Leaf Area Index Data Research Articles

LAI‐based reduction in growth of semiarid grasslands and its responses to climate change on China's Loess Plateau

AbstractUnderstanding the response of vegetation growth to climate change can provide key information for predicting future ecosystem dynamics. Over the past 6 years, the semiarid regions of the Loess Plateau in China have experienced a transition from wet to dry years, but little is known about the response of grass growth to this change. Thus, this study aimed to analyze and evaluate the effect of climate change on grass growth. Using observed climate data and leaf area index (LAI) data obtained with hemispherical photography from 2016 to 2021, we investigated changes in trends of five climatic factors, including precipitation (P), temperature (T), relative humidity (RH), wind speed (WS), and solar radiation (Rs), and their impact on the growth of alfalfa (Medicago sativa) and Stipa bungeana plantations in the semiarid area of the Loess Plateau. The results showed a tendency to become colder in spring and drier in summer and autumn. In response, LAI tended to decrease in alfalfa and S. bungeana plots with interannual variation rates of 0.21 year−1 and 0.12 year−1, respectively. Additionally, linear regression methods showed that P had the greatest effect on LAI of two grasslands during the growing season (R2 > 0.84, p < 0.01), followed by RH and T (0.30 < R2 < 0.50, p < 0.01), a negative effect of WS (R2 = 0.36, p < 0.01), and no significant effect of Rs on LAI were found. Conversely, the path analysis indicated that Rs and P were the largest decision coefficients affecting the monthly LAI of alfalfa and S. bungeana, respectively, due to the different time‐to‐peak LAI and root growth depth of these two grass species. This study can provide theoretical support for ecological restoration and system management of semiarid grasslands.

Effect Of Nitrogen Fertilizer on The Growth of Hybrid Sorghum Sudan Grass

A pot experiment was conducted at Experimental area, Agronomy Department PMAS- University of Arid Agriculture Rawalpindi to see the effect of different levels of nitrogen fertilizer on the growth and physiological traits of Sorghum Sudan Grass during March, 2022. Completely randomized designed was applied along with three replications. Seeds of Sorghum Sudan grass were collected from NARC Islamabad. Nitrogen fertilizer was applied in pots on 30th March, 2022, according to the treatments. Nitrogen played a significant role in the growth and physiology of Sorghum Sudan Grass. Nitrogen @ 150 kgha-1 is the best among other four treatments and attained the highest plant heights in three times (21.355, 65. 153 and 100.620 cm) respectively. However, results of nitrogen @ 200kgha-1 are approximately statistically at par with NPK. Nitrogen @ 150 kgha-1 gained the highest position in two times data (0.7050 and 1.4250) respectively. Nitrogen @ 200 kgha-1 gained less value than nitrogen @150kgha-1 might be excessive dose. Nitrogen nutrition played a key role in the enhancement of leaf area values; however nitrogen @150kgha-1nutrition had the top position (1.140 and 1.950) during two times respectively. Leaf area index data had statistically significant results but among the nutrition treatments nitrogen @150kgha-1nutrition is the best option attaining the highest values (91.475 and 152.550) respectively in two times. Nitrogen fertilizer at different levels had statistically significant effect on crop growth rate of sorghum plants; however nitrogen @150kgha-1nutrition treatment attained the highest value (0.5333) which was statically at par @200kgha-1nutrition treatment. Nitrogen @150kgha-1nutrition treatment had the top position (4061.9) but other three doses of nitrogen values having minute difference with nitrogen @150kgha-1 and higher than control. Four doses of nitrogen fertilizer produced more net assimilation rates than control and nitrogen @150kgha-1gained the highest value (45.653) which was statistically at par with nitrogen @200kgha-1(43.521).Nitrogen fertilizer is an inorganic fertilizer provides the nutrition the plant fastly and provides better performance at nitrogen @150kgha-1. Nitrogen @200kgha-1 showed better performance than control but less than nitrogen @150kgha-1, therefore this finding improves the economics of the study.

Reprocessed MODIS Version 6.1 Leaf Area Index Dataset and Its Evaluation for Land Surface and Climate Modeling

Satellite-based leaf area index (LAI) products, such as the MODIS LAI, play an essential role in land surface and climate modeling research, from regional to global scales. However, data gaps and high-level noise can exist, thus limiting their applications to a broader scope. Our previous work has reprocessed the MODIS LAI Collection 5 (C5) product, and the reprocessed data have been widely used these years. In this study, the MODIS C6.1 LAI data were reprocessed to broaden its application as a successor. We updated the integrated two-step method that is used for MODIS C5 LAI and implemented it into the MODIS C6.1 LAI product. Comprehensive evaluations for the original and reprocessed products were conducted. The results showed that the reprocessed LAI data had better performance in validation against reference maps. In addition, the site scale time series of reprocessed data was much smoother and more consistent with adjacent values. The global scale comparison showed that, though the MODIS C6.1 LAI does have improvements in ground validation with LAI reference maps, its spatial continuity, temporal continuity, and consistency showed little improvement when compared to C5. In contrast, the reprocessed data were more spatiotemporally continuous and consistent. Based on this evaluation, some suggestions for using various MODIS LAI products were given. This study assessed the quality of these different versions of MODIS LAI products and demonstrated the improvement of the reprocessed C6.1 data, which we recommended for use as a substitute for the reprocessed C5 data in land surface and climate modeling.

Evaluation of Yield Prediction Performance of DSSAT CSM-CERES-Wheat Model in Some Bread Wheat Varieties

Product simulation programs with DSSAT are based on the principle of predicting the potentials of yield and other phenological parameters of wheat varieties with different fertilizer application doses in different climatic and soil conditions. For this purpose, different wheat varieties (Bayraktar, Tosunbey) were used in order to test the use of the DSSAT simulation model in semi-arid conditions in the Ikizce experimental area of the Haymana District of Ankara Province, Field Crops Central Research Institute, during the 2017-2018 and 2018-2019 periods. The aim of this study is to predict yield in wheat varieties (Bayraktar, Tosunbey) using CERES and CROPGRO sub-models of DSSAT v.4.7.5 simulation model. In the study, the model was run at different nitrogen application doses (0, 6, 12, 18 kg/da) to reveal the yield prediction potential of the wheat cultivars in semi-arid conditions. For the calibration of the model, the grain yield, plant height and Leaf area index (LAI) data obtained were used in the first year of wheat development stage.The accuracy of the model, which was calibrated with the first year data, was tested with the second year data. For Bayraktar variety, the average measured yield obtained from different nitrogen dose applications (N0,N6,N18) for the 2017-2018 period is 373.3 kg/da, the simulated yield is 373.7 kg/da (N12 dose is neglected), the measured yield for 2018-2019 300. 5 kg/da, the simulated yield was found to be 291.3 kg/da.For the Tosunbey variety, the average yield measured for the 2017-2018 period was 370.0 kg/da, the simulated yield was 338.0 kg/da, the measured yield for the 2018-2019 year was 217.58 kg/da, and the estimated yield was 237.83 kg/da.

A double-layer model for improving the estimation of wheat canopy nitrogen content from unmanned aerial vehicle multispectral imagery

The accurate and rapid estimation of canopy nitrogen content (CNC) in crops is the key to optimizing in-season nitrogen fertilizer application in precision agriculture. However, the determination of CNC from field sampling data for leaf area index (LAI), canopy photosynthetic pigments (CPP; including chlorophyll a, chlorophyll b and carotenoids) and leaf nitrogen concentration (LNC) can be time-consuming and costly. Here we evaluated the use of high-precision unmanned aerial vehicle (UAV) multispectral imagery for estimating the LAI, CPP and CNC of winter wheat over the whole growth period. A total of 23 spectral features (SFs; five original spectrum bands, 17 vegetation indices and the gray scale of the RGB image) and eight texture features (TFs; contrast, entropy, variance, mean, homogeneity, dissimilarity, second moment, and correlation) were selected as inputs for the models. Six machine learning methods, i.e., multiple stepwise regression (MSR), support vector regression (SVR), gradient boosting decision tree (GBDT), Gaussian process regression (GPR), back propagation neural network (BPNN) and radial basis function neural network (RBFNN), were compared for the retrieval of winter wheat LAI, CPP and CNC values, and a double-layer model was proposed for estimating CNC based on LAI and CPP. The results showed that the inversion of winter wheat LAI, CPP and CNC by the combination of SFs+TFs greatly improved the estimation accuracy compared with that by using only the SFs. The RBFNN and BPNN models outperformed the other machine learning models in estimating winter wheat LAI, CPP and CNC. The proposed double-layer models (R2=0.67–0.89, RMSE=13.63–23.71 mg g–1, MAE=10.75–17.59 mg g–1) performed better than the direct inversion models (R2=0.61–0.80, RMSE=18.01–25.12 mg g–1, MAE=12.96–18.88 mg g–1) in estimating winter wheat CNC. The best winter wheat CNC accuracy was obtained by the double-layer RBFNN model with SFs+TFs as inputs (R2=0.89, RMSE=13.63 mg g–1, MAE=10.75 mg g–1). The results of this study can provide guidance for the accurate and rapid determination of winter wheat canopy nitrogen content in the field.

Assessing the consistency of crop leaf area index derived from seasonal Sentinel-2 and Landsat 8 imagery over Manitoba, Canada

To take full advantage of cloud-free optical remote sensing data for crop leaf area index (LAI) retrieval throughout the growing season, integration of multi-sensor satellite data is increasingly resorted. However, the consistencies of LAI products derived from different satellites using different retrieval approaches should be assessed. This study aimed at understanding the spatiotemporal consistency of crop LAI derived from S2/MSI (Multi Spectral Instrument, Sentinel-2) and L8/OLI (Operational Land Imager, Landsat 8) data using two retrieving approaches, a field-data-driven (FDD) approach driven by field measured LAI and a hybrid approach based on simulations of the PROSAIL radiative transfer model. Results from the two approaches were assessed using field measured LAI for six types of crops in 2016 over Manitoba, one of the major agricultural provinces in the Canadian Prairies. LAI estimates from S2/MSI data obtained unbiased root-mean-square-error (ubRMSE) of 0.39 and 0.51 cm2 cm−2 for the FDD and hybrid approaches respectively. For the L8/OLI, ubRMSE of LAI estimates were 0.82 and 0.92 cm2cm−2 for the FDD and hybrid approaches, respectively. To evaluate the temporal consistency of crop LAI derived from the two satellites, daily crop LAI was reconstructed using the Parametric Double-Hyperbolic Tangent model (PDHT) with the aid of Bayesian statistical approach. Time series crop LAI of the hybrid approach had lower variability of estimated parameters in the PDHT and reconstructed daily LAI compared with that of the FDD approach. Based on the Spatial Efficiency Metric (SPAEF) that is a spatial performance metric for assessing spatial pattern similarity, the hybrid approach obtained relatively strong spatial consistency of LAI derived from the two satellites compared with the FDD approach. The study revealed that hybrid approach can achieve good spatiotemporal consistencies in seasonal LAI estimates from the two satellites combined when large numbers of field measurements are not available.

Opportunities for Adaptation to Climate Change of Extensively Grazed Pastures in the Central Apennines (Italy)

Future climate change is expected to significantly alter the growth of vegetation in grassland systems, in terms of length of the growing season, forage production, and climate-altering gas emissions. The main objective of this work was, therefore, to simulate the future impacts of foreseen climate change in the context of two pastoral systems in the central Italian Apennines and test different adaptation strategies to cope with these changes. The PaSim simulation model was, therefore, used for this purpose. After calibration by comparison with observed data of aboveground biomass (AGB) and leaf area index (LAI), simulations were able to produce various future outputs, such as length of growing season, AGB, and greenhouse gas (GHG) emissions, for two time windows (i.e., 2011–2040 and 2041–2070) using 14 global climate models (GCMs) for the generation of future climate data, according to RCP (Representative Concentration Pathways) 4.5 and 8.5 scenarios under business-as-usual management (BaU). As a result of increasing temperatures, the fertilizing effect of CO2, and a similar trend in water content between present and future, simulations showed a lengthening of the season (i.e., mean increase: +8.5 and 14 days under RCP4.5 and RCP8.5, respectively, for the period 2011–2040, +19 and 31.5 days under RCP4.5 and RCP8.5, respectively, for the period 2041–2070) and a rise in forage production (i.e., mean biomass peak increase of the two test sites under BaU: +53.7% and 62.75% for RCP4.5. and RCP8.5, respectively, in the 2011–2040 period, +115.3% and 176.9% in RCP4.5 and RCP8.5 in 2041–2070, respectively,). Subsequently, three different alternative management strategies were tested: a 20% rise in animal stocking rate (+20 GI), a 15% increase in grazing length (+15 GL), and a combination of these two management factors (+20 GI × 15 GL). Simulation results on alternative management strategies suggest that the favorable conditions for forage production could support the increase in animal stocking rate and grazing length of alternative management strategies (i.e., +20 GI, +15 GL, +20 GI × 15 GL). Under future projections, net ecosystem exchange (NEE) and nitrogen oxide (N2O) emissions decreased, whereas methane (CH4) rose. The simulated GHG future changes varied in magnitude according to the different adaptation strategies tested. The development and assessment of adaptation strategies for extensive pastures of the Central Apennines provide a basis for appropriate agricultural policy and optimal land management in response to the ongoing climate change.

Improving the MODIS leaf area index product for a cropland with the nonlinear autoregressive neural network with eXogenous input model

The leaf area index (LAI) is a crucial descriptive parameter of the dynamic change of ground vegetation. The widely used MODIS LAI product, however, does not satisfy the requirements of regional eco-environment modeling. There is an urgent need to improve the product’s overall accuracy. Under this circumstance, this study proposed an improvement scheme based on the nonlinear autoregressive neural network with eXogenous input (NARXNN) model and the high-quality time series LAI inversion result. Case studies were implemented for two seasons a year croplands in Wuzhi, Xinzheng, and Xiangcheng in Henan province. This research acquired 46 periods of the NARXNN model-improved LAI, which went through rigid in situ LAI validation. The in situ measured LAI by LAI-2000 was used to validate the accuracy of NARXNN-enhanced LAI data. The R2 values of the improved LAI of the three research areas are 0.54, 0.41, and 0.51, while the RMSE decreased by 0.07, 0.1, and 0.03, and the bias also decreased to a certain extent. Direct validation using the in situ measured LAI demonstrates that the NARXNN model-enhanced LAI data were more accurate and had a lower bias than MCD15A2H. A comparison of the time series change indicates that the NARXNN-enhanced LAI shows a smoother bimodal change trend and is more conformed to the actual cropland growth than the original MODIS product. The results indicated that the NARXNN neural network further increased the accuracy of the MODIS product and has a particular practical value in future research.

Integrated effects of co-evolutions among climate, land use and vegetation growing dynamics to changes of runoff quantity and quality

Climates, Land use/land cover (LULC) and vegetation growing dynamics have been regarded as the main factors affecting terrestrial hydrological process. However, the mechanisms underlying their integrated effects on terrestrial runoff and nutrient dynamics are not understood well. Here, we constructed a framework to disentangle and quantify the independent and coupled contributions of climate, LULC and vegetation leaf area index (LAI) changes to watershed runoff and nutrient yields changes. Long series of changing meteorological, LULC and LAI data between 1990 and 2020 were integrated into a factor-controlled simulation protocol in a distributed hydrological model, to quantify their comprehensive contributions (individual contribution of single factor change and coupling contribution of multiple factor synchronous changes) to runoff and nutrient changes. The results showed that changes of runoff and nutrient yields are more induced by climate change, rather than LULC and LAI transformations. Increase in annual precipitation significantly elevated runoff and nutrient yields. TP yield was more sensitive to climate change than runoff and TN yields. LULC transformation and climate change have synergistic effects on runoff and nutrient yields. Shift of vegetation areas to construction lands will amplify the effect of climate change on runoff and nutrient yields. Single LAI change has weak effect on runoff and nutrient yields, but it can significantly alter the hydrological effects derived from climate change and the synergistic effects between climate change and LULC transformation. This study considered the coupling and potential synergistic effects among climate change, LULC conversion and LAI variation, which elucidated the comprehensive effects of changing environment on runoff and nutrients evolutions in a more systematic and integrated perspective.

Multi-source remote sensing data shows a significant increase in vegetation on the Tibetan Plateau since 2000

In recent years, there has been growing concern that vegetation changes on the Tibetan Plateau are associated with climate change (temperature and precipitation) and human activities. This study used six types of remote sensing vegetation data, including GIMMS (Global Inventory Modelling and Mapping Studies) NDVI (Normalized Difference Vegetation Index), MODIS (Moderate Resolution Imaging Spectroradiometer) NDVI, MODIS EVI (Enhanced Vegetation Index), SPOT Vegetation (Spot-VGT) NDVI, LAI (Leaf Area Index) and NPP (Net Primary Productivity), and applied the maximum synthesis method, trend analysis, correlation analysis, and multivariate statistical analysis to investigate vegetation change processes since the 1980s. The study showed that the amount of vegetation on the TP had increased significantly since 2000 ( p < .01), especially in the northeastern part of the TP. There was no significant change prior to 2000. The different vegetation data sources varied greatly. Four remote sensing indices, MODIS EVI, Spot-VGT NDVI, LAI, and NPP, showed a significant increase in vegetation from 2000, accounting for 16.18%, 44.55%, 30.44% and 8.94% of the total area, respectively ( p < .05). Multiple data sources provided a more comprehensive understanding, whereas a single data source had substantial uncertainty. Human activities, such as the implementation of large-scale ecological projects, played a dominant role in increasing vegetation, while climate change played a subsidiary role. The MODIS EVI, Spot-VGT NDVI, LAI, and NPP data showed that the area of increased vegetation caused by human activities accounted for 53.51%, 45.68%, 37.52%, and 31.79% of the total area of the TP, respectively. The relative increase from climate change was 10.28%, 17.49%, 13.15%, and 8.82%, respectively. The current study applied multi-source remotely sensed vegetation data, which effectively reduced the uncertainty caused by individual data sources and provided more rigorous and scientific research conclusions.

Disentangling the independent and interacting impacts of biophysical factors on the transpiration of a black locust (Robinia pseudoacacia) plantation in the semiarid Loess Plateau, China

AbstractTranspiration (Tr) is the largest continental water flux. Tr is jointly affected by biophysical factors, including atmospheric demand, soil water supply, and plant physiological features, represented by potential evapotranspiration (PET), soil water content (SWC), and leaf area index (LAI) respectively. Disentangling the independent and interacting impacts of the biophysical factors on Tr provides a better understanding on the mechanism of ecohydrological controls on forest water use. In this study, taking the black locust plantation of the Loess Plateau as an example, independent contributions of the three factors and their interactions on Tr were quantified based on field observation, model development, and factorial analysis. First, sapflow density, meteorological variables, SWC, and LAI were observed during five growing seasons. Second, a coupled Tr model (R2 = 0.85, RMSE = 5.92) was established by the long‐term observed data of Tr, PET, SWC, and LAI. Finally, factorial experiment analysis showed that contributions of LAI, PET, and SWC and their interactions (PET×LAI, LAI × SWC, and PET×SWC) on Tr accounted for 50.82%, 9.77%, 9.30%, 13.88%, 13.75%, and 2.47%, respectively. The results indicated that LAI and PET×LAI had the largest independent and interacting impact on Tr of black locust. This study suggested that regulation of canopy leaf area through forest cultivation practices could reduce forest water use, which was beneficial for soil desiccation mitigation and meeting the soil water capacity for vegetation in this region. This study also provided a methodological reference for disentangling the impacts of biophysical factors on forest transpiration.

Efficiency of assimilating leaf area index into a soybean model to assess within-field yield variability

Methods for accurately estimating within-field yield are essential to improve site-specific crop management and resource use efficiencies, which would be a major step toward sustainable intensification of agricultural systems. We set out to assess the accuracy of within-field soybean yields predicted by two data assimilation methods and to assess these methods’ assimilation efficiency (AE). Yields were estimated by assimilating remotely sensed leaf area index (LAI) data from Sentinel-2 into a soybean crop growth model on a pixel basis. The LAI data was integrated into the model by Ensemble Kalman Filtering (EnKF) or by recalibrating with the Subplex algorithm (recalibration-based). An open-loop setting which only integrates information on the soil layers was used as a baseline scenario for quantifying the AE. We assessed both data assimilation techniques on eight fields (3067 pixels) in the Corn Belt region (Nebraska, Kansas and Kentucky) in the United States. The data set encompassed substantial variation in crop growth conditions: three growing seasons (2018, 2019 and 2020), rainfed and irrigated fields, and early and late planting dates. Ground truth yield acquired from combine monitors was used to validate the yield estimations. Agreement between predicted and observed yield at pixel level was two times higher for both data assimilation methods compared to the open-loop. The root mean square error (RMSE) was 476 kg.ha−1 (RRMSE of 10 %) in the recalibration-based method and 573 kg.ha−1 (RRMSE of 12 %) in the EnKF-based method. For both data assimilation methods, assimilating the LAI improved predictions for 68 % of the pixels. For a further 12 % of pixels, there was no accuracy improvement. For the remaining 20 %, AE was positive for one of the two assimilation methods. The high proportion of pixels with positive AE indicates the potential for overcoming the limitations in applying crop models at high spatial resolution by integrating a crop growth indicator. Assimilating an in-season indicator of crop growth (LAI) into a soybean model made it possible to adjust the simulation pathway, thereby greatly improving the accuracy of the yield estimations at the pixel level. This study elucidates the practical applications of data assimilation strategies for fine-scale within-field crop yield mapping.

Ecological Response to Climate Change Across China From Combined Soil Temperature and Moisture Changes

AbstractA coupled soil temperature (ST) and moisture (SM) balance reflects a synthetic climate regime, having huge ecological impacts. This paper used ST and SM data from the European Center for Medium‐Range Weather Forecasts climate reanalysis‐Land and the Coupled Model Intercomparison Project Phase 6 and leaf area index (LAI) data from the Global Land Surface Satellite Product Suite. The focus was on understanding joint ST‐SM changes and the resulting ecological response across China. The results show that during 2000–2020, 24.5% of the land area in China experienced a warming‐drying trend resulting in a 9.7% LAI decrease, while 6.4% of the area experienced a warming‐wetting trend leading to an 8.6% LAI increase. During 2015–2100, 30.6% of the land area in China will be warmer and drier, while 55.2% of the area will be warmer but wetter across three shared socioeconomic pathways (SSP126, 245, and 585). Superimposed on the long‐term trends, there are also significant spatiotemporal variabilities in ST and SM on annual to decadal timescales. The LAI also showed substantial short‐term fluctuations in both typical regions and ecosystems despite consistent long‐term increases. Our findings suggest that ecosystems could be impaired on annual to decadal scales by adverse soil conditions in the twenty‐first century, but in terms of long‐term trends, ecosystems may be resilient partly because of the compensating effects of global warming and regional hydrological changes. Impact studies should thus focus more on annual to decadal soil‐ecosystem anomalous events.

Improving methods to predict aboveground biomass of Pinus sylvestris in urban forest using UFB model, LiDAR and digital hemispherical photography

The article proposes methods for combining Airborne Laser Scanning (ALS) with Digital Hemispherical Photography (DHP) data required by the Urban Forest Biomass (UFB) model to predict the aboveground biomass (AGB) of Scotch pine (Pinus sylvestris L.) in urban forests of Lublin (Poland). The article also demonstrates the potential of ALS and DHP data in urban AGB estimation. ALS and Leaf Area Index (LAI) data were calculated using a voxels-vector approach based on the measurements taken at eight permanent sample plots (PSPs). The research was conducted in 2014 and the prediction was made until 2030. It was found that the determination coefficients (R2) for the Basal Area (BA) of the trees are 0.97, and the BA modeling parameters have a high correlation with those observed in the field (model efficiency (ME) 0.94). 83 % growth trajectory based on the measured BA was appropriately modeled using the UFB model (P > 0.9). The results for AGB show that the degree of fitting and accuracy are greatest for the Monte Carlo (MC) simulation technique based on ALS and DHP data (UBF with ALS and DHP) where R2 = 0.98, RMSE = 2.97 t/ha, MAE = 2.35 t/ha, rRMSE = 1.28 %, which performed better than MC simulation technique without ALS and DHP (UBF without ALS and DHP) where R2 = 0.94, RMSE = 4.58 t/ha, MAE = 3.64 t/ha, rRMSE = 3.29 %. The results indicate that the proposed method based on combining the UFB model, LiDAR and DHP allows us to improve the accuracy of the AGB prediction.

Evaluating Satellite-Observed Ecosystem Function Changes and the Interaction with Drought in Songnen Plain, Northeast China

Drought is considered one of the devastating natural disasters worldwide. In the context of global climate change, the frequency and intensity of drought have increased, thereby affecting terrestrial ecosystems. To date, the interactions between ecosystem change and drought, especially their mutual lag and cumulative effects is unclear. The Songnen Plain in northeastern China is one of the three major black soil areas in the world and is highly sensitive to global change. Herein, to quantify the interaction between drought and ecosystem function changes in the Songnen Plain, integrating with time-series moderate resolution imaging spectroradiometer (MODIS), leaf area Index (LAI), evapotranspiration (ET), and gross primary productivity (GPP) data, we calculated the standardized precipitation and evapotranspiration index (SPEI) based on the meteorological data, diagnosed the causal relationship between SPEI and the ecosystem function indicators i.e., LAI, ET, and GPP, and analyzed the time-lag and cumulative effects between the degree of drought and three ecosystem function indicators using impulse response analysis. The results showed that the trend of SPEI (2000–2020) was positive in the Songnen Plain, indicating that the drought extent had eased towards wetness. LAI showed insignificant changes (taking up 88.34% of the total area), except for the decrease in LAI found in some forestland and grassland, accounting for 9.43%. The pixels showing a positive trend of ET and GPP occupied 24.86% and 54.94%, respectively. The numbers of pixels with Granger causality between LAI and SPEI (32.31%), SPEI and GPP (52.8%) were greater at the significance 0.05 level. Impulse responses between each variable pair were stronger mainly between the 6th and 8th months, but differed significantly between vegetation types. Grassland and cropland were more susceptible to drought than forest. The cumulative impulse response coefficients values indicated that the mutual impacts between all variables were mainly positive. The increased wetness positively contributed to ecosystem function, and in turn enhanced ecosystem function improved regional drought conditions to some extent. However, in the northeastern forest areas, the SPEI showed a significant negative response to increased ET and GPP, suggesting that the improved physiological functions of forest might lead to regional drought. There were regional differences in the interaction between drought conditions and ecosystem function in the Songnen Plain over the past 21 years.

Changes in land use and cover and their consequences on the spatial distribution of evapotranspiration and climatic elements in southwest Piauí, MATOPIBA

With the advance of agriculture in Brazil, the occurrence of environmental disturbances that can reach different levels, such as the change of use and land cover, is perceived. In the southwest of Piauí, it is noticeable the large portion of Cerrado that has been replaced by crops and pastures, resulting in several impacts, such as changes in evapotranspiration. In the literature there are several methods to estimate it, among them the remote sensing techniques stand out. Therefore, the objective of this work was to quantify agricultural expansion and understand its consequences on real evapotranspiration and on the configuration of meteorological elements in the municipality of Baixa Grande do Ribeiro, through the products of the United States Geological Survey (USGS) accessed through Google Earth. Engine. Platform (GEE), between 2000 and 2020. The real evapotranspiration (ETa) and leaf area index (LAI) data come from the MODIS sensor products. The meteorological data were obtained from the website of the National Aeronautics and Space Administration Prediction Of Worldwide Energy Resources (NASA POWER). The maps were made in QGIS 3.18.1 software from data processed on the GEE platform. The evolution of forest and agricultural areas and images of land use and cover were obtained using the MapBiomas platform. It was observed that between 2000 and 2020, about 27% of the area of native vegetation was replaced, and soybean cultivation grew by approximately 2618%. In addition, the removal of native vegetation for the establishment of pastures and agricultural crops has contributed to the reduction of evapotranspiration and the leaf area index, in addition, it may be associated with the increase in surface and air temperatures and reduction of the relative humidity of the air. This implies the need to create and implement public policies aimed at controlling deforestation in the region, as well as greater inspection in the sphere of environmental crimes.

Feasibility of yield estimation based on leaf area dynamics measurements in rice paddy fields of farmers

Rice yield is an important factor for farmers when reviewing crop management planning and cultivation operations. In previous studies, a non-destructive, labor-saving method has been proposed to determine yield using spectral information from remote sensing. This method assumes that molecule number (e.g., chlorophyll content in leaves) and biophysical information (e.g., leaf area) cause spectral variations. However, spectral information cannot reflect the influence of physiological factors because of the limitations of this method. In this study, we investigated solar radiation use efficiency (RUE), which reportedly reflects physiological changes in crops. Time-series data of leaf area index were collected over five years in rice paddy fields of farmers to calculate and investigate the variation in RUE. The results showed that RUE varied among the survey points but did not differ significantly by year. The mean absolute error of yield estimation ranged from 68.2 g m −2 to 102.2 g m −2 depending on the yield estimation method and from 36.8 g m −2 to 454.1 g m −2 depending on the RUE. Among the estimation methods, the highest accuracy was achieved using leaf area dynamics. These results provide insights into both the effectiveness of leaf area dynamics in yield estimation and the potential errors when using remote sensing. • Solar radiation use efficiency differs by location, planting method, and year. • Error in yield estimation using leaf area differs by solar radiation use efficiency. • Leaf area dynamics is effective in improving the accuracy of rice yield estimation.

Estimation of shrub willow biophysical parameters across time and space from Sentinel-2 and unmanned aerial system (UAS) data

Shrub willow can be used as a perennial energy crop to produce biomass for biofuels, bioproducts, and bioenergy to help address environmental issues. Leaf chlorophyll content (LCC), leaf area index (LAI), and canopy chlorophyll content (CCC), which are closely related to vegetation health status, provide valuable information to assess willow vigor and crop management activities. We developed models for estimating LCC, LAI, and CCC from Sentinel-2 and unmanned aerial system (UAS) data using a number of vegetation indices (VIs) and applying four analytical tools: regression analysis, partial least squares regression (PLSR), random forest (RF), and neural networks (NNs). We investigated twenty VIs using regression analysis, finding the red-edge normalized difference vegetation index (NDVIre) as the optimal VI based on predictive performance and so this was included as an input variable for the PLSR, RF, and NN methods. For RF and NN models, sensitivity analysis was implemented to help tune hyperparameters and provide information to guide future studies. Results showed that the best averaged performance of Sentinel-2 and UAS data for LCC, LAI, and CCC (normalized root mean square error (NRMSE) = 9%, 12%, and 10%) of the same site in the same year was derived from models based on the NN algorithm using blue (B), green (G), red (R), red-edge (RE), and near-infrared (NIR) bands. In order to test model transferability, we collected UAS and Sentinel-2 data across different years and sites. Based on NRMSE, NN models using B, G, R, RE, and NIR bands provided the best averaged predictive performance for LCC estimation (16%) and the regression modeling using NDVIre performed best for LAI (21%) and CCC (13%) estimation for both Sentinel-2 and UAS data. Moreover, CCC estimation had smaller NRMSE values than LCC and LAI estimation for both Sentinel-2 and UAS data. Based on this study, we found that UAS data has the potential to complement Sentinel-2 data by mitigating limitations caused by temporal resolution and cloud cover. We also recommend use of the CCC parameter to evaluate shrub willow health status and perform time-series monitoring across time and space.

Effects of satellite LAI data on modelling land surface temperature and related energy budget in the Noah-MP land surface model

• Satellite LAI scheme can provide more accurate vegetation dynamic information. • Satellite LAI scheme improved LST simulation in Noah-MP land surface model. • Mechanism of LAI on LST simulation was revealed by energy budget analysis in model. Leaf area index (LAI) is essential for modelling the land surface temperature (LST) and related energy budget in the land surface models (LSMs). Due to the lack of a parameterization scheme based on satellite LAI data, it is unclear whether satellite LAI data can improve the performance of Noah with multi-parameterization (Noah-MP) LSM to model LST in China land data assimilation system (CLDAS). Here, by using satellite LAI data reconstructed by the advanced deep learning method, we established a new vegetation scheme based on satellite data for the Noah-MP LSM in the CLDAS. We quantitatively evaluated simulation effects of different vegetation schemes including the parameter table scheme (TAB), the dynamic vegetation scheme (DVEG), and the satellite LAI data scheme (OBS), on modelling LAI, LST and their related energy budget in China from 2016 to 2018. We found that except for the cropland, the LAI of the vegetation in growing season observed by satellite was lower than that simulated by the Noah-MP LSM. Validations based on the automatic weather stations in the vegetation-covered area showed that compared with the TAB and DVEG scheme, the OBS scheme improved the negative bias and high unbiased root mean square error (ubRMSE) of simulated LST. Compared with the TAB and DVEG scheme, the optimal improvements of the OBS scheme for annual mean bias and annual mean ubRMSE were 0.32 °C and 0.17 °C, respectively. We further revealed the mechanism of LAI on LST simulation in the Noah-MP LSM by decomposed temperature metric method. With the adoption of the new satellite LAI-based scheme, the absorbed radiation (SW), sensible heat flux (H), latent heat flux (LE), and ground heat flux (G) simulated by the Noah-MP LSM tend to change in different directions, leading to an overall increase in the net energy absorbed by the surface, which is consistent with the LST changes. The LAI changes of dense vegetation affect the LST simulation mainly through the competition of the absorbed shortwave radiation and turbulent heat fluxes. Due to the influence of exposed soil, the LAI changes of sparse vegetation mainly affect the LST simulation mainly through the interaction of the absorbed shortwave radiation and ground heat flux. Our study highlighted that the application of satellite LAI data has the potential to improve simulations of multiple land surface processes in the Noah-MP LSM.

Estimation of global GPP from GOME-2 and OCO-2 SIF by considering the dynamic variations of GPP-SIF relationship

Previous studies have indicated that gross primary production (GPP) and solar-induced chlorophyll fluorescence (SIF) have a strong linear relationship, and usually exhibit similar spatial and temporal patterns. However, the responses of GPP and SIF to the environment may be different, which will lead to a variant GPP-SIF relationship. To better investigate the impact of the dynamics in GPP-SIF relationship on GPP estimation, we established two GPP models. An inconstant GPP/SIF ratio model (Dynamic-Ratio model, DR model) was first established using meteorological variables and leaf area index (LAI) based on random forest regression algorithm. The model was then used to estimate GPP (referred to as GPP_DR) with different satellite SIF datasets i.e., downscaled fine resolution SIF from the Orbiting Carbon Observatory-2 (GOSIF) and Global Ozone Monitoring Experiment‐2 SIF (downscaled GOME-2 SIF). The second model (SIF-Climate-LAI model, SCL model) was also based on the random forest algorithm but was directly driven by meteorological variables, LAI and SIF data, and no GPP/SIF ratio was used in the model. As a comparison, the linear relationship between GPP and SIF was also established using eddy covariance tower GPP (GPP_EC) and SIF datasets based on linear regression without considering variations of GPP-SIF relationship (Fixed-Ratio model, FR model). Considering the spatio-temporal variations of GPP-SIF relationship can improve the GPP simulation to a certain extent by mitigating the underestimation of peak GPP values. This improvement was found for both DR and SCL models. Owing to the dynamic variations of GPP/SIF ratio and associated uncertainties, the performance of DR model was not as good as that of SCL model. GPP estimation derived from GOSIF matched better with GPP_EC than that from downscaled GOME-2 SIF for DR, SCL and FR models. Our findings suggested that GPP can be better derived from satellite SIF by considering the variations of GPP-SIF relationship.