Evaluation of MODIS Land Surface Temperature Data to Estimate Near-Surface Air Temperature in Northeast China

Air temperature (Tair) near the ground surface is a fundamental descriptor of terrestrial environment conditions and one of the most widely used climatic variables in global change studies. The main objective of this study was to explore the possibility of retrieving high-resolution Tair from the Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) products, covering complex terrain in Northeast China. The All Subsets Regression (ASR) method was adopted to select the predictors and build optimal multiple linear regression models for estimating maximum (Tmax), minimum (Tmin), and mean (Tmean) air temperatures. The relative importance of predictors in these models was evaluated via the Standardized Regression Coefficients (SRCs) method. The results indicated that the optimal models could estimate the Tmax, Tmin, and Tmean with relatively high accuracies (Model Efficiency ≥ 0.90). Both LST and day length (DL) predictors were important in estimating Tmax (SRCs: daytime LST = 0.53, DL = 0.35), Tmin (SRCs: nighttime LST = 0.74, DL = 0.23), and Tmean (SRCs: nighttime LST = 0.72, DL = 0.28). Models predicting Tmin and Tmean had better performance than the one predicting Tmax. Nighttime LST was better at predicting Tmin and Tmean than daytime LST data at predicting Tmax. Land covers had noticeable influences on estimating Tair, and even seasonal vegetation greening could result in temporal variations of model performance. Air temperature could be accurately estimated using remote sensing, but the model performance was varied across different spatial and temporal scales. More predictors should be incorporated for the purpose of improving the estimation of near surface Tair from the MODIS LST production

Spatial crop yield estimation based on remotely sensed stress index

The improvement of methods to evaluate the real impact of soil moisture availability on crop systems is crucial because the importance for world economy and food production. The relationship between the remote sensed stress index TVDI, root-zone soil moisture and soybean yield was analyzed in a sandy region of Argentine Pampas. High correlation (R2 =0.68) between TVDI and soybean yield was observed. The obtained adjustment allows us to evaluate the spatial variability of yield during a humid and dry period 2-3 months before harvest. Since the method requires remote sensed data, it could be applied over areas with poor data coverage.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Remote Sensing of Land Surface Phenology

Land surface phenology (LSP) uses remote sensing to monitor seasonal dynamics in vegetated land surfaces and retrieve phenological metrics (transition dates, rate of change, annual integrals, etc.). LSP has developed rapidly in the last few decades. Both regional and global LSP products have been routinely generated and play prominent roles in modeling crop yield, ecological surveillance, identifying invasive species, modeling the terrestrial biosphere, and assessing impacts on urban and natural ecosystems. Recent advances in field and spaceborne sensor technologies, as well as data fusion techniques, have enabled novel LSP retrieval algorithms that refine retrievals at even higher spatiotemporal resolutions, providing new insights into ecosystem dynamics. Meanwhile, rigorous assessment of the uncertainties in LSP retrievals is ongoing, and efforts to reduce these uncertainties represent an active research area. Open source software and hardware are in development, and have greatly facilitated the use of LSP metrics by scientists outside the remote sensing community. This reprint covers the latest developments in sensor technologies, LSP retrieval algorithms and validation strategies, and the use of LSP products in a variety of fields. It aims to summarize the ongoing diverse LSP developments and boost discussions on future research prospects

Spatial crop yield estimation based on remotely sensed stress index

The improvement of methods to evaluate the real impact of soil moisture availability on crop systems is crucial because the importance for world economy and food production. The relationship between the remote sensed stress index TVDI, root-zone soil moisture and soybean yield was analyzed in a sandy region of Argentine Pampas. High correlation (R2 =0.68) between TVDI and soybean yield was observed. The obtained adjustment allows us to evaluate the spatial variability of yield during a humid and dry period 2-3 months before harvest. Since the method requires remote sensed data, it could be applied over areas with poor data coverage

Spatial crop yield estimation based on remotely sensed stress index

The improvement of methods to evaluate the real impact of soil moisture availability on crop systems is crucial because the importance for world economy and food production. The relationship between the remote sensed stress index TVDI, root-zone soil moisture and soybean yield was analyzed in a sandy region of Argentine Pampas. High correlation (R2 =0.68) between TVDI and soybean yield was observed. The obtained adjustment allows us to evaluate the spatial variability of yield during a humid and dry period 2-3 months before harvest. Since the method requires remote sensed data, it could be applied over areas with poor data coverage

Evaluating k-nearest neighbor (kNN) imputation models for species-level aboveground forest biomass mapping in Northeast China

Quantifying spatially explicit or pixel-level aboveground forest biomass (AFB) across large regions is critical for measuring forest carbon sequestration capacity, assessing forest carbon balance, and revealing changes in the structure and function of forest ecosystems. When AFB is measured at the species level using widely available remote sensing data, regional changes in forest composition can readily be monitored. In this study, wall-to-wall maps of species-level AFB were generated for forests in Northeast China by integrating forest inventory data with Moderate Resolution Imaging Spectroradiometer (MODIS) images and environmental variables through applying the optimal k-nearest neighbor (kNN) imputation model. By comparing the prediction accuracy of 630 kNN models, we found that the models with random forest (RF) as the distance metric showed the highest accuracy. Compared to the use of single-month MODIS data for September, there was no appreciable improvement for the estimation accuracy of species-level AFB by using multi-month MODIS data. When k > 7, the accuracy improvement of the RF-based kNN models using the single MODIS predictors for September was essentially negligible. Therefore, the kNN model using the RF distance metric, single-month (September) MODIS predictors and k = 7 was the optimal model to impute the species-level AFB for entire Northeast China. Our imputation results showed that average AFB of all species over Northeast China was 101.98 Mg/ha around 2000. Among 17 widespread species, larch was most dominant, with the largest AFB (20.88 Mg/ha), followed by white birch (13.84 Mg/ha). Amur corktree and willow had low AFB (0.91 and 0.96 Mg/ha, respectively). Environmental variables (e.g., climate and topography) had strong relationships with species-level AFB. By integrating forest inventory data and remote sensing data with complete spatial coverage using the optimal kNN model, we successfully mapped the AFB distribution of the 17 tree species over Northeast China. We also evaluated the accuracy of AFB at different spatial scales. The AFB estimation accuracy significantly improved from stand level up to the ecotype level, indicating that the AFB maps generated from this study are more suitable to apply to forest ecosystem models (e.g., LINKAGES) which require species-level attributes at the ecotype scale

Evaluación de la humedad del suelo mediante imágenes de temperatura radiactiva e índice de vegetación

El objetivo es proponer un método de estimación de humedad del suelo sólo con datos de satélite. Se utilizaron imágenes diarias MODIS/Aqua de temperatura de superficie, de 1 km y de índice de vegetación EVI, calculado a partir de imágenes de distintas bandas reflectivas, a 500 metros de resolución. Con ellas se calculó el índice de estrés Temperature Vegetation Dryness Index (TVDI), a 500 metros de resolución. Posteriormente se comparó este índice con la humedad del suelo en el norte de la provincia de Buenos Aires, medida a 60 cm de profundidad. Se halló una fuerte relación entre estas dos variables (R2 de 0,69). Se concluye que mediante el índice TVDI se puede lograr una correcta estimación de la humedad del suelo, posibilitando su aplicación en diversos campos de la hidrología como el estudio de la humedad en la zona no saturada, humedad antecedente o la recarga.The objective is to propose a method of soil moisture estimation from satellite without ancillary data. We used daily MODIS/Aqua LST L3 Global 1 Km Grid products and Enhanced Vegetattion Index, obtained from reflectances at 500 m spatial resolution. Then Temperature-Vegetation Dryness Index was calculated at 500 m. This index was compared with measurements of soil moisture at 60 cm depth in the North of Buenos Aires province. A strong correlation between these variables was obtained (R2=0.69). In conclusion, an adequate estimation of soil moisture can be achieved through TVDI. This index can be used in several hydrological studies, like wetness in unsaturated zone, antecedent moisture conditions or aquifer recharge.Universidad Nacional de La Plat

Competition and Burn Severity Determine Post-Fire Sapling Recovery in a Nationally Protected Boreal Forest of China: An Analysis from Very High-Resolution Satellite Imagery

Anticipating how boreal forest landscapes will change in response to changing fire regime requires disentangling the effects of various spatial controls on the recovery process of tree saplings. Spatially explicit monitoring of post-fire vegetation recovery through moderate resolution Landsat imagery is a popular technique but is filled with ambiguous information due to mixed pixel effects. On the other hand, very-high resolution (VHR) satellite imagery accurately measures crown size of tree saplings but has gained little attention and its utility for estimating leaf area index (LAI, m2/m2) and tree sapling abundance (TSA, seedlings/ha) in post-fire landscape remains untested. We compared the explanatory power of 30 m Landsat satellite imagery with 0.5-m WorldView-2 VHR imagery for LAI and TSA based on field sampling data, and subsequently mapped the distribution of LAI and TSA based on the most predictive relationships. A random forest (RF) model was applied to assess the relative importance and causal mechanisms of spatial controls on tree sapling recovery. The results showed that pixel percentage of canopy trees (PPCT) derived from VHR imagery outperform all Landsat-derived spectral indices for explaining variance of LAI (R2VHR = 0.676 vs. R2Landsat = 0.427) and TSA (R2VHR = 0.508 vs. R2Landsat = 0.499). The RF model explained an average of 55.5% (SD = 3.0%, MSE = 0.382, N = 50) of the variation of estimated LAI. Understory vegetation coverage (competition) and post-fire surviving mature trees (seed sources) were the most important spatial controls for LAI recovery, followed by burn severity (legacy effect), topographic factors (environmental filter) and nearest distance to unburned area (edge effect). These analyses allow us to conclude that in our study area, mitigating wildfire severity and size may increase forest resilience to wildfire damage. Given the easily-damaged seed banks and relatively short seed dispersal distance of coniferous trees, reasonable human help to natural recovery of coniferous forests is necessary for severe burns with a large patch size, particularly in certain areas. Our research shows the VHR WorldView-2 imagery better resolves key characteristics of forest landscapes like LAI and TSA than Landsat imagery, providing a valuable tool for land managers and researchers alike

Mapping Spatial Variations of Structure and Function Parameters for Forest Condition Assessment of the Changbai Mountain National Nature Reserve

Forest condition is the baseline information for ecological evaluation and management. The National Forest Inventory of China contains structural parameters, such as canopy closure, stand density and forest age, and functional parameters, such as stand volume and soil fertility. Conventionally forest conditions are assessed through parameters collected from field observations, which could be costly and spatially limited. It is crucial to develop modeling approaches in mapping forest assessment parameters from satellite remote sensing. This study mapped structure and function parameters for forest condition assessment in the Changbai Mountain National Nature Reserve (CMNNR). The mapping algorithms, including statistical regression, random forests, and random forest kriging, were employed with predictors from Advanced Land Observing Satellite (ALOS)-2, Sentinel-1, Sentinel-2 satellite sensors, digital surface model of ALOS, and 1803 field sampled forest plots. Combined predicted parameters and weights from principal component analysis, forest conditions were assessed. The models explained spatial dynamics and characteristics of forest parameters based on an independent validation with all r values above 0.75. The root mean square error (RMSE) values of canopy closure, stand density, stand volume, forest age and soil fertility were 4.6%, 33.8%, 29.4%, 20.5%, and 14.3%, respectively. The mean assessment score suggested that forest conditions in the CMNNR are mainly resulted from spatial variations of function parameters such as stand volume and soil fertility. This study provides a methodology on forest condition assessment at regional scales, as well as the up-to-date information for the forest ecosystem in the CMNNR