial patterns of normalized difference vegetation index (NDVI VGT ) and NDWI VGT . The NDWI VGT temporal dynamics were sensitive enough to capture the substantial increases of surface water due to flooding and rice transplanting at paddy rice fields. A land use thematic map for the timing and location of flooding and rice transplanting was generated for the study area. Our results indicate that NDWI and NDVI temporal anomalies may provide a simple and effective tool for detection of flooding and rice transplanting across the landscape.     

Crop yield prediction from remotely sensed vegetation indices and primary productivity in arid and semi-arid lands

Global demands for biomass and arable lands are expected to double in the next 35 years. Scarcity of water resources in arid and semi-arid areas poses a serious threat to their agricultural productivity and hence their food security. In this study, we examine whether crop yields can be predicted from remotely sensed vegetation indices and remotely sensed estimates of primary productivity. Spatial relationships between remotely sensed enhanced vegetation index (EVI), net photosynthesis (P_Net), and gross and net primary production (GPP and NPP, respectively) in irrigated semi-arid and arid agro-ecosystems since the beginning of the century are analysed. The conflict-affected country of Syria is selected as the case study. Relationships between EVI and crop yield are investigated in an effort to enhance food production estimates in affected areas outside governmental jurisdictions. Estimates of NPP derived from reported irrigated agriculture crop data in a semi-arid and an arid zone are compared to remotely sensed NPP in a geospatial environment. Results show that winter crop yields are correlated with spring GPP in semi-arid zones of the study area (R² = 0.85). Summer crop yield can be predicted from either cumulative summer EVI (R² = 0.77) or P_Net in most zones. Where fully irrigated fields are surrounded by hyper-arid landscape, summer P_Net was negative in all instances and EVI was inversely correlated with yield. NPP from crops was much higher (290 gC m^?2 year^?1) in those regions than MOD17 NPP (70 gC m^–2), where 1.0 g of carbon is equivalent to 2.2 g of oven-dry organic matter (= 45% carbon by weight). The gap was less in semi-arid zones (2–39% difference). Overall crop-derived NPP for the period 2000–2013 was 322 versus 300 gC m^–2 for that remotely sensed within the cropped zones of the political units. The results of this study are crucial to derive accurate estimates of irrigated agriculture productivity and to study the effect of the latter on net ecosystem carbon storage.     

Assessing corn water stress using spectral reflectance

Multiple remote-sensing techniques have been developed to identify crop-water stress; however, some methods may be difficult for farmers to apply. If spectral reflectance data can be used to monitor crop-water stress, growers could use this information as a quick low-cost guideline for irrigation management, thus helping save water by preventing over-irrigating and achieving desired crop yields. Data was collected in the 2013 growing season near Greeley, Colorado, where drip irrigation was used to irrigate 12 corn (Zea mays L.) treatments with varying water-deficit levels. Ground-based multispectral data were collected and three different vegetation indices were evaluated. These included the normalized difference vegetation index (NDVI), the optimized soil-adjusted vegetation index (OSAVI), and the Green normalized difference vegetation index (GNDVI). The three vegetation indices were compared to water stress as indicated by the stress coefficient (K_s), and water deficit in the root zone was calculated using a soil water balance. To compare the indices to K_s, vegetation ratios were developed from vegetation indices in the process of normalization. Vegetation ratios are defined as the non-stressed vegetation index divided by the stressed vegetation index. Results showed that vegetation ratios were sensitive to water stress as indicated by the good coefficient of determination (R² > 0.46) values and low root mean square error (RMSE < 0.076) values when compared to K_s. To use spectral reflectance to manage crop-water stress, an example irrigation trigger point of 0.93 for the vegetation ratios was determined for a 10–12% loss in yield. These results were validated using data collected from a different field. The performance of the vegetation ratio approach was better than when applied to the main field giving higher goodness of fit values (R² > 0.63), and lower error values (RMSE < 0.043) between K_s and the vegetation indices.     

MODIS‐derived visible atmospherically resistant index for monitoring chaparral moisture content

Time series of normalized difference indices (NDIs) derived from MODIS surface reflectance data provide potentially useful information for monitoring fuel moisture content (FMC) for fire risk assessment. The visible atmospherically resistant index (VARI) and normalized difference water index (NDWI) were compared for monitoring live FMC of chaparral shrublands. Regression coefficients are encouraging given disparate spatial resolutions of ground‐based FMC measurements and MODIS‐derived NDIs. VARI exhibited greater temporal co‐variability (0.79>r ²<0.94) and spatial robustness with FMC than NDWI, even though the former is based solely on visible waveband reflectance data.     

Effect of leaf and spike morphological traits on the relationship between spectral reflectance indices and yield in wheat

While certain spectral reflectance indices have been shown to be sensitive to the expression of a range of performance-related traits in crops, knowledge of the potentially confounding effects associated with plant anatomy could help improve their application in phenotyping. Morphological traits (leaf and spike wax content, leaf and spike orientation, and awns on spikes) were studied in 20 contrasting advanced wheat lines to determine their influence on spectral indices and in their association with grain yield under well-irrigated conditions. Canopy reflectance (400–1100 nm) was determined at heading and grain filling during two growing seasons and three vegetation indices (VIs; red normalized difference vegetation index (RNDVI), green normalized difference vegetation index (GNDVI), and simple ratio (SR)), and five water indices (WIs; one simple WI and four normalized WIs (NWI-1, NWI-2, NWI-3, and NWI-4)) were calculated. The major reflectance fluctuations caused by the differences in leaf and spike morphology mainly occurred in the infrared region (700–1100 nm) and little variation in the visible region (400–700 nm). The NWI-3 ((R₉₇₀ – R₈₈₀)/(R₉₇₀ + R₈₈₀)) consistently showed a stronger association with yield than the RNDVI by using uncorrected canopy reflectance (original raw data) and data adjusted by scattering and smoothing. When canopy reflectance was corrected by a scattering method, the NWI-3 and a modified RNDVI with 958 nm showed the strongest correlations with grain yield by grouping lines for waxy leaves and spikes, curved leaves, and erect and awnless spikes. The results showed that the relationship between the spectral indices and grain yield can be improved (higher correlations) by correcting canopy reflectance for confounding effects associated with differences in leaf and spike morphology.     

The effect of soil salinity on the use of the universal triangle method to estimate saline soil moisture from Landsat data: application to the SMAPEx-2 and SMAPEx-3 campaigns

In this article, a method based on UTM called salinity-based soil moisture content (S_SMC) is developed. Since the soil moisture depends on the soil salinity (SS) in semi-arid regions, the S_SMC method employs the SS as an effective and augmented variable in conventional UTM to estimate SMC in these areas. In calibration step, initially, a linear regression model between the land surface temperature (LST), the normalized difference vegetation index (NDVI), and the SS is applied using in situ measurements to assess the influence of the SS in SMC estimation. Then, a non-linearity model is conducted through insertion of more terms in the linear equation and an optimal model of S_SMC is yielded. Moreover, the SS is obtained using a linear model from two selected salinity indices derived from Landsat images and in situ measurements. In estimation step, the LST, NDVI, and the SS are obtained using Landsat data. The S_SMC method is evaluated in the Soil Moisture Active Passive Experiment (SMAPEx)-2 and SMAPEx-3 campaigns in wet and dry conditions, respectively, over two scenes of Landsat images. The results demonstrated that the S_SMC method is appropriate in non-irrigated areas. In these areas, the S_SMC method improves R² (coefficient of determination) from 22% to 65% in SMAPEx-2 and from 24% to 50% in SMAPEx-3. Moreover, the results have shown that the SMC can be estimated at satellite level with a root mean square error of 0.06 and 0.02 (m³ m^?3) in wet and dry condition, respectively. Therefore, the SS is a key parameter to adjust conventional UTM to improve the SMC estimation by the S_SMC method.     

Relationships between vegetation indices and root zone soil moisture under maize and soybean canopies in the US Corn Belt: a comparative study using a close-range sensing approach

Understanding the relationships between root zone soil moisture and vegetation spectral signals will enhance our ability to manage water resources and monitor drought-related stress in vegetation. In this article, the relationships between vegetation indices (VIs) and in situ soil moisture under maize and soybean canopies were analysed using close-range reflectance data acquired at a rainfed cropland site in the US Corn Belt. Because of the deep rooting depths of maize plants, maize-based VIs exhibited significant correlations with soil moisture at a depth of 100 cm (P < 0.01) and kept soil moisture memory for a long period of time (45 days). Among the VIs applied to maize, the chrolophyll red-edge index (CI_red-edge) correlated best with the concurrent soil moisture at 100 cm depth (P < 0.01) for up to 20 day lag periods. The same index showed a significant correlation with soil moisture at a 50 cm depth for lag periods from 10 (P < 0.05) to 60 days (P < 0.01). VIs applied to soybean resulted in statistically significant correlations with soil moisture at the shallower 10 and 25 cm depths, and the correlation coefficients declined with increasing depths. As opposed to maize, soybean held a shorter soil moisture memory as the correlations for all VIs versus soil moisture at 10 cm depth were strongest for the 5 day lag period. Wide dynamic range VI and normalized difference VI performed better in characterizing soil moisture at the 10 and 25 cm depths under soybean canopies when compared with enhanced VI and CI_red-edge.     

Use of Normalized Difference Water Index for monitoring live fuel moisture

Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) were compared for monitoring live fuel moisture in a shrubland ecosystem. Both indices were calculated from 500?m spatial resolution Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance data covering a 33‐month period from 2000 to 2002. Both NDVI and NDWI were positively correlated with live fuel moisture measured by the Los Angeles County Fire Department (LACFD). NDVI had R ² values ranging between 0.25 to 0.60, while NDWI had significantly higher R ² values, varying between 0.39 and 0.80. Water absorption measures, such as NDWI, may prove more appropriate for monitoring live fuel moisture than measures of chlorophyll absorption such as NDVI.     

Remote sensing used to detect moisture status of pecan orchards grown in a desert environment

Remote-sensing techniques can detect and up-scale leaf-level physiological responses to large areas, and provide significant and reliable information on water use and irrigation management. The objectives of this study were to screen leaf-level physiological changes that occur during the cyclic irrigation of pecan orchards to determine which responses best represent changes in moisture status of plants and link plant physiological changes to remotely sensed surface reflectance data derived from the Landsat Thematic Mapper and Enhanced Thematic Mapper Plus (ETM+). The study was conducted simultaneously on two southern New Mexico mature pecan orchards. For both orchards, plant physiological responses and remotely sensed surface reflectance data were collected from trees that were either well watered or in water deficit. Remotely sensed variables included reflectance in band 1, the ratio between shortwave infrared (SWIR) bands (B5:B7), the normalized difference vegetation index, and SWIR moisture indices. Midday stem water potential (Ψ_smd) was the best performing leaf-level physiological response variable for detecting moisture status in pecans. The B5:B7 ratio positively and significantly correlated with Ψ_smd in five of six irrigation cycles while multiple linear regression weighted with six remotely sensed surface reflectance variables revealed a significant relationship with moisture status in all cycles in both orchards (R² > 0.73). Because changes in the B5:B7 band ratio and multiple regression of spectral variables correlate with the moisture status of pecan orchards, we conclude that remotely sensed data hold promise for detecting the moisture status of pecans.     

A new methodology to map irrigated areas using multi-temporal MODIS and ancillary data: An application example in the continental US

We present a dryland irrigation mapping methodology that relies on remotely sensed inputs from the MODerate Resolution Imaging Spectroradiometer (MODIS) instrument, globally extensive ancillary sources of gridded climate and agricultural data and on an advanced image classification algorithm. The methodology involves four steps. First, we use climate-based indices of surface moisture status and a map of cultivated areas to generate a potential irrigation index. Next, we identify remotely-sensed temporal and spectral signatures that are associated with presence of irrigation defined as full or partial artificial application of water to agricultural areas under dryland conditions excluding irrigated pastures, paddy rice fields, and other semiaquatic crops. Here, the temporal indices are based on the difference in annual evolution of greenness between irrigated and non-irrigated crops, while spectral indices are based on the reflectance in the green and are sensitive to vegetation chlorophyll content associated with moisture stress. Third, we combine the climate-based potential irrigation index, remotely sensed indices, and learning samples within a decision tree supervised classification tool to make a binary irrigated/non-irrigated map. Finally, we apply a tree-based regression algorithm to derive the fraction of irrigated area within each pixel that has been identified as irrigated. Application of the proposed procedure over the continental US in the year 2001 produces an objective and comprehensive map that exhibits expected patterns: there is a strong east-west divide where the majority of irrigated areas is concentrated in the arid west along dry lowland valleys. Qualitative assessment of the map across different climatic and agricultural zones reveals a high quality product with sufficient detail when compared to existing large area irrigation databases. Accuracy assessment indicates that the map is highly accurate in the western US but less accurate in the east. Comparison of area estimates made with the new procedure to those reported at the state and county levels shows a strong correlation with a small bias and an estimated RMSE of 2500 km², or little over 2% of the total irrigated area in the US. As a result, the future application of the new procedure at a global scale is promising but may require a better potential irrigation index, as well as the use of remotely sensed skin temperature measurements.     

Upscaling ground observations of vegetation water content, canopy height, and leaf area index during SMEX02 using aircraft and Landsat imagery

Microwave-based remote sensing algorithms for mapping soil moisture are sensitive to water contained in surface vegetation at moderate levels of canopy cover. Correction schemes require spatially distributed estimates of vegetation water content at scales comparable to that of the microwave sensor footprint (10¹ to 10⁴ m). This study compares the relative utility of high-resolution (1.5 m) aircraft and coarser-resolution (30 m) Landsat imagery in upscaling an extensive set of ground-based measurements of canopy biophysical properties collected during the Soil Moisture Experiment of 2002 (SMEX02) within the Walnut Creek Watershed. The upscaling was accomplished using expolinear relationships developed between spectral vegetation indices and measurements of leaf area index, canopy height, and vegetation water content. Of the various indices examined, a Normalized Difference Water Index (NDWI), derived from near- and shortwave-infrared reflectances, was found to be least susceptible to saturation at high levels of leaf area index. With the aircraft data set, which did not include a short-wave infrared water absorption band, the Optimized Soil Adjusted Vegetation Index (OSAVI) yielded best correlations with observations and highest saturation levels. At the observation scale (10 m), LAI was retrieved from both NDWI and OSAVI imagery with an accuracy of 0.6, vegetation water content at 0.7 kg m⁻², and canopy height to within 0.2 m. Both indices were used to estimate field-scale mean canopy properties and variability for each of the intensive soil-moisture-sampling sites within the watershed study area. Results regarding scale invariance over the SMEX02 study area in transformations from band reflectance and vegetation indices to canopy biophysical properties are also presented.     

Identifying potential areas of Cannabis sativa plantations using object-based image analysis of SPOT-5 satellite data

The rapid and efficient detection of illicit drug cultivation, such as that of Cannabis sativa, is important in reducing consumption. The objective of this study was to identify potential sites of illicit C. sativa plantations located in the semi-arid, southern part of Pernambuco State, Brazil. The study was conducted using an object-based image analysis (OBIA) of Système Pour l'Observation de la Terre high-resolution geometric (SPOT-5 HRG) images (overpass: 31 May, 2007). OBIA considers the target's contextual and geometrical attributes to overcome the difficulties inherent in detecting illicit crops associated with the grower's strategies to conceal their fields and optimizes the spectral information extracted to generate land-cover maps. The capabilities of the SPOT-5 near-infrared and shortwave infrared bands to discriminate herbaceous vegetation with high water content, and employment of the support vector machine classifier, contributed to accomplishing this task. Image classification included multiresolution segmentation with an algorithm available in the eCognition Developer software package. In addition to a SPOT-5 HRG multispectral image with 10 m spatial resolution and a panchromatic image with 2.5 m spatial resolution, first-order indices such as the normalized difference vegetation index and ancillary data including land-cover classes, anthropogenic areas, slope, and distance to water sources were also employed in the OBIA. The classification of segments (objects) related to illegal cultivation employed fuzzy logic and fixed-threshold membership functions to describe the following spectral, geometrical, and contextual properties of targets: vegetation density, topography, neighbourhood, and presence of water supplies for irrigation. The results of OBIA were verified from a weight of evidence analysis. Among 15 previously known C. sativa sites identified during police operations conducted on 5–17 June 2007, eight sites were classified as maximum-alert areas (total area of 22.54 km² within a total area of object-oriented image classification of ～1800 km²). The approach proposed in this study is feasible for reducing the area to be searched for illicit cannabis cultivation in semi-arid regions.     

An appraisal of the accuracy of operational soil moisture estimates from SMOS MIRAS using validated in situ observations acquired in a Mediterranean environment

Acquiring information on the spatio-temporal variability of soil moisture is of key importance in extending our capability to understand the Earth system’s physical processes, and is also required in many practical applications. Earth observation (EO) provides a promising avenue to observe the distribution of soil moisture at different observational scales, with a number of products distributed at present operationally. Validation of such products at a range of climate and environmental conditions across continents is a fundamental step related to their practical use. Various in situ soil moisture ground observational networks have been established globally providing suitable data for evaluating the accuracy of EO-based soil moisture products. This study aimed at evaluating the accuracy of soil moisture estimates provided from the Soil Moisture and Ocean Salinity Mission (SMOS) global operational product at test sites from the REMEDHUS International Soil Moisture Network (ISMN) in Spain. For this purpose, validated observations from in situ ground observations acquired nearly concurrent to SMOS overpass were utilized. Overall, results showed a generally reasonable agreement between the SMOS product and the in situ soil moisture measurements in the 0–5 cm soil moisture layer (root mean square error (RMSE) = 0.116 m³ m^?3). An improvement in product accuracy for the overall comparison was shown when days of high radio frequency interference were filtered out (RMSE = 0.110 m³ m^?3). Seasonal analysis showed highest agreement during autumn, followed by summer, winter, and spring seasons. A systematic soil moisture underestimation was also found for the overall comparison and during the four seasons. Overall, the result provides supportive evidence of the potential value of this operational product for meso-scale studies and practical applications.     

Assessing the capability of broadband indices derived from Landsat 8 Operational Land Imager to monitor above ground biomass and salinity in semiarid saline environments of the Bahía Blanca Estuary,Argentina

In arid and semi-arid ecosystems, salinisation and desertification are the most common processes of land degradation, and satellite data may provide a valuable tool to assess land surface condition and vegetation status. The aim of this study was to evaluate the capability of Landsat 8 OLI (Operational Land Imager) remote sensing information and broadband indices derived from it, to monitor above ground biomass (AGB) and salinity in two different semiarid saline environments (unit a and unit b) in the Bahía Blanca Estuary. Unit a (Ua) is composed of bushes of Cyclolepis genistoides in association with Atriplex undulata and 41% of bare soil. Unit b (Ub) is composed of dense thickets of Allenrolfea patagonica in association with C. genistoides and 34% of bare soil. Pearson’s correlation analyses were performed between field estimates of AGB and salinity (soil salinity and interstitial water salinity) and remote sensing estimates. Satellite data include surface reflectance of individual bands, vegetation indices (NDVI [normalised difference vegetation index], SAVI [soil-adjusted vegetation index], MSAVI2 [modified soil-adjusted vegetation index], NDII [normalised difference infrared index], GNDVI [green normalised difference vegetation index], GRNDI [green-red normalised difference index], OSAVI [optimised soil-adjusted vegetation index], SR [simple ratio]), and salinity indices (SI1, SI2, SI3 [salinity index 1, 2 and 3, respectively] and BI [brightness index]). Correlation analyses involving AGB were performed twice; first considering all months and then again excluding the months with higher soil salinities. In Ua, soil adjusted vegetation indices SAVI and MSAVI2 showed to be suitable to detect changes in the total green AGB and C. genistoides green AGB (the major contributor to total green AGB). After excluding data from December and January (the months with the highest soil salinity), green AGB of A. undulata also showed a significant positive correlation with soil adjusted indices SAVI, MSAVI2 and OSAVI. Although proportionally this species was not a large contributor to the total biomass, it is characterised by a high leaf reflectance, which makes it suitable for biomass retrieval. In Ub, significant positive correlations were obtained between NDVI, SAVI, NDII, OSAVI and SR indices and the AGB green ratio, but significant negative correlations were obtained between A. patagonica red AGB and these vegetation indices. When December and January were excluded from the analysis the negative correlations between vegetation indices NDVI, OSAVI and SR and red AGB remained significant (r = ?0.68, ?0.76 and ?0.7, respectively). The positive correlations between these indices and AGB green ratio (r = 0.73, 0.78 and 0.75, respectively) remained significant as well. Significant negative correlations were also found between NDVI, NDII, GNDVI, OSAVI and SR indices and field salinity estimates. As soil salinisation induces A. patagonica reddening, red AGB and soil salinity covariate in the field, and the negative correlation with vegetation indices may be useful to retrieve information on both variables combined, which are indicative of water stress. Correlation analysis between field estimates of salinity and spectral salinity indices showed significant positive correlation for all the tested indices. The obtained results highlight the importance of a thoughtful selection of remote sensing indices to account for changes in vegetation biomass, especially in arid and semiarid environments particularly sensitive to desertification and salinisation. Also, ground truth cannot be overlooked, and field work is necessary to test index performance in every case.     

A modified linear extrapolation approach towards red edge position detection and stress monitoring of wheat crop using hyperspectral data

The inflection point of spectral reflectance of crop in the red edge region (680–780 nm) is termed as the red edge position (REP), which is sensitive to crop biochemical and biophysical parameters. We propose a technique for automatic detection of four dynamic wavebands, i.e. two in the far-red and two in the near-infrared (NIR) region from hyperspectral data, for REP estimation using the linear extrapolation method. A field experiment was conducted at the SHIATS Farm, Allahabad, India, with four levels of nitrogen and irrigation treatments to assess the sensitivity of REP towards crop stress. A correlation analysis was carried out between REPs and different biophysical parameters, such as leaf area index (LAI) and chlorophyll content index (CCI), recorded in each plot at 50, 70, and 90 days after sowing of wheat crop under the field experiment. The inter-comparison among different REP extraction techniques revealed that the proposed technique, i.e. the modified linear extrapolation (MLE) method, has a better ability to distinguish different crop stress conditions. REPs extracted using the MLE technique showed high correlations with a wide range of LAI, CCI, and LAI × CCI, being comparable with results obtained using the traditional linear extrapolation and polynomial fitting techniques. The behaviour of the new techniques was found to be stable at both narrower and broader bandwidth, i.e. 2 and 10 nm. A new red-edge-based index, i.e. area under REP (A_REP), was used to detect the cumulative stress over wheat crop by utilizing the REP and its rate of change information at different crop growth stages. A high coefficient of determination (R² = 0.89) was found between A_REP and dry grain yield (Q ha^?1) up to 50 Q ha^?1 of wheat crop, whereas, beyond this range the relationship was found to be diminishing.     

Mapping the effects of water stress on Sphagnum: Preliminary observations using airborne remote sensing

Remote sensing of near-surface hydrological conditions within northern peatlands has the potential to provide important large-scale hydrological information regarding ecological and carbon-balance processes occurring within such systems. This article details how field knowledge of the spectral properties of Sphagnum spp., airborne remote sensing data and a range of image analysis approaches, may be combined to provide a suitable proxy for near-surface wetness. Co-incident field and airborne remote sensing data were acquired in May and September 2002 over an important UK raised bog (Cors Fochno). A combination of laboratory-tested NIR and SWIR water-based and biophysical spectral reflectance indices were applied to field and airborne reflectance spectra of Sphagnum pulchrum to elucidate changes in near-surface moisture conditions. Field results showed significant correlations between water-based indices (moisture stress index (MSI) and floating water band indices (fWBI₉₈₀ and fWBI₁₂₀₀₎)) and measures of both near-surface volumetric moisture content (VMC) and water-table position. Spectral indices formulated from the NIR (fWBI₉₈₀ and fWBI₁₂₀₀) proved to be the most useful for indicating near-surface wetness across the widest range of moisture conditions because of their ability to penetrate deeper into the Sphagnum canopy. Correlations between a biophysical index based upon chlorophyll content and both hydrological measures were not significant, possibly due to relatively high levels of surface wetness at the field site in both May and September. S. pulchrum lawns were successfully located and mapped from airborne imagery using the mixed tuned match filtering (MTMF) algorithm. Importantly, MSI derived from airborne data was significantly correlated with both field moisture and the water-table position. Relationships between measures of near-surface wetness and the MSI for naturally heterogeneous canopies were, however, found to be weaker for airborne imagery than for associated field data. This is likely to be a result of the formulation of the MSI itself and the possible preferential detection of “wetter” pixels within the imagery. This effectively reduced the ability of MSI to detect subtle changes in near-surface wetness under high moisture conditions, but would not impede the use of the index under drier conditions. Results from the field data suggest that indices formulated from the NIR may be more suitable for detailed estimations of near-surface and surface wetness at the landscape-scale although reliable hyperspectral data are required to test fully the performance of such indices. The relative merits of using such an approach to determine near-surface hydrological conditions across entire peatland complexes are also discussed.