Temporal and Spatial Comparison of Agricultural Drought Indices from Moderate Resolution Satellite Soil Moisture Data over Northwest Spain

Abstract

During the last decade, a variety of agricultural drought indices have been developed using soil moisture (SM), or any of its surrogates, as the primary drought indicator. In this study, a comprehensive study of four innovative SM-based indices, the Soil Water Deficit Index (SWDI), the Soil Moisture Agricultural Drought Index (SMADI), the Soil Moisture Deficit Index (SMDI) and the Soil Wetness Deficit Index (SWetDI), is conducted over a large semi-arid crop region in northwest Spain. The indices were computed on a weekly basis from June 2010 to December 2016 using 1-km satellite SM estimations from Soil Moisture and Ocean Salinity (SMOS) and/or Moderate Resolution Imaging Spectroradiometer (MODIS) data. The temporal dynamics of the indices were compared to two well-known agricultural drought indices, the atmospheric water deficit (AWD) and the crop moisture index (CMI), to analyze the levels of similarity, correlation, seasonality and number of weeks with drought. In addition, the spatial distribution and intensities of the indices were assessed under dry and wet SM conditions at the beginning of the growing season. The results showed that the SWDI and SMADI were the appropriate indices for developing an efficient drought monitoring system, with higher significant correlation coefficients (R ≈ 0.5-0.8) when comparing with the AWD and CMI, whereas lower values (R ≤ 0.3) were obtained for the SMDI and SWetDI.

Satellite Soil Moisture for Agricultural Drought Monitoring: Assessment of SMAP-Derived Soil Water Deficit Index in Xiang River Basin, China

“…There are several ways to define θ FC and θ WP : (1) the 5th and 95th soil moisture data of the time series denote θ WP and θ FC ; (2) the soil moisture at a soil water potential of −33 kPa and −1500 kPa are considered equal to θ WP and θ FC ; and (3) θ WP and θ FC are calculated by basic soil physical characteristics such as the proportion of clay and sand via pedo-transfer functions [53]. These three methods all show good performance in defining θ WP and θ FC , and the first method is the simplest way for calculation [53][54][55]. Agricultural drought is linked with soil moisture availability during the growing season [53].…”

Spatial Downscaling Methods of Soil Moisture Based on Multisource Remote Sensing Data and Its Application

“…Soil moisture is an important component of both the water cycle and the surface energy cycle; it is also an important indicator for reflecting land degradation and characterizing surface drought information [1][2][3][4][5]. Soil moisture is related to a number of factors, which include vegetation growth, crop growth, and food production, as well as important parameters in the fields of hydrology, climate research, agriculture, and ecology [6,7].…”

Estudio de la humedad superficial del suelo en la cuenca del Duero mediante la integración multiescala de técnicas basadas en teledetección, modelización y observaciones in situ

“…Los datos de humedad del suelo son esenciales para una amplia gama de aplicaciones en hidrología (Pauwels et al, 2002;Western et al, 2004;Robinson et al, 2008), meteorología (Dai et al, 2004;Koster et al, 2004;Loew et al, 2013), climatología (Mintz and Serafni, 1992;Hollinger y Isard, 1994;, manejo de los recursos hídricos (Dobriyal et al, 2012;Engman, 1991;Bastiaanssen et al, 2000), modelización de la humedad del suelo y sus aplicaciones Gumuzzio et al, 2016) y estudios y prevención de riesgos ambientales tales como inundaciones, sequía y deslizamientos (Brocca et al 2011a;Brocca et al,2016;Pablos et al, 2017;Sánchez et al, 2018).…”

“…Durante las últimas décadas se han realizado grandes esfuerzos para comprender la dinámica de la humedad del suelo en un contexto espacial y temporal y para determinar los factores de control y las propiedades de escala relacionadas con su variabilidad (Corradini, 2014). Los estudios de la humedad del suelo que integran datos con diferentes escalas espaciales comprenden: aplicaciones dirigidas a la estimación de la humedad del suelo a través de la teledetección (Brocca et al, 2017b;Colliander et al, 2018;Muñoz-Sabater et al, 2016;Petropoulos et al, 2015;Sánchez et al, 2018); diferentes técnicas de monitorización in situ de la humedad del suelo a diferentes escalas (Mohanty et al, 2017;Oschner et al, 2013); implementación de datos de humedad del suelo por satélite en aplicaciones hidrológicas y climáticas (Bogena et al, 2015;Tebbs et al, 2016); estudios relacionados con los efectos de la sequía y pronóstico de inundaciones (AghaKouchak et al, 2014;Brocca et al, 2011a;Martínez-Fernández et al, 2016;Pablos et al, 2017;Sánchez et al, 2018;Wanders et al, 2014) y la evaluación y modelización CAPÍTULO 1 espacio-temporal de la humedad del suelo Brocca et al, 2007, Brocca et al, 2013dMartínez-Fernández and Ceballos 2005;Rötzer et al, 2012;Vereecken et al, 2014, Vereecken et al, 2016.…”

“…El algoritmo se ha actualizado en la versión 3, solventando los problemas relacionados con la nubosidad e integra datos de SMOS, Moderate Resolution Imaging Spectroradiometer (MODIS) y Era Retrospective Analysis (ERA) -Interim data (BEC-Team. Quality Report, 2015; Khodayar et al, 2018;Pablos et al, 2017).…”