Estimating Soil Moisture with Landsat Data and Its Application in Extracting the Spatial Distribution of Winter Flooded Paddies

In the face of rapid global change it is imperative to preserve geodiversity for the overall conservation of biodiversity. Geodiversity is important for understanding complex biogeochemical and physical processes and is directly and indirectly linked to biodiversity on all scales of ecosystem organization. Despite the great importance of geodiversity, there is a lack of suitable monitoring methods. Compared to conventional in-situ techniques, remote sensing (RS) techniques provide a pathway towards cost-effective, increasingly more available, comprehensive, and repeatable, as well as standardized monitoring of continuous geodiversity on the local to global scale. This paper gives an overview of the state-of-the-art approaches for monitoring soil characteristics and soil moisture with unmanned aerial vehicles (UAV) and air- and spaceborne remote sensing techniques. Initially, the definitions for geodiversity along with its five essential characteristics are provided, with an explanation for the latter. Then, the approaches of spectral traits (ST) and spectral trait variations (STV) to record geodiversity using RS are defined. LiDAR (light detection and ranging), thermal and microwave sensors, multispectral, and hyperspectral RS technologies to monitor soil characteristics and soil moisture are also presented. Furthermore, the paper discusses current and future satellite-borne sensors and missions as well as existing data products. Due to the prospects and limitations of the characteristics of different RS sensors, only specific geotraits and geodiversity characteristics can be recorded. The paper provides an overview of those geotraits.

Section: Direct and Indirect Measurements By Optical And Thermal Sensorsmentioning

confidence: 99%

Linking Remote Sensing and Geodiversity and Their Traits Relevant to Biodiversity—Part I: Soil Characteristics

Lausch

Baade

Bannehr³

et al. 2019

“…Passive microwave data have coarse spatial resolutions (The Advanced Microwave Scanning Radiometer for EOS, AMSR-E: 5 km; The Soil Moisture and Ocean Salinity, SMOS: 50 km) and are inappropriate for monitoring SMC variation on a local scale. Active microwave data, which provides even better spatial resolutions (10-100 m), are still not suitable for monitoring the SMC during the short dry season in tropical regions, due to its poor temporal resolution and high cost requirements [9,10]. Therefore, finding suitable optical remote sensing data to couple with microwave remote sensing data for effective monitoring of SMC has been encouraged and carried out.…”

Section: Introductionmentioning

confidence: 99%

“…The newest Landsat, Landsat 8 (L8), which was launched recently in 2013, was also explored for SMC estimation [19][20][21]. As with the use of previous Landsats for SMC measurement purposes, the SMC estimation models using L8 were developed in these studies, mostly based on the empirical linear relationship between in situ SMC with vegetation indices, such as the Normalized Difference Tillage Index (NDTI) and TDVI [10], and/or the Normalized Difference Water Index (NDWI) [20] and/or LST [19] and/or NDVI [22]. The spectral response of various SMCs has not been considered and integrated in the model development process, making it difficult to interpret and evaluate the performance of the proposed models.…”

Section: Introductionmentioning

confidence: 99%

Effective Band Ratio of Landsat 8 Images Based on VNIR-SWIR Reflectance Spectra of Topsoils for Soil Moisture Mapping in a Tropical Region

vThi

Dang

et al. 2019

Effective mapping and monitoring of soil moisture content (SMC) in space and time is an expected application of remote sensing for agricultural development and drought mitigation, particularly in the context of global climate change impact, given that agricultural drought is occurring more frequently and severely worldwide. This study aims to develop a regional algorithm for estimating SMC by using Landsat 8 (L8) imagery, based on analyses of the response of soil reflectance, by corresponding L8 bands with the change of SMC from dry to saturated states, in all 103 soil samples taken in the central region of Vietnam. The L8 spectral band ratio of the near-infrared band (NIR: 850–880 nm, band 5) versus the short-wave infrared 2 band (SWIR2: 2110 to 2290 nm, band 7) shows the strongest correlation to SMC by a logarithm function (R2 = 0.73 and the root mean square error, RMSE ~ 12%) demonstrating the high applicability of this band ratio for estimating SMC. The resultant maps of SMC estimated from the L8 images were acquired over the northern part of the Central Highlands of Vietnam in March 2015 and March 2016 showed an agreement with the pattern of severe droughts that occurred in the region. Further discussions on the relationship between the estimated SMC and the satellite-based retrieved drought index, the Normal Different Drought Index, from the L8 image acquired in March 2016, showed a strong correlation between these two variables within an area with less than 20% dense vegetation (R2 = 0.78 to 0.95), and co-confirms the bad effect of drought on almost all areas of the northern part of the Central Highlands of Vietnam. Directly estimating SMC from L8 imagery provides more information for irrigation management and better drought mitigation than by using the remotely sensed drought index. Further investigations on various soil types and optical sensors (i.e., Sentinel 2A, 2B) need to be carried out, to extend and promote the applicability of the prosed algorithm, towards better serving agricultural management and drought mitigation.

“…NTL data has shown a positive correlation with human activities, such as the gross domestic product (GDP) and the built-up area at the significant level [13][14][15][16][17]. Numerous studies have highlighted that NTL provides a reliable source to map the regional and global urban extent [18], utilizing methods, such as SVM-based classification [19], one-class classification, and thresholding [20,21]. The thresholding method, including both the global-fixed and locally optimized thresholding [22][23][24], is often used to extract urban areas because of its simplicity.…”

Section: Introductionmentioning

confidence: 99%

Delineating Urban Boundaries Using Landsat 8 Multispectral Data and VIIRS Nighttime Light Data

Xue

Zhu

et al. 2018

Administering an urban boundary (UB) is increasingly important for curbing disorderly urban land expansion. The traditionally manual digitalization is time-consuming, and it is difficult to connect UB in the urban fringe due to the fragmented urban pattern in daytime data. Nighttime light (NTL) data is a powerful tool used to map the urban extent, but both the blooming effect and the coarse spatial resolution make the urban product unable to meet the requirements of high-precision urban study. In this study, precise UB is extracted by a practical and effective method using NTL data and Landsat 8 data. Hangzhou, a megacity experiencing rapid urban sprawl, was selected to test the proposed method. Firstly, the rough UB was identified by the search mode of the concentric zones model (CZM) and the variance-based approach. Secondly, a buffer area was constructed to encompass the precise UB that is near the rough UB within a certain distance. Finally, the edge detection method was adopted to obtain the precise UB with a spatial resolution of 30 m. The experimental results show that a good performance was achieved and that it solved the largest disadvantage of the NTL data-blooming effect. The findings indicated that cities with a similar level of socio-economic status can be processed together when applied to larger-scale applications.