Shortwave Infrared Water Stress Index Research Articles

Surface Soil Moisture Inversion and Distribution Based on Spatio-Temporal Fusion of MODIS and Landsat

Soil moisture plays an important role in hydrology, climate, agriculture, and ecology, and remote sensing is one of the most important tools for estimating the soil moisture over large areas. Soil moisture, which is calculated by remote sensing inversion, is affected by the uneven distribution of vegetation and therefore the results cannot accurately reflect the spatial distribution of the soil moisture in the study area. This study analyzes the soil moisture of different vegetation covers in the Wushen Banner of Inner Mongolia, recorded in 2016, and using Landsat and MODIS images fused with multispectral bands. Firstly, we compared and analyzed the ability of the visible optical and short-wave infrared drought index (VSDI), the normalized differential infrared index (NDII), and the short-wave infrared water stress index (SIWSI) in monitoring the soil moisture in different vegetation cover soils. Secondly, we used the stepwise multiple regression analysis method in order to correlate the multispectral fusion bands with the field-measured soil water content and established a soil moisture inversion model based on the multispectral fusion bands. As the results show, there was a strong correlation between the established model and the measured soil water content of the different vegetation cover soils: in the bare soil, R2 was 0.86; in the partially vegetated cover soil, R2 was 0.84; and in the highly vegetated cover soil, R2 was 0.87. This shows that the established model could better reflect the actual condition of the surface soil moisture in the different vegetation covers.

Mapping Forest Stability within Major Biomes Using Canopy Indices Derived from MODIS Time Series

Deforestation and forest degradation from human land use, including primary forest loss, are of growing concern. The conservation of old-growth and other forests with important environmental values is central to many international initiatives aimed at protecting biodiversity, mitigating climate change impacts, and supporting sustainable livelihoods. Current remote-sensing products largely focus on deforestation rather than forest degradation and are dependent on machine learning, calibrated with extensive field measurements. To help address this, we developed a novel approach for mapping forest ecosystem stability, defined in terms of constancy, which is a key characteristic of long-undisturbed (including primary) forests. Our approach categorizes forests into stability classes based on satellite-data time series related to plant water–carbon relationships. Specifically, we used long-term dynamics of the fraction of photosynthetically active radiation intercepted by the canopy (fPAR) and shortwave infrared water stress index (SIWSI) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) for the period 2003–2018. We calculated a set of variables from annual time series of fPAR and SIWSI for representative forest regions at opposite ends of Earth’s climatic and latitudinal gradients: boreal forests of Siberia (southern taiga, Russia) and tropical rainforests of the Amazon basin (Kayapó territory, Brazil). Independent validation drew upon high-resolution Landsat imagery and forest cover change data. The results indicate that the proposed approach is accurate and applicable across forest biomes and, thereby, provides a timely and transferrable method to aid in the identification and conservation of stable forests. Information on the location of less stable forests is equally relevant for ecological restoration, reforestation, and proforestation activities.

Oil palm dry season analysis based on moderate-resolution imaging spectroradiometer (MODIS) satellite indices

ABSTRACTOil palm is the most important crop in Malaysia. It plays a key role in maintaining carbon balance and it possesses high economic value. However, oil palm productivity is really sensitive to environmental changes especially during the long-term dry season. This has warranted studies on frequent observation of dry season for the plantation of oil palm. In this study, Moderate-Resolution Imaging Spectroradiometer (MODIS) surface reflectance data were utilized to generate an index for the crop assessment at Felcra Mendom Oil Plantation area, Negeri Sembilan. The indices of Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and shortwave indices, namely Shortwave Infrared Water Stress Index (SWISI), at three different channels of 2, 5, and 6 were utilized. The main aim is to identify drought year based on MODIS-SPI-based index calculated and evaluated indices developed from MODIS images. The precipitation data retrieved from Malaysia Meteorology Department starting from year 1997 until 2017 and later are classified using Standardised Precipitation Index (SPI). Then by using the data from precipitation and SPI, the MODIS images were extracted from Land Process Distributed Active Archive Center (LP DAAC). Next, the images were processed and analysed by using ENVI Version 5.3 software to extract the indices used. The SPI-3 and SPI-1 were classified year 2015 as extreme drought with ‘extreme’ and ‘mild’, respectively. Meanwhile, the vegetation indices were calculated and average showed both NDVI and EVI approaching +1.00 value at the highest NDVI is 0.79 during 2017 and the highest EVI is 0.62 during years 2005 and 2013, respectively. Both SIWSI showed similar pattern, indicating high-moisture content as the index approaching −1.00. The result does not represent the overall condition of the specific study area, but the SPI is reliable to display the whole area and the utilization of MODIS and SPI can be used in further study.

Integrating Early Growth Information to Monitor Winter Wheat Powdery Mildew Using Multi-Temporal Landsat-8 Imagery

Powdery mildew is one of the dominant diseases in winter wheat. The accurate monitoring of powdery mildew is important for crop management and production. Satellite-based remote sensing monitoring has been proven as an efficient tool for regional disease detection and monitoring. However, the information provided by single-date satellite scene is hard to achieve acceptable accuracy for powdery mildew disease, and incorporation of early period contextual information of winter wheat can improve this situation. In this study, a multi-temporal satellite data based powdery mildew detecting approach had been developed for regional disease mapping. Firstly, the Lansat-8 scenes that covered six winter wheat growth periods (expressed in chronological order as periods 1 to 6) were collected to calculate typical vegetation indices (VIs), which include disease water stress index (DSWI), optimized soil adjusted vegetation index (OSAVI), shortwave infrared water stress index (SIWSI), and triangular vegetation index (TVI). A multi-temporal VIs-based k-nearest neighbors (KNN) approach was then developed to produce the regional disease distribution. Meanwhile, a backward stepwise elimination method was used to confirm the optimal multi-temporal combination for KNN monitoring model. A classification and regression tree (CART) and back propagation neural networks (BPNN) approaches were used for comparison and validation of initial results. VIs of all periods except 1 and 3 provided the best multi-temporal data set for winter wheat powdery mildew monitoring. Compared with the traditional single-date (period 6) image, the multi-temporal images based KNN approach provided more disease information during the disease development, and had an accuracy of 84.6%. Meanwhile, the accuracy of the proposed approach had 11.5% and 3.8% higher than the multi-temporal images-based CART and BPNN models’, respectively. These results suggest that the use of satellite images for early critical disease infection periods is essential for improving the accuracy of monitoring models. Additionally, satellite imagery also assists in monitoring powdery mildew in late wheat growth periods.

Modelling spatial and temporal dynamics of gross primary production in the Sahel from earth-observation-based photosynthetic capacity and quantum efficiency

Abstract. It has been shown that vegetation growth in semi-arid regions is important to the global terrestrial CO2 sink, which indicates the strong need for improved understanding and spatially explicit estimates of CO2 uptake (gross primary production; GPP) in semi-arid ecosystems. This study has three aims: (1) to evaluate the MOD17A2H GPP (collection 6) product against GPP based on eddy covariance (EC) for six sites across the Sahel; (2) to characterize relationships between spatial and temporal variability in EC-based photosynthetic capacity (Fopt) and quantum efficiency (α) and vegetation indices based on earth observation (EO) (normalized difference vegetation index (NDVI), renormalized difference vegetation index (RDVI), enhanced vegetation index (EVI) and shortwave infrared water stress index (SIWSI)); and (3) to study the applicability of EO upscaled Fopt and α for GPP modelling purposes. MOD17A2H GPP (collection 6) drastically underestimated GPP, most likely because maximum light use efficiency is set too low for semi-arid ecosystems in the MODIS algorithm. Intra-annual dynamics in Fopt were closely related to SIWSI being sensitive to equivalent water thickness, whereas α was closely related to RDVI being affected by chlorophyll abundance. Spatial and inter-annual dynamics in Fopt and α were closely coupled to NDVI and RDVI, respectively. Modelled GPP based on Fopt and α upscaled using EO-based indices reproduced in situ GPP well for all except a cropped site that was strongly impacted by anthropogenic land use. Upscaled GPP for the Sahel 2001–2014 was 736 ± 39 g C m−2 yr−1. This study indicates the strong applicability of EO as a tool for spatially explicit estimates of GPP, Fopt and α; incorporating EO-based Fopt and α in dynamic global vegetation models could improve estimates of vegetation production and simulations of ecosystem processes and hydro-biochemical cycles.

Evaluating EO-based canopy water stress from seasonally detrended NDVI and SIWSI with modeled evapotranspiration in the Senegal River Basin

Satellite remote sensing of vegetation parameters and stress is a key issue for semi-arid areas such as the Sahel, where vegetation is an important part of the natural resource base. In this study we examine if additional information can be obtained on intra-seasonal short term scale by using the Shortwave Infrared Water Stress Index (SIWSI) as compared to Normalized Difference Vegetation Index (NDVI). We perform a spatio-temporal evaluation of NDVI and SIWSI using geostationary remote sensing imagery from the Spinning Enhanced Visible and Infrared Imager (SEVIRI). The indices and their seasonally detrended anomalies are evaluated using a gridded rainfall product (RFE2) and modeled actual evapotranspiration (ETa) for the Senegal River basin in 2008. Daily NDVI and SIWSI were found spatially highly correlated to ETa with r=0.73 for both indices, showing the importance of the north/south vegetation gradient in the river catchment. The hypothesis that short term evolution of index anomalies are related to canopy water status was tested by comparing 10-day averages of ETa with short term changes in daily NDVI and SIWSI anomalies, and moderate to strong coefficients of determination where found when anomaly variations where aggregated by Land Cover Classes (LCCs) with R2 values of 0.65 for savanna, 0.60 for grassland, 0.72 for shrubland, and 0.58 for barren or sparsely vegetated areas. This is higher than for the same method applied to NDVI anomalies, with R2 values of 0.57 for savanna, 0.50 for grassland, 0.32 for shrubland, and 0.57 for barren or sparsely vegetated areas. The approach of detrending NIR/SWIR based indices and spatially aggregating the anomalies do offer improved detection of intra-seasonal stress. However, quite coarse spatial aggregation is found necessary for a significant analysis outcome.

Analysis of the application of MODIS shortwave infrared water stress index in monitoring agricultural drought

Analysis of the application of MODIS shortwave infrared water stress index in monitoring agricultural drought

Sensitivity Analysis of Vegetation Reflectance to Biochemical and Biophysical Variables at Leaf, Canopy, and Regional Scales

The objective of this paper is to investigate the sensitivity of reflectance to the variation in biochemical and biophysical variables at leaf, canopy, and regional scales using a modeling approach. The results show that, at the leaf scale, the variations in chlorophyll a+b content, the leaf structure parameter, and the water content dominate the reflectance variance in the visible light (VIS), near infrared (NIR), and short-wave infrared (SWIR) regions, respectively. At the canopy scale, the sensitivity of reflectance to variation in the leaf structure parameter is very slight. For sparse foliage cover (leaf area index ), LAI is the most important variable to the canopy reflectance. As LAI increases, the sensitivity of reflectance to variation in LAI is reduced to a very low value. Moreover, chlorophyll a+b, dry matter, and water content control the variation of canopy reflectance in the VIS, NIR, and SWIR regions, respectively. At the regional scale, the sensitivity of reflectance to variation in vegetation variables is highly influenced by the mixed pixels. Thirty-six vegetation indices (VIs) are chosen in this paper to illustrate the scale dependence of the estimation accuracy of vegetation variables. The results show that the relationships between the VIs and the variables highly depend on the observation scale. For chlorophyll a+b content estimation, transformed chlorophyll absorption in reflectance index (TCARI), Blue Green pigment Index, leaf chlorophyll index (LCI), modified Normalized Difference (mND705), and Plant Biochemical Index at the leaf scale and canopy scale of and TCARI at the canopy scale of are highly related. The correlation between the indices and chlorophyll content in the regional scale is, however, much lower. For water content estimation, disease water stress index (DSWI), leaf water vegetation index 2 (LWVI_2), moisture stress index (MSI), normalized difference infrared index (NDII), normalized difference water index (NDWI), hyperspectral perpendicular vegetation index (RVI), SWIR water stress index (SIWSI), SR water index (SRWI), and water index (WI) are good choices at the leaf scale and canopy scale of , while at the canopy scale of and the regional scale, the correlation between the indices and water content is very low. For LAI estimation, VIs, including the Greenness Index, simple ratio (SR), Normalized Difference VI, modified soil-adjusted vegetation index (MSAVI), modified triangular vegetation index 1 (MTVI1), modified triangular vegetation index 2 (MTVI2), optimized soil-adjusted vegetation index (OSAVI), modified chlorophyll absorption ratio index 1 (MCARI1), modified chlorophyll absorption ratio index 2 (MCARI2), Enhanced VI, LAI Determining Index, renormalized difference vegetation index (RDVI), Spectral Polygon VI, Wide Dynamic Range VI, and triangular vegetation index (TVI), have high correlation with LAI at the canopy scale of while a low correlation at the canopy scale of and the regional scale.

Relation between Seasonally Detrended Shortwave Infrared Reflectance Data and Land Surface Moisture in Semi‑Arid Sahel

In the Sudano-Sahelian areas of Africa droughts can have serious impacts on natural resources, and therefore land surface moisture is an important factor. Insufficient conventional sites for monitoring land surface moisture make the use of Earth Observation data for this purpose a key issue. In this study we explored the potential of using reflectance data in the Red, Near Infrared (NIR), and Shortwave Infrared (SWIR) spectral regions for detecting short term variations in land surface moisture in the Sahel, by analyzing data from three test sites and observations from the geostationary Meteosat Second Generation (MSG) satellite. We focused on responses in surface reflectance to soil- and surface moisture for bare soil and early to mid- growing season. A method for implementing detrended time series of the Shortwave Infrared Water Stress Index (SIWSI) is examined for detecting variations in vegetation moisture status, and is compared to detrended time series of the Normalized Difference Vegetation Index (NDVI). It was found that when plant available water is low, the SIWSI anomalies increase over time, while the NDVI anomalies decrease over time, but less systematically. Therefore SIWSI may carry important complementary information to NDVI in terms of vegetation water status, and can provide this information with the unique combination of temporal and spatial resolution from optical geostationary observations over Sahel. However, the relation between SIWSI anomalies and periods of water stress were not found to be sufficiently robust to be used for water stress detection.

Drought Mapping Using Two Shortwave Infrared Water Indices with MODIS Data under Vegetated Season

Drought monitoring is a critical element for agricultural production, food security, water resource management, sustainable development, and a healthy environment. In this study, shortwave infrared (SWIR) bands with strong water absorption features were used to establish a physically significant water stress index. Two types of indices including SWIR water stress index (SIWSI) and SWIR perpendicular water stress index (SPSI) were constructed using near-infrared (NIR) and SWIR bands. A representative (semi) arid region in the Ningxia Plain of northwestern China, where droughts are frequent, was used to assess the state of dryness using the SIWSI and SPSI indices derived from NIR channel 2 (858 nm) and SWIR channel 6 (1640 nm) or channel 7 (2130 nm) of moderate-resolution imaging spectroradiometer (MODIS) sensor in combination with ground measurements. Fitted regressions indicate significant correlations (P < 0.01) among both indices with the in-situ measurements. Generally, larger indices indicate drier lands, and correlations in the 10-cm range were better than those in the 20-cm range. Although SIWSI6, 2 (r^2 = 0.75, 0.74) performs slightly better than SIWSI7, 2 (r^2 = 0.73, 0.71), SPSI6, 2 (r^2 = 0.70, 0.69) performs marginally weaker than SPSI7, 2 (r^2 = 0.76, 0.74). Ultimately, all four indices reflected dry state under clear sky conditions in the Ningxia Plain.

Tropical forest phenology in French Guiana from MODIS time series

Despite a growing need for knowledge on how terrestrial ecosystems function, phenological studies are rather rare. This study characterizes the temporal evolution of forest covers in French Guiana using variations in the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite signal and vegetation indices, such as the Shortwave Infrared Water Stress Index and the Enhanced Vegetation Index. The principle consists of reconstructing a temporal cycle mosaiced from a 9-year database. Rather than extract the phenological information from each pixel, dynamics were extracted from homogeneous groups of forest pixels enabling the identification of different phenological phases in forest ecosystems. A north–south gradient of chlorophyll activity emerged. The further we moved to the south, the more forest groups consist of fast-growing and drought-tolerant species, whereas a north–south shift appeared at the start dates of phenological cycles. This is a promising approach that paves the way for a much deeper understanding of the mechanisms governing the rainforest in French Guiana, as well as the responses of the Amazon forest to climate change and drought in the long term.

Detecting Canopy Water Status Using Shortwave Infrared Reflectance Data From Polar Orbiting and Geostationary Platforms

Various canopy water status estimates have been developed from recent advances in Earth Observation (EO) technology. A promising methodology is based on the sensitivity of shortwave infrared (SWIR) reflectance to variations in leaf water content. This study explores the potential of SWIR-based canopy water status detection from geostationary Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI) data as compared to polar orbiting environmental satellite (POES)-based moderate resolution imaging spectroradiometer (MODIS) data. The EO-based SWIR water stress index (SIWSI) is evaluated against in situ measured canopy water content indicators at a semi-arid grassland savanna site in Senegal 2008. Daily SIWSI from both MODIS and SEVIRI data show an overall inverse relation to Normalized Difference Vegetation Index (NDVI) throughout the growing season. SIWSI observations from SEVIRI are furthermore sensitive to short-term variations of in situ measured plant water content indicators when aboveground biomass increases from 500 to 900 gm-2 (LAI ≈ 1-2). MODIS SIWSI observations in contrast do not covary with in situ measured moisture indicators. Spatio-temporal trend analyses performed on SEVIRI SIWSI during a dry period within the growing season support these findings. These results suggest that the combined advantage of an improved temporal resolution and a fixed viewing angle potentially makes the SEVIRI sensor an interesting complementary data source to POES data for SWIR-based canopy water status and stress monitoring in a semi-arid environment.

Evaluation of satellite based indices for gross primary production estimates in a sparse savanna in the Sudan

Abstract. One of the more frequently applied methods for integrating controls on primary production through satellite data is the Light Use Efficiency (LUE) approach. Satellite indices such as the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and the Shortwave Infrared Water Stress Index (SIWSI) have previously shown promise as predictors of primary production in several different environments. In this study, we evaluate NDVI, EVI and SIWSI derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite sensor against in-situ measurements from central Sudan in order to asses their applicability in LUE-based primary production modeling within a water limited environment. Results show a strong correlation between vegetation indices and gross primary production (GPP), demonstrating the significance of vegetation indices for deriving information on primary production with relatively high accuracy at similar areas. Evaluation of SIWSI however, reveal that the fraction of vegetation apparently is to low for the index to provide accurate information on canopy water content, indicating that the use of SIWSI as a predictor of water stress in satellite data-driven primary production modeling in similar semi-arid ecosystems is limited.

Evaluation of satellite based primary production modelling in the semi-arid Sahel

The MODIS (Moderate Resolution Imaging Spectroradiometer) primary productivity products are evaluated against observed Above-ground Net Primary Production (AGNPP) in the semi-arid Senegal 2001. MODIS net primary productivity (NPP) modelling is a light use efficiency (LUE) based approach incorporating constraints on vegetation productivity arising from simulated radiation, water demand and temperature data from NASA's Data Assimilation Office (DAO). Annually integrated MODIS PSN (MOD17A2 net photosynthesis, Collection 4) explains more of the observed biomass variation ( r 2 = 0.77) than MODIS fAPAR (fraction Absorbed Photosynthetically Active Radiation, Collection 4) ( r 2 = 0.72), indicating the effect of including the canopy stress scalar ( ε s) based on DAO data combined with modelled maintenance respiration costs (of leaf and fine roots). Annual MODIS NPP (MOD17A3, Collection 4 (C4) and Collection 4.5 (C4.5)) including growth respiration and live wood maintenance respiration costs and modified DAO input (C4.5) however increases the residual unexplained observed AGNPP variance (C4 NPP; r 2 = 0.49) (C4.5 NPP; r 2 = 0.37). The overall quality of the annual NPP MODIS C4 and C4.5 products are moderate for the semi-arid Senegal because of the annual respiration cost modelling and a change in C4.5 biome-specific parameters stored in a Biome Properties Look-Up Table (BPLUT) is the main contributor to the observed discrepancy between C4 and C4.5 NPP. The dynamic range of the values of all MOD17 products was too low when compared to observed AGNPP. An estimate of canopy water stress (SIWSI; Shortwave Infrared Water Stress Index) derived from MODIS channels 2 and 6 and photosynthetically active radiation (PAR) irradiance derived from geostationary METEOSAT data were tested for primary production modelling using a stepwise linear regression analysis. PAR irradiance was combined with MODIS fAPAR into APAR (Absorbed Photosynthetically Active Radiation) explaining 79% of the observed AGNPP variation. Introducing SIWSI significantly increased the explained variance of observed AGNPP ( r 2 = 0.89). MODIS-derived percentage tree cover was tested as a predictor based on the hypothesis that tree cover provides information on differences in respiratory costs between trees and grasses thereby accounting for variations in the LUE conversion efficiency ε. No significant reduction in residual unexplained AGNPP variance was found. Earth observation based derivation of PAR and canopy water stress from SIWSI suggest potential improvements to primary production models in semi-arid biomes that can be implemented in general NPP modelling LUE methodology.

Derivation of a shortwave infrared water stress index from MODIS near- and shortwave infrared data in a semiarid environment

Two different configurations of a shortwave infrared water stress index (SIWSI) are derived from the MODIS near- and shortwave infrared data. A large absorption by leaf water occurs in the shortwave infrared wavelengths (SWIR) and the reflectance from plants thereby is negatively related to leaf water content. Two configurations of a water stress index, SIWSI(6,2) and SIWSI(5,2) are derived on a daily basis from the MODIS satellite data using the information from the near infrared (NIR) channel 2 (841–876 nm) and the shortwave infrared channel 5 (1230–1250 nm) or 6 (1628–1652 nm), respectively, which are wavelength bands at which leaf water content influence the radiometric response. The indices are compared to in situ top layer soil moisture measurements from the semiarid Senegal 2001 and 2002, serving as an indicator of canopy water content. The year 2001 rainfall in the region was slightly below average and the results show a strong correlation between SIWSI and soil moisture. The SIWSI(6,2) performs slightly better than the SIWSI(5,2) ( r 2=0.87 and 0.79). The fieldwork in 2002 did not verify the results found in 2001. However, year 2002 was an extremely dry year and the vegetation cover apparently was too sparse to provide information on the canopy water content. To test the robustness of the SIWSI findings in 2001, soil moisture has been modelled from daily rainfall data at 10 sites in the central and northern part of Senegal. The correlations between SIWSI and simulated soil moisture are generally high with a median r 2=0.72 for both configurations of the SIWSI. It is therefore suggested that the combined information from the MODIS near- and shortwave infrared wavelengths is useful as an indicator of canopy water stress in the semiarid Sahelian environment.