scholarly journals Remote Sensing-Informed Zonation for Understanding Snow, Plant and Soil Moisture Dynamics within a Mountain Ecosystem

2020 ◽  
Vol 12 (17) ◽  
pp. 2733 ◽  
Author(s):  
Jashvina Devadoss ◽  
Nicola Falco ◽  
Baptiste Dafflon ◽  
Yuxin Wu ◽  
Maya Franklin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Soil Moisture ◽  
High Resolution ◽  
Time Lapse ◽  
Resolution Time ◽  
Topographic Wetness Index ◽  
Wetness Index ◽  
Plant Dynamics ◽  
Moisture Conditions

In the headwater catchments of the Rocky Mountains, plant productivity and its dynamics are largely dependent upon water availability, which is influenced by changing snowmelt dynamics associated with climate change. Understanding and quantifying the interactions between snow, plants and soil moisture is challenging, since these interactions are highly heterogeneous in mountainous terrain, particularly as they are influenced by microtopography within a hillslope. Recent advances in satellite remote sensing have created an opportunity for monitoring snow and plant dynamics at high spatiotemporal resolutions that can capture microtopographic effects. In this study, we investigate the relationships among topography, snowmelt, soil moisture and plant dynamics in the East River watershed, Crested Butte, Colorado, based on a time series of 3-meter resolution PlanetScope normalized difference vegetation index (NDVI) images. To make use of a large volume of high-resolution time-lapse images (17 images total), we use unsupervised machine learning methods to reduce the dimensionality of the time lapse images by identifying spatial zones that have characteristic NDVI time series. We hypothesize that each zone represents a set of similar snowmelt and plant dynamics that differ from other identified zones and that these zones are associated with key topographic features, plant species and soil moisture. We compare different distance measures (Ward and complete linkage) to understand the effects of their influence on the zonation map. Results show that the identified zones are associated with particular microtopographic features; highly productive zones are associated with low slopes and high topographic wetness index, in contrast with zones of low productivity, which are associated with high slopes and low topographic wetness index. The zones also correspond to particular plant species distributions; higher forb coverage is associated with zones characterized by higher peak productivity combined with rapid senescence in low moisture conditions, while higher sagebrush coverage is associated with low productivity and similar senescence patterns between high and low moisture conditions. In addition, soil moisture probe and sensor data confirm that each zone has a unique soil moisture distribution. This cluster-based analysis can tractably analyze high-resolution time-lapse images to examine plant-soil-snow interactions, guide sampling and sensor placements and identify areas likely vulnerable to ecological change in the future.

scholarly journals Effect of GPR-derived within-field soil moisture variability on the runoff response using a distributed hydrologic model

2010 ◽  
Vol 7 (6) ◽  
pp. 8947-8986 ◽  
Author(s):  
J. Minet ◽  
E. Laloy ◽  
S. Lambot ◽  
M. Vanclooster
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Spatial Variability ◽  
Hydrologic Model ◽  
Topographic Wetness Index ◽  
Antecedent Soil Moisture ◽  
Distributed Hydrologic Model ◽  
Ground Penetrating ◽  
Moisture Conditions ◽  
Soil Moisture Variability

Abstract. The importance of the spatial variability of the antecedent soil moisture conditions on the runoff response is widely acknowledged in hillslope hydrology. Using a distributed hydrologic model, this paper aims at investigating the effects of soil moisture spatial variability on the runoff in various field conditions and at finding the soil moisture scenario that behaves the most closely as the measured soil moisture pattern in term of runoff hydrograph. Soil moisture was surveyed in ten different field campaigns using a proximal ground penetrating radar (GPR) that allowed to perform high-resolution (~m) mapping at the field scale (several ha). Based on these soil moisture measurements, seven scenarios of antecedent soil moisture were used to feed hydrological simulations and the resulting hydrographs were compared. The novelty of this work is to benefit from high-resolution soil moisture measurements using an advanced GPR in various soil moisture conditions. Accounting for the spatial variability of soil moisture resulted in a larger discharge than using a spatially constant soil moisture. The ranges of possible hydrographs were delineated by the extreme scenarios where soil moisture was directly and inversely arranged according to the topographic wetness index (TWI). These behaviours could be explained in terms of runoff contributing areas, with respect to their sizes and their relative locations within the field. The most efficient scenario for soil moisture appeared to be when soil moisture is directly arranged according to the TWI. This was related to the correlation of the measured soil moisture and the TWI. These observations generalised some of the statements pointed out in previous studies. Similar findings are thus expected under similar soil and rainfall forcing conditions.

scholarly journals High-Resolution Rice Mapping Based on SNIC Segmentation and Multi-Source Remote Sensing Images

2021 ◽  
Vol 13 (6) ◽  
pp. 1148
Author(s):  
Lingbo Yang ◽  
Limin Wang ◽  
Ghali Abdullahi Abubakar ◽  
Jingfeng Huang
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
High Resolution ◽  
Synthetic Aperture Radar ◽  
Classification Accuracy ◽  
Synthetic Aperture ◽  
Resolution Time ◽  
Medium Resolution ◽  
Crop Mapping ◽  
Sentinel 2

High-resolution crop mapping is of great significance in agricultural monitoring, precision agriculture, and providing critical information for crop yield or disaster monitoring. Meanwhile, medium resolution time-series optical and synthetic aperture radar (SAR) images can provide useful phenological information. Combining high-resolution satellite data and medium resolution time-series images provides a great opportunity for fine crop mapping. Simple Non-Iterative Clustering (SNIC) is a state-of-the-art image segmentation algorithm that shows the advantages of efficiency and high accuracy. However, the application of SNIC in crop mapping based on the combination of high-resolution and medium-resolution images is unknown. Besides, there is still little research on the influence of the superpixel size (one of the key user-defined parameters of the SNIC method) on classification accuracy. In this study, we employed a 2 m high-resolution GF-1 pan-sharpened image and 10 m medium resolution time-series Sentinel-1 C-band Synthetic Aperture Radar Instrument (C-SAR) and Sentinel-2 Multispectral Instrument (MSI) images to carry out rice mapping based on the SNIC method. The results show that with the increase of the superpixel size, the classification accuracy increased at first and then decreased rapidly after reaching the summit when the superpixel size is 27. The classification accuracy of the combined use of optical and SAR data is higher than that using only Sentinel-2 MSI or Sentinel-1 C-SAR vertical transmitted and vertical received (VV) or vertical transmitted and horizontal received (VH) data, with overall accuracies of 0.8335, 0.8282, 0.7862, and 0.7886, respectively. Meanwhile, the results also indicate that classification based on superpixels obtained by SNIC significantly outperforms classification based on original pixels. The overall accuracy, producer accuracy, and user accuracy of SNIC superpixel-based classification increased by 9.14%, 17.16%, 27.35% and 1.36%, respectively, when compared with the pixel-based classification, based on the combination of optical and SAR data (using the random forest as the classifier). The results show that SNIC superpixel segmentation is a feasible method for high-resolution crop mapping based on multi-source remote sensing data. The automatic selection of the optimal superpixel size of SNIC will be focused on in future research.

High-Resolution, Time-Lapse Imaging for Ecosystem-Scale Phenotyping in the Field

2012 ◽  
pp. 71-96 ◽  
Author(s):  
Tim Brown ◽  
Christopher Zimmermann ◽  
Whitney Panneton ◽  
Nina Noah ◽  
Justin Borevitz
Keyword(s):  
High Resolution ◽  
Time Lapse ◽  
Resolution Time ◽  
Time Lapse Imaging

High-resolution root zone soil moisture-based indices for drought monitoring in the Ebro basin

2021 ◽  
Author(s):  
Vivien-Georgiana Stefan ◽  
Maria-José Escorihuela ◽  
Pere Quintana-Seguí
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
High Resolution ◽  
Root Zone ◽  
Cost Effective ◽  
Strong Relationship ◽  
Drought Indices ◽  
Ebro Basin ◽  
Small Areas ◽  
Cost Effective Approach

<h3>Agriculture is an important factor on water resources, given the constant population growth and the strong relationship between water availability and food production. In this context, root zone soil moisture (RZSM) measurements are used by modern irrigators in order to detect the onset of crop water stress and to trigger irrigations. Unfortunately, in situ RZSM measurements are costly; combined with the fact they are available only over small areas and that they might not be representative at the field scale, remote sensing is a cost-effective approach for mapping and monitoring extended areas. A recursive formulation of an exponential filter was used in order to derive 1 km resolution RZSM estimates from SMAP (Soil Moisture Active Passive) surface soil moisture (SSM) over the Ebro basin. The SMAP SSM was disaggregated to a 1 km resolution by using the DISPATCH (DISaggregation based on a Physical And Theoretical scale CHange) algorithm. The pseudodiffusivity parameter of the exponential filter was calibrated per land cover type, by using ISBA-DIF (Interaction Soil Biosphere Atmosphere) surface and root zone soil moisture data as an intermediary step. The daily 1 km RZSM estimates were then used to derive 1 km drought indices such as soil moisture anomalies and soil moisture deficit indices (SMDI), on a weekly time-scale, covering the entire 2020 year. Results show that both drought indices are able to capture rainfall and drying events, with the weekly anomaly being more responsive to sudden events such as heavy rainfalls, while the SMDI is slower to react do the inherent inertia it has. Moreover, a quantitative comparison with drought indices derived from a model-based RZSM estimates has also been performed, with results showing a strong correspondence between the different indices. For comparison purposes, the weekly soil moisture anomalies and SMDI derived using 1 km SMAP-derived SSM were also estimated. The analysis shows that the anomalies and SMDI based on the RZSM are more representative of the hydric stress level of the plants, given that the RZSM is better suited than the SSM to describe the moisture conditions at the deeper layers, which are the ones used by plants during growth and development.</h3><h3>The study provides an insight into obtaining robust, high-resolution remote-sensing derived drought indices based on remote-sensing derived RZSM estimates. The 1 km resolution proves an improvement from other currently available drought indices, such as the European Drought Observatory’s 5 km resolution drought index, which is not able to capture as well the spatial variability present within heterogeneous areas. Moreover, the SSM-derived drought indices are currently used in a drought observatory project, covering a region in the Tarragona province of Catalonia, Spain. The project aims at offering irrigation recommendations to water agencies, and the introduction of RZSM-derived drought indices will further improve such advice.</h3>

scholarly journals Land-Cover Dependent Relationships between Fire and Soil Moisture

Fire ◽  
2019 ◽  
Vol 2 (4) ◽  
pp. 55 ◽  
Author(s):  
Alexander J. Schaefer ◽  
Brian I. Magi
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Land Cover ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Fire Activity ◽  
Steep Decline ◽  
Human Use ◽  
Moisture Conditions ◽  
In Fire

For this study, we characterized the dependence of fire counts (FCs) on soil moisture (SM) at global and sub-global scales using 15 years of remote sensing data. We argue that this mathematical relationship serves as an effective way to predict fire because it is a proxy for the semi-quantitative fire–productivity relationship that describes the tradeoff between fuel availability and climate as constraints on fire activity. We partitioned the globe into land-use and land-cover (LULC) categories of forest, grass, cropland, and pasture to investigate how the fire–soil moisture (fire–SM) behavior varies as a function of LULC. We also partitioned the globe into four broadly defined biomes (Boreal, Grassland-Savanna, Temperate, and Tropical) to study the dependence of fire–SM behavior on LULC across those biomes. The forest and grass LULC fire–SM curves are qualitatively similar to the fire–productivity relationship with a peak in fire activity at intermediate SM, a steep decline in fire activity at low SM (productivity constraint), and gradual decline as SM increases (climate constraint), but our analysis highlights how forests and grasses differ across biomes as well. Pasture and cropland LULC are a distinctly human use of the landscape, and fires detected on those LULC types include intentional fires. Cropland fire–SM curves are similar to those for grass LULC, but pasture fires are evident at higher SM values than other LULC. This suggests a departure from the expected climate constraint when burning is happening at non-optimal flammability conditions. Using over a decade of remote sensing data, our results show that quantifying fires relative to a single physical climate variable (soil moisture) is possible on both cultivated and uncultivated landscapes. Linking fire to observable soil moisture conditions for different land-cover types has important applications in fire management and fire modeling.

Sign in / Sign up

Export Citation Format

Share Document