Quantitative assessment of crop residues in no-till technology according to remote sensing data and field soil cover survey

The key feature of the no-till technology is the preservation of crop residues on the soil surface. Crop residues quantitative assessment is an important task when introducing technology into production. On the basis of field and remote sensing data, different approaches to this assessment are considered. The research was carried out in the Budennovsky district of the Stavropol Territory in the fields of farms using both traditional technology (TT) and no-till (ПП). Images of the Sentinel-2 system were used as remote sensing data, on the basis of which the spectral indices NDTI and NDVI were calculated. Three methods were used to estimate the projective cover by plant residues: 1) weight accounting of plant residues per unit area; 2) field determination of the projective cover by the method of line transects; 3) desk analysis of photographs of the soil surface. Based on the obtained results, models of the linear dependence of NDTI values on the projective cover of the soil surface with plant residues were constructed. The possibility of quantitative accounting of plant residues only on the basis of remote sensing data was also analyzed. The highest coefficient of determination (R2 = 0.97) with the smallest square root of the standard error (RMSE = 7.93) was obtained by modeling based on the analysis of photographs of the soil surface covered with plant residues. Based on the model of the dependence of NDTI values on the projective cover of plant residues obtained as a result of the analysis of photographs based on Sentinel -2 satellite data for the growing season 2020–2021, data were obtained on the dynamics of soil coverage with plant residues (CRC) on the scale of a single field an d different tillage technologies. As an approbation of the approach and an assessment of its use for solving production problems, the dynamics of the projective cover with plant residues was analyzed under different crops and different relief conditions. An analysis of the dynamics of CRC values made it possible to distinguish between different stages of crop cultivation under traditional technology (TT) and no-till (ПП), and also on the scale of an individual field revealed the heterogeneity of the projective soil cover with plant residues associated with the features of the mesorelief

Remote sensing for crop residue cover recognition: A review

Nowadays, using of conservation tillage instead of conventional tillage has been changing attitudes from conventional agriculture to sustainable agriculture. The tillage method affects directly soil and water quality. Actions relative to optimized agricultural management such as conservation tillage methods has adopted at recent years by agronomists and agricultures, due to agricultural and environment advantages. These advantages consist of soil and water quality improving, wind and water erosion prevention, evaporation reduction, soil surface temperature reduction, greenhouse gases reduction, fuel consumption reduction, and etc. In conversation tillage, more than 30% agricultural production residues remain on the ground. For evaluation of residues cover in the fields, information of crop residue obtain from line-transect method. This method has great accuracy, but it is very time consuming and costly for large areas. Remote sensing using satellite information processing can help the researchers to gather the data from the field and the extraction the information. Tillage indices and textural features are two most applicable approach in remote sensing crop residue cover assessment. The aim of this paper was to study of conservation tillage advantages and remote sensing methods to residue cover crop measurement at vast regions through satellite imagery.

Estimasi Produksi Jagung (Zea Mays L.) Menggunakan Pendekatan Ekologi Spasial Di Kabupaten Jeneponto

Jeneponto Regency is one of the biggest corn producers in South Sulawesi. Jeneponto Regency is the most suitable area for estimating corn crop production because it is the largest corn-producing region in South Sulawesi Province and has quite complex terrain variations. Agricultural management requires accurate and accurate information or data that can increase productivity and economic benefits. Get accurate and up-to-date data or information about parts of an accurate agricultural information system to support proper planning. The purpose of this study is to map climatic conditions (rainfall) and physical conditions (slope, height, soil type) and to estimate the amount of corn production and maize production maps through spatial assessment. This research was conducted in the Jeneponto Regency, which is located in the southern part of the South Sulawesi Province. The results of the study show that spatial ecology based on agro-ecosystem zones or agricultural unit units in the estimation of special maize production can increase estimation results with high accuracy. Based on the analysis of the four physical maps that have been mapped are rainfall, soil type, slope, and height which are regulated in the agro-ecosystem zone, the estimated amount with spatial ecological calculations is 159.584,05 tons. The accuracy of the estimation model results with field data reaches 95%. Based on the results of the study can conclude the results of spatial ecological research can be used as a method of estimating production on corn

ESTIMASI PRODUKSI JAGUNG (Zea Mays L.) DENGAN MENGGUNAKAN CITRA SENTINEL 2A DI SEBAGIAN WILAYAH KABUPATEN JENEPONTO PROVINSI SULAWESI SELATAN

Production is a real benchmark in successful crop management which is the most important output economically. Currently, corn production estimates are generally done by conventional means through field surveys. This conventional way requires a high cost and a long time. Appropriate agricultural management requires precise and accurate information or data to increase production and economic benefits. Sentinel 2A remote sensing satellite data is potential to be used in assessment of corn production estimation. The purpose of this research is to make land use mapping and corn production estimation by using spectral approach. Estimated data were obtained from Sentinel 2A image by mapping land use and modeling of vegetation index (NDVI, SAVI, MSAVI, TSAVI, EVI, and ARVI) then compared with data of corn production in the field. The result of data analysis shows land use mapping using Sentinel 2A image has 91% confidence level. Calculation of production estimation can show the accuracy of 74% with RMSE 0.69. The highest correlation is estimated production with EVI index model with regression correlation equal to 74% which shows strong correlation on both variables. Estimated production of corn in 2017 in Jeneponto Regency is 178,660,69 tons

VALUASI JUMLAH AIR DI EKOSISTEM LAHAN GAMBUT DENGAN DATA LANDSAT 8 OLI/TIRS (WATER CONTENT VALUATION IN PEATLAND ECOSYSTEM BY USING LANDSAT 8 OLI/TIRS)

The water content of peatland ecosystems stored as gasses in the air and as liquid in the peat soil and vegetation. The presence of water was influential to the value of spectral radians received by satellite sensors. Objective of study were develop empirical model to be applied in the Landsat 8 satellite imagery interpretation to estimate water content of peatland ecosystem. Method consisted of field measurements and satellite data interpretation. Field activities aimed to obtain weather parameters such as radiation, air temperature, surface temperature, evapotranspiration (ET), relative humidity (RH), soil water content (KAT), and biomass for each land cover in peatland ecosystems. Field measurements results were used to validate the parameters derived from Landsat 8 satellite data. Water content in the air was assessed by the ET and RH, in the soil was assessed by soil heat flux (G) and in the vegetation by biomass. The results of the validation of the data field measurement with Landsat 8 showed only ET (r2 = 0.71), RH (r2 = 0.71), and biomass (r2 = 0.87) had a strong relationship, while between G and KAT had weak relationship. Conclusion of this study indicated Landsat 8 satellite data could be used to calculate the water content in the air and vegetation. Thus, estimating water content in the peatland ecosystem with satellite data can only be done on the surface. AbstrakEkosistem lahan gambut menyimpan air dalam bentuk gas di udara, dan cair dalam tanah gambut dan vegetasi. Keberadaannya mempengaruhi nilai spektral radians yang diterima oleh sensor satelit. Tujuan penelitian ini adalah untuk mendapatkan model empirik yang dapat diaplikasikan untuk interpretasi citra satelit dalam pendugaan jumlah air di ekosistem lahan gambut. Metode penelitian terdiri dari pengukuran lapangan dan interpretasi data satelit LANDSAT 8. Parameter cuaca seperti radiasi, suhu udara, suhu permukaan, evapotranspirasi (ET), kelembaban udara (RH), kadar air tanah (KAT) dan biomassa diukur di lapangan pada setiap jenis tutupan lahan. Hasil-hasil pengukuran lapangan digunakan untuk memvalidasi parameter-parameter yang diturunkan dari data satelit LANDSAT 8. Jumlah air di udara yang dinilai dari ET dan RH, jumlah air di tanah dinilai dengan laju pemanasan tanah (G) dan jumlah air di vegetasi dengan biomassa. Hasil validasi antara data lapangan dengan data LANDSAT 8 menunjukkan hanya nilai ET (r2=0,71), RH (r2=0,71), dan biomassa ((r2=0,87) mempunyai hubungan yang kuat, sedangkan nilai G tidak mempunyai hubungan yang kuat dengan KAT. Penelitian ini menyimpulkan bahwa data satelit LANDSAT 8 hanya dapat digunakan untuk menghitung jumlah air yang tersimpan di udara dan vegetasi. Oleh karena itu, pendugaan jumlah air di ekosistem lahan gambut dengan data satelit hanya dapat dilakukan di atas permukaan

Combining Unmanned Aerial Vehicle (UAV)-Based Multispectral Imagery and Ground-Based Hyperspectral Data for Plant Nitrogen Concentration Estimation in Rice

Plant nitrogen concentration (PNC) is a critical indicator of N status for crops, and can be used for N nutrition diagnosis and management. This work aims to explore the potential of multispectral imagery from unmanned aerial vehicle (UAV) for PNC estimation and improve the estimation accuracy with hyperspectral data collected in the field with a hyperspectral radiometer. In this study we combined selected vegetation indices (VIs) and texture information to estimate PNC in rice. The VIs were calculated from ground and aerial platforms and the texture information was obtained from UAV-based multispectral imagery. Two consecutive years (2015 & 2016) of experiments were conducted, involving different N rates, planting densities and rice cultivars. Both UAV flights and ground spectral measurements were taken along with destructive samplings at critical growth stages of rice (Oryza sativa L.). After UAV imagery preprocessing, both VIs and texture measurements were calculated. Then the optimal normalized difference texture index (NDTI) from UAV imagery was determined for separated stage groups and the entire season. Results demonstrated that aerial VIs performed well only for pre-heading stages (R2 = 0.52–0.70), and photochemical reflectance index and blue N index from ground (PRIg and BNIg) performed consistently well across all growth stages (R2 = 0.48–0.65 and 0.39–0.68). Most texture measurements were weakly related to PNC, but the optimal NDTIs could explain 61 and 51% variability of PNC for separated stage groups and entire season, respectively. Moreover, stepwise multiple linear regression (SMLR) models combining aerial VIs and NDTIs did not significantly improve the accuracy of PNC estimation, while models composed of BNIg and optimal NDTIs exhibited significant improvement for PNC estimation across all growth stages. Therefore, the integration of ground-based narrow band spectral indices with UAV-based textural information might be a promising technique in crop growth monitoring

Estimation of Maize Residue Cover Using Landsat-8 OLI Image Spectral Information and Textural Features

The application of crop residue has become increasingly important for providing a barrier against water and wind erosion and improving soil organic matter content, infiltration, evaporation, temperature, and soil structure. The objectives of this work were to: (i) estimate maize residue cover (MRC) from Landsat-8 OLI images using seven vegetation indices (VIs) and eight textural features; and (ii) compare the VI method, textural feature method, and combination method (integration of textural features and spectral information) for estimating MRC with partial least squares regression (PLSR). The results showed that the normalized difference tillage index (NDTI), simple tillage index (STI), normalized difference index 7 (NDI7), and shortwave red normalized difference index (SRNDI) were significantly correlated with MRC. The MRC model based on NDTI outperformed (R2 = 0.84 and RMSE = 12.33%) the models based on the other VIs. Band3mean, Band4mean, and Band5mean were highly correlated with MRC. The regression between Band3mean and MRC was stronger (R2 = 0.71 and RMSE = 15.21%) than those between MRC and the other textural features. The MRC estimation accuracy using the combination method (R2 = 0.96 and RMSE = 8.11%) was better than that based on only the VI (R2 = 0.88 and RMSE = 11.34%) or textural feature (R2 = 0.90 and RMSE = 9.82%) methods. The results suggest that the combination method can be used to estimate MRC on a regional scale