Comparing the Normalized Difference Infrared Index (NDII) with root zone storage in a lumped conceptual model

Abstract

Abstract. With remote sensing we can readily observe the Earth's surface, but direct observation of the sub-surface remains a challenge. In hydrology, but also in related disciplines such as agricultural and atmospheric sciences, knowledge of the dynamics of soil moisture in the root zone of vegetation is essential, as this part of the vadose zone is the core component controlling the partitioning of water into evaporative fluxes, drainage, recharge, and runoff. In this paper, we compared the catchment-scale soil moisture content in the root zone of vegetation, computed by a lumped conceptual model, with the remotely sensed Normalized Difference Infrared Index (NDII) in the Upper Ping River basin (UPRB) in northern Thailand. The NDII is widely used to monitor the equivalent water thickness (EWT) of leaves and canopy. Satellite data from the Moderate Resolution Imaging Spectroradiometer (MODIS) were used to determine the NDII over an 8-day period, covering the study area from 2001 to 2013. The results show that NDII values decrease sharply at the end of the wet season in October and reach lowest values near the end of the dry season in March. The values then increase abruptly after rains have started, but vary in an insignificant manner from the middle to the late rainy season. This paper investigates if the NDII can be used as a proxy for moisture deficit and hence for the amount of moisture stored in the root zone of vegetation, which is a crucial component of hydrological models. During periods of moisture stress, the 8-day average NDII values were found to correlate well with the 8-day average soil moisture content (Su) simulated by the lumped conceptual hydrological rainfall–runoff model FLEX for eight sub-catchments in the Upper Ping basin. Even the deseasonalized Su and NDII (after subtracting the dominant seasonal signal) showed good correlation during periods of moisture stress. The results illustrate the potential of the NDII as a proxy for catchment-scale root zone moisture deficit and as a potentially valuable constraint for the internal dynamics of hydrological models. In dry periods, when plants are exposed to water stress, the EWT (reflecting leaf water deficit) decreases steadily, as moisture stress in the leaves is connected to moisture deficits in the root zone. Subsequently, when the soil moisture is replenished as a result of rainfall, the EWT increases without delay. Once leaf water is close to saturation – mostly during the heart of the wet season – leaf characteristics and NDII values are not well correlated. However, for both hydrological modelling and water management, the stress periods are most important, which is why this product has the potential of becoming a highly efficient model constraint, particularly in ungauged basins.

Recent Drought-Induced Vitality Decline of Black Pine (Pinus nigra Arn.) in South-West Hungary—Is This Drought-Resistant Species under Threat by Climate Change?

“…The possible reason is that the NDII is proportional to the water content of the canopy [77] that is sensitive to changes in available water in the soil, and thus could react to changing conditions fast. This interpretation is confirmed by a recent paper of Sriwongsitanon et al [78] where the NDII was successfully used to monitor the moisture content of the root zone especially during dry periods. Besides the different nature of the indices, the high daily maximum temperatures of August in 1992 (about 2.7 • C higher than in August of 2012) may have more negatively influenced the photosynthesis and consequently the NDVI than the NDII [79].…”

A hydrological model for root zone water storage simulation on a global scale

“…In FLEX model, the runoff generation is calculated based on the widely used beta function of the Xinanjiang model (Zhao, 1992) that is a function of the relative soil moisture in the unsaturated soil layer. The beta function for calculation of runoff in WAYS is replaced by a modified version from the work of Sriwongsitanon et al (2016) to link the function to the water storage in the root zone layer. Depending on the root zone water storage S rz , 15 a part of effective precipitation turns into runoff and the rest are infiltrated into soil and recharges the root zone layer.…”

“…The development of WAYS is based on a lumped conceptual model with an HBV-like model structure, called the FLEX model (Fenicia et al, 2011;Gao et al, 2014a). The FLEX model has been widely used and validated at the basin scale to simulate the soil moisture content and root zone water storage (Gao et al, 2014b;Nijzink et al, 2016;de Boer-Euser et al, 2016;Sriwongsitanon et al, 2016). Benefit from its flexible modelling framework, we have now extended it to a spatially distributed global hydrological model.…”

Time Series Analysis of MODIS-Derived NDVI for the Hluhluwe-Imfolozi Park, South Africa: Impact of Recent Intense Drought

“…The BAI was also included in order to determine the possible vegetation burning activity, which may have been triggered by drier conditions associated with an intense drought period. NDII has been recently proven to be a robust indicator for monitoring the moisture content in the root-zone from the observed moisture state of vegetation [19,21]. These spectral indices were calculated using the formulas: where R, NIR, and SWIR1 are spectral bands in the blue (450-500 nm), red (600-700 nm), near-infrared (700-1300 nm), and shortwave infrared (1550-1750 nm) regions.…”

“…Remote sensing's systematic observation allows us to track vegetation conditions from the 1970s to the present [14] and provides the means to integrate the record with causal factors. This study investigates vegetative drought which is the vegetation stress as a function of moisture deficit [15].Several drought studies based on satellite-derived measurements have exploited key indicators such as the (a) normalized difference vegetation index (NDVI), a ratio of the difference between the near-infrared and red bands of the spectrum over the sum of the near-infrared and red bands [16,17], which is a robust indicator of vegetation productivity [18]; (b) the normalized difference infrared index (NDII) which contain additional information on water availability in the soil for use by vegetation [19] as measured by the ratios of the near-infrared and short-wave infrared [20]; and (c) the evapotranspiration (ET) which includes both the loss of root zone soil water through transpiration (influenced by stomatal conductance), as well as evaporation from bare soil [21]. These studies have enhanced our understanding of how vegetation reacts to drought events over time [22][23][24][25].Hitherto, numerous studies have explored vegetation changes using NDVI in response to climatic variability.…”