Soil Moisture by Remote Sensing Retrieval in the Tropic of Cancer of Yunnan Province

Abstract

Soil moisture (SM) is an important indicator of climate and environmental drought, the basis of sustainable and healthy operation of global ecosystems [1], and an important parameter for crop growth monitoring, yield estimation and drought monitoring [2]. Large-scale soil moisture monitoring is an important component of agricultural water management and crop drought monitoring [3]. The region of the Tropic of Cancer in Yunnan Province is characterized by unique geographical conditions, high vegetation coverage, outstanding biodiversity, abundant products (such as Panax notoginseng, purple rice, Punica granatum and Yunnan large leaf tea), and "living fossil" plants (such as Alsophila spinulosa and Manglietiastrum sinicum). The lack of soil moisture has a significant impact on the leaf area and yield of these crops [4]. The quality of crops can be improved and the efficiency of water resource utilization can be effectively increased by regulation

Improving Soil Moisture Estimation by Identification of NDVI Thresholds Optimization: An Application to the Chinese Loess Plateau

“…In detail, the merged Terra 8-day surface reflectance and temperature products (MOD09A1/MOD11A2) are distributed on 8-day synthesis periods of clear sky data accumulation and each 8-day composite pixel contains the best possible observation according to specified criteria [43]. In addition, previous studies applied MOD11A2 LST products and MOD09A1 surface reflectance data (time resolution of 8 days) to Equation (1) [15,34,44]. In this case, our study was also carried out for an 8-day temporal resolution to calculate ATI.…”

“…where b 1 -b 5 and b 7 are the reflectance of bands 1, 2, 3, 4, 5, and 7 of MODIS, respectively [15,42,46,47]. MODIS's six bands are excellent in making the broadband albedo conversions under the general atmospheric conditions [45].…”

“…To evaluate SM estimation, there are many studies on the comparison of SM products and modeled SM on different scales [7][8][9][10][11][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, ATIapparent thermal inertia) [13][14][15][16][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][21][22].…”

“…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 triangular or trapezoidal NDVI (normalized difference vegetation index)-LST (land surface temperature) space, interpreted as TVDI, is widely used for SM estimation [31,32].…”

Soil Moisture Estimation for the Chinese Loess Plateau Using MODIS-derived ATI and TVDI

“…Soil thermal inertia (TI) is a factor that reflects the temperature change of soil surface and is highly correlated with SM [17,18]. Based on this principle, simplified TI, namely ATI, is routinely used to estimate the SM of bare and less-vegetated soil [19][20][21][22][23][24][25][26]. There are no strict limitations on the number and time of day of LST observations using the ATI-based model comparing to other methods [27].…”