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Improving Soil Moisture Estimation by Identification of NDVI Thresholds

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Improving Soil Moisture Estimation by Identification of NDVI Thresholds remote sensing Article Improving Soil Moisture Estimation by Identification of NDVI Thresholds Optimization: An Application to the Chinese Loess Plateau Lina Yuan 1 , Long Li 1,2 , Ting Zhang 1, Longqian Chen 1,*, Jianlin Zhao 3, Weiqiang Liu 4, Liang Cheng 1,5 , Sai Hu 6, Longhua Yang 7 and Mingxin Wen 1 1 School of Public Policy and Management, China University of Mining and Technology, Daxue Road 1, Xuzhou 221116, China; [email protected] (L.Y.); [email protected] or [email protected] (L.L.); [email protected] (T.Z.); [email protected] (L.C.); [email protected] (M.W.) 2 Department of Geography & Earth System Science, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium 3 Department of Geology Engineering and Geomatics, Chang’an University, Yanta Road 120, Xi’an 710054, China; [email protected] 4 School of Environment and Spatial Informatics, China University of Mining and Technology, Daxue Road 1, Xuzhou 221116, China; [email protected] 5 Henry Fok College of Biology and Agriculture, Shaoguan University, Daxue Road 26, Shaoguan 512005, China 6 School of Humanities and Law, Jiangsu Ocean University, Cangwu Road 59, Lianyungang 222005, China; [email protected] 7 Department of Research and Development, Shanghai Gongjing Environmental Protection Co., Ltd., Yuanjiang Road 525, Shanghai 201100, China; [email protected] * Correspondence: [email protected]; Tel.: +86-516-8359-1327 Abstract: Accuracy soil moisture estimation at a relevant spatiotemporal scale is scarce but beneficial Citation: Yuan, L.; Li, L.; Zhang, T.; for understanding ecohydrological processes and improving weather forecasting and climate models, Chen, L.; Zhao, J.; Liu, W.; Cheng, L.; particularly in arid and semi-arid regions like the Chinese Loess Plateau (CLP). This study proposed Hu, S.; Yang, L.; Wen, M. Improving Criterion 2, a new method to improve relative soil moisture (RSM) estimation by identification of Soil Moisture Estimation by normalized difference vegetation index (NDVI) thresholds optimization based on our previously Identification of NDVI Thresholds Optimization: An Application to the proposed iteration procedure of Criterion 1. Apparent thermal inertia (ATI) and temperature veg- Chinese Loess Plateau. Remote Sens. etation dryness index (TVDI) were applied to subregional RSM retrieval for the CLP throughout 2021, 13, 589. https://doi.org/ 2017. Three optimal NDVI thresholds (NDVI0 was used for computing TVDI, and both NDVIATI 10.3390/rs13040589 and NDVITVDI for dividing the entire CLP) were firstly identified with the best validation results (R) of subregions for 8-day periods. Then, we compared the selected optimal NDVI thresholds and Academic Editor: Marion Pause estimated RSM with each criterion. Results show that NDVI thresholds were optimized to robust Received: 26 December 2020 RSM estimation with Criterion 2, which characterized RSM variability better. The estimated RSM Accepted: 4 February 2021 with Criterion 2 showed increased accuracy (maximum R of 0.82 ± 0.007 for Criterion 2 and of Published: 7 February 2021 0.75 ± 0.008 for Criterion 1) and spatiotemporal coverage (45 and 38 periods (8-day) of RSM maps and the total RSM area of 939.52 × 104 km2 and 667.44 × 104 km2 with Criterion 2 and Criterion 1, Publisher’s Note: MDPI stays neutral respectively) than with Criterion 1. Moreover, the additional NDVI thresholds we applied was with regard to jurisdictional claims in another strategy to acquire wider coverage of RSM estimation. The improved RSM estimation with published maps and institutional affil- Criterion 2 could provide a basis for forecasting drought and precision irrigation management. iations. Keywords: MODIS; relative soil moisture; Chinese Loess Plateau; ATI; TVDI Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. 1. Introduction This article is an open access article distributed under the terms and Accurate and timely soil moisture (SM) information has essential applications in conditions of the Creative Commons different fields, such as flood/drought forecasting, climate and weather modeling, water re- Attribution (CC BY) license (https:// sources, and agriculture management [1]. Proper water resource management is crucial in creativecommons.org/licenses/by/ declining vulnerability to drought and other extreme events that may occur with increasing 4.0/). frequency because of climate change. This has been widely recognized in the arid and Remote Sens. 2021, 13, 589. https://doi.org/10.3390/rs13040589 https://www.mdpi.com/journal/remotesensing Remote Sens. 2021, 13, 589 2 of 25 semi-arid regions of the Chinese Loess Plateau (CLP), which is particularly susceptible to soil erosion and water shortage due to intensive rainstorms, fractured and steep terrain, low vegetation cover, highly erodible loess soil, and a semiarid to arid climate [2–4]. Microwave sensors have been used for SM retrieval due to the direct relationship between microwave radiation and soil dielectric, though providing a coarse spatial res- olution [5]. As for indirect approaches, SM estimation from visible and infrared data is based on land surface reflectance at much higher spatial resolutions [6]. To evaluate SM estimation, there are many studies on the comparison of SM products and modeled SM on different scales [7–12]. In order to explore the potential of optical and thermal remote sensing imagery for SM estimation, different indices (e.g., VCI—vegetation condition index, TVDI—temperature vegetation dryness index, TCI—temperature condition index, ATI— apparent thermal inertia) [13–17] and models (SVAT—soil vegetation atmosphere transfer, EF—evaporative fraction model) [18,19] have been applied to different climate conditions. The majority of previous studies derived SM from optical and infrared remote sensing imagery concentrating on vegetation-growing seasons [20–22]. In addition, existing SM estimation algorithms are not applicable to steep regions [23]. The continuous evaluation of SM throughout the year at a moderate 1-km resolution (compared with coarse resolution of a few tens of kilometers and fine resolution of the tens of meters [5,24,25]) over the CLP at a regional scale of 640,000 km2 (local ≤ 104 km2, 104 km2 < regional < 107 km2, and global≥107 km2 [26]) is, however, scarce. Because geology and soil composition change only over a long period, short-term changes in thermal inertia (TI) can be associated with variations in SM [27]. Originally proposed by Price [28], ATI is a simplified calculation for TI and has been routinely used to estimate SM in bare soil and sparsely vegetated land [15,29,30]. For a densely vegetated surface, the tri- angular or trapezoidal NDVI (normalized difference vegetation index)—LST (land surface temperature) space, interpreted as TVDI, is widely used for SM estimation [31,32]. Moreover, a viable method for time series SM monitoring could overcome the limitations of a single method (PDI—perpendicular drought index or TVDI) [33], and the ATI and TVDI models were applied to retrieval the relative soil moisture (RSM) in Guangxi, south China [34]. RSM represents the percentage of SM that accounts for the moisture storage capacity and describes the SM levels in the present study. A detailed explanation regarding RSM is presented in Section 3.1.2. Yuan et al. [35] applied the MODIS-derived ATI and TVDI mod- els to subregional RSM estimation for the CLP. They highlighted the identification of three optimal NDVI thresholds (NDVI0 was for computing TVDI, both NDVIATI and NDVITVDI were used for dividing the whole CLP) and concluded that the ATI/TVDI joint models were more applicable (accounting for 36/38 8-day periods) and accurate (R: 0.75 ± 0.008 on DOY—day of the year (hereafter referred to as DOY), 313) than the ATI-based and TVDI- based models. Here, the limiting condition (NDVI0 ≤ NDVIATI ≤ NDVITVDI) when select- ing optimal NDVI thresholds, as proposed by Yuan et al. [35], is regarded as Criterion 1. It failed to select the highest R in certain subregions, resulting in no corresponding sub- regional RSM maps being produced, thereby causing incomplete RSM maps for 8-day periods. To produce more complete RSM maps, additional criteria (Criterion 2) for de- termining optimal NDVI thresholds should be tested. Importantly, because the NDVI0 and NDVIATI thresholds with Criterion 2 do not influence each other, highest R for an individual subregion could have more opportunities to be selected. In this case, we could obtain more subregional RSM maps to produce more complete RSM maps. The main objective of this study is to improve RSM estimation by applying Criterion 2 (NDVIATI < NDVITVDI) for optimizing the identification of NDVI thresholds. Three optimal NDVI thresholds were firstly identified with Criterion 2 for each 8-day period and 8-day RSM maps were generated using selected optimal NDVI thresholds. Then, in order to evaluate the accuracy and coverage of RSM estimation, we compared the selected optimal NDVI thresholds and estimated RSM with each criterion. Monthly, seasonal, and yearly RSM maps of the CLP in 2017 were produced via the 8-day RSM maps and examined lastly. Remote Sens. 2021, 13, x FOR PEER REVIEW 3 of 26 to evaluate the accuracy and coverage of RSM estimation, we compared the selected op- timal NDVI thresholds and estimated RSM with each criterion. Monthly, seasonal, and Remote Sens. 2021, 13, 589 yearly RSM maps of the CLP in 2017 were produced via the 8-day RSM maps and exam-3 of 25 ined lastly. 2. Study Area and Data 2. Study Area and Data 2.1. Study Area 2.1. Study Area The CLP (Chinese Loess Plateau) is located at 100°54′–114°33′ E and 33°43′–41°16′ N The CLP (Chinese Loess Plateau) is located at 100◦540–114◦330E and 33◦430–41◦160N and has a total area of 64,000 km2 covering seven northern Chinese provinces (Figure 1a). and has a total area of 64,000 km2 covering seven northern Chinese provinces (Figure1a).
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