Simple Ratio Index Research Articles

Revealing irrigation uniformity with remote sensing: A comparative analysis of satellite-derived uniformity coefficient

This study investigates the use of satellite-derived Christiansen Uniformity Coefficient (SDCUC) values for evaluating irrigation uniformity. In the context of global water scarcity and the imperative for sustainable water management, we explore the potential of remote sensing methods to evaluate irrigation uniformity across large agricultural areas. The findings reveal a consistent tendency for SDCUC to overestimate irrigation uniformity, with an average overestimation rate of 7.83 %. However, accuracy improved with the appropriate method, vegetation index, or spectral band selection. Employing the entire satellite image for SDCUC (SDCUCTOT) assessment improved accuracy. For Sentinel-1 (S1), using the dual-band cross-polarization horizontal transmit/vertical receive band (VH), the bias confidence interval was −0.39–0.69 %, while for Sentinel-2 (S2), using the normalized difference red edge 3 index (NDRE3), it was −1.47–0.66 %, and for Landsat 8 (L8) and Landsat 9 (L9) using the shortwave infrared water stress index (SIWSI) it ranged from 0.36 % to 2.28 %. Improved results were also observed when the normalized difference vegetation index (NDVI) ranged between 0.4 and 0.8 or the evapotranspiration and potential evapotranspiration ratio (ET/PET) ranged between 0.30 and 0.55. In these conditions, SDCUCTOT for the S2, L8, and L9 using the simple ratio index (SR) ranged from 1.00 % to 2.33 %, 0.00–1.83 %, 0.23–2.00 %, respectively, and for S2, the normalized difference water index (NDWI) and NDRE3 ranged from −1.39–0.71 %, and −1.43–2.31 % respectively. These findings underscore the potential of remote sensing techniques to revolutionize water resource management and promote sustainable agriculture, emphasizing the synergistic role of ground-based measurements and the need for continued methodological refinements to improve accuracy. Further advancements and research are warranted to refine the methodology and enhance the accuracy and reliability of remote sensing-based irrigation uniformity assessment, ultimately contributing to more sustainable agricultural irrigation practices.

Pre-Harvest UV-A Supplementation in Plant Factory with Artificial Lighting Improves Growth, Photosynthesis, and Phytonutrients in Kale

UV-A (315–400 nm) is a component of solar radiation, which impacts plant physiology. Consequently, the impact was investigated of UV-A supplementation on various aspects of kale cultivation in a plant factory with artificial lighting. Three UV-A intensity treatments (5, 10, or 15 W/m2) were applied to kale plants for 5 days before harvesting. The results revealed that 5 or 10 W/m2 UV-A supplementation provided significant advantages for kale growth. The fresh weight of the kale shoots increased by over 35–50% after UV-A exposure. In particular, the 10 W/m2 UV-A exposure significantly improved the photosynthetic rate, water-use efficiency, and quantum efficiency of photosystem II (Y(II)). In addition, the treatment with UV-A positively impacted changes in leaf spectral indices, resulting in decreases in the simple ratio index (SR), the normalized phaeophytinization index (NPQI), and the normalized difference red edge index (NDRE). Furthermore, the phytonutrients in kale (Vitamin C, soluble protein, and total phenolics) were enhanced using the 10 W/m2 UV-A treatment. One notable outcome was the consistent reduction in nitrate contents across all UV-A treatment intensities. Therefore, supplementing kale cultivation with UV-A radiation was a valuable strategy for enhancing kale yield and quality in plant factory production.

Спектральные характеристики засоленной пашни в предгорной долине Заилийского Алатау (Северный Тянь-Шань) в период 2016–2022 гг. по спутниковым данным Sentinel-2

Introduction. The foothill catchment of the Zailiisky Alatau (Northern Tien Shan), adjacent to the ridge from the north, occupies an altitude range of 475–925 m above sea level and has an area of about 8500 km2. The Almaty urban agglomeration with a population of 2.5 million people is located here and there is a developed agriculture. Crop production in the sharply continental, arid and hot climate of Central Asia is based on irrigation agriculture. For irrigation in the foothill catchment, river runoff from the Zailiysky Alatau Ridge is used. In some parts of the foothill catchment there are salt stores and salt outbreaks in the landscape, which contribute to the salinization of arable land. Secondary salinization of arable land is a negative factor that reduces their quality and productivity. Monitoring the salinity of arable land is practically an important task. The most effective technologies for detailed assessment of soil salinity are based on the processing of satellite data. The purpose of the work. The purpose of this study was to analyze the long-term spectral characteristics of agricultural land potentially susceptible to secondary salinization. This information is necessary to determine optimal strategies in the task of remote assessment of salinization of arable land in the foothill catchment of the Zailiysky Alatau Ridge. Materials and research methodology. The 323 cloudless Sentinel-2 images (10 m resolution) of the period 2016–2022 were selected. Four spectral indices: Normalized Difference Vegetation Index (NDVI); Soil Adjusted Vegetation Index (SAVI); Vegetation Soil Salinity Index (VSSI); and Simple Ratio Index (SRI); were used to monitor the test site, which was located in the northeastern part of the foothill catchment of the Trans-Ili Alatau Ridge. The test site included three polygons of irrigated arable land subjected to secondary salinization, and two polygons of saline abandoned land. The satellite monitoring of spectral indices was used to determine the most informative periods within the season, as well as to assess the inter-seasonal stability of the results. Results and discussion. The dynamics of the considered vegetation indices (NDVI, SAVI and SRI) differed most significantly in July-August, i.e., in conditions of the most developed vegetation. The VSSI index was the most informative in April-May and December-February. The vegetation condition of saline arable lands is controlled by the moisture content and salinity of the rootinhabited soil layer. The precipitation deficit leads to deterioration in the state of vegetation, which masks the negative impact of secondary salinization of fields. Thus, the assessment of the significance of the factor of secondary salinization in the state of vegetation requires annual ground sub-satellite surveys. Conclusion. The variations in the values of the spectral indices of test polygons are very large and depend on the seasonal weather characteristics. Therefore, practically significant satellite methods for assessing the salinity of arable land in the foothill catchment of the Trans-Ili Alatau Ridge should be based on annual sub-satellite surveys. This limits the range of significant remote sensing technologies, methods of extrapolation of sub-satellite surveys data to regular grids, within territories with similar hydrological characteristics and land-use types.

Spectral Analysis of Pink Mealy Bug Damaged Mulberry Plants Using Hyperspectral Radiometry

Mulberry (Morus spp.) foliage forms a sole food to silkworms, Bombyx mori L. Its quality plays a pivotal role in superior silk fiber production. Like any other plants, mulberry is susceptible to detrimental diseases and infested by insect pests. Many sucking pests cause damage to mulberry among which, pink mealybug causes severe damage leading to significant loss in mulberry leaf yield (12-25 %). Conventional approaches to assess and manage damage caused by pink mealybugs demand substantial manpower and expertise, resulting in time-consuming and inefficient processes. However, leveraging modern tools and technologies like remote sensing has emerged as a remarkably effective strategy for crop protection and management.Specifically, hyperspectral remote sensing proves invaluable by furnishing insights into the biophysical and biochemical traits of crops. This is achieved through the recording of narrow wave bands that reflect specific plant characteristics. The main objectives were to understand the spectral reflectance characteristics of healthy and pink mealy bug damaged mulberry crops, to identify the optimal spectral bands and vegetative indices for detecting pink mealy bug damage in mulberry and to explore the feasibility of estimating pest damage levels based on the spectral properties of mulberry crops. Top of FormAt the mulberry garden of the Department of Sericulture, Tamil Nadu Agricultural University (TNAU), Coimbatore, studies were conducted on spectral analysis of pink mealy bug damaged mulberry plants using hyperspectral radiometry during 2018. In this trial, at 15-day intervals during the active damage stage, the percentage damage was observed in plots infested with pink mealy bugs and those that were healthy. Measurements of spectral reflectance across various wavelengths and vegetation indices (VIs) were taken using a hyperspectral radiometer. The analysis included evaluating the sensitivity of different spectral bands and VIs to pink mealybug damage, along with employing correlation and regression analyses between the extent of pest damage and the VIs. The findings revealed distinct differences in the spectral reflectance profiles of mulberry plants compared to their healthy counterparts. Typically, affected plants exhibited increased reflectance in the red (620–680 nm) and green (520–590 nm) bands and reduced near-infrared (NIR) reflectance (770–860 nm). The average values of the Normalized Difference Vegetation Index (NDVI), Green Red Vegetation Index (GRVI), and Ratio Vegetation Index (RVI) were significantly lower in the pink mealybug-damaged plants across all measurements. Notably, red reflectance demonstrated the greatest sensitivity to pink mealybug-induced damage. The study highlighted the Simple Ratio (SR) index as particularly effective for identifying pest damage. Damage estimation by pink mealybugs was refined using linear regression models based on spectral indices, specifically NDVI, RVI, and GRVI. The correlation intensity analysis pinpointed the green region at 516.73 nm as showing the most significant negative correlation (r = -0.02), whereas the highest positive correlation with pink mealybug damage was observed in the NIR region (r = 0.77), underscoring the precise wavelengths and indices most indicative of pest impact. Thus, using hyperspectral radiometry identification of damage caused by pink mealy bug possible, through analysis of spectral bands and indices.

Effect of social relationships on dairy cows’ decision to move to and from an outdoor area

Leader-follower behaviours have been described in cattle housed on pasture and when entering the milking parlour. To our knowledge, no study has investigated these behaviours in cattle provided free-choice access to the outdoors. This study explores whether the timing of cows exiting and entering the barn from an outdoor area is associated with the movement of their herd mates. We followed 18 lactating Holstein cows housed indoors in a freestall barn together with 18 non-experimental cows, for on average 77 d (range: 74 – 80). The 18 experimental cows were allowed free access to an outdoor open pack (sand covered with woodchips) through an automated selection gate which automatically recorded when a cow exited the barn. To enter the barn from outside, cows went through a one-way gate; time of entry was recorded from video. Cows moved in and out of the barn on average 269 times (range: 68 – 472). A Gaussian mixture model was applied to the log of the frequency distribution of time intervals between each cow and the next for each entry and exit. On the basis of where the curves intersected, we identified a threshold interval of 42 s between consecutive exits or entries to consider movements as associated. Applying this threshold, 13.8% of events were associated with the movement of another cow: 11.2% of exits and 16.4% of entries. We created a leader-follower matrix for each direction: the leader being the cow initiating the movement, and the follower the one walking through the same gate within 42 s of the leader. These association matrices were used to build weighted and directed social networks for exits and entries; we found a moderate positive correlation between the networks (RQAP = 0.69, p < 0.001), meaning the patterns of association between cows observed when exiting the barn were consistent with those when they entered. Just over a third of the relations in the networks were asymmetrical; in contrast, leader-follower theory would predict that one individual within each pair would lead consistently. We identified 17 of 153 possible between-cow pairings as preferentially bonded; these pairs had a simple ratio index of association more than twice the average of all possible pairs. We conclude that some of the cow movements to and from an outdoor pack were influenced by the movements of other group members, and these associations were particularly strong between some pairs of cows.

Assessing Forest Species Diversity in Ghana’s Tropical Forest Using PlanetScope Data

This study utilized a remotely sensed dataset with a high spatial resolution of 3 m to predict species diversity in the Bobiri Forest Reserve (BFR), a moist semi-deciduous tropical forest in Ghana. We conducted a field campaign of tree species measurements to achieve this objective for species diversity estimation. Thirty-five field plots of 50 m × 20 m were established, and the most dominant tree species within the forest were identified. Other measurements, such as diameter at breast height (DBH ≥ 5 cm), tree height, and each plot’s GPS coordinates, were recorded. The following species diversity indices were estimated from the field measurements: Shannon–Wiener (H′), Simpson diversity index (D2), species richness (S), and species evenness (J′). The PlanetScope surface reflectance data at 3 m spatial resolution was acquired and preprocessed for species diversity prediction. The spectral/pixel information of all bands, except the coastal band, was extracted for further processing. Vegetation indices (VIs) (NDVI—normalized difference vegetation index, EVI—enhanced vegetation index, SRI—simple ratio index, SAVI—soil adjusted vegetation index, and NDRE—normalized difference red edge index) were also calculated from the spectral bands and their pixel value extracted. A correlation analysis was then performed between the spectral bands and VIs with the species diversity index. The results showed that spectral bands 6 (red) and 2 (blue) significantly correlated with the two main species diversity indices (S and H′) due to their influence on vegetation properties, such as canopy biomass and leaf chlorophyll content. Furthermore, we conducted a stepwise regression analysis to investigate the most important spectral bands to consider when estimating species diversity from the PlanetScope satellite data. Like the correlation results, bands 6 (red) and 2 (blue) were the most important bands to be considered for predicting species diversity. The model equations from the stepwise regression were used to predict tree species diversity. Overall, the study’s findings emphasize the relevance of remotely sensed data in assessing the ecological condition of protected areas, a tool for decision-making in biodiversity conservation.

Pre-Harvest Corn Grain Moisture Estimation Using Aerial Multispectral Imagery and Machine Learning Techniques

Corn grain moisture (CGM) is critical to estimate grain maturity status and schedule harvest. Traditional methods for determining CGM range from manual scouting, destructive laboratory analyses, and weather-based dry down estimates. Such methods are either time consuming, expensive, spatially inaccurate, or subjective, therefore they are prone to errors or limitations. Realizing that precision harvest management could be critical for extracting the maximum crop value, this study evaluates the estimation of CGM at a pre-harvest stage using high-resolution (1.3 cm/pixel) multispectral imagery and machine learning techniques. Aerial imagery data were collected in the 2022 cropping season over 116 experimental corn planted plots. A total of 24 vegetation indices (VIs) were derived from imagery data along with reflectance (REF) information in the blue, green, red, red-edge, and near-infrared imaging spectrum that was initially evaluated for inter-correlations as well as subject to principal component analysis (PCA). VIs including the Green Normalized Difference Index (GNDVI), Green Chlorophyll Index (GCI), Infrared Percentage Vegetation Index (IPVI), Simple Ratio Index (SR), Normalized Difference Red-Edge Index (NDRE), and Visible Atmospherically Resistant Index (VARI) had the highest correlations with CGM (r: 0.68–0.80). Next, two state-of-the-art statistical and four machine learning (ML) models (Stepwise Linear Regression (SLR), Partial Least Squares Regression (PLSR), Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forest (RF), and K-nearest neighbor (KNN)), and their 120 derivates (six ML models × two input groups (REFs and REFs+VIs) × 10 train–test data split ratios (starting 50:50)) were formulated and evaluated for CGM estimation. The CGM estimation accuracy was impacted by the ML model and train-test data split ratio. However, the impact was not significant for the input groups. For validation over the train and entire dataset, RF performed the best at a 95:5 split ratio, and REFs+VIs as the input variables (rtrain: 0.97, rRMSEtrain: 1.17%, rentire: 0.95, rRMSEentire: 1.37%). However, when validated for the test dataset, an increase in the train–test split ratio decreased the performances of the other ML models where SVM performed the best at a 50:50 split ratio (r = 0.70, rRMSE = 2.58%) and with REFs+VIs as the input variables. The 95:5 train–test ratio showed the best performance across all the models, which may be a suitable ratio for relatively smaller or medium-sized datasets. RF was identified to be the most stable and consistent ML model (r: 0.95, rRMSE: 1.37%). Findings in the study indicate that the integration of aerial remote sensing and ML-based data-run techniques could be useful for reliably predicting CGM at the pre-harvest stage, and developing precision corn harvest scheduling and management strategies for the growers.

A robust model for diagnosing water stress of winter wheat by combining UAV multispectral and thermal remote sensing

Diagnosing water conditions timely and accurately is crucial for seasonal irrigation scheduling in crop production. The purpose of this study was to establish robust water deficit models of winter wheat in different growing seasons by combining unmanned aerial vehicle (UAV) multispectral and thermal images. In 2021 and 2022, a water deficit field experiment on winter wheat was carried out in Hebei Province, China. Five-band multispectral and thermal images were obtained with a UAV at six key growth stages of winter wheat. Fourteen vegetation indices (VIs) and two thermal indices (TIs) were calculated. Simultaneously, wheat stomatal conductance and soil water content were measured. On this basis, normalized stomatal conductance (NGS) and effective water content (EWC) were calculated. TIs had the highest correlation with NGS and EWC at early growth stages, whereas ratio vegetation index (RVI), modified simple ratio index (MSR) and normalized difference vegetation index (NDVI) were highly correlated at later stages. Partial least squares (PLS), support vector machine (SVM) and gradient boosting decision tree (GBDT) were used to predict NGS and EWC for each growth stage, with the data of 2021 as the training set and the data of 2022 as independent test set. In general, GBDT outperformed PLS and SVM, and NGS was better predicted than EWC. Including VIs and TIs effectively improved the estimation accuracy of the predictive models. The test set results of the NGS and EWC models built by GBDT achieved the best performance in flowering stage (coefficient of determination (R2) = 0.88, root mean square error (RMSE) = 0.08, normalized root mean square error (NRMSE) = 14.7%) and filling stage (R2 = 0.90, RMSE = 0.05, NRMSE = 15.9%), respectively. The models of the post-heading stage were better than those of the pre-heading stage for both NGS and EWC. This study provides a robust method for diagnosing water stress only using UAV remote sensing data.

Remote Sensing Inversion and Mapping of Typical Forest Stand Age in the Loess Plateau

The accuracy of vegetation indices (VIs) in estimating forest stand age is significantly inadequate due to insufficient consideration of the differences in the physiological functions of forest ecosystems, which limits the accuracy of carbon sink simulation. In this study, remote sensing inversion and mapping of forest stand age were carried out on the Loess Plateau under consideration of the remote sensing mechanism of VIs and the physiological function and canopy structure of the forest using multiple linear regression (MLR) and random forest (RF) models. The main conclusions are as follows: (1) The canopy reflectance of different forest stands has a significant change pattern, and the older the forest stands, the lower the NIR reflectance. The relationship between forest stands and red edge is the most significant, and r is 0.53, and the relationship between Simple Ratio Index (SR), near-infrared reflectance of vegetation (NIRv), normalized difference vegetation index (NDVI), Global Vegetation Index and forest stands is more nonlinear than linear. (2) Principal component analysis (PCA) of canopy spectral information shows that SR, NDVI and red edge (B5) could explain 98% of all spectral information. SR, NDVI and red edge (B5) were used to construct a multiple linear regression model and random forest (RF) algorithm model, and RF has high estimation accuracy (R2 = 0.63). (3) The accuracy of the model was evaluated using reference data, and it was found that the accuracy of the RF model (R2 = 0.63) was higher than that of the linear regression model (R2 = 0.61), but both models underestimated the forest stand age when the forest stand age was greater than 50a, which may be caused by the saturation of the reflectance of the old forest canopy. The RF model was used to generate the dataset of forest stand information in the Loess Plateau, and it was found that the forest is dominated by young forests (&lt;20a), accounting for 38.26% of the forest area, and the average age of forests in the Loess Plateau is 56.1a. This study not only improves the method of forest stand age estimation, but also provides data support for vegetation construction in the Loess Plateau.

Spatio-Temporal Evolution of Forest Landscape in China’s Giant Panda National Park: A Case Study of Jiudingshan Nature Reserve

The continuous prohibition of commercial logging and intensifying conservation endeavors have encompassed the implementation of the Natural Forest Conservation Program (NFCP) and the Grain-to-Green Program (GTGP) by the Chinese government since 1999. Nevertheless, the efficacy of the commercial logging ban and its effectiveness in halting deforestation remain uncertain. Likewise, the destructive aftermath of the 7.9 magnitude Wenchuan earthquake in 2008 continues to be under scrutiny, necessitating ongoing study and analysis. Thus, there exists a pressing need to comprehensively monitor the spatio-temporal evolution of the forest habitat and assess the ecological status over the past two decades. The Jiudingshan Nature Reserve (JNR) is situated in the upper reaches of the Tuojiang River basin in Sichuan province, China, constituting an integral part of the Giant Panda National Park (GPNP). In this study, we classified land cover types and conducted a meticulous monitoring of forest habitat alterations within JNR, by a multilayer perceptron model (MLP) with a highly learning-sensitive algorithm. To quantify these changes, the Simple Ratio Index (SRI) and the Normalized Difference Vegetation Index (NDVI) were computed from Landsat TM/OLI images of four years (i.e., 1997, 2007, 2008, and 2018). Additionally, elevation, slope, aspect, and other topographic data were acquired from the Digital Elevation Model (DEM). The findings of our study unveil a notable expansion in both the scope and proportion of mixed conifer and broadleaf forest from 1997 to 2004. The growth of coniferous forest and the augmented areas of mixed conifer and broadleaf forest signify a substantial improvement in panda habitat. However, the seismic event of 2008 exhibited a pronounced adverse impact on vegetation, particularly within forested regions. Although there is evidence of forest recovery spanning 21 years, concerns regarding fragmentation linger. It is pivotal to acknowledge the potential long-term adverse implications arising from widespread socio-economic development and a multitude of geohazards. Hence, sustained long-term monitoring coupled with effective management strategies remain pivotal for the preservation and rehabilitation of the Giant Panda National Park (GPNP) and giant panda habitat in the future.

Cooperation increases bottlenose dolphins' (Tursiops truncatus) social affiliation.

Dolphins live in a fission-fusion society, where strong social bonds and alliances can last for decades. However, the mechanism that allows dolphins to form such strong social bonds is still unclear. Here, we hypothesized the existence of a positive feedback mechanism in which social affiliation promotes dolphins' cooperation, which in turn promotes their social affiliation. To test it, we stimulated the cooperation of the 11 dolphins studied by providing a cooperative enrichment tool based on a rope-pulling task to access a resource. Then we measured the social affiliation [simple ratio index (SRI)] of each possible pair of dolphins and evaluated whether it increased after cooperation. We also evaluated whether, before cooperation, pairs that cooperated had a higher SRI than those that did not cooperate. Our findings showed that the 11 cooperating pairs had significantly stronger social affiliation before cooperation than the 15 non-cooperating pairs. Furthermore, cooperating pairs significantly increased their social affiliation after cooperation, while non-cooperating pairs did not. As a result, our findings provide support to our hypothesis, and suggest that the previous social affiliation between dolphins facilitates cooperation, which in turn promotes their social affiliation.

A cooperative scheme for late leaf spot estimation in peanut using UAV multispectral images.

In Australia, peanuts are mainly grown in Queensland with tropical and subtropical climates. The most common foliar disease that poses a severe threat to quality peanut production is late leaf spot (LLS). Unmanned aerial vehicles (UAVs) have been widely investigated for various plant trait estimations. The existing works on UAV-based remote sensing have achieved promising results for crop disease estimation using a mean or a threshold value to represent the plot-level image data, but these methods might be insufficient to capture the distribution of pixels within a plot. This study proposes two new methods, namely measurement index (MI) and coefficient of variation (CV), for LLS disease estimation on peanuts. We first investigated the relationship between the UAV-based multispectral vegetation indices (VIs) and the LLS disease scores at the late growth stages of peanuts. We then compared the performances of the proposed MI and CV-based methods with the threshold and mean-based methods for LLS disease estimation. The results showed that the MI-based method achieved the highest coefficient of determination and the lowest error for five of the six chosen VIs whereas the CV-based method performed the best for simple ratio (SR) index among the four methods. By considering the strengths and weaknesses of each method, we finally proposed a cooperative scheme based on the MI, the CV and the mean-based methods for automatic disease estimation, demonstrated by applying this scheme to the LLS estimation in peanuts.

Predicting key soil properties from Vis-NIR spectra by applying dual-wavelength indices transformations and stacking machine learning approaches

Soil visible and near-infrared (Vis-NIR) spectroscopy has been widely used for soil characterization, due to its high potential for assessing different soil properties. However, despite great successes achieved by applying the method, there is a potential for further improvement, especially regarding data (pre-) processing and modeling. In this study, we employ simple ratio indices (SRI) and normalized difference indices (NDI), calculated from all combinations of different wavebands of Vis-NIR spectra, for improving the prediction of key soil properties such as calcium carbonate content (CaCO3), cation exchange capacity (CEC), clay content (Clay), sand content (Sand), nitrogen content (N), organic carbon content (OC), and pH in CaCl2 (pH). For this, we use the topsoil physicochemical and multispectral reflectance data from the Land Use/Land Cover Area Frame Survey (LUCAS) dataset. Various machine learning techniques were employed for the modeling, including partial least squares regression (PLSR), ridge regression, principal component regression (PCR), and two stacked algorithms. Based on the results obtained, both the SRI and NDI transformations significantly improved the performance of all the models compared to the untransformed data. However, SRI was superior to NDI. The stacked model PCR-poly applied on the SRI data yielded the best prediction performances for all the soil properties. The model gave RMSE (R2) values of 25.71 (0.96), 6.89 (0.80), 5.41 (0.82), 1.11 (0.92), 21.34 (0.95), 0.35 (0.94) and 13.41 (0.73) for estimation of CaCO3, CEC, Clay, N, OC, pH and sand, respectively. The findings of this study showed that applying the SRI and NDI transformations on Vis-NIR data and employing stacked models for calibration, yield performances for soil properties prediction that are similar to those obtained by using deep learning based methods on the same dataset in previous works, while being computationally less demanding.

A Normalized Spectral Angle Index for Estimating the Probability of Viewing Sunlit Leaves From Satellite Data

The probability of viewing sunlit leaves (PT) is a crucial variable influencing observed canopy spectra. Proper determination of PT is necessary for the quantitative retrieval of vegetation parameters using remote sensing. This paper describes a spectral index for estimating PT from satellite-observed canopy spectra. For this purpose, we propose a normalized spectral angle index (NSAI) at near-infrared (NIR) wavelengths, based on the spectral shapes of leaf and soil background. The performance of NSAI in estimating PT was evaluated using one ground-based high-resolution imaging dataset, one synthetic satellite dataset, and one satellite-ground synchronous observation dataset. The results demonstrate that NSAI is more suitable for estimating PT from satellite data than five commonly used spectral indices, including Enhanced Vegetation Index (EVI), Normalized Difference Spectral Index (NDSI), Normalized Difference Vegetation Index (NDVI), Simple Ratio Index (SR), and Photochemical Reflectance Index (PRI). NSAI exhibits a significant linear correlation with PT. The empirical model for estimating PT based on NSAI has the best transferability from simulated to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> satellite data. For the fine spectral-spatial resolution (Hyperion) data, the normalized Root Mean Square Error (nRMSE) and adjusted R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of estimated PT were 14.9% and 0.744, respectively. For MODIS images, PT was estimated with satisfactory accuracy, with an nRMSE of 18.71% and an adjusted R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of 0.670. NSAI is potentially applicable to satellite images for direct estimation of PT to improve the inversion accuracy of vegetation parameters.

Wheat yield estimation using remote sensing data based on machine learning approaches.

Accurate predictions of wheat yields are essential to farmers'production plans and to the international trade in wheat. However, only poor approximations of the productivity of wheat crops in China can be obtained using traditional linear regression models based on vegetation indices and observations of the yield. In this study, Sentinel-2 (multispectral data) and ZY-1 02D (hyperspectral data) were used together with 15709 gridded yield data (with a resolution of 5m × 5m) to predict the winter wheat yield. These estimates were based on four mainstream data-driven approaches: Long Short-Term Memory (LSTM), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and Support Vector Regression (SVR). The method that gave the best estimate of the winter wheat yield was determined, and the accuracy of the estimates based on multispectral and hyperspectral data were compared. The results showed that the LSTM model, for which the RMSE of the estimates was 0.201 t/ha, performed better than the RF (RMSE = 0.260 t/ha), GBDT (RMSE = 0.306 t/ha), and SVR (RMSE = 0.489 t/ha) methods. The estimates based on the ZY-1 02D hyperspectral data were more accurate than those based on the 30-m Sentinel-2 data: RMSE = 0.237 t/ha for the ZY-1 02D data, which is about a 5% improvement on the RSME of 0.307 t/ha for the 30-m Sentinel-2 data. However, the 10-m Sentinel-2 data performed even better, giving an RMSE of 0.219 t/ha. In addition, it was found that the greenness vegetation index SR (simple ratio index) outperformed the traditional vegetation indices. The results highlight the potential of the shortwave infrared bands to replace the visible and near-infrared bands for predicting crop yields Our study demonstrates the advantages of the deep learning method LSTM over machine learning methods in terms of its ability to make accurate estimates of the winter wheat yield.

A comparison of methods to estimate leaf area index using either crop-specific or generic proximal hyperspectral datasets

The leaf area index (LAI) is an important parameter indicating the crop growth status. Many vegetation indices and models have been developed to estimate LAI of different crops. However, the utility of and differences between crop-specific and generic algorithms for LAI estimation covering several crops, dates, and sites need to be compared. The main objectives of this study were to: (1) evaluate the suitability and robustness of different vegetation indices for LAI estimation of different crops; (2) compare the performance of crop-specific and generic algorithms; (3) evaluate the performance of statistical regression, machine learning regression, and the PROSAIL-D with different inversion strategies. Results showed that: (1) the simple ratio index (SR) performed best for cotton and winter wheat, the MERIS terrestrial chlorophyll index (MTCI) performed best for maize; (2) the models trained over specific crop types performed better than those trained over all crop types together; (3) for crop-specific models, artificial neural networks (ANN) performed better than support vector machine regression (SVR), partial least square regression (PLSR), and statistical regression methods, while the PROSAIL-D yielded similar or slightly better performance as compared with ANN method. Furthermore, the look-up table (LUT) strategy performed better than iterative optimization strategy (Shuffled Complex Evolution method developed at the University of Arizona, SCE-UA). Results indicated that there was not an optimal model that could be used to precisely estimate LAI of different crops since the relationship between the specific hyperspectral reflectance and the LAI of different crops were different. In addition, the PROSAIL-D with crop specific input parameters is a potential method for LAI estimation of different crops with hyperspectral reflectance.

Dry season forage assessment across senegalese rangelands using earth observation data

Strengthening of feed security in the Sahel is urgently needed given the climate change and growing human population. A prerequisite to this is sustainable use of rangeland forage resources for livestock. Many studies have focused on the assessment of rangeland resources during the rainy season, while only a few have focused on the dry season which is the longest and most demanding period for livestock in Sahelian rangelands. The objective of this study is to develop remote sensing-based models for estimating dry season forage vegetation mass. To that end, 29 vegetation indices calculated from each of the MODIS-MCD43A4 (500 m), Landsat-8 (30 m), and Sentinel-2 (10 m) satellite products were used and tested against in situ data collected during three field-measurement campaigns in 2021 at eleven monitoring sites across Senegalese rangelands. Four statistical models were tested, namely, random forest, gradient boosting machines, and simple linear and multiple linear regressions. The two main vegetation mass variables modeled from remote sensing imagery were the standing herbaceous and litter dry mass (BH) and total forage dry mass (BT) with a dry mass of woody plant leaves added to BH. Overall, Sentinel-2 data provided the best performance for the assessment of BH with multiple linear regression (R2 = 0.74; RMSE = 378 kg DM/ha) using NDI5 (Normalized Difference Index5), GRCI (Green Residue Cover Index), SRI (Simple Ratio Index), TCARI (Transformed Chlorophyll Absorption in Reflectance Index), and DFI (Dead Fuel Index) indices. For BT, the best model was also obtained from Sentinel-2 data, including RVI3 (Ratio Vegetation Index3) (R2 = 0.78; RMSE = 496 kg DM/ha). Results showed the suitability of combining the red, green, blue, NIR, SWIR1, and SWIR2 bands in monitoring forage availability during the dry season. Our study revealed that the spectral richness of the optical sensor systems Sentinel-2, Landsat-8, and MODIS-MCD43A4 allowed for accurate assessments of dry-season forage mass of semi-arid rangelands. Adding to this, the high spatial and temporal resolution of Sentinel-2 satellite imagery makes this a promising data source for timely monitoring. These findings can support the monitoring of the animal feed balance in Sahelian countries and contribute to enhancing the resilience of pastoralism toward feed shortage through early warning systems.

Comparison of UAV RGB Imagery and Hyperspectral Remote-Sensing Data for Monitoring Winter Wheat Growth

Although crop-growth monitoring is important for agricultural managers, it has always been a difficult research topic. However, unmanned aerial vehicles (UAVs) equipped with RGB and hyperspectral cameras can now acquire high-resolution remote-sensing images, which facilitates and accelerates such monitoring. To explore the effect of monitoring a single crop-growth indicator and multiple indicators, this study combines six growth indicators (plant nitrogen content, above-ground biomass, plant water content, chlorophyll, leaf area index, and plant height) into the new comprehensive growth index (CGI). We investigate the performance of RGB imagery and hyperspectral data for monitoring crop growth based on multi-time estimation of the CGI. The CGI is estimated from the vegetation indices based on UAV hyperspectral data treated by linear, nonlinear, and multiple linear regression (MLR), partial least squares (PLSR), and random forest (RF). The results are as follows: (1) The RGB-imagery indices red reflectance (r), the excess-red index (EXR), the vegetation atmospherically resistant index (VARI), and the modified green-red vegetation index (MGRVI), as well as the spectral indices consisting of the linear combination index (LCI), the modified simple ratio index (MSR), the simple ratio vegetation index (SR), and the normalized difference vegetation index (NDVI), are more strongly correlated with the CGI than a single growth-monitoring indicator. (2) The CGI estimation model is constructed by comparing a single RGB-imagery index and a spectral index, and the optimal RGB-imagery index corresponding to each of the four growth stages in order is r, r, r, EXR; the optimal spectral index is LCI for all four growth stages. (3) The MLR, PLSR, and RF methods are used to estimate the CGI. The MLR method produces the best estimates. (4) Finally, the CGI is more accurately estimated using the UAV hyperspectral indices than using the RGB-image indices.

Forecasting rainfed rice yield with biomass of early phenophases, peak intercepted PAR and ground based remotely sensed vegetation indices

Rice is the main staple food of the country but crop productivity in some years declines due to erratic monsoon and non-uniformity in spatial and temporal distribution of rainfall. Hence, assessing productivity of the rice crop in advance using meteorological and plant physiological attributes will be helpful for planners to take decision on contingency measures. In this investigation, dry biomass of early phenophases (active tillering, panicle initiation, boot leaf stages, flowering),peak intercepted photosynthetically active radiation (IPAR), peak spectral reflectance based vegetation indices of 3 rice varieties under 3 nitrogen levels (50, 100 and 130 kg ha-1) were made correlated with grain yield.Based on interrelationship it was found that biomass of flowering period was better correlated with grain yield with the R2 value of 0.75.Inter-relationship between peak IPAR(%), remotely sensed peak simple ratio index (IR/R) and normalized difference vegetation index (NDVI)with the rice yield were also established. Multiple regression modelwas developed by interrelating yield as dependant variable with dry biomass of flowering stage, peak IPAR(%) and peak IR/R and NDVI as independent variables which may be used as an effective tool for early prediction of rice yield, at least 30-40 days in advance. The grain yield was also estimated through developed algorithm using MODIS satellite derived NDVI and compared with that of actual yield.

Retrieval of Paddy Crop Nutrient Content at Plot Scale Using Optimal Synthetic Bands of High Spectral and Spatial Resolution Satellite Imagery

The main aim of the present study is to introduce Hyperion narrow band information into high spatial broad band imagery so that it will be helpful for the estimation of crop parameters at small field scale from multispectral data. Here, we are targeting to retrieve the nitrogen content of summer paddy at plot scale. Our findings concluded that paddy crop heterogeneity can be assessed by implementing narrow band index regression model designed for paddy crop from multispectral LISS IV data. Moreover, the proposed models to construct the synthetic narrow bands in green (GS), red (RS) and NIR (NS) regions of the spectrum for high-resolution satellite imagery (LISS IV) are also presented here {\(GS\rho_{LISS \, IV} = 0.032 \cdot G\rho_{IH} + 17.296\); \(RS\rho_{LISS \, IV} = 0.072 \cdot R\rho_{IH} + 16.025\); \(NS\rho_{LISS \, IV} = 0.087 \cdot N\rho_{IH} + 24.923\)}. It showed the spatial variability of nitrogen content ranged from 3.37–6.04% obtained by leaf nitrogen concentration (LNC: R705, R717, R491) index regression model and 3.82–5.96% by simple ratio (SR: R533, R565) index regression model over a rice agriculture system. Thus, the details of spatial variability at crop field level over a rice agriculture system can be assessed from multispectral LISS IV data with 5.6 m spatial resolution through the synthetic broad bands. The results conclude that narrow band-based index regression model derived from Hyperion bands by implementing band average method can effectively evaluate the nitrogen content distribution of croplands subjected to varying field heterogeneity. It confirmed that the proposed methodology can be applied for the variability mapping of crop parameters at plot-scale level within the fields from space platform, which can be beneficial in achieving operational precision farming more efficiently.